diff --git a/docs/reconcile-dashboard.html b/docs/reconcile-dashboard.html
index dbbf63f2..bf1f25b5 100644
--- a/docs/reconcile-dashboard.html
+++ b/docs/reconcile-dashboard.html
@@ -118,7 +118,7 @@ <h2>Inheritance: what each file takes from its parents</h2>
 
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"assay_type", "declared": 2, "mixed": 0, "parent_conflict": 2, "parent_not_classified": 0, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_IGVF_Mouse_R1", "role": "indexed", "slot": "instrument_model", "declared": 2, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 2, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_IGVF_Mouse_R1", "role": "indexed", "slot": "variant_kind", "declared": 2, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 2, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_IGVF_Mouse_R1", "role": "indexed", "slot": "variant_origin", "declared": 2, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 2, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_IGVF_Mouse_R1", "role": "reads", "slot": "data_modality", "declared": 2, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 0, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_IGVF_Mouse_R1", "role": "reads", "slot": "platform", "declared": 2, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 0, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_IGVF_Mouse_R1", "role": "reads", "slot": "assay_type", "declared": 2, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 0, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_IGVF_Mouse_R1", "role": "reads", "slot": "instrument_model", "declared": 2, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 0, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_MAGE", "role": "indexed", "slot": "data_modality", "declared": 795, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 36, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_MAGE", "role": "indexed", "slot": "platform", "declared": 0, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 831, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_MAGE", "role": "indexed", "slot": "reference_assembly", "declared": 787, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 44, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_MAGE", "role": "indexed", "slot": "assay_type", "declared": 791, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 40, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_MAGE", "role": "indexed", "slot": "instrument_model", "declared": 0, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 831, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_MAGE", "role": "indexed", "slot": "variant_kind", "declared": 792, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 39, "parent_not_in_run": 0, "list_unresolved": 0}, {"dataset": "AnVIL_MAGE", "role": "indexed", "slot": "variant_origin", "declared": 792, "mixed": 0, "parent_conflict": 0, "parent_not_classified": 39, "parent_not_in_run": 0, "list_unresolved": 0}], "inheritance_columns": ["declared", "mixed", "parent_conflict", "parent_not_classified", "parent_not_in_run", "list_unresolved"], "samples": [{"dataset": "ANVIL_1000G_PRIMED_data_model", "headers": {"named": 46, "unrecognised": 0, "no_sample": 2854, "unread": 0}, "header_unrecognised": {}, "settled": {"agreed": 0, "header_only": 46, "table_only": 0, "inherited": 23, "conflict": 0, "incomparable": 0, "none": 11424}, "conflict_examples": [], "incomparable_examples": [], "table_unrecognised": [], "name_disagreements": 0, "name_disagreement_examples": [], "inheritance": {"IndexActivity": {"passed": 23}}, "parent_disagreements": {}, "parent_disagreement_examples": []}, {"dataset": "ANVIL_1000G_high_coverage_2019", "headers": {"named": 6426, "unrecognised": 3, "no_sample": 25, "unread": 0}, "header_unrecognised": {"NA12546B": 1, "NA12830A": 1, "NA18874A": 1}, "settled": {"agreed": 6383, "header_only": 43, "table_only": 3199, "inherited": 16079, "conflict": 0, "incomparable": 0, "none": 312}, "conflict_examples": [], "incomparable_examples": [], "table_unrecognised": [], "name_disagreements": 0, "name_disagreement_examples": [], "inheritance": {"ChecksumActivity": {"parent_none": 156, "passed": 12852}, "IndexActivity": {"parent_none": 28, "passed": 6426}}, "parent_disagreements": {}, "parent_disagreement_examples": []}, {"dataset": "ANVIL_HPRC", "headers": {"named": 356, "unrecognised": 1004, "no_sample": 574, "unread": 0}, "header_unrecognised": {"(a read group without SM)": 545, "Bio Sample 1": 3, "Bio Sample 15": 3, "Bio Sample 2": 3, "Bio Sample 3": 3, "Bio Sample 4": 3, "Bio Sample 5": 3, "Bio Sample 7": 3, "CHM13": 2, "HG002.f1_assembly_v2.1.dipcall/HG002.f1_assembly_v2.1.hap1.bam": 2, "HG002.f1_assembly_v2.1.dipcall/HG002.f1_assembly_v2.1.hap2.bam": 2, "HG002.f1_assembly_v2.dipcall/HG002.f1_assembly_v2.hap1.bam": 2, "HG002.f1_assembly_v2.dipcall/HG002.f1_assembly_v2.hap2.bam": 2, "HG00423_Fraction2_Fraction3_480cFc": 4, "HG00438.f1_assembly_v2.dipcall/HG00438.f1_assembly_v2.hap1.bam": 2, "HG00438.f1_assembly_v2.dipcall/HG00438.f1_assembly_v2.hap2.bam": 2, "HG005.f1_assembly_v2.dipcall/HG005.f1_assembly_v2.hap1.bam": 2, "HG005.f1_assembly_v2.dipcall/HG005.f1_assembly_v2.hap2.bam": 2, "HG005.f1_assembly_v2_genbank.dipcall/HG005.f1_assembly_v2_genbank.hap1.bam": 2, "HG005.f1_assembly_v2_genbank.dipcall/HG005.f1_assembly_v2_genbank.hap2.bam": 2, "HG00544_Fraction2_Fraction3_480dck": 4, "HG00609_Fraction2_Fraction3_480dcs": 3, "HG00621.f1_assembly_v2.dipcall/HG00621.f1_assembly_v2.hap1.bam": 2, "HG00621.f1_assembly_v2.dipcall/HG00621.f1_assembly_v2.hap2.bam": 2, "HG00673.f1_assembly_v2.dipcall/HG00673.f1_assembly_v2.hap1.bam": 2, "HG00673.f1_assembly_v2.dipcall/HG00673.f1_assembly_v2.hap2.bam": 2, "HG00733.f1_assembly_v2.dipcall/HG00733.f1_assembly_v2.hap1.bam": 2, "HG00733.f1_assembly_v2.dipcall/HG00733.f1_assembly_v2.hap2.bam": 2, "HG00735.f1_assembly_v2.dipcall/HG00735.f1_assembly_v2.hap1.bam": 2, "HG00735.f1_assembly_v2.dipcall/HG00735.f1_assembly_v2.hap2.bam": 2, "HG00738_Fraction2_Fraction3_480dcm": 4, "HG01433_Fraction2_Fraction3_480dcp": 4, "HG02004_Fraction2_Fraction3_480dcw": 4, "HG02027_Fraction2_Fraction3_480cFd": 4, "HG02083_Fraction2_Fraction3_480cIG": 4, "HG02602_Fraction2_Fraction3_480dcn": 4, "HG03654_Fraction2_Fraction3_480dcj": 4, "HG03669_Fraction2_Fraction3_480dco": 4, "HG03831_Fraction2_480dcq": 4, "syndip": 148}, "settled": {"agreed": 141, "header_only": 215, "table_only": 4731, "inherited": 558, "conflict": 0, "incomparable": 1, "none": 17539}, "conflict_examples": [], "incomparable_examples": ["HG005.paternal.GRCh38_no_alt.bam"], "table_unrecognised": [{"column": "maternal_id", "files": 14, "raw_value": "mHomSap2", "table": "sample"}, {"column": "paternal_id", "files": 10, "raw_value": "mHomSap1", "table": "sample"}, {"column": "sample_id", "files": 4, "raw_value": "mHomSap3", "table": "sample"}], "name_disagreements": 0, "name_disagreement_examples": [], "inheritance": {"ChecksumActivity": {"parent_conflict": 1, "parent_none": 206, "passed": 493}, "IndexActivity": {"parent_conflict": 1, "parent_none": 104, "passed": 251}}, "parent_disagreements": {}, "parent_disagreement_examples": []}, {"dataset": "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open", "headers": {"named": 12398, "unrecognised": 58, "no_sample": 3, "unread": 0}, "header_unrecognised": {"(a read group without SM)": 3, "HG00733_reseq": 2, "SAMPLE": 15, "Sample": 32, "default": 6}, "settled": {"agreed": 0, "header_only": 12398, "table_only": 0, "inherited": 0, "conflict": 0, "incomparable": 0, "none": 136}, "conflict_examples": [], "incomparable_examples": [], "table_unrecognised": [], "name_disagreements": 0, "name_disagreement_examples": [], "inheritance": {}, "parent_disagreements": {}, "parent_disagreement_examples": []}, {"dataset": "ANVIL_T2T", "headers": {"named": 112739, "unrecognised": 129, "no_sample": 318, "unread": 0}, "header_unrecognised": {"GIABvCHM13_20200921_mm2/alignments/HG002vCHM13_20200921_mm2_ONT.sort.bam": 2, "GIABvCHM13_20200921_mm2/alignments/HG002vCHM13_20200921_mm2_PBCCS.sort.bam": 2, "GIABvCHM13_20200921_mm2/alignments/HG003vCHM13_20200921_mm2_ONT.sort.bam": 2, "GIABvCHM13_20200921_mm2/alignments/HG003vCHM13_20200921_mm2_PBCCS.sort.bam": 2, "GIABvCHM13_20200921_mm2/alignments/HG004vCHM13_20200921_mm2_ONT.sort.bam": 2, "GIABvCHM13_20200921_mm2/alignments/HG004vCHM13_20200921_mm2_PBCCS.sort.bam": 2, "GIABvCHM13_20200921_mm2/alignments/HG005vCHM13_20200921_mm2_ONT.sort.bam": 2, "GIABvCHM13_20200921_mm2/alignments/HG005vCHM13_20200921_mm2_PBCCS.sort.bam": 2, "GIABvCHM13_20200921_mm2/alignments/HG006vCHM13_20200921_mm2_ONT.sort.bam": 2, "GIABvCHM13_20200921_mm2/alignments/HG006vCHM13_20200921_mm2_PBCCS.sort.bam": 2, "GIABvCHM13_20200921_mm2/alignments/HG007vCHM13_20200921_mm2_ONT.sort.bam": 2, "GIABvCHM13_20200921_mm2/alignments/HG007vCHM13_20200921_mm2_PBCCS.sort.bam": 2, "GIABvCHM13_20200921_wm/alignments/HG002vCHM13_20200921_wm_PBCCS.sort.bam": 5, "GIABvCHM13_20200921_wm/alignments/HG003vCHM13_20200921_wm_PBCCS.sort.bam": 2, "GIABvCHM13_20200921_wm/alignments/HG004vCHM13_20200921_wm_PBCCS.sort.bam": 2, "GIABvCHM13_20200921_wm/alignments/HG005vCHM13_20200921_wm_PBCCS.sort.bam": 3, "GIABvCHM13_20200921_wm/alignments/HG007vCHM13_20200921_wm_PBCCS.sort.bam": 2, "GIABvGRCh38_wm/alignments/HG002vGRCh38_wm_PBCCS.sort.bam": 5, "GIABvGRCh38_wm/alignments/HG005vGRCh38_wm_PBCCS.sort.bam": 3, "Sample": 6}, "settled": {"agreed": 3202, "header_only": 109537, "table_only": 28818, "inherited": 140620, "conflict": 0, "incomparable": 0, "none": 7027}, "conflict_examples": [], "incomparable_examples": [], "table_unrecognised": [], "name_disagreements": 0, "name_disagreement_examples": [], "inheritance": {"CohortMergeActivity": {"passed": 31155}, "IndexActivity": {"parent_none": 383, "passed": 115869}, "QualityControlActivity": {"passed": 16010}}, "parent_disagreements": {}, "parent_disagreement_examples": []}, {"dataset": "ANVIL_T2T_CHRY", "headers": {"named": 94378, "unrecognised": 0, "no_sample": 0, "unread": 0}, "header_unrecognised": {}, "settled": {"agreed": 94138, "header_only": 240, "table_only": 122057, "inherited": 31398, "conflict": 0, "incomparable": 0, "none": 62146}, "conflict_examples": [], "incomparable_examples": [], "table_unrecognised": [], "name_disagreements": 0, "name_disagreement_examples": [], "inheritance": {"CohortMergeActivity": {"passed": 31158}, "IndexActivity": {"parent_none": 6, "passed": 98138}, "QualityControlActivity": {"passed": 23601}}, "parent_disagreements": {}, "parent_disagreement_examples": []}, {"dataset": "ANVIL_nhp_dGTEx_V1", "headers": {"named": 0, "unrecognised": 756, "no_sample": 0, "unread": 0}, "header_unrecognised": {"CJGTEX001-B-01-N1-E-SM-OJY1S": 1, "CJGTEX002-B-02-N1-E-SM-OJY3C": 1, "CJGTEX003-B-02-N1-E-SM-OJY1Q": 1, "CJGTEX004-B-01-N1-E-SM-OJY1T": 1, "CJGTEX005-B-01-N1-E-SM-OJY1P": 1, "CJGTEX006-B-01-N1-E-SM-OJY37": 1, "CJGTEX007-B-02-N1-F-SM-OOPPB": 1, "CJGTEX009-B-02-N1-E-SM-OJY1U": 1, "CJGTEX010-B-01-N1-E-SM-OJY3G": 1, "CJGTEX011-B-02-N1-F-SM-OJY1R": 1, "CJGTEX013-B-01-N1-E-SM-OOPQ5": 1, "CJGTEX014-B-02-N1-E-SM-OOPPZ": 1, "CJGTEX016-B-01-N1-G-SM-OOPPU": 1, "CJGTEX017-B-02-N1-E-SM-OOP2K": 1, "CJGTEX018-B-01-N1-F-SM-OOPQA": 1, "CJGTEX019-B-02-N1-E-SM-OOPQ9": 1, "CJGTEX020-B-01-N1-E-SM-OOP4Z": 1, "CJGTEX021-B-02-N1-E-SM-OOPPA": 1, "CJGTEX022-B-01-N1-E-SM-OOPPG": 1, "RHGTEX011-601A-01-SM-OD3AA": 1, "SM-NB4YC": 1, "SM-NB4YD": 1, "SM-NB4YF": 1, "SM-NB4YG": 1, "SM-NB4YH": 1, "SM-NB4YI": 1, "SM-NB4YJ": 1, "SM-NB4YK": 1, "SM-NB4YQ": 1, "SM-NB4YR": 1, "SM-NB4YT": 1, "SM-NB4YU": 1, "SM-OGPFL": 1, "SM-OGPFM": 1, "SM-OGPFX": 1, "SM-OGPFY": 1, "SM-OGPGA": 1, "SM-OGPGB": 1, "SM-OGPGL": 1, "sm1": 686}, "settled": {"agreed": 0, "header_only": 0, "table_only": 0, "inherited": 0, "conflict": 0, "incomparable": 0, "none": 3591}, "conflict_examples": [], "incomparable_examples": [], "table_unrecognised": [], "name_disagreements": 0, "name_disagreement_examples": [], "inheritance": {"IndexActivity": {"parent_none": 756}}, "parent_disagreements": {}, "parent_disagreement_examples": []}, {"dataset": "AnVIL_ENCORE_293T", "headers": {"named": 0, "unrecognised": 0, "no_sample": 224, "unread": 0}, "header_unrecognised": {}, "settled": {"agreed": 0, "header_only": 0, "table_only": 0, "inherited": 0, "conflict": 0, "incomparable": 0, "none": 1992}, "conflict_examples": [], "incomparable_examples": [], "table_unrecognised": [], "name_disagreements": 0, "name_disagreement_examples": [], "inheritance": {"CoverageActivity": {"parent_none": 896}}, "parent_disagreements": {}, "parent_disagreement_examples": []}, {"dataset": "AnVIL_ENCORE_RS293", "headers": {"named": 0, "unrecognised": 0, "no_sample": 410, "unread": 0}, "header_unrecognised": {}, "settled": {"agreed": 0, "header_only": 0, "table_only": 0, "inherited": 0, "conflict": 0, "incomparable": 0, "none": 3752}, "conflict_examples": [], "incomparable_examples": [], "table_unrecognised": [], "name_disagreements": 0, "name_disagreement_examples": [], "inheritance": {"CoverageActivity": {"parent_none": 1640}}, "parent_disagreements": {}, "parent_disagreement_examples": []}, {"dataset": "AnVIL_HPRC_R2", "headers": {"named": 737, "unrecognised": 1672, "no_sample": 924, "unread": 0}, "header_unrecognised": {"(a read group without SM)": 870, "BioSample_1": 29, "BioSample_2": 27, "BioSample_3": 30, "BioSample_4": 29, "BioSample_5": 29, "BioSample_6": 28, "BioSample_7": 29, "BioSample_8": 29, "CHM13": 1, "GRCh38": 1, "HG002148": 4, "HG00423_Fraction2_Fraction3_480cFc": 4, "HG00544_Fraction2_Fraction3_480dck": 4, "HG00738_Fraction2_Fraction3_480dcm": 4, "HG01433_Fraction2_Fraction3_480dcp": 4, "HG02004_Fraction2_Fraction3_480dcw": 4, "HG02027_Fraction2_Fraction3_480cFd": 4, "HG02083_Fraction2_Fraction3_480cIG": 4, "HG02602_Fraction2_Fraction3_480dcn": 4, "HG03654_Fraction2_Fraction3_480dcj": 4, "PG00133": 5}, "settled": {"agreed": 735, "header_only": 0, "table_only": 13634, "inherited": 464, "conflict": 2, "incomparable": 0, "none": 1436}, "conflict_examples": ["m54329U_211105_213234-bc1001.5mc.hifi_reads.bam", "m54329U_211111_222027-bc1001.5mc.hifi_reads.bam"], "incomparable_examples": [], "table_unrecognised": [{"column": "sample_id", "files": 3, "raw_value": "CHM13", "table": "assembly"}, {"column": "sample_id", "files": 3, "raw_value": "GRCh38", "table": "assembly"}], "name_disagreements": 0, "name_disagreement_examples": [], "inheritance": {"ChecksumActivity": {"parent_none": 2, "passed": 464}, "IndexActivity": {"parent_none": 6, "passed": 1852}}, "parent_disagreements": {}, "parent_disagreement_examples": []}, {"dataset": "AnVIL_IGVF_Mouse_R1", "headers": {"named": 0, "unrecognised": 0, "no_sample": 2, "unread": 0}, "header_unrecognised": {}, "settled": {"agreed": 0, "header_only": 0, "table_only": 0, "inherited": 0, "conflict": 0, "incomparable": 0, "none": 6786}, "conflict_examples": [], "incomparable_examples": [], "table_unrecognised": [], "name_disagreements": 0, "name_disagreement_examples": [], "inheritance": {"IndexActivity": {"parent_none": 4}}, "parent_disagreements": {}, "parent_disagreement_examples": []}, {"dataset": "AnVIL_MAGE", "headers": {"named": 734, "unrecognised": 48, "no_sample": 0, "unread": 0}, "header_unrecognised": {"GM18861-6-1": 1, "GM18861-6-2": 1, "GM19095-3-1": 1, "GM19095-3-2": 1, "GM19116-15-1": 1, "GM19116-15-2": 1, "GM19117-13-1": 1, "GM19117-13-2": 1, "GM19129-7-1": 1, "GM19129-7-2": 1, "GM19143-15-1": 1, "GM19143-15-2": 1, "GM20502-16-1": 1, "GM20502-16-2": 1, "GM20525-10-1": 1, "GM20525-10-2": 1, "GM20532-7-1": 1, "GM20532-7-2": 1, "GM20543-9-1": 1, "GM20543-9-2": 1}, "settled": {"agreed": 731, "header_only": 3, "table_only": 2385, "inherited": 3, "conflict": 0, "incomparable": 0, "none": 163}, "conflict_examples": [], "incomparable_examples": [], "table_unrecognised": [], "name_disagreements": 0, "name_disagreement_examples": [], "inheritance": {"IndexActivity": {"parent_none": 49, "passed": 782}}, "parent_disagreements": {}, "parent_disagreement_examples": []}]};
     const CONFLICT_KEYS = new Set(DATA.conflict_categories);
     const LABEL = Object.fromEntries(DATA.columns.map(c => [c.key, c.label]));
 
diff --git a/docs/reconcile-report.md b/docs/reconcile-report.md
index f0191c64..05295c24 100644
--- a/docs/reconcile-report.md
+++ b/docs/reconcile-report.md
@@ -5,8 +5,8 @@ Generated by `make reconcile-report` from `output/anvil/20261019_225524/reconcil
 - **Run:** `20261019_225524`, 708,088 files
 - **Input:** `data/anvil/prod/anvil_files_metadata.json` (repository `anvil`, catalog `anvil15`)
 - **Classified by:** commit 5841265470b8, version 0.1.0
-- **Reconciled by:** commit 5841265470b8, version 0.1.0
-- **Translation table sha256:** `0931a644945fdcc526e74cd290b830b3def24e20dc7e21ebbf172881ea674051`
+- **Reconciled by:** commit fa826edc3e16, version 0.1.0
+- **Translation table sha256:** `7ec99f3da63ff27b7a7cc2d2bce96dd01744c8ffec50ec8be5f0b4ed13545ce9`
 
 ## How each slot settled
 
@@ -228,7 +228,7 @@ What each dataset holds, per dimension: every file counted once, under its recon
 
 ## Change since the previous reconciled run
 
-Against `20261019_084533` (708,088 files; the same translation table). Each cell is this run's count minus that run's.
+Against `20261019_084533` (708,088 files; a different translation table). Each cell is this run's count minus that run's.
 
 | dimension | published | published harmonized | submitter | submitter harmonized | external | external harmonized | inference | inherited | conflict (inference) | conflict (sources) | conflict (published) | published unreviewed | not applicable | not classified | added over published | filled over inference |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
diff --git a/docs/review-queue-report.md b/docs/review-queue-report.md
index e257a8dc..2b556c61 100644
--- a/docs/review-queue-report.md
+++ b/docs/review-queue-report.md
@@ -1,6 +1,6 @@
 # Review queue
 
-Source values the slot map or the entity map routed to a slot but that no authored translation rule reads yet, so they make no claim (contract 5.2), by kind of source and then by slot. A value is listed once per source, dataset, table and column it was found in; "rule it would use" names the seeded rule that matches it, or — where none does. For a donor's slot (phenotypic_sex, genetic_ancestry, organism_type, reported_ethnicity), files counts donors, and for a sample's slot (biosample_type), samples, each dataset's identifiers apart. 27 listed, from evidence under data/source_evidence and data/entity_evidence.
+Source values the slot map or the entity map routed to a slot but that no authored translation rule reads yet, so they make no claim (contract 5.2), by kind of source and then by slot. A value is listed once per source, dataset, table and column it was found in; "rule it would use" names the seeded rule that matches it, or — where none does. For a donor's slot (phenotypic_sex, genetic_ancestry, organism_type, reported_ethnicity), files counts donors, and for a sample's slot (biosample_type), samples, each dataset's identifiers apart. 156 listed, from evidence under data/source_evidence and data/entity_evidence.
 
 ## Published: the catalog's published columns
 
@@ -75,7 +75,139 @@ Source values the slot map or the entity map routed to a slot but that no author
 
 ## External: other catalogs
 
-No unreviewed values.
+### genetic_ancestry
+
+| files | raw value | source | dataset | table | column | rule it would use |
+| --- | --- | --- | --- | --- | --- | --- |
+| 15 | `'Papuan'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 4 | `'Dai'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 4 | `'Ju_hoan_North'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 4 | `'Mbuti'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 4 | `'Punjabi'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'Dinka'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'Even'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'Finnish'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'French'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'Han'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'Japanese'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'Jordanian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'Karitiana'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'Mandenka'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'Mixe'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'Quechua'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'Sardinian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 3 | `'Yoruba'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Abkhasian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Adygei'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Aleut'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Ami'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Armenian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Balochi'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'BantuHerero'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'BantuKenya'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'BantuTswana'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Basque'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'BedouinB'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Bengali'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Bergamo'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Biaka'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Bougainville'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Brahmin'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Brahui'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Bulgarian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Burmese'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Burusho'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Cambodian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Crete'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Druze'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Dusun'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'English'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Esan'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Eskimo_Naukan'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Eskimo_Sireniki'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Estonian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Gambian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Georgian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Greek'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Hazara'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Hezhen'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Hungarian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Icelandic'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Igorot'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Iranian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Iraqi_Jew'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Irula'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Kalash'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Kapu'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Khomani_San'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Kinh'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Korean'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Kusunda'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Kyrgyz'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Lahu'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Lezgin'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Luhya'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Luo'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Madiga'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Makrani'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Mala'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Mansi'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Masai'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Mayan'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Mende'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Miao'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Mixtec'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Mongola'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Mozabite'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Naxi'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'North_Ossetian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Orcadian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Oroqen'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Palestinian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Pathan'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Piapoco'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Pima'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Relli'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Russian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Saami'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Saharawi'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'She'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Sindhi'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Spanish'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Surui'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Tajik'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Thai'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Tlingit'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Tu'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Tubalar'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Tujia'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Turkish'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Tuscan'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Ulchi'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Uygur'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Xibo'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Yadava'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Yakut'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Yemenite_Jew'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Yi'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 2 | `'Zapotec'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Albanian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Altaian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Atayal'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Australian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Chane'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Chechen'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Chukchi'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Czech'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Daur'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Eskimo_Chaplin'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Hawaiian'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Itelman'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Khonda_Dora'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Maori'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Polish'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Samaritan'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
+| 1 | `'Somali'` | `sgdp` |  | `SGDP_metadata` | `Population_ID` | — |
 
 ## Authored mappings: what each translation rule declares
 
@@ -170,6 +302,8 @@ Every authored translation rule, by slot and most files first: the source values
 | 5,243 | 1,822 | 3,421 | 0 | phenotypic_sex.male | `any` | `'male'` | phenotypic_sex: Male | The term itself, as T2T, HPRC and NIA_CARD write it lower case and PRIMED and AnVIL capitalised. |
 | 1,603 | 0 | 1,603 | 0 | phenotypic_sex.pedigree_2 | `anvil / ANVIL_1000G_high_coverage_2019` | `'2'` | phenotypic_sex: Female | 1000 Genomes' `pedigree.sex` is a PED file's sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `2` means this only in a PED file. |
 | 1,599 | 0 | 1,599 | 0 | phenotypic_sex.pedigree_1 | `anvil / ANVIL_1000G_high_coverage_2019` | `'1'` | phenotypic_sex: Male | 1000 Genomes' `pedigree.sex` is a PED file's sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `1` means this only in a PED file. |
+| 174 | 0 | 0 | 174 | phenotypic_sex.sgdp_m | `sgdp` | `'M'` | phenotypic_sex: Male | SGDP's metadata file writes a genome's sex in `Gender` as M, F or U (not known); scoped to SGDP, since a bare `M` means male only there (#559). Its U has no row and makes no claim. |
+| 101 | 0 | 0 | 101 | phenotypic_sex.sgdp_f | `sgdp` | `'F'` | phenotypic_sex: Female | SGDP's metadata file writes a genome's sex in `Gender` as M, F or U (not known); scoped to SGDP, since a bare `F` means female only there (#559). Its U has no row and makes no claim. |
 
 ### genetic_ancestry
 
diff --git a/docs/review-queue.html b/docs/review-queue.html
index 4da3b110..cc218e60 100644
--- a/docs/review-queue.html
+++ b/docs/review-queue.html
@@ -31,7 +31,7 @@
     <a href="rules-dashboard.html" style="color: #0969da; text-decoration: none;">Rules Report</a>
   </nav>
   <h1>Translation Review Queue</h1>
-<p class="note">Source values the slot map or the entity map routed to a slot but that no authored translation rule reads yet, so they make no claim (contract 5.2), by kind of source and then by slot. A value is listed once per source, dataset, table and column it was found in; &quot;rule it would use&quot; names the seeded rule that matches it, or — where none does. For a donor&#x27;s slot (phenotypic_sex, genetic_ancestry, organism_type, reported_ethnicity), files counts donors, and for a sample&#x27;s slot (biosample_type), samples, each dataset&#x27;s identifiers apart. 27 listed, from evidence under data/source_evidence and data/entity_evidence.</p>
+<p class="note">Source values the slot map or the entity map routed to a slot but that no authored translation rule reads yet, so they make no claim (contract 5.2), by kind of source and then by slot. A value is listed once per source, dataset, table and column it was found in; &quot;rule it would use&quot; names the seeded rule that matches it, or — where none does. For a donor&#x27;s slot (phenotypic_sex, genetic_ancestry, organism_type, reported_ethnicity), files counts donors, and for a sample&#x27;s slot (biosample_type), samples, each dataset&#x27;s identifiers apart. 156 listed, from evidence under data/source_evidence and data/entity_evidence.</p>
 <h2>Published: the catalog&#x27;s published columns</h2>
 <h3>reference_assembly</h3>
 <div class="scroll"><table><tr><th>files</th><th>raw value</th><th>source</th><th>dataset</th><th>table</th><th>column</th><th>rule it would use</th></tr><tr><td class="num">220</td><td class="catalog">&#x27;[&quot;GRCm39&quot;]&#x27;</td><td class="catalog">anvil</td><td class="catalog">AnVIL_IGVF_Mouse_R1</td><td class="catalog">anvil_file</td><td class="catalog">reference_assembly</td><td class="plain">—</td></tr></table></div>
@@ -51,7 +51,8 @@ <h3>assay_type</h3>
 <h3>genetic_ancestry</h3>
 <div class="scroll"><table><tr><th>files</th><th>raw value</th><th>source</th><th>dataset</th><th>table</th><th>column</th><th>rule it would use</th></tr><tr><td class="num">1</td><td class="catalog">&#x27;ASL&#x27;</td><td class="catalog">anvil</td><td class="catalog">AnVIL_HPRC_R2</td><td class="catalog">sample_metadata</td><td class="catalog">population_abbreviation</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;MKK&#x27;</td><td class="catalog">anvil</td><td class="catalog">AnVIL_HPRC_R2</td><td class="catalog">sample_metadata</td><td class="catalog">population_abbreviation</td><td class="plain">—</td></tr></table></div>
 <h2>External: other catalogs</h2>
-<p>No unreviewed values.</p>
+<h3>genetic_ancestry</h3>
+<div class="scroll"><table><tr><th>files</th><th>raw value</th><th>source</th><th>dataset</th><th>table</th><th>column</th><th>rule it would use</th></tr><tr><td class="num">15</td><td class="catalog">&#x27;Papuan&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">4</td><td class="catalog">&#x27;Dai&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">4</td><td class="catalog">&#x27;Ju_hoan_North&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">4</td><td class="catalog">&#x27;Mbuti&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">4</td><td class="catalog">&#x27;Punjabi&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;Dinka&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;Even&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;Finnish&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;French&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;Han&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;Japanese&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;Jordanian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;Karitiana&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;Mandenka&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;Mixe&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;Quechua&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;Sardinian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">3</td><td class="catalog">&#x27;Yoruba&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Abkhasian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Adygei&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Aleut&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Ami&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Armenian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Balochi&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;BantuHerero&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td 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class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Brahui&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Bulgarian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Burmese&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Burusho&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Cambodian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Crete&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Druze&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Dusun&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;English&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Esan&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Eskimo_Naukan&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Eskimo_Sireniki&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Estonian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Gambian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Georgian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Greek&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Hazara&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Hezhen&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Hungarian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Icelandic&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Igorot&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Iranian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Iraqi_Jew&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Irula&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Kalash&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Kapu&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Khomani_San&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Kinh&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Korean&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Kusunda&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Kyrgyz&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Lahu&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Lezgin&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Luhya&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Luo&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Madiga&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Makrani&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Mala&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Mansi&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Masai&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Mayan&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Mende&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Miao&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Mixtec&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Mongola&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Mozabite&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Naxi&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;North_Ossetian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Orcadian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Oroqen&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Palestinian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Pathan&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Piapoco&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Pima&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Relli&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Russian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Saami&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Saharawi&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;She&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Sindhi&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Spanish&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Surui&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Tajik&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Thai&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Tlingit&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Tu&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Tubalar&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Tujia&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Turkish&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Tuscan&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Ulchi&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Uygur&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Xibo&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Yadava&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Yakut&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Yemenite_Jew&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Yi&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">2</td><td class="catalog">&#x27;Zapotec&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Albanian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Altaian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Atayal&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Australian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Chane&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Chechen&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Chukchi&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Czech&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Daur&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Eskimo_Chaplin&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Hawaiian&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Itelman&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Khonda_Dora&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Maori&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Polish&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Samaritan&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr><tr><td class="num">1</td><td class="catalog">&#x27;Somali&#x27;</td><td class="catalog">sgdp</td><td class="catalog"></td><td class="catalog">SGDP_metadata</td><td class="catalog">Population_ID</td><td class="plain">—</td></tr></table></div>
 <h2>Authored mappings: what each translation rule declares</h2>
 <p class="note">Every authored translation rule, by slot and most files first: the source values it matches in that slot, wherever the slot map or the entity map found them, and what it declares. Files are those it matched in the same evidence as the queue above, per kind of source; a row that matched nothing shows 0.</p>
 <h3>data_modality</h3>
@@ -65,7 +66,7 @@ <h3>reference_assembly</h3>
 <h3>assay_type</h3>
 <div class="scroll"><table><tr><th>files</th><th>published</th><th>submitter</th><th>external</th><th>rule</th><th>scope</th><th>matches</th><th>declares</th><th>reason</th></tr><tr><td class="num">19,388</td><td class="num">0</td><td class="num">12,426</td><td class="num">6,962</td><td class="plain">assay_type.wgs</td><td class="catalog">any</td><td class="catalog">&#x27;WGS&#x27;</td><td class="plain">assay_type: WGS; data_modality: genomic</td><td class="plain">Whole-genome sequencing, spelled as our term; a WGS library is genomic DNA.</td></tr><tr><td class="num">6,733</td><td class="num">0</td><td class="num">6,733</td><td class="num">0</td><td class="plain">assay_type.single_nucleus_rna_sequencing_assay</td><td class="catalog">any</td><td class="catalog">[&#x27;single-nucleus RNA sequencing assay&#x27;]</td><td class="plain">assay_type: snRNA-seq; data_modality: transcriptomic</td><td class="plain">IGVF&#x27;s `file.assay_titles`. Single-nucleus RNA sequencing, EFO:0009809 (#533); RNA-seq measures the transcriptome.</td></tr><tr><td class="num">3,770</td><td class="num">0</td><td class="num">3,770</td><td class="num">0</td><td class="plain">assay_type.hi_c</td><td class="catalog">any</td><td class="catalog">&#x27;Hi-C&#x27; · &#x27;hic&#x27;</td><td class="plain">data_modality: epigenomic.3d_contact_maps</td><td class="plain">Hi-C measures chromatin contacts, which MODAL&#x27;s `3D contact maps` names (#563); the modality is the information measured, not the use HPRC puts it to (scaffolding). The vocabulary has no assay term for it yet (#399), so nothing is declared for assay_type. `hic` is the table-name span of the same data.</td></tr><tr><td class="num">12</td><td class="num">0</td><td class="num">12</td><td class="num">0</td><td class="plain">assay_type.single_nucleus_atac_seq+single_nucleus_rna_sequencing_assay</td><td class="catalog">anvil / AnVIL_IGVF_Mouse_R1</td><td class="catalog">[&#x27;single-nucleus ATAC-seq&#x27;, &#x27;single-nucleus RNA sequencing assay&#x27;]</td><td class="plain">assay_type: SHARE-seq</td><td class="plain">IGVF&#x27;s `file.assay_titles`. Both assays of one SHARE-seq experiment: IGVF&#x27;s analysis sets that carry the pair (IGVFDS5339UKYM, IGVFDS5530HYKI) give `preferred_assay_titles: [&quot;SHARE-seq&quot;]`, EFO:0022962. Nothing is declared for data_modality. The value is the analysis set&#x27;s label, on every file of the set, and each file holds one half: the count matrices and kallisto outputs are RNA, the fragment files ATAC. Any one modality would be wrong for some of them, and the vocabulary holds one per file, so each file&#x27;s own evidence decides. Scoped to this dataset: the two titles alone do not name the protocol, since another joint assay (10x Multiome for one) could carry the same pair, so elsewhere the value selects no row and is queued.</td></tr><tr><td class="num">10</td><td class="num">0</td><td class="num">10</td><td class="num">0</td><td class="plain">assay_type.single_nucleus_atac_seq</td><td class="catalog">any</td><td class="catalog">[&#x27;single-nucleus ATAC-seq&#x27;]</td><td class="plain">assay_type: snATAC-seq; data_modality: epigenomic.chromatin_accessibility</td><td class="plain">IGVF&#x27;s `file.assay_titles`. Single-nucleus ATAC-seq: our own term, under EFO&#x27;s sc/snATAC-seq, which has no single-nucleus child (#533). ATAC-seq measures open chromatin. In AnVIL_IGVF_Mouse_R1 the value is on the reads (FASTQ) and seqspec files of the ATAC half of a SHARE-seq experiment.</td></tr></table></div>
 <h3>phenotypic_sex</h3>
-<div class="scroll"><table><tr><th>files</th><th>published</th><th>submitter</th><th>external</th><th>rule</th><th>scope</th><th>matches</th><th>declares</th><th>reason</th></tr><tr><td class="num">5,293</td><td class="num">1,845</td><td class="num">3,448</td><td class="num">0</td><td class="plain">phenotypic_sex.female</td><td class="catalog">any</td><td class="catalog">&#x27;female&#x27;</td><td class="plain">phenotypic_sex: Female</td><td class="plain">The term itself, as T2T, HPRC and NIA_CARD write it lower case and PRIMED and AnVIL capitalised.</td></tr><tr><td class="num">5,243</td><td class="num">1,822</td><td class="num">3,421</td><td class="num">0</td><td class="plain">phenotypic_sex.male</td><td class="catalog">any</td><td class="catalog">&#x27;male&#x27;</td><td class="plain">phenotypic_sex: Male</td><td class="plain">The term itself, as T2T, HPRC and NIA_CARD write it lower case and PRIMED and AnVIL capitalised.</td></tr><tr><td class="num">1,603</td><td class="num">0</td><td class="num">1,603</td><td class="num">0</td><td class="plain">phenotypic_sex.pedigree_2</td><td class="catalog">anvil / ANVIL_1000G_high_coverage_2019</td><td class="catalog">&#x27;2&#x27;</td><td class="plain">phenotypic_sex: Female</td><td class="plain">1000 Genomes&#x27; `pedigree.sex` is a PED file&#x27;s sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `2` means this only in a PED file.</td></tr><tr><td class="num">1,599</td><td class="num">0</td><td class="num">1,599</td><td class="num">0</td><td class="plain">phenotypic_sex.pedigree_1</td><td class="catalog">anvil / ANVIL_1000G_high_coverage_2019</td><td class="catalog">&#x27;1&#x27;</td><td class="plain">phenotypic_sex: Male</td><td class="plain">1000 Genomes&#x27; `pedigree.sex` is a PED file&#x27;s sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `1` means this only in a PED file.</td></tr></table></div>
+<div class="scroll"><table><tr><th>files</th><th>published</th><th>submitter</th><th>external</th><th>rule</th><th>scope</th><th>matches</th><th>declares</th><th>reason</th></tr><tr><td class="num">5,293</td><td class="num">1,845</td><td class="num">3,448</td><td class="num">0</td><td class="plain">phenotypic_sex.female</td><td class="catalog">any</td><td class="catalog">&#x27;female&#x27;</td><td class="plain">phenotypic_sex: Female</td><td class="plain">The term itself, as T2T, HPRC and NIA_CARD write it lower case and PRIMED and AnVIL capitalised.</td></tr><tr><td class="num">5,243</td><td class="num">1,822</td><td class="num">3,421</td><td class="num">0</td><td class="plain">phenotypic_sex.male</td><td class="catalog">any</td><td class="catalog">&#x27;male&#x27;</td><td class="plain">phenotypic_sex: Male</td><td class="plain">The term itself, as T2T, HPRC and NIA_CARD write it lower case and PRIMED and AnVIL capitalised.</td></tr><tr><td class="num">1,603</td><td class="num">0</td><td class="num">1,603</td><td class="num">0</td><td class="plain">phenotypic_sex.pedigree_2</td><td class="catalog">anvil / ANVIL_1000G_high_coverage_2019</td><td class="catalog">&#x27;2&#x27;</td><td class="plain">phenotypic_sex: Female</td><td class="plain">1000 Genomes&#x27; `pedigree.sex` is a PED file&#x27;s sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `2` means this only in a PED file.</td></tr><tr><td class="num">1,599</td><td class="num">0</td><td class="num">1,599</td><td class="num">0</td><td class="plain">phenotypic_sex.pedigree_1</td><td class="catalog">anvil / ANVIL_1000G_high_coverage_2019</td><td class="catalog">&#x27;1&#x27;</td><td class="plain">phenotypic_sex: Male</td><td class="plain">1000 Genomes&#x27; `pedigree.sex` is a PED file&#x27;s sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `1` means this only in a PED file.</td></tr><tr><td class="num">174</td><td class="num">0</td><td class="num">0</td><td class="num">174</td><td class="plain">phenotypic_sex.sgdp_m</td><td class="catalog">sgdp</td><td class="catalog">&#x27;M&#x27;</td><td class="plain">phenotypic_sex: Male</td><td class="plain">SGDP&#x27;s metadata file writes a genome&#x27;s sex in `Gender` as M, F or U (not known); scoped to SGDP, since a bare `M` means male only there (#559). Its U has no row and makes no claim.</td></tr><tr><td class="num">101</td><td class="num">0</td><td class="num">0</td><td class="num">101</td><td class="plain">phenotypic_sex.sgdp_f</td><td class="catalog">sgdp</td><td class="catalog">&#x27;F&#x27;</td><td class="plain">phenotypic_sex: Female</td><td class="plain">SGDP&#x27;s metadata file writes a genome&#x27;s sex in `Gender` as M, F or U (not known); scoped to SGDP, since a bare `F` means female only there (#559). Its U has no row and makes no claim.</td></tr></table></div>
 <h3>genetic_ancestry</h3>
 <div class="scroll"><table><tr><th>files</th><th>published</th><th>submitter</th><th>external</th><th>rule</th><th>scope</th><th>matches</th><th>declares</th><th>reason</th></tr><tr><td class="num">893</td><td class="num">0</td><td class="num">893</td><td class="num">0</td><td class="plain">genetic_ancestry.afr</td><td class="catalog">any</td><td class="catalog">&#x27;AFR&#x27;</td><td class="plain">genetic_ancestry: AFR</td><td class="plain">1000 Genomes&#x27; superpopulation code, the term itself (HANCESTRO:2000).</td></tr><tr><td class="num">633</td><td class="num">0</td><td class="num">633</td><td class="num">0</td><td class="plain">genetic_ancestry.eur</td><td class="catalog">any</td><td class="catalog">&#x27;EUR&#x27;</td><td class="plain">genetic_ancestry: EUR</td><td class="plain">1000 Genomes&#x27; superpopulation code, the term itself (HANCESTRO:2003).</td></tr><tr><td class="num">601</td><td class="num">0</td><td class="num">601</td><td class="num">0</td><td class="plain">genetic_ancestry.sas</td><td class="catalog">any</td><td class="catalog">&#x27;SAS&#x27;</td><td class="plain">genetic_ancestry: SAS</td><td class="plain">1000 Genomes&#x27; superpopulation code, the term itself (HANCESTRO:2004).</td></tr><tr><td class="num">587</td><td class="num">0</td><td class="num">587</td><td class="num">0</td><td class="plain">genetic_ancestry.gwd</td><td class="catalog">any</td><td class="catalog">&#x27;GWD&#x27; · &#x27;GWD | AFR&#x27;</td><td class="plain">genetic_ancestry: GWD</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0664); PRIMED&#x27;s `population_label` writes it with its superpopulation, `GWD | AFR`, which the term sits under.</td></tr><tr><td class="num">585</td><td class="num">0</td><td class="num">585</td><td class="num">0</td><td class="plain">genetic_ancestry.eas</td><td class="catalog">any</td><td class="catalog">&#x27;EAS&#x27;</td><td class="plain">genetic_ancestry: EAS</td><td class="plain">1000 Genomes&#x27; superpopulation code, the term itself (HANCESTRO:2002).</td></tr><tr><td class="num">572</td><td class="num">0</td><td class="num">572</td><td class="num">0</td><td class="plain">genetic_ancestry.yri</td><td class="catalog">any</td><td class="catalog">&#x27;YRI&#x27; · &#x27;YRI | AFR&#x27;</td><td class="plain">genetic_ancestry: YRI</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0668); PRIMED&#x27;s `population_label` writes it with its superpopulation, `YRI | AFR`, which the term sits under.</td></tr><tr><td class="num">567</td><td class="num">0</td><td class="num">567</td><td class="num">0</td><td class="plain">genetic_ancestry.ceu</td><td class="catalog">any</td><td class="catalog">&#x27;CEU&#x27; · &#x27;CEU | EUR&#x27;</td><td class="plain">genetic_ancestry: CEU</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0766); PRIMED&#x27;s `population_label` writes it with its superpopulation, `CEU | EUR`, which the term sits under.</td></tr><tr><td class="num">531</td><td class="num">0</td><td class="num">531</td><td class="num">0</td><td class="plain">genetic_ancestry.chs</td><td class="catalog">any</td><td class="catalog">&#x27;CHS&#x27; · &#x27;CHS | EAS&#x27;</td><td class="plain">genetic_ancestry: CHS</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0756); PRIMED&#x27;s `population_label` writes it with its superpopulation, `CHS | EAS`, which the term sits under.</td></tr><tr><td class="num">502</td><td class="num">0</td><td class="num">502</td><td class="num">0</td><td class="plain">genetic_ancestry.ibs</td><td class="catalog">any</td><td class="catalog">&#x27;IBS&#x27; · &#x27;IBS | EUR&#x27;</td><td class="plain">genetic_ancestry: IBS</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0768); PRIMED&#x27;s `population_label` writes it with its superpopulation, `IBS | EUR`, which the term sits under.</td></tr><tr><td class="num">490</td><td class="num">0</td><td class="num">490</td><td class="num">0</td><td class="plain">genetic_ancestry.amr</td><td class="catalog">any</td><td class="catalog">&#x27;AMR&#x27;</td><td class="plain">genetic_ancestry: AMR</td><td class="plain">1000 Genomes&#x27; superpopulation code, the term itself (HANCESTRO:2001).</td></tr><tr><td class="num">486</td><td class="num">0</td><td class="num">486</td><td class="num">0</td><td class="plain">genetic_ancestry.esn</td><td class="catalog">any</td><td class="catalog">&#x27;ESN&#x27; · &#x27;ESN | AFR&#x27;</td><td class="plain">genetic_ancestry: ESN</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0661); PRIMED&#x27;s `population_label` writes it with its superpopulation, `ESN | AFR`, which the term sits under.</td></tr><tr><td class="num">477</td><td class="num">0</td><td class="num">477</td><td class="num">0</td><td class="plain">genetic_ancestry.pjl</td><td class="catalog">any</td><td class="catalog">&#x27;PJL&#x27; · &#x27;PJL | SAS&#x27;</td><td class="plain">genetic_ancestry: PJL</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0804); PRIMED&#x27;s `population_label` writes it with its superpopulation, `PJL | SAS`, which the term sits under.</td></tr><tr><td class="num">469</td><td class="num">0</td><td class="num">469</td><td class="num">0</td><td class="plain">genetic_ancestry.pur</td><td class="catalog">any</td><td class="catalog">&#x27;PUR&#x27; · &#x27;PUR | AMR&#x27;</td><td class="plain">genetic_ancestry: PUR</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0687); PRIMED&#x27;s `population_label` writes it with its superpopulation, `PUR | AMR`, which the term sits under.</td></tr><tr><td class="num">439</td><td class="num">0</td><td class="num">439</td><td class="num">0</td><td class="plain">genetic_ancestry.clm</td><td class="catalog">any</td><td class="catalog">&#x27;CLM&#x27; · &#x27;CLM | AMR&#x27;</td><td class="plain">genetic_ancestry: CLM</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0684); PRIMED&#x27;s `population_label` writes it with its superpopulation, `CLM | AMR`, which the term sits under.</td></tr><tr><td class="num">431</td><td class="num">0</td><td class="num">431</td><td class="num">0</td><td class="plain">genetic_ancestry.beb</td><td class="catalog">any</td><td class="catalog">&#x27;BEB&#x27; · &#x27;BEB | SAS&#x27;</td><td class="plain">genetic_ancestry: BEB</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0802); PRIMED&#x27;s `population_label` writes it with its superpopulation, `BEB | SAS`, which the term sits under.</td></tr><tr><td class="num">413</td><td class="num">0</td><td class="num">413</td><td class="num">0</td><td class="plain">genetic_ancestry.pel</td><td class="catalog">any</td><td class="catalog">&#x27;PEL&#x27; · &#x27;PEL | AMR&#x27;</td><td class="plain">genetic_ancestry: PEL</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0686); PRIMED&#x27;s `population_label` writes it with its superpopulation, `PEL | AMR`, which the term sits under.</td></tr><tr><td class="num">408</td><td class="num">0</td><td class="num">408</td><td class="num">0</td><td class="plain">genetic_ancestry.khv</td><td class="catalog">any</td><td class="catalog">&#x27;KHV&#x27; · &#x27;KHV | EAS&#x27;</td><td class="plain">genetic_ancestry: KHV</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0755); PRIMED&#x27;s `population_label` writes it with its superpopulation, `KHV | EAS`, which the term sits under.</td></tr><tr><td class="num">395</td><td class="num">0</td><td class="num">395</td><td class="num">0</td><td class="plain">genetic_ancestry.acb</td><td class="catalog">any</td><td class="catalog">&#x27;ACB&#x27; · &#x27;ACB | AFR&#x27;</td><td class="plain">genetic_ancestry: ACB</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0660); PRIMED&#x27;s `population_label` writes it with its superpopulation, `ACB | AFR`, which the term sits under.</td></tr><tr><td class="num">375</td><td class="num">0</td><td class="num">375</td><td class="num">0</td><td class="plain">genetic_ancestry.stu</td><td class="catalog">any</td><td class="catalog">&#x27;STU&#x27; · &#x27;STU | SAS&#x27;</td><td class="plain">genetic_ancestry: STU</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0805); PRIMED&#x27;s `population_label` writes it with its superpopulation, `STU | SAS`, which the term sits under.</td></tr><tr><td class="num">357</td><td class="num">0</td><td class="num">357</td><td class="num">0</td><td class="plain">genetic_ancestry.tsi</td><td class="catalog">any</td><td class="catalog">&#x27;TSI&#x27; · &#x27;TSI | EUR&#x27;</td><td class="plain">genetic_ancestry: TSI</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0769); PRIMED&#x27;s `population_label` writes it with its superpopulation, `TSI | EUR`, which the term sits under.</td></tr><tr><td class="num">356</td><td class="num">0</td><td class="num">356</td><td class="num">0</td><td class="plain">genetic_ancestry.jpt</td><td class="catalog">any</td><td class="catalog">&#x27;JPT&#x27; · &#x27;JPT | EAS&#x27;</td><td class="plain">genetic_ancestry: JPT</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0754); PRIMED&#x27;s `population_label` writes it with its superpopulation, `JPT | EAS`, which the term sits under.</td></tr><tr><td class="num">352</td><td class="num">0</td><td class="num">352</td><td class="num">0</td><td class="plain">genetic_ancestry.itu</td><td class="catalog">any</td><td class="catalog">&#x27;ITU&#x27; · &#x27;ITU | SAS&#x27;</td><td class="plain">genetic_ancestry: ITU</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0806); PRIMED&#x27;s `population_label` writes it with its superpopulation, `ITU | SAS`, which the term sits under.</td></tr><tr><td class="num">344</td><td class="num">0</td><td class="num">344</td><td class="num">0</td><td class="plain">genetic_ancestry.gih</td><td class="catalog">any</td><td class="catalog">&#x27;GIH&#x27; · &#x27;GIH | SAS&#x27;</td><td class="plain">genetic_ancestry: GIH</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0803); PRIMED&#x27;s `population_label` writes it with its superpopulation, `GIH | SAS`, which the term sits under.</td></tr><tr><td class="num">342</td><td class="num">0</td><td class="num">342</td><td class="num">0</td><td class="plain">genetic_ancestry.chb</td><td class="catalog">any</td><td class="catalog">&#x27;CHB&#x27; · &#x27;CHB | EAS&#x27;</td><td class="plain">genetic_ancestry: CHB</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0753); PRIMED&#x27;s `population_label` writes it with its superpopulation, `CHB | EAS`, which the term sits under.</td></tr><tr><td class="num">338</td><td class="num">0</td><td class="num">338</td><td class="num">0</td><td class="plain">genetic_ancestry.msl</td><td class="catalog">any</td><td class="catalog">&#x27;MSL&#x27; · &#x27;MSL | AFR&#x27;</td><td class="plain">genetic_ancestry: MSL</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0667); PRIMED&#x27;s `population_label` writes it with its superpopulation, `MSL | AFR`, which the term sits under.</td></tr><tr><td class="num">334</td><td class="num">0</td><td class="num">334</td><td class="num">0</td><td class="plain">genetic_ancestry.fin</td><td class="catalog">any</td><td class="catalog">&#x27;FIN&#x27; · &#x27;FIN | EUR&#x27;</td><td class="plain">genetic_ancestry: FIN</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0767); PRIMED&#x27;s `population_label` writes it with its superpopulation, `FIN | EUR`, which the term sits under.</td></tr><tr><td class="num">331</td><td class="num">0</td><td class="num">331</td><td class="num">0</td><td class="plain">genetic_ancestry.lwk</td><td class="catalog">any</td><td class="catalog">&#x27;LWK&#x27; · &#x27;LWK | AFR&#x27;</td><td class="plain">genetic_ancestry: LWK</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0666); PRIMED&#x27;s `population_label` writes it with its superpopulation, `LWK | AFR`, which the term sits under.</td></tr><tr><td class="num">322</td><td class="num">0</td><td class="num">322</td><td class="num">0</td><td class="plain">genetic_ancestry.mxl</td><td class="catalog">any</td><td class="catalog">&#x27;MXL&#x27; · &#x27;MXL | AMR&#x27;</td><td class="plain">genetic_ancestry: MXL</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0685); PRIMED&#x27;s `population_label` writes it with its superpopulation, `MXL | AMR`, which the term sits under.</td></tr><tr><td class="num">313</td><td class="num">0</td><td class="num">313</td><td class="num">0</td><td class="plain">genetic_ancestry.cdx</td><td class="catalog">any</td><td class="catalog">&#x27;CDX&#x27; · &#x27;CDX | EAS&#x27;</td><td class="plain">genetic_ancestry: CDX</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0752); PRIMED&#x27;s `population_label` writes it with its superpopulation, `CDX | EAS`, which the term sits under.</td></tr><tr><td class="num">311</td><td class="num">0</td><td class="num">311</td><td class="num">0</td><td class="plain">genetic_ancestry.gbr</td><td class="catalog">any</td><td class="catalog">&#x27;GBR&#x27; · &#x27;GBR | EUR&#x27;</td><td class="plain">genetic_ancestry: GBR</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0765); PRIMED&#x27;s `population_label` writes it with its superpopulation, `GBR | EUR`, which the term sits under.</td></tr><tr><td class="num">257</td><td class="num">0</td><td class="num">257</td><td class="num">0</td><td class="plain">genetic_ancestry.asw</td><td class="catalog">any</td><td class="catalog">&#x27;ASW&#x27; · &#x27;ASW | AFR&#x27;</td><td class="plain">genetic_ancestry: ASW</td><td class="plain">1000 Genomes&#x27; population code, the term itself (HANCESTRO:0659); PRIMED&#x27;s `population_label` writes it with its superpopulation, `ASW | AFR`, which the term sits under.</td></tr></table></div>
 <h3>organism_type</h3>
diff --git a/docs/rules-dashboard.html b/docs/rules-dashboard.html
index 8b5de281..58bd6968 100644
--- a/docs/rules-dashboard.html
+++ b/docs/rules-dashboard.html
@@ -70,7 +70,7 @@ <h2 id="undeclared-head"></h2>
   <div id="undeclared"></div>
 
   <script>
-    const DATA = {"run": "20261019_225524", "records": 708088, "provenance": "188 rules, 23 edge rules and 16 sample and donor rules. Counts from the reconciled records of 708,088 files, in the run started on 19 October 2026 (output folder 20261019_225524), and the run's 3,416 donor records, counted as files under (donor records), and its 5,434 sample records, under (sample records), and the 25 sample maps held outside AnVIL its steps read through (#710), under (external sample maps).", "rules": [{"id": "index_file", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "extensions: [\".bai\", \".crai\", \".tbi\", \".csi\", \".fai\", \".gzi\", \".idx\", \".pbi\"]", "effect": "data_type: index", "expanded": [], "rationale": "Index file \u2014 an index; what it indexes is the parent's to say", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "checksum_file", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".md5\"]", "effect": "data_type: checksum\nstatus:\n  data_modality: not_applicable\n  reference_assembly: not_applicable\n  assay_type: not_applicable\n  platform: not_applicable\n  instrument_model: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Checksum file \u2014 a checksum, and no data of its own", "dataset": "general", "claims": 113864, "files": 14233, "won": 14233, "datasets": {"ANVIL_1000G_high_coverage_2019": 13008, "ANVIL_HPRC": 759, "AnVIL_HPRC_R2": 466}, "source_types": {}}, {"id": "log_file", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".log\"]", "effect": "data_type: log\nstatus:\n  data_modality: not_applicable\n  reference_assembly: not_applicable\n  assay_type: not_applicable\n  platform: not_applicable\n  instrument_model: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Log file \u2014 a log, and no data of its own", "dataset": "general", "claims": 29096, "files": 3637, "won": 3637, "datasets": {"ANVIL_1000G_PRIMED_data_model": 2852, "ANVIL_HPRC": 785}, "source_types": {}}, {"id": "variant_default_genomic", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "extensions: [\".vcf\", \".bcf\", \".gvcf\", \".g.vcf\"]", "effect": "data_modality: genomic\ndata_type: variants", "expanded": [], "rationale": "VCF files contain variant calls (genomic data)", "dataset": "general", "claims": 408298, "files": 204149, "won": 204149, "datasets": {"ANVIL_T2T": 109863, "ANVIL_T2T_CHRY": 90618, "ANVIL_1000G_high_coverage_2019": 3252, "ANVIL_HPRC": 304, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 59, "ANVIL_1000G_PRIMED_data_model": 46, "AnVIL_MAGE": 3, "AnVIL_HPRC_R2": 2, "ANVIL_nhp_dGTEx_V1": 2}, "source_types": {}}, {"id": "plink_genomic", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "extensions: [\".pgen\", \".pvar\", \".psam\"]", "effect": "data_modality: genomic\ndata_type: genotypes", "expanded": [], "rationale": "PLINK files contain genotype data (genomic)", "dataset": "general", "claims": 17124, "files": 8562, "won": 8562, "datasets": {"ANVIL_1000G_PRIMED_data_model": 8562}, "source_types": {}}, {"id": "idat_array_signal", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "extensions: [\".idat\"]", "effect": "data_type: array_signal\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "IDAT is Illumina's BeadChip intensity format, written by genotyping and methylation chips alike, so the extension cannot say which chip wrote the file, only that it is array signal (#603). Its intensities are keyed by probe, not by genome coordinate, so no reference applies until genotypes are called against a chip manifest", "dataset": "general", "claims": 640, "files": 160, "won": 160, "datasets": {"ANVIL_HPRC": 160}, "source_types": {}}, {"id": "sc_matrix_default", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "extensions: [\".h5ad\", \".loom\", \".mtx\"]", "effect": "data_modality: transcriptomic\ndata_type: expression_matrix\nassay_type: sc/snRNA-seq\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Single-cell matrix format: transcriptomic data from a single-cell or single-nucleus assay, which one the format does not say (#563, #533)", "dataset": "general", "claims": 1750, "files": 350, "won": 350, "datasets": {"AnVIL_IGVF_Mouse_R1": 350}, "source_types": {}}, {"id": "image_svs_histology", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".svs\"]", "effect": "data_modality: imaging.microscopy\ndata_type: images\nassay_type: Histology\nstatus:\n  platform: not_applicable\n  instrument_model: not_applicable\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "SVS files are Aperio whole-slide histology images used for pathology analysis", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "image_derived", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".png\", \".jpg\", \".jpeg\", \".tiff\", \".tif\"]", "effect": "data_type: images\nstatus:\n  data_modality: not_applicable\n  platform: not_applicable\n  instrument_model: not_applicable\n  reference_assembly: not_applicable\n  assay_type: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Image files are derived visualizations (QC plots, assembly graphs) \u2014 not primary experimental data", "dataset": "general", "claims": 24592, "files": 3074, "won": 3074, "datasets": {"ANVIL_HPRC": 3074}, "source_types": {}}, {"id": "nanopore_fast5", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".fast5\"]", "effect": "data_type: raw_signal\nplatform: ONT\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "FAST5 files contain raw Oxford Nanopore electrical signal data. Reference not applicable until basecalling/alignment", "dataset": "general", "claims": 62545, "files": 12509, "won": 12509, "datasets": {"ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 12394, "ANVIL_HPRC": 115}, "source_types": {}}, {"id": "nanopore_pod5", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".pod5\"]", "effect": "data_type: raw_signal\nplatform: ONT\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "POD5 files contain raw Oxford Nanopore electrical signal data (newer format replacing FAST5)", "dataset": "general", "claims": 55, "files": 11, "won": 11, "datasets": {"AnVIL_IGVF_Mouse_R1": 11}, "source_types": {}}, {"id": "reads_base", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".fastq\", \".fq\"]", "effect": "data_type: reads\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "FASTQ files contain raw sequencing reads. Reference not applicable until alignment", "dataset": "general", "claims": 85080, "files": 21270, "won": 21270, "datasets": {"ANVIL_T2T": 6404, "AnVIL_IGVF_Mouse_R1": 3959, "ANVIL_HPRC": 3856, "AnVIL_HPRC_R2": 3661, "AnVIL_MAGE": 1558, "AnVIL_ENCORE_RS293": 820, "ANVIL_T2T_CHRY": 558, "AnVIL_ENCORE_293T": 448, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 6}, "source_types": {}}, {"id": "fasta_base", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "format: FASTA", "effect": "data_type: sequence\nstatus:\n  platform: not_applicable\n  instrument_model: not_applicable\n  assay_type: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "FASTA file detected; header inspection needed to determine if de novo assembly, reference genome, or transcript sequences", "dataset": "general", "claims": 5880, "files": 980, "won": 980, "datasets": {"ANVIL_HPRC": 476, "AnVIL_HPRC_R2": 466, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 27, "ANVIL_T2T_CHRY": 6, "ANVIL_T2T": 5}, "source_types": {}}, {"id": "pangenome_graph", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "extensions: [\".gfa\", \".rgfa\", \".gbz\", \".vg\", \".gbwt\", \".xg\"]", "effect": "data_modality: genomic\ndata_type: pangenome\nstatus:\n  platform: not_applicable\n  instrument_model: not_applicable\n  assay_type: not_applicable", "expanded": [], "rationale": "Sequence-graph formats (GFA/rGFA/GBZ/vg/GBWT/xg) encode a pangenome graph \u2014 a genomic assembly product, so no sequencing platform or assay applies", "dataset": "general", "claims": 1710, "files": 342, "won": 342, "datasets": {"ANVIL_HPRC": 336, "AnVIL_HPRC_R2": 6}, "source_types": {}}, {"id": "isoseq_filename", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bam\", \".cram\"]\nfilename_pattern: \"(?i)\\\\.flnc\\\\.\"", "effect": "data_modality: transcriptomic", "expanded": [], "rationale": "Filename contains IsoSeq indicator (flnc = full-length non-chimeric transcript reads)", "dataset": "general", "claims": 230, "files": 230, "won": 230, "datasets": {"AnVIL_HPRC_R2": 230}, "source_types": {}}, {"id": "hifi_filename", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bam\", \".cram\", \".sam\"]\nfilename_pattern: \"(?i)(^|[._-])hifi|_pb_\"", "effect": "platform: PACBIO", "expanded": [], "rationale": "HiFi names PacBio's read chemistry: it says the platform, not the assay or the modality \u2014 Iso-Seq is HiFi too (#430)", "dataset": "general", "claims": 1240, "files": 1240, "won": 1240, "datasets": {"AnVIL_HPRC_R2": 899, "ANVIL_HPRC": 341}, "source_types": {}}, {"id": "star_filename", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bam\", \".cram\", \".sam\"]\nfilename_pattern: \"(?i)Aligned\\\\.sortedByCoord|_STAR_|\\\\.star\\\\.\"", "effect": "data_modality: transcriptomic\nassay_type: RNA-seq", "expanded": [], "rationale": "STAR aligner output indicates RNA-seq", "dataset": "general", "claims": 2640, "files": 1320, "won": 1320, "datasets": {"ANVIL_nhp_dGTEx_V1": 686, "AnVIL_ENCORE_RS293": 410, "AnVIL_ENCORE_293T": 224}, "source_types": {}}, {"id": "pangenome_reference_mc", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".gfa\", \".rgfa\", \".gbz\", \".vg\", \".gbwt\", \".xg\"]\nfilename_pattern: \"(?i)-mc-\"", "effect": "data_modality: genomic\ndata_type: pangenome.reference", "expanded": [], "rationale": "Minigraph-Cactus (mc) HPRC graphs are published pangenome references used as the alignment coordinate system", "dataset": "general", "claims": 52, "files": 26, "won": 26, "datasets": {"ANVIL_HPRC": 20, "AnVIL_HPRC_R2": 6}, "source_types": {}}, {"id": "filename_ref_grch38", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: &reference_bearing_extensions\n  [\".bam\", \".cram\", \".sam\",\n   \".vcf\", \".bcf\", \".gvcf\", \".g.vcf\",\n   \".bed\", \".narrowpeak\", \".broadpeak\", \".bigwig\", \".bw\", \".bedgraph\",\n   \".fasta\", \".fa\", \".fna\", \".dict\", \".sizes\",\n   \".gfa\", \".rgfa\", \".gbz\", \".vg\", \".gbwt\", \".xg\",\n   \".snarls\", \".dist\", \".min\", \".gg\", \".hapl\", \".trans\",\n   \".chain\", \".paf\", \".delta\", \".hal\",\n   \".gff3\",\n   \".pgen\", \".pvar\",\n   \".bai\", \".crai\", \".tbi\", \".csi\", \".fai\"]\nfilename_pattern: \"(?i)hg38|grch38|hs38\"", "effect": "reference_assembly: GRCh38", "expanded": [], "rationale": "Filename contains GRCh38/hg38 reference indicator", "dataset": "general", "claims": 1582, "files": 1582, "won": 1441, "datasets": {"AnVIL_HPRC_R2": 930, "ANVIL_T2T": 372, "ANVIL_HPRC": 266, "ANVIL_T2T_CHRY": 10, "ANVIL_1000G_PRIMED_data_model": 2, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 2}, "source_types": {}}, {"id": "filename_ref_grch37", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions:\n- .bam\n- .cram\n- .sam\n- .vcf\n- .bcf\n- .gvcf\n- .g.vcf\n- .bed\n- .narrowpeak\n- .broadpeak\n- .bigwig\n- .bw\n- .bedgraph\n- .fasta\n- .fa\n- .fna\n- .dict\n- .sizes\n- .gfa\n- .rgfa\n- .gbz\n- .vg\n- .gbwt\n- .xg\n- .snarls\n- .dist\n- .min\n- .gg\n- .hapl\n- .trans\n- .chain\n- .paf\n- .delta\n- .hal\n- .gff3\n- .pgen\n- .pvar\n- .bai\n- .crai\n- .tbi\n- .csi\n- .fai\nfilename_pattern: (?i)hg19|grch37|hs37|b37", "effect": "reference_assembly: GRCh37", "expanded": ["reference_bearing_extensions"], "rationale": "Filename contains GRCh37/hg19 reference indicator", "dataset": "general", "claims": 1, "files": 1, "won": 1, "datasets": {"ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 1}, "source_types": {}}, {"id": "filename_ref_chm13", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions:\n- .bam\n- .cram\n- .sam\n- .vcf\n- .bcf\n- .gvcf\n- .g.vcf\n- .bed\n- .narrowpeak\n- .broadpeak\n- .bigwig\n- .bw\n- .bedgraph\n- .fasta\n- .fa\n- .fna\n- .dict\n- .sizes\n- .gfa\n- .rgfa\n- .gbz\n- .vg\n- .gbwt\n- .xg\n- .snarls\n- .dist\n- .min\n- .gg\n- .hapl\n- .trans\n- .chain\n- .paf\n- .delta\n- .hal\n- .gff3\n- .pgen\n- .pvar\n- .bai\n- .crai\n- .tbi\n- .csi\n- .fai\nfilename_pattern: (?i)chm13|t2t|CHM13v2|hs1", "effect": "reference_assembly: CHM13", "expanded": ["reference_bearing_extensions"], "rationale": "Filename contains CHM13/T2T reference indicator", "dataset": "general", "claims": 1709, "files": 1709, "won": 1672, "datasets": {"AnVIL_HPRC_R2": 930, "ANVIL_T2T": 501, "ANVIL_HPRC": 266, "ANVIL_T2T_CHRY": 10, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 2}, "source_types": {}}, {"id": "fasta_assembly_filename", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "format: FASTA\nfilename_pattern: \"(?i)(^|[._-])(assembly(?!\\\\d)|hap[12])|[._](pat|mat)(ernal)?[._]\"", "effect": "data_modality: genomic\ndata_type: assembly\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Filename contains assembly/assembler indicator \u2014 de novo assembly", "dataset": "general", "claims": 4680, "files": 936, "won": 936, "datasets": {"ANVIL_HPRC": 472, "AnVIL_HPRC_R2": 464}, "source_types": {}}, {"id": "fasta_haplotype_filename", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "format: FASTA\nfilename_pattern: \"(?i)haplotype|(^|[._-])(phased|hapdup)\"", "effect": "data_modality: genomic\ndata_type: assembly\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Filename contains haplotype/phasing indicator \u2014 de novo assembly", "dataset": "general", "claims": 120, "files": 24, "won": 24, "datasets": {"ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 24}, "source_types": {}}, {"id": "bed_methylation", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bed\"]\nfilename_pattern: \"(?i)modbam2bed|(^|[._-])cpg\"", "effect": "data_modality: epigenomic.methylation\ndata_type: annotations\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "BED file is modbam2bed output or names CpG methylation calls; which assay produced them is not in the name", "dataset": "general", "claims": 112, "files": 28, "won": 28, "datasets": {"ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 28}, "source_types": {}}, {"id": "bed_expression", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bed\"]\nfilename_pattern: \"(?i)(^|[._-])(TMM|counts)|leafcutter|\\\\.TSS\\\\.\"", "effect": "data_modality: transcriptomic\ndata_type: annotations\nassay_type: RNA-seq\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "BED file contains gene expression quantification or splicing data", "dataset": "general", "claims": 60, "files": 12, "won": 12, "datasets": {"AnVIL_MAGE": 12}, "source_types": {}}, {"id": "bed_assembly_qc", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bed\"]\nfilename_pattern: \"(?i)(\\\\.hap[12]\\\\.|[\\\\./_]paternal[\\\\./_]|[\\\\./_]maternal[\\\\./_]|\\\\.dip\\\\.bed|\\\\.switch\\\\.|(^|[._-])flagger|\\\\.lowQ\\\\.|unreliable|issues\\\\.bed|_genbank\\\\.)\"", "effect": "data_modality: genomic\ndata_type: annotations\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "BED file is assembly QC output (haplotype regions, error flags) on de novo genomic assemblies", "dataset": "general", "claims": 6860, "files": 1715, "won": 1715, "datasets": {"ANVIL_HPRC": 1713, "ANVIL_T2T_CHRY": 2}, "source_types": {}}, {"id": "bed_mosdepth_regions", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bed\"]\nfilename_pattern: \"\\\\.regions\\\\.bed\\\\.gz$\"", "effect": "data_type: annotations.coverage\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "mosdepth's `\u003cprefix>.regions.bed.gz`: mean read depth per genome window, measured from an alignment", "dataset": "general", "claims": 20991, "files": 6997, "won": 6997, "datasets": {"ANVIL_T2T_CHRY": 3760, "ANVIL_T2T": 3237}, "source_types": {}}, {"id": "star_signal_coverage", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bw\", \".bigwig\"]\nfilename_pattern: \"[._-]Signal\\\\.Unique(Multiple)?\\\\.strand[+-]\\\\.\"", "effect": "data_modality: transcriptomic\ndata_type: annotations.coverage\nassay_type: RNA-seq\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "A `Signal.Unique[Multiple].strand\u00b1` bigWig: STAR's per-base read depth, and STAR is an RNA-seq aligner", "dataset": "general", "claims": 12680, "files": 2536, "won": 2536, "datasets": {"AnVIL_ENCORE_RS293": 1640, "AnVIL_ENCORE_293T": 896}, "source_types": {}}, {"id": "intervals_targets", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 1, "condition": "extensions: [\".bed\"]\n# Not bare `regions`: it names mosdepth's coverage tracks and assembly QC\n# regions as often as a target list (#541).\nfilename_pattern: \"(?i)(^|[._-])(targets?|capture|exome)\"", "effect": "status:\n  data_modality: not_applicable\n  data_type: not_applicable\n  assay_type: not_applicable\n  platform: not_applicable\n  instrument_model: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Target/capture region file \u2014 reference data, not primary experimental data", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "text_stats", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".txt\", \".tsv\"]\nfilename_pattern: \"(?i)\\\\.stats\\\\.txt$|samtools\\\\.stats|(^|[._-])mosdepth\"", "effect": "data_type: qc_report\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "QC report (samtools stats, mosdepth, bcftools stats) about another file", "dataset": "general", "claims": 112644, "files": 37548, "won": 37548, "datasets": {"ANVIL_T2T_CHRY": 19937, "ANVIL_T2T": 17566, "ANVIL_HPRC": 45}, "source_types": {}}, {"id": "text_expression", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".txt\", \".tsv\", \".csv\"]\nfilename_pattern: \"(?i)expression|tpm\\\\.txt|fpkm\\\\.txt\"", "effect": "data_modality: transcriptomic\ndata_type: expression_matrix\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Filename contains `expression`, `tpm.txt` or `fpkm.txt`", "dataset": "general", "claims": 20, "files": 5, "won": 5, "datasets": {"AnVIL_MAGE": 5}, "source_types": {}}, {"id": "salmon_quant", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".sf\"]\nfilename_pattern: \"(?i)(^|[._])quant\\\\.sf$\"", "effect": "data_modality: transcriptomic\ndata_type: quantification\nassay_type: RNA-seq\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Salmon/Sailfish transcript-level quantification (quant.sf)", "dataset": "general", "claims": 3170, "files": 634, "won": 634, "datasets": {"AnVIL_ENCORE_RS293": 410, "AnVIL_ENCORE_293T": 224}, "source_types": {}}, {"id": "platform_pacbio", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@RG\"\nheader_field: \"PL\"\nheader_pattern: \"PACBIO\"", "effect": "platform: PACBIO", "expanded": [], "rationale": "PL:PACBIO indicates PacBio long-read sequencing", "dataset": "general", "claims": 1810, "files": 1810, "won": 1810, "datasets": {"AnVIL_HPRC_R2": 1260, "ANVIL_HPRC": 550}, "source_types": {}}, {"id": "platform_illumina", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@RG\"\nheader_field: \"PL\"\nheader_pattern: \"ILLUMINA\"", "effect": "platform: ILLUMINA", "expanded": [], "rationale": "PL:ILLUMINA in @RG header \u2014 definitive Illumina platform tag", "dataset": "general", "claims": 10623, "files": 10623, "won": 10623, "datasets": {"ANVIL_T2T_CHRY": 3760, "ANVIL_T2T": 3237, "ANVIL_1000G_high_coverage_2019": 3202, "AnVIL_HPRC_R2": 200, "ANVIL_HPRC": 156, "ANVIL_nhp_dGTEx_V1": 68}, "source_types": {}}, {"id": "platform_ont", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@RG\"\nheader_field: \"PL\"\nheader_pattern: \"ONT\"", "effect": "platform: ONT", "expanded": [], "rationale": "PL:ONT indicates Oxford Nanopore long-read sequencing", "dataset": "general", "claims": 1300, "files": 1300, "won": 1300, "datasets": {"AnVIL_HPRC_R2": 947, "ANVIL_HPRC": 350, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 3}, "source_types": {}}, {"id": "instrument_model_revio", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@RG\"\nheader_field: \"PM\"\nheader_pattern: \"^REVIO$\"\nheader_match_all: true", "effect": "instrument_model: Revio\nplatform: PACBIO", "expanded": [], "rationale": "@RG PM is REVIO, which names the PacBio Revio; the model implies the platform", "dataset": "general", "claims": 1368, "files": 684, "won": 684, "datasets": {"AnVIL_HPRC_R2": 684}, "source_types": {}}, {"id": "instrument_model_novaseq_x", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@RG\"\nheader_field: \"PM\"\nheader_pattern: \"^NovaSeq X$\"\nheader_match_all: true", "effect": "instrument_model: Illumina NovaSeq X\nplatform: ILLUMINA", "expanded": [], "rationale": "PM:NovaSeq X names the Illumina NovaSeq X, and so the platform; `NovaSeq X Plus` is a different value and not matched", "dataset": "general", "claims": 136, "files": 68, "won": 68, "datasets": {"ANVIL_nhp_dGTEx_V1": 68}, "source_types": {}}, {"id": "ont_basecall_dna", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@RG\"\nheader_field: \"DS\"\nheader_pattern: \"basecall_model=[^;\\\\s]*dna_\"", "effect": "data_modality: genomic", "expanded": [], "rationale": "ONT basecall model containing dna_ indicates DNA sequencing (genomic data). Handles both modern (dna_r10...) and legacy date-prefixed (2021-05-05_dna_r9...) formats", "dataset": "general", "claims": 1300, "files": 1300, "won": 1300, "datasets": {"AnVIL_HPRC_R2": 947, "ANVIL_HPRC": 350, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 3}, "source_types": {}}, {"id": "program_star", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@PG\"\nheader_field: \"PN\"\nheader_pattern: \"STAR\"", "effect": "data_modality: transcriptomic\nassay_type: RNA-seq", "expanded": [], "rationale": "STAR (Spliced Transcripts Alignment to a Reference) is the most widely used RNA-seq aligner. Presence strongly indicates RNA-seq data", "dataset": "general", "claims": 4198, "files": 2099, "won": 2099, "datasets": {"AnVIL_MAGE": 779, "ANVIL_nhp_dGTEx_V1": 686, "AnVIL_ENCORE_RS293": 410, "AnVIL_ENCORE_293T": 224}, "source_types": {}}, {"id": "program_bwa", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@PG\"\nheader_field: \"PN\"\nheader_pattern: \"bwa\"", "effect": "data_modality: genomic", "expanded": [], "rationale": "BWA (Burrows-Wheeler Aligner) is the standard short-read aligner for DNA sequencing. Commonly used for WGS, WES, and ChIP-seq", "dataset": "general", "claims": 10555, "files": 10555, "won": 10555, "datasets": {"ANVIL_T2T_CHRY": 3760, "ANVIL_T2T": 3237, "ANVIL_1000G_high_coverage_2019": 3202, "AnVIL_HPRC_R2": 200, "ANVIL_HPRC": 156}, "source_types": {}}, {"id": "program_minimap2", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@PG\"\nheader_field: \"PN\"\nheader_pattern: \"minimap2\"", "effect": "data_modality: genomic", "expanded": [], "rationale": "Minimap2 is a versatile aligner for long reads (PacBio, ONT) and assemblies. Can also be used for direct RNA with -ax splice preset", "dataset": "general", "claims": 1400, "files": 1400, "won": 1400, "datasets": {"ANVIL_HPRC": 738, "AnVIL_HPRC_R2": 659, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 3}, "source_types": {}}, {"id": "program_ccs", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@PG\"\nheader_field: \"PN\"\nheader_pattern: \"ccs\"", "effect": "platform: PACBIO", "expanded": [], "rationale": "The 'ccs' program generates HiFi reads from PacBio subreads. Confirms PacBio platform (used for both WGS and IsoSeq)", "dataset": "general", "claims": 1810, "files": 1810, "won": 1810, "datasets": {"AnVIL_HPRC_R2": 1260, "ANVIL_HPRC": 550}, "source_types": {}}, {"id": "aligned_has_sq", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@SQ\"\nheader_present: true", "effect": "data_type: alignments\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "@SQ lines declare a reference, so the file is aligned to it. The reads themselves are not read (#538)", "dataset": "general", "claims": 42852, "files": 14284, "won": 14284, "datasets": {"ANVIL_T2T_CHRY": 3760, "ANVIL_T2T": 3299, "ANVIL_1000G_high_coverage_2019": 3202, "AnVIL_HPRC_R2": 1124, "AnVIL_MAGE": 779, "ANVIL_nhp_dGTEx_V1": 754, "ANVIL_HPRC": 730, "AnVIL_ENCORE_RS293": 410, "AnVIL_ENCORE_293T": 224, "AnVIL_IGVF_Mouse_R1": 2}, "source_types": {}}, {"id": "unaligned_no_sq", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@SQ\"\nheader_absent: true", "effect": "data_type: reads\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "No @SQ lines: the header declares no reference, so the file holds unaligned reads. Common for PacBio HiFi and ONT mod-base deliverables", "dataset": "general", "claims": 12452, "files": 3113, "won": 3113, "datasets": {"AnVIL_HPRC_R2": 2207, "ANVIL_HPRC": 900, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 6}, "source_types": {}}, {"id": "vcf_ref_grch38", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".vcf\", \".bcf\", \".gvcf\", \".g.vcf\"]\nvcf_header_type: \"##reference\"\nvcf_pattern: \"(?i)(grch38|hg38|hs38|GCA_000001405\\\\.(1[5-9]|[2-9]\\\\d|\\\\d{3,}))\"", "effect": "reference_assembly: GRCh38", "expanded": [], "rationale": "##reference line containing GRCh38, hg38, or the GRCh38 GenBank accession indicates variants called against GRCh38", "dataset": "general", "claims": 37811, "files": 37811, "won": 4658, "datasets": {"ANVIL_T2T": 34339, "ANVIL_1000G_high_coverage_2019": 3252, "ANVIL_HPRC": 148, "ANVIL_T2T_CHRY": 72}, "source_types": {}}, {"id": "vcf_ref_chm13", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".vcf\", \".bcf\", \".gvcf\", \".g.vcf\"]\nvcf_header_type: \"##reference\"\nvcf_pattern: \"(?i)(chm13|t2t|hs1)\"", "effect": "reference_assembly: CHM13", "expanded": [], "rationale": "##reference line containing CHM13 or T2T indicates variants called against T2T-CHM13", "dataset": "general", "claims": 33449, "files": 33449, "won": 33449, "datasets": {"ANVIL_T2T": 33281, "ANVIL_T2T_CHRY": 168}, "source_types": {}}, {"id": "vcf_contig_grch38", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".vcf\", \".bcf\", \".gvcf\", \".g.vcf\"]\nvcf_header_type: \"##contig\"\nvcf_pattern: \"(?i)(grch38|hg38|hs38|GCA_000001405\\\\.(1[5-9]|[2-9]\\\\d|\\\\d{3,}))\"", "effect": "reference_assembly: GRCh38", "expanded": [], "rationale": "##contig assembly subfield naming GRCh38, hg38, hs38, or the GRCh38 GenBank accession declares the reference genome", "dataset": "general", "claims": 33147, "files": 33147, "won": 0, "datasets": {"ANVIL_T2T": 33147}, "source_types": {}}, {"id": "vcf_contig_chm13", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".vcf\", \".bcf\", \".gvcf\", \".g.vcf\"]\nvcf_header_type: \"##contig\"\nvcf_pattern: \"(?i)(chm13|t2t|hs1)\"", "effect": "reference_assembly: CHM13", "expanded": [], "rationale": "##contig assembly subfield naming CHM13, T2T, or hs1 declares the reference genome", "dataset": "general", "claims": 33315, "files": 33315, "won": 33315, "datasets": {"ANVIL_T2T": 33147, "ANVIL_T2T_CHRY": 168}, "source_types": {}}, {"id": "fastq_illumina_modern", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \"^@[A-Z0-9-]+:\\\\d+:[A-Z0-9]+:\\\\d+:\\\\d+:\\\\d+:\\\\d+\"", "effect": "platform: ILLUMINA", "expanded": [], "rationale": "Modern Illumina read names (Casava 1.8+) follow the format @instrument:run:flowcell:lane:tile:x:y", "dataset": "general", "claims": 19608, "files": 19608, "won": 19608, "datasets": {"ANVIL_T2T": 6404, "AnVIL_IGVF_Mouse_R1": 3948, "ANVIL_HPRC": 3428, "AnVIL_HPRC_R2": 3002, "AnVIL_MAGE": 1558, "AnVIL_ENCORE_RS293": 820, "AnVIL_ENCORE_293T": 448}, "source_types": {}}, {"id": "fastq_illumina_hiseq_desc", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \" HS2[05]00[-_]\"", "effect": "platform: ILLUMINA", "expanded": [], "rationale": "HiSeq 2000/2500 instrument identifier found in read description", "dataset": "general", "claims": 558, "files": 558, "won": 558, "datasets": {"ANVIL_T2T_CHRY": 558}, "source_types": {}}, {"id": "fastq_illumina_ena_hiseq", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \"^@[EDS]RR\\\\d+\\\\.\\\\d+ HS2[05]00\"", "effect": "platform: ILLUMINA", "expanded": [], "rationale": "ENA/SRA-reformatted read names with HiSeq 2000/2500 instrument ID in description", "dataset": "general", "claims": 558, "files": 558, "won": 558, "datasets": {"ANVIL_T2T_CHRY": 558}, "source_types": {}}, {"id": "fastq_pacbio_ccs", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \"^@m\\\\d+[A-Za-z]*_\\\\d+_\\\\d+/\\\\d+/ccs\"", "effect": "platform: PACBIO\ndata_type: reads\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "PacBio CCS (HiFi) read names follow @movie/zmw/ccs format. The 'ccs' suffix indicates high-accuracy long reads (>Q20)", "dataset": "general", "claims": 3868, "files": 967, "won": 967, "datasets": {"AnVIL_HPRC_R2": 659, "ANVIL_HPRC": 308}, "source_types": {}}, {"id": "fastq_pacbio_generic", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \"^@m\\\\d+[A-Za-z]*_\\\\d+_\\\\d+/\\\\d+\"", "effect": "platform: PACBIO\ndata_type: reads\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Generic PacBio read names follow @movie/zmw format", "dataset": "general", "claims": 3868, "files": 967, "won": 967, "datasets": {"AnVIL_HPRC_R2": 659, "ANVIL_HPRC": 308}, "source_types": {}}, {"id": "fastq_ont_uuid", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \"^@[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{12}\"", "effect": "platform: ONT\ndata_type: reads\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "ONT read names are UUIDs (format: 8-4-4-4-12 hex characters). Uniquely identifies Oxford Nanopore data", "dataset": "general", "claims": 548, "files": 137, "won": 137, "datasets": {"ANVIL_HPRC": 120, "AnVIL_IGVF_Mouse_R1": 11, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 6}, "source_types": {}}, {"id": "fastq_ont_metadata", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \"runid=[a-f0-9]+\"", "effect": "platform: ONT\ndata_type: reads\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "ONT reads often include 'runid=' metadata in the header line", "dataset": "general", "claims": 504, "files": 126, "won": 126, "datasets": {"ANVIL_HPRC": 120, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 6}, "source_types": {}}, {"id": "fastq_modality_unknown", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]", "effect": "status:\n  data_modality: not_classified", "expanded": [], "rationale": "FASTQ modality cannot be determined from reads alone \u2014 could be genomic, transcriptomic, or epigenomic depending on assay", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "contig_length_detection", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "BAM/CRAM/SAM header: the @SQ contig names and lengths for the family; for the build within it, chr1 and chrY's lengths and M5 checksums, and the reference name @SQ UR or an @PG command line declares", "expanded": [], "effect": "reference_assembly", "rationale": "An alignment's @SQ lines list the reference it was aligned to, contig by contig, and a primary chromosome's length differs between the reference_contig_lengths assemblies that share its name, so matching them identifies the reference family. Where the build within it has a term of its own (#473), chr1 and chrY's lengths and checksums pick it, and a declared reference name only breaks a tie among the builds those allow.", "dataset": "general", "claims": 14282, "files": 14282, "won": 14282, "datasets": {"ANVIL_T2T_CHRY": 3760, "ANVIL_T2T": 3299, "ANVIL_1000G_high_coverage_2019": 3202, "AnVIL_HPRC_R2": 1124, "AnVIL_MAGE": 779, "ANVIL_nhp_dGTEx_V1": 754, "ANVIL_HPRC": 730, "AnVIL_ENCORE_RS293": 410, "AnVIL_ENCORE_293T": 224}, "source_types": {}}, {"id": "vcf_contig_length", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header: the ##contig names and lengths for the family; for the build within it, chr1 and chrY's lengths, and the reference name ##reference or a command line declares (not for a lifted-over VCF)", "expanded": [], "effect": "reference_assembly", "rationale": "A VCF's ##contig lines declare the reference its positions are on. Their lengths identify the reference family as @SQ lengths do. ##contig carries no checksum, so builds that differ only in sequence are told apart only by a declared reference name, and without one the value is the family.", "dataset": "general", "claims": 206840, "files": 206840, "won": 206840, "datasets": {"ANVIL_T2T": 109708, "ANVIL_T2T_CHRY": 90618, "ANVIL_1000G_high_coverage_2019": 3252, "ANVIL_1000G_PRIMED_data_model": 2900, "ANVIL_HPRC": 299, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 59, "AnVIL_HPRC_R2": 2, "ANVIL_nhp_dGTEx_V1": 2}, "source_types": {}}, {"id": "vcf_gvcf", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header: the reference-confidence mode (-ERC) on the command line of the step that made the file (the end of its data flow), where that step is a GATK HaplotypeCaller; and the ALT column of the first records the head read", "expanded": [], "effect": "data_type", "rationale": "A gVCF represents every site, variant or not, each in a record of its own or in a block record covering a run of sites with no variant, and carries the symbolic \u003cNON_REF> allele in every record's ALT (GATK, 'GVCF - Genomic Variant Call Format', https://gatk.broadinstitute.org/hc/en-us/articles/360035531812). HaplotypeCaller writes one when run with -ERC GVCF (non-variant sites condensed into blocks) or -ERC BP_RESOLUTION (a record per site); NONE, the default, is regular calling (GATK, 'HaplotypeCaller', https://gatk.broadinstitute.org/hc/en-us/articles/360037225632). Both must say so: the step that made the file is a HaplotypeCaller in one of those modes; and every record read has \u003cNON_REF> among its ALT alleles, at least one with it alone, a site with no variant (#607). The step alone is not enough: a tool that writes no command line leaves the header's last step in place, so a gVCF filtered to its variant sites would still read as made by HaplotypeCaller; its records then have no site with \u003cNON_REF> alone. The records alone are not either: GenotypeGVCFs drops \u003cNON_REF> in a normal run, but keeps it at some sites under --include-non-variant-sites (GATK forum, https://gatk.broadinstitute.org/hc/en-us/community/posts/360056352871). The ##ALT=\u003cID=NON_REF> and ##GVCFBlock header lines, and an earlier step's -ERC GVCF line, carry over into the joint-called VCFs and PLINK .pvar files made from gVCFs, so none of them is read.", "dataset": "general", "claims": 168825, "files": 168825, "won": 168825, "datasets": {"ANVIL_T2T_CHRY": 90378, "ANVIL_T2T": 75245, "ANVIL_1000G_high_coverage_2019": 3202}, "source_types": {}}, {"id": "vcf_step_caller_kind", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header: its command lines, as steps; the producing step (the end of their data flow), walked back through steps that keep the kind (SelectVariants, GenotypeGVCFs, FilterMutectCalls, ApplyVQSR, ApplyRecalibration, bcftools view, norm, annotate) to the step that called the variants", "expanded": [], "effect": "variant_kind", "rationale": "The caller calls one kind of variant, so its kind is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no kind (#654). The records cannot settle it: a mixed callset shows a structural variant in its first records only by chance (#630). Where the chain names no caller (no one producing step, or a kept step's input written by no step the header records), the header's steps that do not keep the kind name it: a merge among them (vcf_merge_small_callers), else the one tool they are, if they are exactly one. A command line the reader does not know gives no kind.", "dataset": "general", "claims": 168840, "files": 168840, "won": 168840, "datasets": {"ANVIL_T2T_CHRY": 90378, "ANVIL_T2T": 75245, "ANVIL_1000G_high_coverage_2019": 3202, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 15}, "source_types": {}}, {"id": "vcf_source_caller_kind", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header whose command lines name no caller (or that has none): its ##source lines and a ##DeepVariant_version line", "expanded": [], "effect": "variant_kind", "rationale": "The caller calls one kind of variant, so its kind is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no kind (#654). The records cannot settle it: a mixed callset shows a structural variant in its first records only by chance (#630). Structural-variant callers (Sniffles, SVIM) and DeepVariant record themselves only so. Taken only where they name exactly one tool, spellings of one caller counted once: a header naming two tools gives no kind.", "dataset": "general", "claims": 157, "files": 157, "won": 157, "datasets": {"ANVIL_T2T": 134, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 23}, "source_types": {}}, {"id": "vcf_merge_small_callers", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header whose caller is a merge (bcftools concat, merge; GATK GenomicsDBImport, CombineGVCFs): every caller its command lines and ##source lines name, and its ##INFO and ##ALT declarations of structural variants", "expanded": [], "effect": "variant_kind", "rationale": "A merge holds what its inputs held. It is small variants where every caller the header names calls small variants and the header declares no structural-variant field: a merged header can lose an input's ##source line, but the VCF spec requires a structural variant to be declared, so an input's structural variants leave their declarations behind (#654).", "dataset": "general", "claims": 51, "files": 51, "won": 51, "datasets": {"ANVIL_1000G_high_coverage_2019": 50, "ANVIL_T2T": 1}, "source_types": {}}, {"id": "vcf_step_caller_origin", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header: its command lines, as steps; the caller vcf_step_caller_kind finds behind the producing step, and that caller's own command line for a somatic mode's flags; its ##source spellings; and its SOMATIC INFO, ##tumor_sample and ##normal_sample declarations", "expanded": [], "effect": "variant_origin", "rationale": "Each caller the table lists calls variants of one origin, germline or somatic, or, for one listed with a somatic mode, of one origin unless a mode flag is set (a caller that mixes them, as Mutect2 can, is not listed), so its origin is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no origin (#658). A caller with a somatic mode (Sniffles2's --non-germline, --mosaic and two more) gives its origin only where the header records its command line without the mode's flags, or names it by a ##source spelling of a version without the mode (Sniffles 1). A germline reading is refused where the header declares the SOMATIC INFO flag or ##tumor_sample / ##normal_sample. The caller is found as for the kind; the header's one other step, taken where the chain names none, is refused where the header names itself a tool of another origin, or a caller whose somatic mode it leaves open.", "dataset": "general", "claims": 168840, "files": 168840, "won": 168840, "datasets": {"ANVIL_T2T_CHRY": 90378, "ANVIL_T2T": 75245, "ANVIL_1000G_high_coverage_2019": 3202, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 15}, "source_types": {}}, {"id": "vcf_source_caller_origin", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header whose command lines name no caller (or that has none): its ##source lines and a ##DeepVariant_version line, and its SOMATIC INFO, ##tumor_sample and ##normal_sample declarations", "expanded": [], "effect": "variant_origin", "rationale": "Each caller the table lists calls variants of one origin, germline or somatic, or, for one listed with a somatic mode, of one origin unless a mode flag is set (a caller that mixes them, as Mutect2 can, is not listed), so its origin is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no origin (#658). A caller with a somatic mode (Sniffles2's --non-germline, --mosaic and two more) gives its origin only where the header records its command line without the mode's flags, or names it by a ##source spelling of a version without the mode (Sniffles 1). A germline reading is refused where the header declares the SOMATIC INFO flag or ##tumor_sample / ##normal_sample. Taken only where they name exactly one tool, as for vcf_source_caller_kind.", "dataset": "general", "claims": 157, "files": 157, "won": 157, "datasets": {"ANVIL_T2T": 134, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 23}, "source_types": {}}, {"id": "vcf_merge_germline_callers", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header whose caller is a merge (bcftools concat, merge; GATK GenomicsDBImport, CombineGVCFs): every caller its command lines and ##source lines name, those callers' command lines for a somatic mode's flags, and its declarations of somatic calls", "expanded": [], "effect": "variant_origin", "rationale": "A merge holds what its inputs held. It is germline where every caller the header names calls germline variants, none in a somatic mode, and the header declares neither the SOMATIC INFO flag nor a tumour or normal sample: a merged header can lose an input's ##source line, so a somatic input is also looked for in what the header declares (#658).", "dataset": "general", "claims": 63, "files": 63, "won": 63, "datasets": {"ANVIL_1000G_high_coverage_2019": 50, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 12, "ANVIL_T2T": 1}, "source_types": {}}, {"id": "tar_step_caller_kind", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "GenomicsDB workspace tar: its vcfheader.vcf, the header GenomicsDBImport writes, which records the import's command line beside command lines carried over from its gVCFs' headers, read as a VCF's is: VCF header: its command lines, as steps; the producing step (the end of their data flow), walked back through steps that keep the kind (SelectVariants, GenotypeGVCFs, FilterMutectCalls, ApplyVQSR, ApplyRecalibration, bcftools view, norm, annotate) to the step that called the variants", "expanded": [], "effect": "variant_kind", "rationale": "A workspace holds the calls it imported, and its header records the steps behind it, so its caller is read as a VCF's (#678), the workspace's name (the tar's, less .tar and any .gz) standing for the file's, since the import's output is the directory the tar was made from; a tar named otherwise reads the same, through the header's import, a merge. The caller calls one kind of variant, so its kind is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no kind (#654). The records cannot settle it: a mixed callset shows a structural variant in its first records only by chance (#630). Where the chain names no caller (no one producing step, or a kept step's input written by no step the header records), the header's steps that do not keep the kind name it: a merge among them (tar_merge_small_callers), else the one tool they are, if they are exactly one. A command line the reader does not know gives no kind. A workspace whose header was not read gives no kind.", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "tar_source_caller_kind", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "GenomicsDB workspace tar: its vcfheader.vcf, the header GenomicsDBImport writes, which records the import's command line beside command lines carried over from its gVCFs' headers, read as a VCF's is: VCF header whose command lines name no caller (or that has none): its ##source lines and a ##DeepVariant_version line", "expanded": [], "effect": "variant_kind", "rationale": "A workspace holds the calls it imported, and its header records the steps behind it, so its caller is read as a VCF's (#678), the workspace's name (the tar's, less .tar and any .gz) standing for the file's, since the import's output is the directory the tar was made from; a tar named otherwise reads the same, through the header's import, a merge. The caller calls one kind of variant, so its kind is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no kind (#654). The records cannot settle it: a mixed callset shows a structural variant in its first records only by chance (#630). Structural-variant callers (Sniffles, SVIM) and DeepVariant record themselves only so. Taken only where they name exactly one tool, spellings of one caller counted once: a header naming two tools gives no kind.", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "tar_merge_small_callers", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "GenomicsDB workspace tar: its vcfheader.vcf, the header GenomicsDBImport writes, which records the import's command line beside command lines carried over from its gVCFs' headers, read as a VCF's is: VCF header whose caller is a merge (bcftools concat, merge; GATK GenomicsDBImport, CombineGVCFs): every caller its command lines and ##source lines name, and its ##INFO and ##ALT declarations of structural variants", "expanded": [], "effect": "variant_kind", "rationale": "A workspace holds the calls it imported, and its header records the steps behind it, so its caller is read as a VCF's (#678), the workspace's name (the tar's, less .tar and any .gz) standing for the file's, since the import's output is the directory the tar was made from; a tar named otherwise reads the same, through the header's import, a merge. A merge holds what its inputs held. It is small variants where every caller the header names calls small variants and the header declares no structural-variant field: a merged header can lose an input's ##source line, but the VCF spec requires a structural variant to be declared, so an input's structural variants leave their declarations behind (#654).", "dataset": "general", "claims": 124335, "files": 124335, "won": 124335, "datasets": {"ANVIL_T2T_CHRY": 93180, "ANVIL_T2T": 31155}, "source_types": {}}, {"id": "tar_step_caller_origin", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "GenomicsDB workspace tar: its vcfheader.vcf, the header GenomicsDBImport writes, which records the import's command line beside command lines carried over from its gVCFs' headers, read as a VCF's is: VCF header: its command lines, as steps; the caller tar_step_caller_kind finds behind the producing step, and that caller's own command line for a somatic mode's flags; its ##source spellings; and its SOMATIC INFO, ##tumor_sample and ##normal_sample declarations", "expanded": [], "effect": "variant_origin", "rationale": "A workspace holds the calls it imported, and its header records the steps behind it, so its caller is read as a VCF's (#678), the workspace's name (the tar's, less .tar and any .gz) standing for the file's, since the import's output is the directory the tar was made from; a tar named otherwise reads the same, through the header's import, a merge. Each caller the table lists calls variants of one origin, germline or somatic, or, for one listed with a somatic mode, of one origin unless a mode flag is set (a caller that mixes them, as Mutect2 can, is not listed), so its origin is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no origin (#658). A caller with a somatic mode (Sniffles2's --non-germline, --mosaic and two more) gives its origin only where the header records its command line without the mode's flags, or names it by a ##source spelling of a version without the mode (Sniffles 1). A germline reading is refused where the header declares the SOMATIC INFO flag or ##tumor_sample / ##normal_sample. The caller is found as for the kind; the header's one other step, taken where the chain names none, is refused where the header names itself a tool of another origin, or a caller whose somatic mode it leaves open. A workspace whose header was not read gives no origin.", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "tar_source_caller_origin", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "GenomicsDB workspace tar: its vcfheader.vcf, the header GenomicsDBImport writes, which records the import's command line beside command lines carried over from its gVCFs' headers, read as a VCF's is: VCF header whose command lines name no caller (or that has none): its ##source lines and a ##DeepVariant_version line, and its SOMATIC INFO, ##tumor_sample and ##normal_sample declarations", "expanded": [], "effect": "variant_origin", "rationale": "A workspace holds the calls it imported, and its header records the steps behind it, so its caller is read as a VCF's (#678), the workspace's name (the tar's, less .tar and any .gz) standing for the file's, since the import's output is the directory the tar was made from; a tar named otherwise reads the same, through the header's import, a merge. Each caller the table lists calls variants of one origin, germline or somatic, or, for one listed with a somatic mode, of one origin unless a mode flag is set (a caller that mixes them, as Mutect2 can, is not listed), so its origin is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no origin (#658). A caller with a somatic mode (Sniffles2's --non-germline, --mosaic and two more) gives its origin only where the header records its command line without the mode's flags, or names it by a ##source spelling of a version without the mode (Sniffles 1). A germline reading is refused where the header declares the SOMATIC INFO flag or ##tumor_sample / ##normal_sample. Taken only where they name exactly one tool, as for tar_source_caller_kind.", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "tar_merge_germline_callers", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "GenomicsDB workspace tar: its vcfheader.vcf, the header GenomicsDBImport writes, which records the import's command line beside command lines carried over from its gVCFs' headers, read as a VCF's is: VCF header whose caller is a merge (bcftools concat, merge; GATK GenomicsDBImport, CombineGVCFs): every caller its command lines and ##source lines name, those callers' command lines for a somatic mode's flags, and its declarations of somatic calls", "expanded": [], "effect": "variant_origin", "rationale": "A workspace holds the calls it imported, and its header records the steps behind it, so its caller is read as a VCF's (#678), the workspace's name (the tar's, less .tar and any .gz) standing for the file's, since the import's output is the directory the tar was made from; a tar named otherwise reads the same, through the header's import, a merge. A merge holds what its inputs held. It is germline where every caller the header names calls germline variants, none in a somatic mode, and the header declares neither the SOMATIC INFO flag nor a tumour or normal sample: a merged header can lose an input's ##source line, so a somatic input is also looked for in what the header declares (#658).", "dataset": "general", "claims": 124335, "files": 124335, "won": 124335, "datasets": {"ANVIL_T2T_CHRY": 93180, "ANVIL_T2T": 31155}, "source_types": {}}, {"id": "bed_coordinate_reference", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "BED body: the largest coordinate seen per chromosome, and whether names carry a chr prefix", "expanded": [], "effect": "reference_assembly", "rationale": "A coordinate past a chromosome's end in a human reference rules that reference out, whether the names carry a chr prefix or not; where more than one is left, a chr prefix also rules out GRCh37; and a reference is claimed only when exactly one is left.", "dataset": "general", "claims": 7542, "files": 7542, "won": 7542, "datasets": {"ANVIL_T2T_CHRY": 3763, "ANVIL_T2T": 3259, "ANVIL_HPRC": 509, "AnVIL_MAGE": 8, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 3}, "source_types": {}}, {"id": "bed_nonstandard_contigs", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "BED body: chromosome names", "expanded": [], "effect": "reference_assembly", "rationale": "When no chromosome name is a standard one (1-22, X, Y, M or MT, with or without chr), the intervals are on an assembly's own contigs, not a standard reference, so reference_assembly does not apply.", "dataset": "general", "claims": 3922, "files": 3922, "won": 3922, "datasets": {"AnVIL_HPRC_R2": 2310, "ANVIL_HPRC": 1612}, "source_types": {}}, {"id": "fasta_transcript_contigs", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "FASTA header lines: sequence names", "expanded": [], "effect": "data_modality, data_type", "rationale": "Sequence names in transcript id form (ENST, NM_, NR_, XM_, rna-) outnumbering reference chromosome names mean the file holds transcript sequences: transcriptomic, data_type sequence.", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "fasta_reference_contigs", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "FASTA header lines: sequence names", "expanded": [], "effect": "reference_assembly, data_modality, data_type", "rationale": "At least 20 sequence names that are one known reference's chromosomes mean the file is that reference genome. A tie between references names none (#88).", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "fasta_assembler_contigs", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "FASTA header lines: sequence names", "expanded": [], "effect": "data_modality, data_type, reference_assembly", "rationale": "Sequence names in the form assemblers write (h1tg, ptg, utg, ctg, scaffold_, contig_) mean a de novo assembly: genomic, data_type assembly, and no reference applies, since an assembly is its own.", "dataset": "general", "claims": 1134, "files": 378, "won": 378, "datasets": {"ANVIL_HPRC": 378}, "source_types": {}}, {"id": "fasta_many_contigs", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "FASTA header lines: how many sequences, and whether any is a reference chromosome", "expanded": [], "effect": "data_modality, data_type, reference_assembly", "rationale": "More than 50 sequences, none of them a reference chromosome and none named as a transcript or by an assembler, are read as a de novo assembly: genomic, data_type assembly, no reference applies.", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "rgfa_stable_rank_reference", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "rGFA segments: the SR (stable rank) and SN (stable name) tags", "expanded": [], "effect": "data_type", "rationale": "Segments at stable rank 0 are the backbone that defines the graph's coordinate system, so a graph that has them is a reference pangenome (pangenome.reference).", "dataset": "general", "claims": 10, "files": 10, "won": 10, "datasets": {"ANVIL_HPRC": 6, "AnVIL_HPRC_R2": 4}, "source_types": {}}, {"id": "tar_inner_format", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "tar archive head: its member names", "expanded": [], "effect": "data_modality, data_type, assay_type", "rationale": "A container has no format of its own (#245), so it takes its contents'. A GenomicsDB member layout is a variant store (genomic, variants); otherwise the archive takes what the rules resolve for its dominant recognized member extension.", "dataset": "general", "claims": 249162, "files": 124564, "won": 124564, "datasets": {"ANVIL_T2T_CHRY": 93180, "ANVIL_T2T": 31155, "ANVIL_HPRC": 163, "AnVIL_IGVF_Mouse_R1": 64, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 2}, "source_types": {}}, {"id": "sample_map_content", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "a GATK sample-name map's rows: two or three tab-separated columns, the second a VCF path", "expanded": [], "effect": "data_type, data_modality, reference_assembly, assay_type, platform, instrument_model, variant_kind, variant_origin", "rationale": "A file whose rows are a sample name and a VCF path is a list of files, not their data (#621): its data_type is sample_map, and the seven dimensions of the data do not apply to it, as for a checksum (#596). Read from the content, so a file that is only named like a map keeps what its name says.", "dataset": "general", "claims": 192, "files": 24, "won": 24, "datasets": {"ANVIL_T2T": 24}, "source_types": {}}, {"id": "idat_chip_type", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "an Illumina IDAT's header: its chip type (field 403) and its number of probes (field 1000)", "expanded": [], "effect": "data_modality, assay_type", "rationale": "Genotyping and methylation BeadChips both write IDAT, so the extension cannot say which (#603). A chip is recognised only as the exact chip type and probe count read from real files: 1-95um_multi-swath_for_8x2-5M with 2,522,340 probes is the HPRC's Infinium Omni2.5-8 v1.3 genotyping BeadChip, as its methods name it (Liao et al. 2023, PMC10172123). Any other chip, a methylation chip included, is left not_classified with what was read.", "dataset": "general", "claims": 320, "files": 160, "won": 160, "datasets": {"ANVIL_HPRC": 160}, "source_types": {}}, {"id": "idat_scanner", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "an Illumina IDAT's run log (field 300): the software each Scan row names", "expanded": [], "effect": "platform, instrument_model", "rationale": "The run log records the software that scanned the chip, not the scanner's model, so the model is inferred from the software: iScan Control Software is the software of Illumina's iScan System, the scanner the HPRC's methods name for its arrays (#603). If another Illumina scanner ran the same software, its files would be called iScan; a source naming the scanner would settle it. Every Scan row must name the same recognised software; otherwise platform and instrument are left not_classified with what was read.", "dataset": "general", "claims": 320, "files": 160, "won": 160, "datasets": {"ANVIL_HPRC": 160}, "source_types": {}}, {"id": "index_by_extension", "kind": "code", "defined_in": "scripts/classify_index_files.py", "basis": "extension", "tier": null, "condition": "file extension: .bai, .crai, .tbi, .csi, .pbi and the others INDEX_TO_PARENT declares", "expanded": [], "effect": "data_type", "rationale": "An index file is an index whatever it indexes, and its extension says so, with or without a parent (#437). The YAML rule index_file says the same for an index this producer misses.", "dataset": "general", "claims": 224726, "files": 224726, "won": 224726, "datasets": {"ANVIL_T2T": 116300, "ANVIL_T2T_CHRY": 98144, "ANVIL_1000G_high_coverage_2019": 6454, "AnVIL_HPRC_R2": 1858, "AnVIL_MAGE": 831, "ANVIL_nhp_dGTEx_V1": 756, "ANVIL_HPRC": 356, "ANVIL_1000G_PRIMED_data_model": 23, "AnVIL_IGVF_Mouse_R1": 4}, "source_types": {}}, {"id": "inherited_from_parent", "kind": "code", "defined_in": "src/meta_disco/reconcile_inherit.py", "basis": "parent_file", "tier": null, "condition": "the settled answers, at reconcile, of the parents in one input role of the file's generated_by", "expanded": [], "effect": "data_modality, platform, reference_assembly, assay_type, instrument_model, variant_kind, variant_origin", "rationale": "A file's step passes what its activity declares for each input role (rules/activities.yaml), from the parents in that role. Parents that agree give their value; parents that differ give mixed; a parent not_classified or in conflict gives nothing (contract 4.9). The claim is weighed with the file's own (4.2-4.6).", "dataset": "general", "claims": 3266662, "files": 534193, "won": 534193, "datasets": {"ANVIL_T2T": 273092, "ANVIL_T2T_CHRY": 247041, "ANVIL_1000G_high_coverage_2019": 6454, "AnVIL_ENCORE_RS293": 2460, "AnVIL_HPRC_R2": 1858, "AnVIL_ENCORE_293T": 1352, "AnVIL_MAGE": 795, "ANVIL_nhp_dGTEx_V1": 756, "ANVIL_HPRC": 356, "ANVIL_1000G_PRIMED_data_model": 23, "AnVIL_IGVF_Mouse_R1": 6}, "source_types": {}}, {"id": "phenotypic_sex.female", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: phenotypic_sex, value: \"female\"}", "effect": "{phenotypic_sex: Female}", "expanded": [], "rationale": "The term itself, as T2T, HPRC and NIA_CARD write it lower case and PRIMED and AnVIL capitalised.", "dataset": "general", "seeded": false, "claims": 5293, "files": 1705, "won": 1704, "datasets": {"(donor records)": 1705}, "source_types": {"repository_metadata": 3448, "published_value": 1845}}, {"id": "phenotypic_sex.male", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: phenotypic_sex, value: \"male\"}", "effect": "{phenotypic_sex: Male}", "expanded": [], "rationale": "The term itself, as T2T, HPRC and NIA_CARD write it lower case and PRIMED and AnVIL capitalised.", "dataset": "general", "seeded": false, "claims": 5243, "files": 1690, "won": 1689, "datasets": {"(donor records)": 1690}, "source_types": {"repository_metadata": 3421, "published_value": 1822}}, {"id": "phenotypic_sex.pedigree_1", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: phenotypic_sex, value: \"1\"}", "effect": "{phenotypic_sex: Male}", "expanded": [], "rationale": "1000 Genomes' `pedigree.sex` is a PED file's sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `1` means this only in a PED file.", "dataset": "source anvil, dataset ANVIL_1000G_high_coverage_2019", "seeded": false, "claims": 1599, "files": 1599, "won": 1598, "datasets": {"(donor records)": 1599}, "source_types": {"repository_metadata": 1599}}, {"id": "phenotypic_sex.pedigree_2", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: phenotypic_sex, value: \"2\"}", "effect": "{phenotypic_sex: Female}", "expanded": [], "rationale": "1000 Genomes' `pedigree.sex` is a PED file's sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `2` means this only in a PED file.", "dataset": "source anvil, dataset ANVIL_1000G_high_coverage_2019", "seeded": false, "claims": 1603, "files": 1603, "won": 1603, "datasets": {"(donor records)": 1603}, "source_types": {"repository_metadata": 1603}}, {"id": "genetic_ancestry.afr", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"AFR\"}", "effect": "{genetic_ancestry: AFR}", "expanded": [], "rationale": "1000 Genomes' superpopulation code, the term itself (HANCESTRO:2000).", "dataset": "general", "seeded": false, "claims": 893, "files": 893, "won": 893, "datasets": {"(donor records)": 893}, "source_types": {"repository_metadata": 893}}, {"id": "genetic_ancestry.amr", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"AMR\"}", "effect": "{genetic_ancestry: AMR}", "expanded": [], "rationale": "1000 Genomes' superpopulation code, the term itself (HANCESTRO:2001).", "dataset": "general", "seeded": false, "claims": 490, "files": 490, "won": 490, "datasets": {"(donor records)": 490}, "source_types": {"repository_metadata": 490}}, {"id": "genetic_ancestry.eas", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"EAS\"}", "effect": "{genetic_ancestry: EAS}", "expanded": [], "rationale": "1000 Genomes' superpopulation code, the term itself (HANCESTRO:2002).", "dataset": "general", "seeded": false, "claims": 585, "files": 585, "won": 585, "datasets": {"(donor records)": 585}, "source_types": {"repository_metadata": 585}}, {"id": "genetic_ancestry.eur", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"EUR\"}", "effect": "{genetic_ancestry: EUR}", "expanded": [], "rationale": "1000 Genomes' superpopulation code, the term itself (HANCESTRO:2003).", "dataset": "general", "seeded": false, "claims": 633, "files": 633, "won": 633, "datasets": {"(donor records)": 633}, "source_types": {"repository_metadata": 633}}, {"id": "genetic_ancestry.sas", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"SAS\"}", "effect": "{genetic_ancestry: SAS}", "expanded": [], "rationale": "1000 Genomes' superpopulation code, the term itself (HANCESTRO:2004).", "dataset": "general", "seeded": false, "claims": 601, "files": 601, "won": 601, "datasets": {"(donor records)": 601}, "source_types": {"repository_metadata": 601}}, {"id": "genetic_ancestry.acb", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"ACB\", alternates: [\"ACB | AFR\"]}", "effect": "{genetic_ancestry: ACB}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0660); PRIMED's `population_label` writes it with its superpopulation, `ACB | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 511, "files": 119, "won": 119, "datasets": {"(donor records)": 119}, "source_types": {"repository_metadata": 511}}, {"id": "genetic_ancestry.asw", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"ASW\", alternates: [\"ASW | AFR\"]}", "effect": "{genetic_ancestry: ASW}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0659); PRIMED's `population_label` writes it with its superpopulation, `ASW | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 331, "files": 74, "won": 74, "datasets": {"(donor records)": 74}, "source_types": {"repository_metadata": 331}}, {"id": "genetic_ancestry.beb", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"BEB\", alternates: [\"BEB | SAS\"]}", "effect": "{genetic_ancestry: BEB}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0802); PRIMED's `population_label` writes it with its superpopulation, `BEB | SAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 562, "files": 131, "won": 131, "datasets": {"(donor records)": 131}, "source_types": {"repository_metadata": 562}}, {"id": "genetic_ancestry.cdx", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"CDX\", alternates: [\"CDX | EAS\"]}", "effect": "{genetic_ancestry: CDX}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0752); PRIMED's `population_label` writes it with its superpopulation, `CDX | EAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 406, "files": 93, "won": 93, "datasets": {"(donor records)": 93}, "source_types": {"repository_metadata": 406}}, {"id": "genetic_ancestry.ceu", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"CEU\", alternates: [\"CEU | EUR\"]}", "effect": "{genetic_ancestry: CEU}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0766); PRIMED's `population_label` writes it with its superpopulation, `CEU | EUR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 746, "files": 179, "won": 179, "datasets": {"(donor records)": 179}, "source_types": {"repository_metadata": 746}}, {"id": "genetic_ancestry.chb", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"CHB\", alternates: [\"CHB | EAS\"]}", "effect": "{genetic_ancestry: CHB}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0753); PRIMED's `population_label` writes it with its superpopulation, `CHB | EAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 445, "files": 103, "won": 103, "datasets": {"(donor records)": 103}, "source_types": {"repository_metadata": 445}}, {"id": "genetic_ancestry.chs", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"CHS\", alternates: [\"CHS | EAS\"]}", "effect": "{genetic_ancestry: CHS}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0756); PRIMED's `population_label` writes it with its superpopulation, `CHS | EAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 694, "files": 163, "won": 163, "datasets": {"(donor records)": 163}, "source_types": {"repository_metadata": 694}}, {"id": "genetic_ancestry.clm", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"CLM\", alternates: [\"CLM | AMR\"]}", "effect": "{genetic_ancestry: CLM}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0684); PRIMED's `population_label` writes it with its superpopulation, `CLM | AMR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 571, "files": 133, "won": 133, "datasets": {"(donor records)": 133}, "source_types": {"repository_metadata": 571}}, {"id": "genetic_ancestry.esn", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"ESN\", alternates: [\"ESN | AFR\"]}", "effect": "{genetic_ancestry: ESN}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0661); PRIMED's `population_label` writes it with its superpopulation, `ESN | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 635, "files": 149, "won": 149, "datasets": {"(donor records)": 149}, "source_types": {"repository_metadata": 635}}, {"id": "genetic_ancestry.fin", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"FIN\", alternates: [\"FIN | EUR\"]}", "effect": "{genetic_ancestry: FIN}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0767); PRIMED's `population_label` writes it with its superpopulation, `FIN | EUR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 433, "files": 99, "won": 99, "datasets": {"(donor records)": 99}, "source_types": {"repository_metadata": 433}}, {"id": "genetic_ancestry.gbr", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"GBR\", alternates: [\"GBR | EUR\"]}", "effect": "{genetic_ancestry: GBR}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0765); PRIMED's `population_label` writes it with its superpopulation, `GBR | EUR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 402, "files": 91, "won": 91, "datasets": {"(donor records)": 91}, "source_types": {"repository_metadata": 402}}, {"id": "genetic_ancestry.gih", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"GIH\", alternates: [\"GIH | SAS\"]}", "effect": "{genetic_ancestry: GIH}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0803); PRIMED's `population_label` writes it with its superpopulation, `GIH | SAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 447, "files": 103, "won": 103, "datasets": {"(donor records)": 103}, "source_types": {"repository_metadata": 447}}, {"id": "genetic_ancestry.gwd", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"GWD\", alternates: [\"GWD | AFR\"]}", "effect": "{genetic_ancestry: GWD}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0664); PRIMED's `population_label` writes it with its superpopulation, `GWD | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 765, "files": 178, "won": 178, "datasets": {"(donor records)": 178}, "source_types": {"repository_metadata": 765}}, {"id": "genetic_ancestry.ibs", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"IBS\", alternates: [\"IBS | EUR\"]}", "effect": "{genetic_ancestry: IBS}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0768); PRIMED's `population_label` writes it with its superpopulation, `IBS | EUR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 659, "files": 157, "won": 157, "datasets": {"(donor records)": 157}, "source_types": {"repository_metadata": 659}}, {"id": "genetic_ancestry.itu", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"ITU\", alternates: [\"ITU | SAS\"]}", "effect": "{genetic_ancestry: ITU}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0806); PRIMED's `population_label` writes it with its superpopulation, `ITU | SAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 459, "files": 107, "won": 107, "datasets": {"(donor records)": 107}, "source_types": {"repository_metadata": 459}}, {"id": "genetic_ancestry.jpt", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"JPT\", alternates: [\"JPT | EAS\"]}", "effect": "{genetic_ancestry: JPT}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0754); PRIMED's `population_label` writes it with its superpopulation, `JPT | EAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 460, "files": 104, "won": 104, "datasets": {"(donor records)": 104}, "source_types": {"repository_metadata": 460}}, {"id": "genetic_ancestry.khv", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"KHV\", alternates: [\"KHV | EAS\"]}", "effect": "{genetic_ancestry: KHV}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0755); PRIMED's `population_label` writes it with its superpopulation, `KHV | EAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 530, "files": 122, "won": 122, "datasets": {"(donor records)": 122}, "source_types": {"repository_metadata": 530}}, {"id": "genetic_ancestry.lwk", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"LWK\", alternates: [\"LWK | AFR\"]}", "effect": "{genetic_ancestry: LWK}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0666); PRIMED's `population_label` writes it with its superpopulation, `LWK | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 430, "files": 99, "won": 99, "datasets": {"(donor records)": 99}, "source_types": {"repository_metadata": 430}}, {"id": "genetic_ancestry.msl", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"MSL\", alternates: [\"MSL | AFR\"]}", "effect": "{genetic_ancestry: MSL}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0667); PRIMED's `population_label` writes it with its superpopulation, `MSL | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 437, "files": 100, "won": 100, "datasets": {"(donor records)": 100}, "source_types": {"repository_metadata": 437}}, {"id": "genetic_ancestry.mxl", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"MXL\", alternates: [\"MXL | AMR\"]}", "effect": "{genetic_ancestry: MXL}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0685); PRIMED's `population_label` writes it with its superpopulation, `MXL | AMR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 419, "files": 97, "won": 97, "datasets": {"(donor records)": 97}, "source_types": {"repository_metadata": 419}}, {"id": "genetic_ancestry.pel", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"PEL\", alternates: [\"PEL | AMR\"]}", "effect": "{genetic_ancestry: PEL}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0686); PRIMED's `population_label` writes it with its superpopulation, `PEL | AMR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 535, "files": 122, "won": 122, "datasets": {"(donor records)": 122}, "source_types": {"repository_metadata": 535}}, {"id": "genetic_ancestry.pjl", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"PJL\", alternates: [\"PJL | SAS\"]}", "effect": "{genetic_ancestry: PJL}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0804); PRIMED's `population_label` writes it with its superpopulation, `PJL | SAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 623, "files": 146, "won": 146, "datasets": {"(donor records)": 146}, "source_types": {"repository_metadata": 623}}, {"id": "genetic_ancestry.pur", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"PUR\", alternates: [\"PUR | AMR\"]}", "effect": "{genetic_ancestry: PUR}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0687); PRIMED's `population_label` writes it with its superpopulation, `PUR | AMR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 608, "files": 139, "won": 139, "datasets": {"(donor records)": 139}, "source_types": {"repository_metadata": 608}}, {"id": "genetic_ancestry.stu", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"STU\", alternates: [\"STU | SAS\"]}", "effect": "{genetic_ancestry: STU}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0805); PRIMED's `population_label` writes it with its superpopulation, `STU | SAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 489, "files": 114, "won": 114, "datasets": {"(donor records)": 114}, "source_types": {"repository_metadata": 489}}, {"id": "genetic_ancestry.tsi", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"TSI\", alternates: [\"TSI | EUR\"]}", "effect": "{genetic_ancestry: TSI}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0769); PRIMED's `population_label` writes it with its superpopulation, `TSI | EUR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 464, "files": 107, "won": 107, "datasets": {"(donor records)": 107}, "source_types": {"repository_metadata": 464}}, {"id": "genetic_ancestry.yri", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"YRI\", alternates: [\"YRI | AFR\"]}", "effect": "{genetic_ancestry: YRI}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0668); PRIMED's `population_label` writes it with its superpopulation, `YRI | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 750, "files": 178, "won": 178, "datasets": {"(donor records)": 178}, "source_types": {"repository_metadata": 750}}, {"id": "organism_type.human", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: organism_type, value: \"Human\"}", "effect": "{organism_type: Human}", "expanded": [], "rationale": "The term itself, as AnVIL's `anvil_donor.organism_type` writes it.", "dataset": "general", "seeded": false, "claims": 9656, "files": 3209, "won": 3209, "datasets": {"(donor records)": 3209}, "source_types": {"published_value": 9656}}, {"id": "organism_type.mouse", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: organism_type, value: \"Mouse\"}", "effect": "{organism_type: Mouse}", "expanded": [], "rationale": "The term itself, as AnVIL's `anvil_donor.organism_type` writes it; every Mouse donor in anvil15 is IGVF's, whose donors are laboratory mice (Mus musculus).", "dataset": "general", "seeded": false, "claims": 183, "files": 183, "won": 183, "datasets": {"(donor records)": 183}, "source_types": {"published_value": 183}}, {"id": "reported_ethnicity.black_or_african_american", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: reported_ethnicity, value: \"Black or African American\"}", "effect": "{reported_ethnicity: Black or African American}", "expanded": [], "rationale": "The term itself, as AnVIL's `anvil_donor.reported_ethnicity` writes it, and NIA_CARD's `subject.race_ethnicity` it is made from.", "dataset": "general", "seeded": false, "claims": 2, "files": 1, "won": 1, "datasets": {"(donor records)": 1}, "source_types": {"repository_metadata": 1, "published_value": 1}}, {"id": "reported_ethnicity.hispanic_or_latino", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: reported_ethnicity, value: \"Hispanic or Latino\"}", "effect": "{reported_ethnicity: Hispanic or Latino}", "expanded": [], "rationale": "The term itself, as AnVIL's `anvil_donor.reported_ethnicity` writes it, and NIA_CARD's `subject.race_ethnicity` it is made from.", "dataset": "general", "seeded": false, "claims": 2, "files": 1, "won": 1, "datasets": {"(donor records)": 1}, "source_types": {"repository_metadata": 1, "published_value": 1}}, {"id": "reported_ethnicity.white", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: reported_ethnicity, value: \"White\"}", "effect": "{reported_ethnicity: White}", "expanded": [], "rationale": "The term itself, as AnVIL's `anvil_donor.reported_ethnicity` writes it, and NIA_CARD's `subject.race_ethnicity` it is made from.", "dataset": "general", "seeded": false, "claims": 2, "files": 1, "won": 1, "datasets": {"(donor records)": 1}, "source_types": {"repository_metadata": 1, "published_value": 1}}, {"id": "biosample_type.tissue", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: biosample_type, value: \"Tissue\"}", "effect": "{biosample_type: Tissue}", "expanded": [], "rationale": "The term itself, as AnVIL's `anvil_biosample.biosample_type` writes it.", "dataset": "general", "seeded": false, "claims": 1518, "files": 1518, "won": 1518, "datasets": {"(sample records)": 1518}, "source_types": {"published_value": 1518}}, {"id": "biosample_type.unknown", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: biosample_type, value: \"Unknown\"}", "effect": "{}", "expanded": [], "rationale": "AnVIL's `Unknown` names no type: reviewed, and no claim. On anvil15 it is every filled HPRC_R2 biosample's (230 of 234; 4 are null) and 135 of IGVF's.", "dataset": "general", "seeded": false, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "assay_type.hi_c", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: assay_type, value: \"Hi-C\", alternates: [\"hic\"]}", "effect": "{data_modality: epigenomic.3d_contact_maps}", "expanded": [], "rationale": "Hi-C measures chromatin contacts, which MODAL's `3D contact maps` names (#563); the modality is the information measured, not the use HPRC puts it to (scaffolding). The vocabulary has no assay term for it yet (#399), so nothing is declared for assay_type. `hic` is the table-name span of the same data.", "dataset": "general", "seeded": false, "claims": 6772, "files": 3770, "won": 3770, "datasets": {"AnVIL_HPRC_R2": 3002, "ANVIL_HPRC": 768}, "source_types": {"repository_metadata": 6772}}, {"id": "assay_type.isoseq", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: assay_type, value: \"isoseq\"}", "effect": null, "expanded": [], "rationale": null, "dataset": "general", "seeded": true, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "assay_type.single_nucleus_atac_seq", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: assay_type, value: [\"single-nucleus ATAC-seq\"]}", "effect": "{assay_type: snATAC-seq, data_modality: epigenomic.chromatin_accessibility}", "expanded": [], "rationale": "IGVF's `file.assay_titles`. Single-nucleus ATAC-seq: our own term, under EFO's sc/snATAC-seq, which has no single-nucleus child (#533). ATAC-seq measures open chromatin. In AnVIL_IGVF_Mouse_R1 the value is on the reads (FASTQ) and seqspec files of the ATAC half of a SHARE-seq experiment.", "dataset": "general", "seeded": false, "claims": 20, "files": 10, "won": 10, "datasets": {"AnVIL_IGVF_Mouse_R1": 10}, "source_types": {"repository_metadata": 20}}, {"id": "assay_type.single_nucleus_atac_seq+single_nucleus_rna_sequencing_assay", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: assay_type, value: [\"single-nucleus ATAC-seq\", \"single-nucleus RNA sequencing assay\"]}", "effect": "{assay_type: SHARE-seq}", "expanded": [], "rationale": "IGVF's `file.assay_titles`. Both assays of one SHARE-seq experiment: IGVF's analysis sets that carry the pair (IGVFDS5339UKYM, IGVFDS5530HYKI) give `preferred_assay_titles: [\"SHARE-seq\"]`, EFO:0022962. Nothing is declared for data_modality. The value is the analysis set's label, on every file of the set, and each file holds one half: the count matrices and kallisto outputs are RNA, the fragment files ATAC. Any one modality would be wrong for some of them, and the vocabulary holds one per file, so each file's own evidence decides. Scoped to this dataset: the two titles alone do not name the protocol, since another joint assay (10x Multiome for one) could carry the same pair, so elsewhere the value selects no row and is queued.", "dataset": "source anvil, dataset AnVIL_IGVF_Mouse_R1", "seeded": false, "claims": 12, "files": 12, "won": 10, "datasets": {"AnVIL_IGVF_Mouse_R1": 12}, "source_types": {"repository_metadata": 12}}, {"id": "assay_type.single_nucleus_rna_sequencing_assay", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: assay_type, value: [\"single-nucleus RNA sequencing assay\"]}", "effect": "{assay_type: snRNA-seq, data_modality: transcriptomic}", "expanded": [], "rationale": "IGVF's `file.assay_titles`. Single-nucleus RNA sequencing, EFO:0009809 (#533); RNA-seq measures the transcriptome.", "dataset": "general", "seeded": false, "claims": 13466, "files": 6733, "won": 6733, "datasets": {"AnVIL_IGVF_Mouse_R1": 6733}, "source_types": {"repository_metadata": 13466}}, {"id": "assay_type.wgs", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: assay_type, value: \"WGS\"}", "effect": "{assay_type: WGS, data_modality: genomic}", "expanded": [], "rationale": "Whole-genome sequencing, spelled as our term; a WGS library is genomic DNA.", "dataset": "general", "seeded": false, "claims": 38776, "files": 19388, "won": 19388, "datasets": {"ANVIL_1000G_high_coverage_2019": 9603, "ANVIL_T2T": 6404, "AnVIL_HPRC_R2": 2817, "ANVIL_T2T_CHRY": 558, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 6}, "source_types": {"repository_metadata": 24852, "external_ground_truth": 13924}}, {"id": "data_modality.genomic_hprc_r2", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: \"GENOMIC\"}", "effect": "{}", "expanded": [], "rationale": "The `library_source` column's SRA value. It names the molecule sequenced, not the information measured: SRA files WGS, bisulfite, ATAC, ChIP and Hi-C alike under GENOMIC, because each sequences DNA, so it is no evidence of a MODAL modality (#563). Hi-C arrived under it here (the `hic` table). Scoped to the datasets whose library_source columns carry it: elsewhere the value selects no row, and so enters the review queue rather than being declined unreviewed. Measured on run 20260927_225823: none of the 5,619 files it reached here depended on it alone for data_modality.", "dataset": "source anvil, dataset AnVIL_HPRC_R2", "seeded": false, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_modality.genomic_1000g_high_coverage", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: \"GENOMIC\"}", "effect": "{}", "expanded": [], "rationale": "The `sample` table's `library_source` column, the same SRA value as `data_modality.genomic_hprc_r2`, for the same reason. Measured on run 20260927_225823: none of the 9,603 files it reached depended on it alone for data_modality.", "dataset": "source anvil, dataset ANVIL_1000G_high_coverage_2019", "seeded": false, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_modality.genomic_ena", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: \"GENOMIC\"}", "effect": "{}", "expanded": [], "rationale": "ENA's `library_source` for a run, the same SRA value as `data_modality.genomic_hprc_r2`, for the same reason: it names the molecule sequenced, not a MODAL modality (#563). A WGS run's modality comes from its `library_strategy` instead (`assay_type.wgs`).", "dataset": "source ena", "seeded": false, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_modality.methylation", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: \"methylation\"}", "effect": "{data_modality: epigenomic.methylation}", "expanded": [], "rationale": "The `ont_methylation` table-name span; its files are bigwig methylation tracks.", "dataset": "general", "seeded": false, "claims": 462, "files": 462, "won": 462, "datasets": {"AnVIL_HPRC_R2": 462}, "source_types": {"repository_metadata": 462}}, {"id": "data_modality.single_nucleus_atac_seq", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: [\"single-nucleus ATAC-seq\"]}", "effect": "{assay_type: snATAC-seq, data_modality: epigenomic.chromatin_accessibility}", "expanded": [], "rationale": "AnVIL's published `anvil_file.data_modality`, which carries IGVF's assay titles. Single-nucleus ATAC-seq: our own term, under EFO's sc/snATAC-seq, which has no single-nucleus child (#533). ATAC-seq measures open chromatin. In AnVIL_IGVF_Mouse_R1 the value is on the reads (FASTQ) and seqspec files of the ATAC half of a SHARE-seq experiment.", "dataset": "general", "seeded": false, "claims": 20, "files": 10, "won": 10, "datasets": {"AnVIL_IGVF_Mouse_R1": 10}, "source_types": {"published_value": 20}}, {"id": "data_modality.single_nucleus_atac_seq+single_nucleus_rna_sequencing_assay", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: [\"single-nucleus ATAC-seq\", \"single-nucleus RNA sequencing assay\"]}", "effect": "{assay_type: SHARE-seq}", "expanded": [], "rationale": "AnVIL's published `anvil_file.data_modality`, which carries IGVF's assay titles. Both assays of one SHARE-seq experiment: IGVF's analysis sets that carry the pair (IGVFDS5339UKYM, IGVFDS5530HYKI) give `preferred_assay_titles: [\"SHARE-seq\"]`, EFO:0022962. Nothing is declared for data_modality. The value is the analysis set's label, on every file of the set, and each file holds one half: the count matrices and kallisto outputs are RNA, the fragment files ATAC. Any one modality would be wrong for some of them, and the vocabulary holds one per file, so each file's own evidence decides. Scoped to this dataset: the two titles alone do not name the protocol, since another joint assay (10x Multiome for one) could carry the same pair, so elsewhere the value selects no row and is queued.", "dataset": "source anvil, dataset AnVIL_IGVF_Mouse_R1", "seeded": false, "claims": 12, "files": 12, "won": 10, "datasets": {"AnVIL_IGVF_Mouse_R1": 12}, "source_types": {"published_value": 12}}, {"id": "data_modality.single_nucleus_rna_sequencing_assay", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: [\"single-nucleus RNA sequencing assay\"]}", "effect": "{assay_type: snRNA-seq, data_modality: transcriptomic}", "expanded": [], "rationale": "AnVIL's published `anvil_file.data_modality`, which carries IGVF's assay titles. Single-nucleus RNA sequencing, EFO:0009809 (#533); RNA-seq measures the transcriptome.", "dataset": "general", "seeded": false, "claims": 13466, "files": 6733, "won": 6733, "datasets": {"AnVIL_IGVF_Mouse_R1": 6733}, "source_types": {"published_value": 13466}}, {"id": "data_modality.transcriptomic", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: \"transcriptomic\"}", "effect": null, "expanded": [], "rationale": null, "dataset": "general", "seeded": true, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_type.alignments", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"alignments\"}", "effect": null, "expanded": [], "rationale": null, "dataset": "general", "seeded": true, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_type.annotation", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"annotation\"}", "effect": "{data_type: annotations}", "expanded": [], "rationale": "The `assembly_annotation` table-name span; its files are feature annotations of an assembly.", "dataset": "general", "seeded": false, "claims": 881, "files": 881, "won": 881, "datasets": {"ANVIL_HPRC": 881}, "source_types": {"repository_metadata": 881}}, {"id": "data_type.assembly", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"assembly\"}", "effect": "{data_type: assembly}", "expanded": [], "rationale": "The `assembly` table-name span; its files are the de-novo assemblies (.fa.gz).", "dataset": "general", "seeded": false, "claims": 466, "files": 466, "won": 464, "datasets": {"AnVIL_HPRC_R2": 466}, "source_types": {"repository_metadata": 466}}, {"id": "data_type.bam", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"bam\"}", "effect": "{}", "expanded": [], "rationale": "A file format, not a data type: the kinnex table's data_type column holds what its filetype column should. Ruled to mean nothing here rather than left queued.", "dataset": "general", "seeded": false, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_type.censat", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"censat\"}", "effect": "{data_type: annotations}", "expanded": [], "rationale": "Centromeric satellite annotation tracks (BED) of an assembly.", "dataset": "general", "seeded": false, "claims": 462, "files": 462, "won": 462, "datasets": {"AnVIL_HPRC_R2": 462}, "source_types": {"repository_metadata": 462}}, {"id": "data_type.censat_centromeres", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"censat_centromeres\"}", "effect": "{data_type: annotations}", "expanded": [], "rationale": "Centromere annotation tracks (BED) of an assembly.", "dataset": "general", "seeded": false, "claims": 462, "files": 462, "won": 462, "datasets": {"AnVIL_HPRC_R2": 462}, "source_types": {"repository_metadata": 462}}, {"id": "data_type.chains", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"chains\"}", "effect": null, "expanded": [], "rationale": null, "dataset": "general", "seeded": true, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_type.fragments", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"fragments\"}", "effect": "{data_modality: epigenomic.chromatin_accessibility}", "expanded": [], "rationale": "IGVF's `file.content_type`. A fragment file: one line per transposase-cut fragment, with its cell barcode. In AnVIL_IGVF_Mouse_R1 the two such files are the ATAC half of a SHARE-seq experiment, whose assay_titles pair declares no modality (#570), and ATAC-seq measures open chromatin. Scoped to this dataset: the word names a file layout, which other chromatin assays (CUT&Tag for one) write too, so elsewhere it does not say accessibility.", "dataset": "source anvil, dataset AnVIL_IGVF_Mouse_R1", "seeded": false, "claims": 2, "files": 2, "won": 2, "datasets": {"AnVIL_IGVF_Mouse_R1": 2}, "source_types": {"repository_metadata": 2}}, {"id": "data_type.gaps", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"gaps\"}", "effect": null, "expanded": [], "rationale": null, "dataset": "general", "seeded": true, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_type.kallisto_single_cell_rnaseq_output", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"kallisto single cell RNAseq output\"}", "effect": "{data_modality: transcriptomic}", "expanded": [], "rationale": "IGVF's `file.content_type`. kallisto's single-cell RNA-seq output, as IGVF names it, so RNA. Most of these files are titled snRNA-seq alone, which already says transcriptomic; on the two of the SHARE-seq pair it is what says which half the file holds (#570). Scoped to this dataset, the one whose vocabulary it is. No data_type: the tar is a bundle of kallisto's outputs \u2014 the cached tar heads hold, variously, BUS files, `matrix.ec`, `kb_info.json` and cells \u00d7 genes `.mtx` matrices \u2014 not one data type, though IGVF files it as a cell-by-gene `MatrixFile`.", "dataset": "source anvil, dataset AnVIL_IGVF_Mouse_R1", "seeded": false, "claims": 304, "files": 304, "won": 304, "datasets": {"AnVIL_IGVF_Mouse_R1": 304}, "source_types": {"repository_metadata": 304}}, {"id": "data_type.liftoff", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"liftoff\"}", "effect": "{data_type: annotations}", "expanded": [], "rationale": "Gene annotations lifted onto an assembly with Liftoff (GFF3).", "dataset": "general", "seeded": false, "claims": 462, "files": 462, "won": 462, "datasets": {"AnVIL_HPRC_R2": 462}, "source_types": {"repository_metadata": 462}}, {"id": "data_type.repeat_masker", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"repeat_masker\"}", "effect": "{data_type: annotations}", "expanded": [], "rationale": "RepeatMasker repeat annotations of an assembly (BED and .out).", "dataset": "general", "seeded": false, "claims": 1018, "files": 1018, "won": 1018, "datasets": {"AnVIL_HPRC_R2": 924, "ANVIL_HPRC": 94}, "source_types": {"repository_metadata": 1018}}, {"id": "data_type.segdups", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"segdups\"}", "effect": "{data_type: annotations}", "expanded": [], "rationale": "Segmental duplication tracks (BED) of an assembly.", "dataset": "general", "seeded": false, "claims": 462, "files": 462, "won": 462, "datasets": {"AnVIL_HPRC_R2": 462}, "source_types": {"repository_metadata": 462}}, {"id": "data_type.sequences", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"sequences\"}", "effect": null, "expanded": [], "rationale": null, "dataset": "general", "seeded": true, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_type.sparse_gene_count_matrix", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"sparse gene count matrix\"}", "effect": "{data_type: expression_matrix, data_modality: transcriptomic}", "expanded": [], "rationale": "IGVF's `file.content_type`. A cell-by-gene count matrix, as IGVF's `summary` on these files says. In AnVIL_IGVF_Mouse_R1 each is RNA: the files titled with snRNA-seq alone, and the two of the SHARE-seq pair (#570), which IGVF derives from the same reads as that experiment's kallisto RNA output. Scoped to this dataset: a gene matrix alone does not say RNA, since one can hold ATAC gene-activity scores.", "dataset": "source anvil, dataset AnVIL_IGVF_Mouse_R1", "seeded": false, "claims": 1032, "files": 516, "won": 516, "datasets": {"AnVIL_IGVF_Mouse_R1": 516}, "source_types": {"repository_metadata": 1032}}, {"id": "data_type.transcript_quantifications", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"transcript quantifications\"}", "effect": "{data_type: quantification, data_modality: transcriptomic}", "expanded": [], "rationale": "IGVF's `file.content_type`. Per-transcript abundances quantify RNA. Scoped to this dataset, the one whose vocabulary it is.", "dataset": "source anvil, dataset AnVIL_IGVF_Mouse_R1", "seeded": false, "claims": 16, "files": 8, "won": 8, "datasets": {"AnVIL_IGVF_Mouse_R1": 8}, "source_types": {"repository_metadata": 16}}, {"id": "data_type.unaligned_reads", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"unaligned reads\"}", "effect": "{data_type: reads}", "expanded": [], "rationale": "Reads, as the source says; whether aligned is not the data type.", "dataset": "general", "seeded": false, "claims": 4690, "files": 4690, "won": 4690, "datasets": {"AnVIL_HPRC_R2": 4690}, "source_types": {"repository_metadata": 4690}}, {"id": "data_type.unaligned_reads_with_5mc_mods", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"unaligned reads with 5mC mods\"}", "effect": "{data_type: reads}", "expanded": [], "rationale": "Reads carrying base-modification calls; the modification tags do not change the data type.", "dataset": "general", "seeded": false, "claims": 641, "files": 641, "won": 641, "datasets": {"AnVIL_HPRC_R2": 641}, "source_types": {"repository_metadata": 641}}, {"id": "data_type.unaligned_reads_with_5mcg_5hmcg_mods", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"unaligned reads with 5mCG_5hmCG mods\"}", "effect": "{data_type: reads}", "expanded": [], "rationale": "Reads carrying base-modification calls; the modification tags do not change the data type.", "dataset": "general", "seeded": false, "claims": 288, "files": 288, "won": 288, "datasets": {"AnVIL_HPRC_R2": 288}, "source_types": {"repository_metadata": 288}}, {"id": "platform.deepconsensus", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"deepconsensus\"}", "effect": "{platform: PACBIO}", "expanded": [], "rationale": "DeepConsensus is PacBio's HiFi read-polishing method; the span names PacBio data. Platform only.", "dataset": "general", "seeded": false, "claims": 619, "files": 619, "won": 619, "datasets": {"AnVIL_HPRC_R2": 619}, "source_types": {"repository_metadata": 619}}, {"id": "platform.gridion", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"GridION\"}", "effect": "{platform: ONT, instrument_model: GridION}", "expanded": [], "rationale": "An Oxford Nanopore instrument, spelled as ENA/SRA's model.", "dataset": "general", "seeded": false, "claims": 4, "files": 2, "won": 2, "datasets": {"AnVIL_HPRC_R2": 2}, "source_types": {"repository_metadata": 4}}, {"id": "platform.hifi", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"hifi\"}", "effect": "{platform: PACBIO}", "expanded": [], "rationale": "HiFi reads are PacBio's; the span says platform only and implies no assay (#430).", "dataset": "general", "seeded": false, "claims": 1292, "files": 1292, "won": 1292, "datasets": {"AnVIL_HPRC_R2": 1069, "ANVIL_HPRC": 223}, "source_types": {"repository_metadata": 1292}}, {"id": "platform.illumina", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"ILLUMINA\", alternates: [\"illumina\"]}", "effect": "{platform: ILLUMINA}", "expanded": [], "rationale": "Spelled as our term, as SRA's platform value and as a table-name span. Identity by ruling.", "dataset": "general", "seeded": false, "claims": 16762, "files": 16562, "won": 16562, "datasets": {"ANVIL_T2T": 6404, "ANVIL_1000G_high_coverage_2019": 6398, "AnVIL_HPRC_R2": 3202, "ANVIL_T2T_CHRY": 558}, "source_types": {"repository_metadata": 9800, "external_ground_truth": 6962}}, {"id": "platform.illumina_hiseq_2000", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"Illumina HiSeq 2000\"}", "effect": "{platform: ILLUMINA, instrument_model: Illumina HiSeq 2000}", "expanded": [], "rationale": "An Illumina instrument model, spelled as ENA/SRA's; the model implies the platform.", "dataset": "general", "seeded": false, "claims": 1116, "files": 558, "won": 558, "datasets": {"ANVIL_T2T_CHRY": 558}, "source_types": {"external_ground_truth": 1116}}, {"id": "platform.illumina_novaseq_6000", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"Illumina NovaSeq 6000\"}", "effect": "{platform: ILLUMINA, instrument_model: Illumina NovaSeq 6000}", "expanded": [], "rationale": "An Illumina instrument model, spelled as ENA/SRA's; the model implies the platform.", "dataset": "general", "seeded": false, "claims": 52422, "files": 21209, "won": 21209, "datasets": {"ANVIL_1000G_high_coverage_2019": 9603, "ANVIL_T2T": 6404, "AnVIL_HPRC_R2": 3202, "AnVIL_IGVF_Mouse_R1": 2000}, "source_types": {"repository_metadata": 39614, "external_ground_truth": 12808}}, {"id": "platform.illumina_novaseq_6001_to_6698", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "slot: platform\nvalue: \"Illumina NovaSeq 6001\"\nalternates: [\n    \"Illumina NovaSeq 6002\", \"Illumina NovaSeq 6003\", \"Illumina NovaSeq 6004\", \"Illumina NovaSeq 6005\", \"Illumina NovaSeq 6006\",\n    \"Illumina NovaSeq 6007\", \"Illumina NovaSeq 6008\", \"Illumina NovaSeq 6009\", \"Illumina NovaSeq 6010\", \"Illumina NovaSeq 6011\",\n    \"Illumina NovaSeq 6012\", \"Illumina NovaSeq 6013\", \"Illumina NovaSeq 6014\", \"Illumina NovaSeq 6015\", \"Illumina NovaSeq 6016\",\n    \"Illumina NovaSeq 6017\", \"Illumina NovaSeq 6018\", \"Illumina NovaSeq 6019\", \"Illumina NovaSeq 6020\", \"Illumina NovaSeq 6021\",\n    \"Illumina NovaSeq 6022\", \"Illumina NovaSeq 6023\", \"Illumina NovaSeq 6024\", \"Illumina NovaSeq 6025\", \"Illumina NovaSeq 6026\",\n    \"Illumina NovaSeq 6027\", \"Illumina NovaSeq 6028\", \"Illumina NovaSeq 6029\", \"Illumina NovaSeq 6030\", \"Illumina NovaSeq 6031\",\n    \"Illumina NovaSeq 6032\", \"Illumina NovaSeq 6033\", \"Illumina NovaSeq 6034\", \"Illumina NovaSeq 6035\", \"Illumina NovaSeq 6036\",\n    \"Illumina NovaSeq 6037\", \"Illumina NovaSeq 6038\", \"Illumina NovaSeq 6039\", \"Illumina NovaSeq 6040\", \"Illumina NovaSeq 6041\",\n    \"Illumina NovaSeq 6042\", \"Illumina NovaSeq 6043\", \"Illumina NovaSeq 6044\", \"Illumina NovaSeq 6045\", \"Illumina NovaSeq 6046\",\n    \"Illumina NovaSeq 6047\", \"Illumina NovaSeq 6048\", \"Illumina NovaSeq 6049\", \"Illumina NovaSeq 6050\", \"Illumina NovaSeq 6051\",\n    \"Illumina NovaSeq 6052\", \"Illumina NovaSeq 6053\", \"Illumina NovaSeq 6054\", \"Illumina NovaSeq 6055\", \"Illumina NovaSeq 6056\",\n    \"Illumina NovaSeq 6057\", \"Illumina NovaSeq 6058\", \"Illumina NovaSeq 6059\", \"Illumina NovaSeq 6060\", \"Illumina NovaSeq 6061\",\n    \"Illumina NovaSeq 6062\", \"Illumina NovaSeq 6063\", \"Illumina NovaSeq 6064\", \"Illumina NovaSeq 6065\", \"Illumina NovaSeq 6066\",\n    \"Illumina NovaSeq 6067\", \"Illumina NovaSeq 6068\", \"Illumina NovaSeq 6069\", \"Illumina NovaSeq 6070\", \"Illumina NovaSeq 6071\",\n    \"Illumina NovaSeq 6072\", \"Illumina NovaSeq 6073\", \"Illumina NovaSeq 6074\", \"Illumina NovaSeq 6075\", \"Illumina NovaSeq 6076\",\n    \"Illumina NovaSeq 6077\", \"Illumina NovaSeq 6078\", \"Illumina NovaSeq 6079\", \"Illumina NovaSeq 6080\", \"Illumina NovaSeq 6081\",\n    \"Illumina NovaSeq 6082\", \"Illumina NovaSeq 6083\", \"Illumina NovaSeq 6084\", \"Illumina NovaSeq 6085\", \"Illumina NovaSeq 6086\",\n    \"Illumina NovaSeq 6087\", \"Illumina NovaSeq 6088\", \"Illumina NovaSeq 6089\", \"Illumina NovaSeq 6090\", \"Illumina NovaSeq 6091\",\n    \"Illumina NovaSeq 6092\", \"Illumina NovaSeq 6093\", \"Illumina NovaSeq 6094\", \"Illumina NovaSeq 6095\", \"Illumina NovaSeq 6096\",\n    \"Illumina NovaSeq 6097\", \"Illumina NovaSeq 6098\", \"Illumina NovaSeq 6099\", \"Illumina NovaSeq 6100\", \"Illumina NovaSeq 6101\",\n    \"Illumina NovaSeq 6102\", \"Illumina NovaSeq 6103\", \"Illumina NovaSeq 6104\", \"Illumina NovaSeq 6105\", \"Illumina NovaSeq 6106\",\n    \"Illumina NovaSeq 6107\", \"Illumina NovaSeq 6108\", \"Illumina NovaSeq 6109\", \"Illumina NovaSeq 6110\", \"Illumina NovaSeq 6111\",\n    \"Illumina NovaSeq 6112\", \"Illumina NovaSeq 6113\", \"Illumina NovaSeq 6114\", \"Illumina NovaSeq 6115\", \"Illumina NovaSeq 6116\",\n    \"Illumina NovaSeq 6117\", \"Illumina NovaSeq 6118\", \"Illumina NovaSeq 6119\", \"Illumina NovaSeq 6120\", \"Illumina NovaSeq 6121\",\n    \"Illumina NovaSeq 6122\", \"Illumina NovaSeq 6123\", \"Illumina NovaSeq 6124\", \"Illumina NovaSeq 6125\", \"Illumina NovaSeq 6126\",\n    \"Illumina NovaSeq 6127\", \"Illumina NovaSeq 6128\", \"Illumina NovaSeq 6129\", \"Illumina NovaSeq 6130\", \"Illumina NovaSeq 6131\",\n    \"Illumina NovaSeq 6132\", \"Illumina NovaSeq 6133\", \"Illumina NovaSeq 6134\", \"Illumina NovaSeq 6135\", \"Illumina NovaSeq 6136\",\n    \"Illumina NovaSeq 6137\", \"Illumina NovaSeq 6138\", \"Illumina NovaSeq 6139\", \"Illumina NovaSeq 6140\", \"Illumina NovaSeq 6141\",\n    \"Illumina NovaSeq 6142\", \"Illumina NovaSeq 6143\", \"Illumina NovaSeq 6144\", \"Illumina NovaSeq 6145\", \"Illumina NovaSeq 6146\",\n    \"Illumina NovaSeq 6147\", \"Illumina NovaSeq 6148\", \"Illumina NovaSeq 6149\", \"Illumina NovaSeq 6150\", \"Illumina NovaSeq 6151\",\n    \"Illumina NovaSeq 6152\", \"Illumina NovaSeq 6153\", \"Illumina NovaSeq 6154\", \"Illumina NovaSeq 6155\", \"Illumina NovaSeq 6156\",\n    \"Illumina NovaSeq 6157\", \"Illumina NovaSeq 6158\", \"Illumina NovaSeq 6159\", \"Illumina NovaSeq 6160\", \"Illumina NovaSeq 6161\",\n    \"Illumina NovaSeq 6162\", \"Illumina NovaSeq 6163\", \"Illumina NovaSeq 6164\", \"Illumina NovaSeq 6165\", \"Illumina NovaSeq 6166\",\n    \"Illumina NovaSeq 6167\", \"Illumina NovaSeq 6168\", \"Illumina NovaSeq 6169\", \"Illumina NovaSeq 6170\", \"Illumina NovaSeq 6171\",\n    \"Illumina NovaSeq 6172\", \"Illumina NovaSeq 6173\", \"Illumina NovaSeq 6174\", \"Illumina NovaSeq 6175\", \"Illumina NovaSeq 6176\",\n    \"Illumina NovaSeq 6177\", \"Illumina NovaSeq 6178\", \"Illumina NovaSeq 6179\", \"Illumina NovaSeq 6180\", \"Illumina NovaSeq 6181\",\n    \"Illumina NovaSeq 6182\", \"Illumina NovaSeq 6183\", \"Illumina NovaSeq 6184\", \"Illumina NovaSeq 6185\", \"Illumina NovaSeq 6186\",\n    \"Illumina NovaSeq 6187\", \"Illumina NovaSeq 6188\", \"Illumina NovaSeq 6189\", \"Illumina NovaSeq 6190\", \"Illumina NovaSeq 6191\",\n    \"Illumina NovaSeq 6192\", \"Illumina NovaSeq 6193\", \"Illumina NovaSeq 6194\", \"Illumina NovaSeq 6195\", \"Illumina NovaSeq 6196\",\n    \"Illumina NovaSeq 6197\", \"Illumina NovaSeq 6198\", \"Illumina NovaSeq 6199\", \"Illumina NovaSeq 6200\", \"Illumina NovaSeq 6201\",\n    \"Illumina NovaSeq 6202\", \"Illumina NovaSeq 6203\", \"Illumina NovaSeq 6204\", \"Illumina NovaSeq 6205\", \"Illumina NovaSeq 6206\",\n    \"Illumina NovaSeq 6207\", \"Illumina NovaSeq 6208\", \"Illumina NovaSeq 6209\", \"Illumina NovaSeq 6210\", \"Illumina NovaSeq 6211\",\n    \"Illumina NovaSeq 6212\", \"Illumina NovaSeq 6213\", \"Illumina NovaSeq 6214\", \"Illumina NovaSeq 6215\", \"Illumina NovaSeq 6216\",\n    \"Illumina NovaSeq 6217\", \"Illumina NovaSeq 6218\", \"Illumina NovaSeq 6219\", \"Illumina NovaSeq 6220\", \"Illumina NovaSeq 6221\",\n    \"Illumina NovaSeq 6222\", \"Illumina NovaSeq 6223\", \"Illumina NovaSeq 6224\", \"Illumina NovaSeq 6225\", \"Illumina NovaSeq 6226\",\n    \"Illumina NovaSeq 6227\", \"Illumina NovaSeq 6228\", \"Illumina NovaSeq 6229\", \"Illumina NovaSeq 6230\", \"Illumina NovaSeq 6231\",\n    \"Illumina NovaSeq 6232\", \"Illumina NovaSeq 6233\", \"Illumina NovaSeq 6234\", \"Illumina NovaSeq 6235\", \"Illumina NovaSeq 6236\",\n    \"Illumina NovaSeq 6237\", \"Illumina NovaSeq 6238\", \"Illumina NovaSeq 6239\", \"Illumina NovaSeq 6240\", \"Illumina NovaSeq 6241\",\n    \"Illumina NovaSeq 6242\", \"Illumina NovaSeq 6243\", \"Illumina NovaSeq 6244\", \"Illumina NovaSeq 6245\", \"Illumina NovaSeq 6246\",\n    \"Illumina NovaSeq 6247\", \"Illumina NovaSeq 6248\", \"Illumina NovaSeq 6249\", \"Illumina NovaSeq 6250\", \"Illumina NovaSeq 6251\",\n    \"Illumina NovaSeq 6252\", \"Illumina NovaSeq 6253\", \"Illumina NovaSeq 6254\", \"Illumina NovaSeq 6255\", \"Illumina NovaSeq 6256\",\n    \"Illumina NovaSeq 6257\", \"Illumina NovaSeq 6258\", \"Illumina NovaSeq 6259\", \"Illumina NovaSeq 6260\", \"Illumina NovaSeq 6261\",\n    \"Illumina NovaSeq 6262\", \"Illumina NovaSeq 6263\", \"Illumina NovaSeq 6264\", \"Illumina NovaSeq 6265\", \"Illumina NovaSeq 6266\",\n    \"Illumina NovaSeq 6267\", \"Illumina NovaSeq 6268\", \"Illumina NovaSeq 6269\", \"Illumina NovaSeq 6270\", \"Illumina NovaSeq 6271\",\n    \"Illumina NovaSeq 6272\", \"Illumina NovaSeq 6273\", \"Illumina NovaSeq 6274\", \"Illumina NovaSeq 6275\", \"Illumina NovaSeq 6276\",\n    \"Illumina NovaSeq 6277\", \"Illumina NovaSeq 6278\", \"Illumina NovaSeq 6279\", \"Illumina NovaSeq 6280\", \"Illumina NovaSeq 6281\",\n    \"Illumina NovaSeq 6282\", \"Illumina NovaSeq 6283\", \"Illumina NovaSeq 6284\", \"Illumina NovaSeq 6285\", \"Illumina NovaSeq 6286\",\n    \"Illumina NovaSeq 6287\", \"Illumina NovaSeq 6288\", \"Illumina NovaSeq 6289\", \"Illumina NovaSeq 6290\", \"Illumina NovaSeq 6291\",\n    \"Illumina NovaSeq 6292\", \"Illumina NovaSeq 6293\", \"Illumina NovaSeq 6294\", \"Illumina NovaSeq 6295\", \"Illumina NovaSeq 6296\",\n    \"Illumina NovaSeq 6297\", \"Illumina NovaSeq 6298\", \"Illumina NovaSeq 6299\", \"Illumina NovaSeq 6300\", \"Illumina NovaSeq 6301\",\n    \"Illumina NovaSeq 6302\", \"Illumina NovaSeq 6303\", \"Illumina NovaSeq 6304\", \"Illumina NovaSeq 6305\", \"Illumina NovaSeq 6306\",\n    \"Illumina NovaSeq 6307\", \"Illumina NovaSeq 6308\", \"Illumina NovaSeq 6309\", \"Illumina NovaSeq 6310\", \"Illumina NovaSeq 6311\",\n    \"Illumina NovaSeq 6312\", \"Illumina NovaSeq 6313\", \"Illumina NovaSeq 6314\", \"Illumina NovaSeq 6315\", \"Illumina NovaSeq 6316\",\n    \"Illumina NovaSeq 6317\", \"Illumina NovaSeq 6318\", \"Illumina NovaSeq 6319\", \"Illumina NovaSeq 6320\", \"Illumina NovaSeq 6321\",\n    \"Illumina NovaSeq 6322\", \"Illumina NovaSeq 6323\", \"Illumina NovaSeq 6324\", \"Illumina NovaSeq 6325\", \"Illumina NovaSeq 6326\",\n    \"Illumina NovaSeq 6327\", \"Illumina NovaSeq 6328\", \"Illumina NovaSeq 6329\", \"Illumina NovaSeq 6330\", \"Illumina NovaSeq 6331\",\n    \"Illumina NovaSeq 6332\", \"Illumina NovaSeq 6333\", \"Illumina NovaSeq 6334\", \"Illumina NovaSeq 6335\", \"Illumina NovaSeq 6336\",\n    \"Illumina NovaSeq 6337\", \"Illumina NovaSeq 6338\", \"Illumina NovaSeq 6339\", \"Illumina NovaSeq 6340\", \"Illumina NovaSeq 6341\",\n    \"Illumina NovaSeq 6342\", \"Illumina NovaSeq 6343\", \"Illumina NovaSeq 6344\", \"Illumina NovaSeq 6345\", \"Illumina NovaSeq 6346\",\n    \"Illumina NovaSeq 6347\", \"Illumina NovaSeq 6348\", \"Illumina NovaSeq 6349\", \"Illumina NovaSeq 6350\", \"Illumina NovaSeq 6351\",\n    \"Illumina NovaSeq 6352\", \"Illumina NovaSeq 6353\", \"Illumina NovaSeq 6354\", \"Illumina NovaSeq 6355\", \"Illumina NovaSeq 6356\",\n    \"Illumina NovaSeq 6357\", \"Illumina NovaSeq 6358\", \"Illumina NovaSeq 6359\", \"Illumina NovaSeq 6360\", \"Illumina NovaSeq 6361\",\n    \"Illumina NovaSeq 6362\", \"Illumina NovaSeq 6363\", \"Illumina NovaSeq 6364\", \"Illumina NovaSeq 6365\", \"Illumina NovaSeq 6366\",\n    \"Illumina NovaSeq 6367\", \"Illumina NovaSeq 6368\", \"Illumina NovaSeq 6369\", \"Illumina NovaSeq 6370\", \"Illumina NovaSeq 6371\",\n    \"Illumina NovaSeq 6372\", \"Illumina NovaSeq 6373\", \"Illumina NovaSeq 6374\", \"Illumina NovaSeq 6375\", \"Illumina NovaSeq 6376\",\n    \"Illumina NovaSeq 6377\", \"Illumina NovaSeq 6378\", \"Illumina NovaSeq 6379\", \"Illumina NovaSeq 6380\", \"Illumina NovaSeq 6381\",\n    \"Illumina NovaSeq 6382\", \"Illumina NovaSeq 6383\", \"Illumina NovaSeq 6384\", \"Illumina NovaSeq 6385\", \"Illumina NovaSeq 6386\",\n    \"Illumina NovaSeq 6387\", \"Illumina NovaSeq 6388\", \"Illumina NovaSeq 6389\", \"Illumina NovaSeq 6390\", \"Illumina NovaSeq 6391\",\n    \"Illumina NovaSeq 6392\", \"Illumina NovaSeq 6393\", \"Illumina NovaSeq 6394\", \"Illumina NovaSeq 6395\", \"Illumina NovaSeq 6396\",\n    \"Illumina NovaSeq 6397\", \"Illumina NovaSeq 6398\", \"Illumina NovaSeq 6399\", \"Illumina NovaSeq 6400\", \"Illumina NovaSeq 6401\",\n    \"Illumina NovaSeq 6402\", \"Illumina NovaSeq 6403\", \"Illumina NovaSeq 6404\", \"Illumina NovaSeq 6405\", \"Illumina NovaSeq 6406\",\n    \"Illumina NovaSeq 6407\", \"Illumina NovaSeq 6408\", \"Illumina NovaSeq 6409\", \"Illumina NovaSeq 6410\", \"Illumina NovaSeq 6411\",\n    \"Illumina NovaSeq 6412\", \"Illumina NovaSeq 6413\", \"Illumina NovaSeq 6414\", \"Illumina NovaSeq 6415\", \"Illumina NovaSeq 6416\",\n    \"Illumina NovaSeq 6417\", \"Illumina NovaSeq 6418\", \"Illumina NovaSeq 6419\", \"Illumina NovaSeq 6420\", \"Illumina NovaSeq 6421\",\n    \"Illumina NovaSeq 6422\", \"Illumina NovaSeq 6423\", \"Illumina NovaSeq 6424\", \"Illumina NovaSeq 6425\", \"Illumina NovaSeq 6426\",\n    \"Illumina NovaSeq 6427\", \"Illumina NovaSeq 6428\", \"Illumina NovaSeq 6429\", \"Illumina NovaSeq 6430\", \"Illumina NovaSeq 6431\",\n    \"Illumina NovaSeq 6432\", \"Illumina NovaSeq 6433\", \"Illumina NovaSeq 6434\", \"Illumina NovaSeq 6435\", \"Illumina NovaSeq 6436\",\n    \"Illumina NovaSeq 6437\", \"Illumina NovaSeq 6438\", \"Illumina NovaSeq 6439\", \"Illumina NovaSeq 6440\", \"Illumina NovaSeq 6441\",\n    \"Illumina NovaSeq 6442\", \"Illumina NovaSeq 6443\", \"Illumina NovaSeq 6444\", \"Illumina NovaSeq 6445\", \"Illumina NovaSeq 6446\",\n    \"Illumina NovaSeq 6447\", \"Illumina NovaSeq 6448\", \"Illumina NovaSeq 6449\", \"Illumina NovaSeq 6450\", \"Illumina NovaSeq 6451\",\n    \"Illumina NovaSeq 6452\", \"Illumina NovaSeq 6453\", \"Illumina NovaSeq 6454\", \"Illumina NovaSeq 6455\", \"Illumina NovaSeq 6456\",\n    \"Illumina NovaSeq 6457\", \"Illumina NovaSeq 6458\", \"Illumina NovaSeq 6459\", \"Illumina NovaSeq 6460\", \"Illumina NovaSeq 6461\",\n    \"Illumina NovaSeq 6462\", \"Illumina NovaSeq 6463\", \"Illumina NovaSeq 6464\", \"Illumina NovaSeq 6465\", \"Illumina NovaSeq 6466\",\n    \"Illumina NovaSeq 6467\", \"Illumina NovaSeq 6468\", \"Illumina NovaSeq 6469\", \"Illumina NovaSeq 6470\", \"Illumina NovaSeq 6471\",\n    \"Illumina NovaSeq 6472\", \"Illumina NovaSeq 6473\", \"Illumina NovaSeq 6474\", \"Illumina NovaSeq 6475\", \"Illumina NovaSeq 6476\",\n    \"Illumina NovaSeq 6477\", \"Illumina NovaSeq 6478\", \"Illumina NovaSeq 6479\", \"Illumina NovaSeq 6480\", \"Illumina NovaSeq 6481\",\n    \"Illumina NovaSeq 6482\", \"Illumina NovaSeq 6483\", \"Illumina NovaSeq 6484\", \"Illumina NovaSeq 6485\", \"Illumina NovaSeq 6486\",\n    \"Illumina NovaSeq 6487\", \"Illumina NovaSeq 6488\", \"Illumina NovaSeq 6489\", \"Illumina NovaSeq 6490\", \"Illumina NovaSeq 6491\",\n    \"Illumina NovaSeq 6492\", \"Illumina NovaSeq 6493\", \"Illumina NovaSeq 6494\", \"Illumina NovaSeq 6495\", \"Illumina NovaSeq 6496\",\n    \"Illumina NovaSeq 6497\", \"Illumina NovaSeq 6498\", \"Illumina NovaSeq 6499\", \"Illumina NovaSeq 6500\", \"Illumina NovaSeq 6501\",\n    \"Illumina NovaSeq 6502\", \"Illumina NovaSeq 6503\", \"Illumina NovaSeq 6504\", \"Illumina NovaSeq 6505\", \"Illumina NovaSeq 6506\",\n    \"Illumina NovaSeq 6507\", \"Illumina NovaSeq 6508\", \"Illumina NovaSeq 6509\", \"Illumina NovaSeq 6510\", \"Illumina NovaSeq 6511\",\n    \"Illumina NovaSeq 6512\", \"Illumina NovaSeq 6513\", \"Illumina NovaSeq 6514\", \"Illumina NovaSeq 6515\", \"Illumina NovaSeq 6516\",\n    \"Illumina NovaSeq 6517\", \"Illumina NovaSeq 6518\", \"Illumina NovaSeq 6519\", \"Illumina NovaSeq 6520\", \"Illumina NovaSeq 6521\",\n    \"Illumina NovaSeq 6522\", \"Illumina NovaSeq 6523\", \"Illumina NovaSeq 6524\", \"Illumina NovaSeq 6525\", \"Illumina NovaSeq 6526\",\n    \"Illumina NovaSeq 6527\", \"Illumina NovaSeq 6528\", \"Illumina NovaSeq 6529\", \"Illumina NovaSeq 6530\", \"Illumina NovaSeq 6531\",\n    \"Illumina NovaSeq 6532\", \"Illumina NovaSeq 6533\", \"Illumina NovaSeq 6534\", \"Illumina NovaSeq 6535\", \"Illumina NovaSeq 6536\",\n    \"Illumina NovaSeq 6537\", \"Illumina NovaSeq 6538\", \"Illumina NovaSeq 6539\", \"Illumina NovaSeq 6540\", \"Illumina NovaSeq 6541\",\n    \"Illumina NovaSeq 6542\", \"Illumina NovaSeq 6543\", \"Illumina NovaSeq 6544\", \"Illumina NovaSeq 6545\", \"Illumina NovaSeq 6546\",\n    \"Illumina NovaSeq 6547\", \"Illumina NovaSeq 6548\", \"Illumina NovaSeq 6549\", \"Illumina NovaSeq 6550\", \"Illumina NovaSeq 6551\",\n    \"Illumina NovaSeq 6552\", \"Illumina NovaSeq 6553\", \"Illumina NovaSeq 6554\", \"Illumina NovaSeq 6555\", \"Illumina NovaSeq 6556\",\n    \"Illumina NovaSeq 6557\", \"Illumina NovaSeq 6558\", \"Illumina NovaSeq 6559\", \"Illumina NovaSeq 6560\", \"Illumina NovaSeq 6561\",\n    \"Illumina NovaSeq 6562\", \"Illumina NovaSeq 6563\", \"Illumina NovaSeq 6564\", \"Illumina NovaSeq 6565\", \"Illumina NovaSeq 6566\",\n    \"Illumina NovaSeq 6567\", \"Illumina NovaSeq 6568\", \"Illumina NovaSeq 6569\", \"Illumina NovaSeq 6570\", \"Illumina NovaSeq 6571\",\n    \"Illumina NovaSeq 6572\", \"Illumina NovaSeq 6573\", \"Illumina NovaSeq 6574\", \"Illumina NovaSeq 6575\", \"Illumina NovaSeq 6576\",\n    \"Illumina NovaSeq 6577\", \"Illumina NovaSeq 6578\", \"Illumina NovaSeq 6579\", \"Illumina NovaSeq 6580\", \"Illumina NovaSeq 6581\",\n    \"Illumina NovaSeq 6582\", \"Illumina NovaSeq 6583\", \"Illumina NovaSeq 6584\", \"Illumina NovaSeq 6585\", \"Illumina NovaSeq 6586\",\n    \"Illumina NovaSeq 6587\", \"Illumina NovaSeq 6588\", \"Illumina NovaSeq 6589\", \"Illumina NovaSeq 6590\", \"Illumina NovaSeq 6591\",\n    \"Illumina NovaSeq 6592\", \"Illumina NovaSeq 6593\", \"Illumina NovaSeq 6594\", \"Illumina NovaSeq 6595\", \"Illumina NovaSeq 6596\",\n    \"Illumina NovaSeq 6597\", \"Illumina NovaSeq 6598\", \"Illumina NovaSeq 6599\", \"Illumina NovaSeq 6600\", \"Illumina NovaSeq 6601\",\n    \"Illumina NovaSeq 6602\", \"Illumina NovaSeq 6603\", \"Illumina NovaSeq 6604\", \"Illumina NovaSeq 6605\", \"Illumina NovaSeq 6606\",\n    \"Illumina NovaSeq 6607\", \"Illumina NovaSeq 6608\", \"Illumina NovaSeq 6609\", \"Illumina NovaSeq 6610\", \"Illumina NovaSeq 6611\",\n    \"Illumina NovaSeq 6612\", \"Illumina NovaSeq 6613\", \"Illumina NovaSeq 6614\", \"Illumina NovaSeq 6615\", \"Illumina NovaSeq 6616\",\n    \"Illumina NovaSeq 6617\", \"Illumina NovaSeq 6618\", \"Illumina NovaSeq 6619\", \"Illumina NovaSeq 6620\", \"Illumina NovaSeq 6621\",\n    \"Illumina NovaSeq 6622\", \"Illumina NovaSeq 6623\", \"Illumina NovaSeq 6624\", \"Illumina NovaSeq 6625\", \"Illumina NovaSeq 6626\",\n    \"Illumina NovaSeq 6627\", \"Illumina NovaSeq 6628\", \"Illumina NovaSeq 6629\", \"Illumina NovaSeq 6630\", \"Illumina NovaSeq 6631\",\n    \"Illumina NovaSeq 6632\", \"Illumina NovaSeq 6633\", \"Illumina NovaSeq 6634\", \"Illumina NovaSeq 6635\", \"Illumina NovaSeq 6636\",\n    \"Illumina NovaSeq 6637\", \"Illumina NovaSeq 6638\", \"Illumina NovaSeq 6639\", \"Illumina NovaSeq 6640\", \"Illumina NovaSeq 6641\",\n    \"Illumina NovaSeq 6642\", \"Illumina NovaSeq 6643\", \"Illumina NovaSeq 6644\", \"Illumina NovaSeq 6645\", \"Illumina NovaSeq 6646\",\n    \"Illumina NovaSeq 6647\", \"Illumina NovaSeq 6648\", \"Illumina NovaSeq 6649\", \"Illumina NovaSeq 6650\", \"Illumina NovaSeq 6651\",\n    \"Illumina NovaSeq 6652\", \"Illumina NovaSeq 6653\", \"Illumina NovaSeq 6654\", \"Illumina NovaSeq 6655\", \"Illumina NovaSeq 6656\",\n    \"Illumina NovaSeq 6657\", \"Illumina NovaSeq 6658\", \"Illumina NovaSeq 6659\", \"Illumina NovaSeq 6660\", \"Illumina NovaSeq 6661\",\n    \"Illumina NovaSeq 6662\", \"Illumina NovaSeq 6663\", \"Illumina NovaSeq 6664\", \"Illumina NovaSeq 6665\", \"Illumina NovaSeq 6666\",\n    \"Illumina NovaSeq 6667\", \"Illumina NovaSeq 6668\", \"Illumina NovaSeq 6669\", \"Illumina NovaSeq 6670\", \"Illumina NovaSeq 6671\",\n    \"Illumina NovaSeq 6672\", \"Illumina NovaSeq 6673\", \"Illumina NovaSeq 6674\", \"Illumina NovaSeq 6675\", \"Illumina NovaSeq 6676\",\n    \"Illumina NovaSeq 6677\", \"Illumina NovaSeq 6678\", \"Illumina NovaSeq 6679\", \"Illumina NovaSeq 6680\", \"Illumina NovaSeq 6681\",\n    \"Illumina NovaSeq 6682\", \"Illumina NovaSeq 6683\", \"Illumina NovaSeq 6684\", \"Illumina NovaSeq 6685\", \"Illumina NovaSeq 6686\",\n    \"Illumina NovaSeq 6687\", \"Illumina NovaSeq 6688\", \"Illumina NovaSeq 6689\", \"Illumina NovaSeq 6690\", \"Illumina NovaSeq 6691\",\n    \"Illumina NovaSeq 6692\", \"Illumina NovaSeq 6693\", \"Illumina NovaSeq 6694\", \"Illumina NovaSeq 6695\", \"Illumina NovaSeq 6696\",\n    \"Illumina NovaSeq 6697\", \"Illumina NovaSeq 6698\"\n]", "effect": "{platform: ILLUMINA}", "expanded": [], "rationale": "Every number from 6001 to 6698 in 1000G's participant.instrument_model, two files each, beside 14,605 lines of `Illumina NovaSeq 6000`. They look like a spreadsheet fill series run down from that value; no such models exist. The vendor is certain, so platform is declared. The model most likely is the NovaSeq 6000, but that is a guess, so instrument_model is not (#532). The correction belongs at the source.", "dataset": "general", "seeded": false, "claims": 1396, "files": 1396, "won": 1396, "datasets": {"ANVIL_1000G_high_coverage_2019": 1396}, "source_types": {"repository_metadata": 1396}}, {"id": "platform.illumina_novaseq_x", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"Illumina NovaSeq X\"}", "effect": "{platform: ILLUMINA, instrument_model: Illumina NovaSeq X}", "expanded": [], "rationale": "An Illumina instrument model, spelled as ENA/SRA's; the model implies the platform.", "dataset": "general", "seeded": false, "claims": 3360, "files": 1680, "won": 1680, "datasets": {"AnVIL_IGVF_Mouse_R1": 1680}, "source_types": {"repository_metadata": 3360}}, {"id": "platform.illumina_nextseq_2000", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"Illumina NextSeq 2000\"}", "effect": "{platform: ILLUMINA, instrument_model: NextSeq 2000}", "expanded": [], "rationale": "An Illumina instrument model; ENA/SRA spells it without the vendor, as `NextSeq 2000`.", "dataset": "general", "seeded": false, "claims": 536, "files": 268, "won": 268, "datasets": {"AnVIL_IGVF_Mouse_R1": 268}, "source_types": {"repository_metadata": 536}}, {"id": "platform.kinnex", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"kinnex\"}", "effect": "{platform: PACBIO}", "expanded": [], "rationale": "Kinnex is a PacBio library kit; the span names PacBio data. Platform only.", "dataset": "general", "seeded": false, "claims": 230, "files": 230, "won": 230, "datasets": {"AnVIL_HPRC_R2": 230}, "source_types": {"repository_metadata": 230}}, {"id": "platform.minion", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"MinION\"}", "effect": "{platform: ONT, instrument_model: MinION}", "expanded": [], "rationale": "An Oxford Nanopore instrument, spelled as ENA/SRA's model.", "dataset": "general", "seeded": false, "claims": 4, "files": 2, "won": 2, "datasets": {"AnVIL_HPRC_R2": 2}, "source_types": {"repository_metadata": 4}}, {"id": "platform.nanopore", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"nanopore\"}", "effect": "{platform: ONT}", "expanded": [], "rationale": "A column-name span naming Oxford Nanopore data.", "dataset": "general", "seeded": false, "claims": 120, "files": 120, "won": 120, "datasets": {"ANVIL_HPRC": 120}, "source_types": {"repository_metadata": 120}}, {"id": "platform.ont", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"ont\"}", "effect": "{platform: ONT}", "expanded": [], "rationale": "Spelled as our term, as a table-name span. Identity by ruling.", "dataset": "general", "seeded": false, "claims": 1391, "files": 1391, "won": 1391, "datasets": {"AnVIL_HPRC_R2": 1391}, "source_types": {"repository_metadata": 1391}}, {"id": "platform.ont_gridion_x5", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"ONT GridION X5\"}", "effect": "{platform: ONT, instrument_model: GridION}", "expanded": [], "rationale": "The GridION X5, ONT's GridION with five flow-cell positions. ENA/SRA has one GridION model and no separate X5, so the model is GridION.", "dataset": "general", "seeded": false, "claims": 40, "files": 20, "won": 20, "datasets": {"AnVIL_IGVF_Mouse_R1": 20}, "source_types": {"repository_metadata": 40}}, {"id": "platform.ont_promethion_2_solo", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"ONT PromethION 2 Solo\"}", "effect": "{platform: ONT, instrument_model: PromethION}", "expanded": [], "rationale": "The PromethION 2 Solo, a two-flow-cell PromethION device. ENA/SRA has one PromethION model and no separate P2 Solo, so the model is PromethION.", "dataset": "general", "seeded": false, "claims": 4, "files": 2, "won": 2, "datasets": {"AnVIL_IGVF_Mouse_R1": 2}, "source_types": {"repository_metadata": 4}}, {"id": "platform.oxford_nanopore", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"OXFORD_NANOPORE\"}", "effect": "{platform: ONT}", "expanded": [], "rationale": "SRA's platform value for Oxford Nanopore.", "dataset": "general", "seeded": false, "claims": 929, "files": 929, "won": 929, "datasets": {"AnVIL_HPRC_R2": 929}, "source_types": {"repository_metadata": 929}}, {"id": "platform.pacbio", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"PACBIO\"}", "effect": "{platform: PACBIO}", "expanded": [], "rationale": "Spelled as our term (the kinnex table's spelling). 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The paths are where the workflow ran, so only the name is matched, and a name two files of the dataset carry names neither (#438), unless, on a GATK 4 line, exactly one of them fits the VCF's contigs: GATK 4 refuses an alignment with a contig its reference lacks or gives another length (unless the line turns that check off, which no header we hold does), and writes that reference's contigs into the VCF, so of two re-alignments of one sample's reads the one whose every contig is the VCF's, at its length, is its parent (#620).", "claims": 165623, "files": 165623, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 90378, "ANVIL_T2T": 75245}, "source_types": {}}, {"id": "merge_by_header", "activity": "MergeActivity", "defined_in": "src/meta_disco/producer_steps.py", "reads": "the VCF's own header, its producing step found as for variant_call_by_header; where that step is bcftools concat, each of its input VCFs' base names, matched case-insensitively within the dataset", "rationale": "bcftools concat records its inputs in its own command line, written into the VCF it writes: T2T's chromosome VCFs name every region VCF they join. 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table: '1KGP_CHM13v2_sample' \u00b7 'SGDP_CHM13v2_sample' \u00b7 'SGDP_GRCh38_sample' \u00b7 'participant'; child_column: 'cram'; parent_column: 'read_1_fastq' \u00b7 'read_2_fastq'", "reads_is_catalog": true, "rationale": "The sample row's CRAM is its two FASTQs aligned.", "claims": 7520, "files": 3760, "won": 0, "datasets": {"ANVIL_T2T": 3202, "ANVIL_T2T_CHRY": 558}, "source_types": {}}, {"id": "activity.ena_run_bwa_mem", "activity": "AlignmentActivity / reads", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'external_ground_truth'; table: 'read_run'; raw_activity: 'bwa mem'; child_column: 'cram'; parent_column: 'fastq_ftp'", "reads_is_catalog": true, "rationale": "A T2T_CHRY CRAM holds the ENA run whose read and base counts its samtools stats equal, and its own @PG says bwa mem took FASTQs: the run's reads, aligned (#594).", "claims": 6404, "files": 3202, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 3202}, "source_types": {}}, {"id": "activity.external_sample_map_import", "activity": "CohortMergeActivity / input_list", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'external_ground_truth'; table: 'sample_maps'; raw_activity: 'GenomicsDBImport'; child_column: 'file_id'; parent_column: '--sample-name-map'", "reads_is_catalog": true, "rationale": "A workspace tar's own vcfheader.vcf says GenomicsDBImport read this sample-name map, and the tar's callset.json names exactly the samples the map's published copy lists: the tar merges the gVCFs the map lists, read through it (#710, a workaround until AnVIL holds the map, #624).", "claims": 31158, "files": 31158, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 31158}, "source_types": {}}, {"id": "activity.t2t_variant_call", "activity": "VariantCallActivity / calls_from", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_metadata'; table: '1KGP_CHM13v2_sample' \u00b7 'SGDP_CHM13v2_sample' \u00b7 'SGDP_GRCh38_sample' \u00b7 'participant'; child_column: 'chr10_hcvcf_gz' \u00b7 'chr11_hcvcf_gz' \u00b7 'chr12_hcvcf_gz' \u00b7 'chr13_hcvcf_gz' \u00b7 'chr14_hcvcf_gz' \u00b7 'chr15_hcvcf_gz' \u00b7 'chr16_hcvcf_gz' \u00b7 'chr17_hcvcf_gz' \u00b7 'chr18_hcvcf_gz' \u00b7 'chr19_hcvcf_gz' \u00b7 'chr1_hcvcf_gz' \u00b7 'chr20_hcvcf_gz' \u00b7 'chr21_hcvcf_gz' \u00b7 'chr22_hcvcf_gz' \u00b7 'chr2_hcvcf_gz' \u00b7 'chr3_hcvcf_gz' \u00b7 'chr4_hcvcf_gz' \u00b7 'chr5_hcvcf_gz' \u00b7 'chr6_hcvcf_gz' \u00b7 'chr7_hcvcf_gz' \u00b7 'chr8_hcvcf_gz' \u00b7 'chr9_hcvcf_gz' \u00b7 'xx_x_hcvcf_gz' \u00b7 'xy_x_nonpar_hcvcf_gz' \u00b7 'xy_x_par_hcvcf_gz' \u00b7 'xy_y_nonpar_hcvcf_gz'; parent_column: 'cram'", "reads_is_catalog": true, "rationale": "HaplotypeCaller's per-chromosome VCFs are called from the sample row's CRAM.", "claims": 90378, "files": 90378, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 90378}, "source_types": {}}, {"id": "activity.t2t_index", "activity": "IndexActivity / indexed", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_metadata'; table: '1KGP_CHM13v2_sample' \u00b7 'SGDP_CHM13v2_sample' \u00b7 'SGDP_GRCh38_sample' \u00b7 'participant'; child_column: 'chr10_hcvcf_index' \u00b7 'chr11_hcvcf_index' \u00b7 'chr12_hcvcf_index' \u00b7 'chr13_hcvcf_index' \u00b7 'chr14_hcvcf_index' \u00b7 'chr15_hcvcf_index' \u00b7 'chr16_hcvcf_index' \u00b7 'chr17_hcvcf_index' \u00b7 'chr18_hcvcf_index' \u00b7 'chr19_hcvcf_index' \u00b7 'chr1_hcvcf_index' \u00b7 'chr20_hcvcf_index' \u00b7 'chr21_hcvcf_index' \u00b7 'chr22_hcvcf_index' \u00b7 'chr2_hcvcf_index' \u00b7 'chr3_hcvcf_index' \u00b7 'chr4_hcvcf_index' \u00b7 'chr5_hcvcf_index' \u00b7 'chr6_hcvcf_index' \u00b7 'chr7_hcvcf_index' \u00b7 'chr8_hcvcf_index' \u00b7 'chr9_hcvcf_index' \u00b7 'cram_index' \u00b7 'mosdepth_regions_bed_idx' \u00b7 'mosdepth_regions_index' \u00b7 'xx_x_hcvcf_index' \u00b7 'xy_x_nonpar_hcvcf_index' \u00b7 'xy_x_par_hcvcf_index' \u00b7 'xy_y_nonpar_hcvcf_index'; parent_column: 'chr10_hcvcf_gz' \u00b7 'chr11_hcvcf_gz' \u00b7 'chr12_hcvcf_gz' \u00b7 'chr13_hcvcf_gz' \u00b7 'chr14_hcvcf_gz' \u00b7 'chr15_hcvcf_gz' \u00b7 'chr16_hcvcf_gz' \u00b7 'chr17_hcvcf_gz' \u00b7 'chr18_hcvcf_gz' \u00b7 'chr19_hcvcf_gz' \u00b7 'chr1_hcvcf_gz' \u00b7 'chr20_hcvcf_gz' \u00b7 'chr21_hcvcf_gz' \u00b7 'chr22_hcvcf_gz' \u00b7 'chr2_hcvcf_gz' \u00b7 'chr3_hcvcf_gz' \u00b7 'chr4_hcvcf_gz' \u00b7 'chr5_hcvcf_gz' \u00b7 'chr6_hcvcf_gz' \u00b7 'chr7_hcvcf_gz' \u00b7 'chr8_hcvcf_gz' \u00b7 'chr9_hcvcf_gz' \u00b7 'cram' \u00b7 'mosdepth_regions_bed' \u00b7 'xx_x_hcvcf_gz' \u00b7 'xy_x_nonpar_hcvcf_gz' \u00b7 'xy_x_par_hcvcf_gz' \u00b7 'xy_y_nonpar_hcvcf_gz'", "reads_is_catalog": true, "rationale": "Each index column on a sample row indexes the file in the column it is named after (`cram_index` the `cram`, `chr1_hcvcf_index` the `chr1_hcvcf_gz`). The two lists match every combination, not only those pairs: no other pair occurs, and any index of one of these files is an index of it.", "claims": 104302, "files": 104302, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 97898, "ANVIL_T2T": 6404}, "source_types": {}}, {"id": "activity.t2t_joint_genotype", "activity": "JointGenotypingActivity / genotyped_from", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_metadata'; table: 'interval'; child_column: 'genotyped_bgzip'; parent_column: 'genomics_db_tar'", "reads_is_catalog": true, "rationale": "The interval row's VCF is its GenomicsDB workspace genotyped across every sample (GATK GenotypeGVCFs reads the workspace) and trimmed to the interval (SelectVariants). The VCF's header carries the workspace's GenomicsDBImport line and SelectVariants', but not GenotypeGVCFs', so the row is what states the link (#610), and with it the calls' kind and origin, which the header no longer names (#678). The SelectVariants step selects intervals, not types (no T2T header names --select-type), so the kind passes.", "claims": 31155, "files": 31155, "won": 0, "datasets": {"ANVIL_T2T": 31155}, "source_types": {}}, {"id": "activity.t2t_variant_filter", "activity": "VariantFilterActivity / processed", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_metadata'; table: 'chromosome'; child_column: 'chm13_pass_vcf_gz' \u00b7 'chm13_recalibrated_vcf_gz' \u00b7 'grch38_pass_vcf_gz'; parent_column: 'chm13_raw_vcf_gz' \u00b7 'chm13_recalibrated_vcf_gz' \u00b7 'grch38_recalibrated_vcf_gz'", "reads_is_catalog": true, "rationale": "On a chromosome row, the recalibrated callset is the raw one with every row labelled by GATK's ApplyVQSR (its header records both passes), and the PASS callset is the recalibrated one with only its passing rows kept (`bcftools view -f PASS`). The row holds CHM13's raw, recalibrated and PASS callsets and GRCh38's recalibrated and PASS ones. The lists match every combination, but the lineage map pairs each chain only within itself, so no line pairs a CHM13 file with a GRCh38 one (#610).", "claims": 72, "files": 72, "won": 0, "datasets": {"ANVIL_T2T": 72}, "source_types": {}}, {"id": "activity.igvf_index", "activity": "IndexActivity / indexed", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_metadata'; table: 'file'; child_column: 'file_path'; parent_column: 'derived_from'; child_data_type: 'index'; parent_data_type: 'alignments' \u00b7 'fragments'", "reads_is_catalog": true, "rationale": "An IGVF index is derived from the alignments or fragments file it indexes.", "claims": 4, "files": 4, "won": 0, "datasets": {"AnVIL_IGVF_Mouse_R1": 4}, "source_types": {}}, {"id": "activity.igvf_alignment", "activity": "AlignmentActivity / reads", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_metadata'; table: 'file'; child_column: 'file_path'; parent_column: 'derived_from'; child_data_type: 'alignments'; parent_data_type: 'reads'", "reads_is_catalog": true, "rationale": "IGVF alignments derived from reads are those reads aligned.", "claims": 6, "files": 2, "won": 0, "datasets": {"AnVIL_IGVF_Mouse_R1": 2}, "source_types": {}}, {"id": "activity.track_bedgraphtobigwig", "activity": "CoverageActivity / covered", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_activity'; table: 'anvil_activity'; raw_activity: 'Track: bedGraphToBigWig'; parent_column: 'used_file_id'", "reads_is_catalog": true, "rationale": "Each step used one STAR BAM and generated its four `Signal.{Unique,UniqueMultiple}.strand\u00b1` bigWigs: STAR's per-strand coverage of that BAM, converted by bedGraphToBigWig, so the raw words name the conversion, not what was computed.", "claims": 2536, "files": 2536, "won": 0, "datasets": {"AnVIL_ENCORE_RS293": 1640, "AnVIL_ENCORE_293T": 896}, "source_types": {}}, {"id": "activity.quantification_salmon", "activity": "ExpressionActivity / quantified", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_activity'; table: 'anvil_activity'; raw_activity: 'Quantificatioin: salmon'; parent_column: 'used_file_id'", "reads_is_catalog": true, "rationale": "salmon quantifies the reads it used; every file this step used in anvil15 is a trimmed FASTQ. `Quantificatioin` is AnVIL's spelling, matched as written.", "claims": 1268, "files": 634, "won": 0, "datasets": {"AnVIL_ENCORE_RS293": 410, "AnVIL_ENCORE_293T": 224}, "source_types": {}}, {"id": "activity.merger_fastq", "activity": "MergeActivity / shard", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_activity'; table: 'anvil_activity'; raw_activity: 'Merger: multiple FASTQ files'; parent_column: 'used_file_id'", "reads_is_catalog": true, "rationale": "The step joins the FASTQs it used into one FASTQ.", "claims": 20, "files": 8, "won": 0, "datasets": {"AnVIL_ENCORE_293T": 8}, "source_types": {}}], "sample_rules": [{"id": "bam_read_group_samples", "reads": "a BAM/CRAM header's @RG lines: each distinct SM, every read group having one", "rationale": "SM is the SAM specification's field for the sample a read group's reads come from. A header whose every read group has an SM, each recognised, names the file's samples; one with a read group without SM, or an SM no identifier rule recognises (`Sample`, `BioSample_3`), names none.", "defined_in": "src/meta_disco/sample_identity.py", "claims": 12020, "files": 12020, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 3760, "ANVIL_T2T": 3237, "ANVIL_1000G_high_coverage_2019": 3202, "AnVIL_HPRC_R2": 735, "AnVIL_MAGE": 731, "ANVIL_HPRC": 355}, "source_types": {}}, {"id": "vcf_sample_columns", "reads": "a VCF header's #CHROM line: each column after FORMAT", "rationale": "The VCF specification gives each sample whose genotypes a file holds a column after FORMAT, named for the sample. A header whose every column is recognised names the file's samples; one with a column no identifier rule recognises (a BAM path, `syndip`) names none, and a sites-only file has no sample columns.", "defined_in": "src/meta_disco/sample_identity.py", "claims": 203374, "files": 203374, "won": 0, "datasets": {"ANVIL_T2T": 109478, "ANVIL_T2T_CHRY": 90618, "ANVIL_1000G_high_coverage_2019": 3224, "ANVIL_1000G_PRIMED_data_model": 46, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 4, "AnVIL_MAGE": 3, "ANVIL_HPRC": 1}, "source_types": {}}, {"id": "sample_map_rows", "reads": "a GATK sample-name map's rows: the sample name in each row's first column", "rationale": "A sample-name map gives each sample a GenomicsDB import joint-calls a row, the sample's name first and its gVCF second; the import writes that name as the sample's column in what it makes (#668). A map whose every name is recognised names the samples of the cohort it lists; one with a name no identifier rule recognises names none.", "defined_in": "src/meta_disco/sample_identity.py", "claims": 24, "files": 24, "won": 0, "datasets": {"ANVIL_T2T": 24}, "source_types": {}}, {"id": "fast5_tracking_sample", "reads": "a nanopore FAST5's first 64 KiB: each attribute named sample_id (MinKNOW writes it in tracking_id)", "rationale": "MinKNOW writes one sequencing run's reads to a FAST5, and records the sample the run sequenced as every read's tracking_id sample_id; the one NIA_CARD file read whole stores it once, shared by its 4,000 reads (#605). A head holding one recognised value names the file's sample; one holding none, an unrecognised value or two or more values names none. The attribute is found by its name and its message parsed whole, not reached by walking the file, which would read about 7.5 MB.", "defined_in": "src/meta_disco/sample_identity.py", "claims": 12394, "files": 12394, "won": 0, "datasets": {"ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 12394}, "source_types": {}}, {"id": "samples_inherited_from_parent", "reads": "the settled samples, at reconcile, of the parent in an input role of the file's generated_by that passes samples (rules/activities.yaml's passes_samples)", "rationale": "An index, checksum, QC report or coverage track describes exactly the file it was made from, and a cohort's merge exactly the samples its list names, so the file holds that parent's samples (#668). A parent with none, or parents naming different lists, pass nothing; the file's own sources and the parent's settle as any two sources do.", "defined_in": "src/meta_disco/reconcile_samples.py", "claims": 339074, "files": 339074, "won": 0, "datasets": {"ANVIL_T2T": 163034, "ANVIL_T2T_CHRY": 152897, "ANVIL_1000G_high_coverage_2019": 19278, "AnVIL_HPRC_R2": 2316, "AnVIL_MAGE": 782, "ANVIL_HPRC": 744, "ANVIL_1000G_PRIMED_data_model": 23}, "source_types": {}}, {"id": "external_sample_map_rows", "reads": "the rows, at reconcile, of a GATK sample-name map held outside AnVIL for the dataset (sources/external_files.yaml): the sample name in each row's first column", "rationale": "A sample-name map's first column names the samples its import joint-called, as a catalog map's does (sample_map_rows, #668). A map whose every name is recognised names them; one with a name no identifier rule recognises names none. A workaround until AnVIL holds the map (#624).", "defined_in": "src/meta_disco/sample_identity.py", "claims": 25, "files": 25, "won": 0, "datasets": {"(external sample maps)": 25}, "source_types": {}}, {"id": "family_id_as_written", "reads": "a submitter table's family id column (a PED file's `familyid`), on the row naming the donor", "rationale": "A family id is a name for a group of relatives, with no vocabulary to translate it into, so the string is the answer as the source wrote it. It means something only among datasets sharing a numbering: HPRC and HPRC_R2 reuse 1000 Genomes' ids, and two sources writing different ids for one donor are a conflict, never a choice.", "defined_in": "src/meta_disco/reconcile_entities.py", "claims": 3574, "files": 3210, "won": 0, "datasets": {"(donor records)": 3210}, "source_types": {}}, {"id": "anatomical_site_as_written", "reads": "the repository's own record of a sample: AnVIL's `anvil_biosample.anatomical_site`", "rationale": "AnVIL's sites are not yet mapped to an ontology (#702), so the string is the answer as AnVIL wrote it; its `Unknown` names no site and makes no claim. A pooled sample's site is AnVIL's join of its members' (`Brain, Heart`), kept as one string until #702 makes the slot a list.", "defined_in": "src/meta_disco/reconcile_entities.py", "claims": 1800, "files": 1800, "won": 0, "datasets": {"(sample records)": 1800}, "source_types": {}}, {"id": "coriell_cell_line_or_dna", "reads": "a string matching ^(?:GM|NA)(\\d{5})$; names a sample and its donor (the sample is collected from the donor of the same identifier)", "rationale": "A Coriell repository number names one person's cell line (GM) or the DNA extracted from it (NA); both prefixes with the same number are one sample, written NA as 1000 Genomes writes it. The number also names the person the line came from, as 1000 Genomes' `pedigree` and PRIMED's `subject` key their people by it.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 81646, "files": 80240, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 59995, "ANVIL_T2T": 10440, "AnVIL_HPRC_R2": 3385, "ANVIL_1000G_high_coverage_2019": 3121, "AnVIL_MAGE": 1112, "(donor records)": 1056, "(sample records)": 1049, "ANVIL_HPRC": 82}, "source_types": {}}, {"id": "coriell_hg", "reads": "a string matching ^HG\\d{5}$; names a sample and its donor (the sample is collected from the donor of the same identifier)", "rationale": "The NHGRI repository's HG numbers at Coriell, as 1000 Genomes, HPRC and T2T name their samples: one number, one person, and that person's cell line.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 176235, "files": 172399, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 124110, "ANVIL_T2T": 21580, "AnVIL_HPRC_R2": 10902, "ANVIL_1000G_high_coverage_2019": 6461, "ANVIL_HPRC": 3005, "(donor records)": 2172, "(sample records)": 2165, "AnVIL_MAGE": 2004}, "source_types": {}}, {"id": "giab_name", "reads": "a string matching ^HG00[1-7]$; names a sample and its donor (the sample is collected from the donor of the same identifier)", "rationale": "Genome in a Bottle names its seven reference samples HG001 to HG007, each one person's cell line, and HPRC's `participant` and NIA_CARD's `subject` key those people by the same name. They are the same people as some Coriell numbers, but joining two namespaces is a crosswalk's (#361), so they stay apart.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 1885, "files": 1879, "won": 0, "datasets": {"ANVIL_HPRC": 1785, "AnVIL_HPRC_R2": 82, "(donor records)": 6, "(sample records)": 6}, "source_types": {}}, {"id": "sra_sample_accession", "reads": "a string matching ^[SED]RS\\d+$; names a sample", "rationale": "An INSDC sample accession (SRA, ENA, DDBJ) names one sample.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "biosample_accession", "reads": "a string matching ^SAM(?:N|EA|D)\\d+$; names a sample", "rationale": "An INSDC BioSample accession (NCBI, EBI, DDBJ) names one sample.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "igvf_donor_accession", "reads": "a string matching ^IGVFDO[0-9A-Z]{8}$; names a donor", "rationale": "The IGVF Data Portal issues one donor accession per donor, unique across the portal; its samples have accessions of their own (IGVFSM\u2026), so the accession names no sample.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 183, "files": 183, "won": 0, "datasets": {"(donor records)": 183}, "source_types": {}}, {"id": "igvf_sample_accession", "reads": "a string matching ^IGVFSM[0-9A-Z]{8}$; names a sample", "rationale": "The IGVF Data Portal issues one sample accession per sample, unique across the portal; its donors have accessions of their own (IGVFDO\u2026), so the accession names no donor.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 1935, "files": 1935, "won": 0, "datasets": {"(sample records)": 1935}, "source_types": {}}, {"id": "sgdp_sample_name", "reads": "a string matching ^simons_data_sample_\\d+$ in ANVIL_T2T_CHRY; names a sample", "rationale": "ANVIL_T2T_CHRY's SGDP sample tables have one row per name, so a name is one sample in that dataset; it is not an identifier anywhere else.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 32927, "files": 32369, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 32090, "(sample records)": 279}, "source_types": {}}], "callers": [{"name": "HaplotypeCaller", "pattern": "HaplotypeCaller", "kind": "small", "origin": "germline", "somatic_mode": "", "reason": "GATK's germline caller of SNVs and indels by local assembly; structural variants are outside what it calls.", "kind_files": 293212, "origin_files": 293212}, {"name": "DeepVariant", "pattern": "DeepVariant", "kind": "small", "origin": "germline", "somatic_mode": "", "reason": "Google's deep-learning germline caller of SNVs and small indels; it does not call structural variants, and its somatic counterpart is a separate tool, DeepSomatic.", "kind_files": 18, "origin_files": 24}, {"name": "PEPPER-Margin-DeepVariant", "pattern": "PEPPER[-_]?Margin[-_]?DeepVariant", "kind": "small", "origin": "germline", "somatic_mode": "", "reason": "DeepVariant on long reads, after PEPPER's candidate finding and Margin's phasing; a germline caller of SNVs and small indels, as DeepVariant is.", "kind_files": 0, "origin_files": 0}, {"name": "Sniffles", "pattern": "sniffles2?(?:[_-]v?[0-9][0-9.]*)?", "kind": "structural", "origin": "germline", "somatic_mode": "flags --combine-output-filtered, --mosaic, --mosaic-include-germline, --non-germline; predated by sniffles. Sniffles2 calls germline structural variants by default, and rare, somatic or mosaic ones with --non-germline (2.0.x) or --mosaic (2.2 on); --mosaic-include-germline (2.2 on) turns mosaic mode on and reports germline SVs beside the mosaic ones, and --combine-output-filtered (2.0.7 on) includes low-confidence / mosaic SVs in multi-calling. As each version's own options say; it records its command line under ##command. Sniffles 1 has no such mode (1.0.12's options were read on 2026-10-10) and writes its ##source as plain \"Sniffles\".", "reason": "A long-read structural-variant caller (Sniffles and Sniffles2): it calls insertions, deletions, duplications, inversions and breakends.", "kind_files": 136, "origin_files": 136}, {"name": "SVIM", "pattern": "svim(?:-asm)?(?:[_-]v?[0-9][0-9.]*)?", "kind": "structural", "origin": "germline", "somatic_mode": "", "reason": "Structural-variant callers from long reads (SVIM) and from assemblies (SVIM-asm), both calling the germline variants of one sample.", "kind_files": 17, "origin_files": 29}], "undeclared": [], "labels": {"yaml": ["When", "Then"], "code": ["Reads", "Sets"], "mapping": ["Match", "Declares"]}, "text": {"intro": "Every rule meta-disco uses to describe a file, such as \"a .bam file holds alignments\" or \"a CRAM whose chromosome lengths match GRCh38 is aligned to GRCh38\". Each row shows what the rule looks at, what it concludes and why, and how often it applied in the classification named below.", "key": [["Kind", "where the rule is written: yaml, the rules file; code, in Python, for checks a rules file cannot express, such as comparing chromosome lengths; mapping, a translation from a value a submitter wrote (\"Revio\") to our term (\"PACBIO\")."], ["Basis", "what the rule looks at: the file's extension, its size, its name, the dataset it belongs to, its contents (headers, chromosome names and lengths, what a tar archive holds), the answer for the file it was made from (parent_file, for an index), or a submitter's value (mapping)."], ["Files", "how many files the rule gave an answer for."], ["Claims", "how many answers it gave: one rule can answer several fields of one file."], ["Won", "how many of those files ended up with the rule's answer, or a more specific form of it (a rule saying CHM13 on a file found to be T2T-CHM13v2.0). It can be lower when a stronger rule, or the submitter's metadata, gave a different answer. For our own rules, this compares with the answer meta-disco worked out on its own, before the submitter's metadata; for a mapping, with the file's final answer."], ["Highlighted", "a rule that gave no answer for any file this time."]], "markers_note": "Not rules. Each is a reason a field of a file was left without an answer.", "edge_rules_note": "Rules that say which file a file was made from, rather than giving a field an answer. The code rules name the parent from the file's own name, or from the command line in its header that made it (#609), and only where exactly one file of the dataset carries that name. The activity-map rows (#584) translate what a source table says about a step, and reconcile resolves the parent that table names (#577). One code rule is reconcile's: the members of a sample map held outside AnVIL for a dataset (#710). Counted by the inputs of the reconciled `generated_by` that cite them, and of the external lists' steps.", "callers_note": "The rows of `rules/callers.yaml` (#654, #658): each variant caller, the kind of variant it calls and their origin, which the caller rules look up once they have found the caller in a VCF's header, or a GenomicsDB workspace's (#678). A caller with a somatic mode gives its origin only where its command line shows none of the mode's flags, or a `##source` spelling predates the mode. Counted by the files whose kind or origin a caller rule took from the row: a merge counts each of its callers.", "undeclared_head": "Rule names with no rule behind them", "undeclared_note": "The counts name these rules, but the rule files in this version of meta-disco do not contain them. That happens when a rule was renamed or removed after the files were classified, or when a change still under review added it. There is nothing to show for them but the count. Once the files are classified again with the current rules, this list is empty.", "none_note": "None: every rule the counts name is in the rule files."}};
+    const DATA = {"run": "20261019_225524", "records": 708088, "provenance": "190 rules, 23 edge rules and 18 sample and donor rules. Counts from the reconciled records of 708,088 files, in the run started on 19 October 2026 (output folder 20261019_225524), and the run's 3,668 donor records, counted as files under (donor records), and its 5,434 sample records, under (sample records), and the 25 sample maps held outside AnVIL its steps read through (#710), under (external sample maps).", "rules": [{"id": "index_file", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "extensions: [\".bai\", \".crai\", \".tbi\", \".csi\", \".fai\", \".gzi\", \".idx\", \".pbi\"]", "effect": "data_type: index", "expanded": [], "rationale": "Index file \u2014 an index; what it indexes is the parent's to say", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "checksum_file", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".md5\"]", "effect": "data_type: checksum\nstatus:\n  data_modality: not_applicable\n  reference_assembly: not_applicable\n  assay_type: not_applicable\n  platform: not_applicable\n  instrument_model: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Checksum file \u2014 a checksum, and no data of its own", "dataset": "general", "claims": 113864, "files": 14233, "won": 14233, "datasets": {"ANVIL_1000G_high_coverage_2019": 13008, "ANVIL_HPRC": 759, "AnVIL_HPRC_R2": 466}, "source_types": {}}, {"id": "log_file", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".log\"]", "effect": "data_type: log\nstatus:\n  data_modality: not_applicable\n  reference_assembly: not_applicable\n  assay_type: not_applicable\n  platform: not_applicable\n  instrument_model: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Log file \u2014 a log, and no data of its own", "dataset": "general", "claims": 29096, "files": 3637, "won": 3637, "datasets": {"ANVIL_1000G_PRIMED_data_model": 2852, "ANVIL_HPRC": 785}, "source_types": {}}, {"id": "variant_default_genomic", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "extensions: [\".vcf\", \".bcf\", \".gvcf\", \".g.vcf\"]", "effect": "data_modality: genomic\ndata_type: variants", "expanded": [], "rationale": "VCF files contain variant calls (genomic data)", "dataset": "general", "claims": 408298, "files": 204149, "won": 204149, "datasets": {"ANVIL_T2T": 109863, "ANVIL_T2T_CHRY": 90618, "ANVIL_1000G_high_coverage_2019": 3252, "ANVIL_HPRC": 304, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 59, "ANVIL_1000G_PRIMED_data_model": 46, "AnVIL_MAGE": 3, "AnVIL_HPRC_R2": 2, "ANVIL_nhp_dGTEx_V1": 2}, "source_types": {}}, {"id": "plink_genomic", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "extensions: [\".pgen\", \".pvar\", \".psam\"]", "effect": "data_modality: genomic\ndata_type: genotypes", "expanded": [], "rationale": "PLINK files contain genotype data (genomic)", "dataset": "general", "claims": 17124, "files": 8562, "won": 8562, "datasets": {"ANVIL_1000G_PRIMED_data_model": 8562}, "source_types": {}}, {"id": "idat_array_signal", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "extensions: [\".idat\"]", "effect": "data_type: array_signal\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "IDAT is Illumina's BeadChip intensity format, written by genotyping and methylation chips alike, so the extension cannot say which chip wrote the file, only that it is array signal (#603). Its intensities are keyed by probe, not by genome coordinate, so no reference applies until genotypes are called against a chip manifest", "dataset": "general", "claims": 640, "files": 160, "won": 160, "datasets": {"ANVIL_HPRC": 160}, "source_types": {}}, {"id": "sc_matrix_default", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "extensions: [\".h5ad\", \".loom\", \".mtx\"]", "effect": "data_modality: transcriptomic\ndata_type: expression_matrix\nassay_type: sc/snRNA-seq\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Single-cell matrix format: transcriptomic data from a single-cell or single-nucleus assay, which one the format does not say (#563, #533)", "dataset": "general", "claims": 1750, "files": 350, "won": 350, "datasets": {"AnVIL_IGVF_Mouse_R1": 350}, "source_types": {}}, {"id": "image_svs_histology", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".svs\"]", "effect": "data_modality: imaging.microscopy\ndata_type: images\nassay_type: Histology\nstatus:\n  platform: not_applicable\n  instrument_model: not_applicable\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "SVS files are Aperio whole-slide histology images used for pathology analysis", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "image_derived", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".png\", \".jpg\", \".jpeg\", \".tiff\", \".tif\"]", "effect": "data_type: images\nstatus:\n  data_modality: not_applicable\n  platform: not_applicable\n  instrument_model: not_applicable\n  reference_assembly: not_applicable\n  assay_type: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Image files are derived visualizations (QC plots, assembly graphs) \u2014 not primary experimental data", "dataset": "general", "claims": 24592, "files": 3074, "won": 3074, "datasets": {"ANVIL_HPRC": 3074}, "source_types": {}}, {"id": "nanopore_fast5", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".fast5\"]", "effect": "data_type: raw_signal\nplatform: ONT\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "FAST5 files contain raw Oxford Nanopore electrical signal data. Reference not applicable until basecalling/alignment", "dataset": "general", "claims": 62545, "files": 12509, "won": 12509, "datasets": {"ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 12394, "ANVIL_HPRC": 115}, "source_types": {}}, {"id": "nanopore_pod5", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".pod5\"]", "effect": "data_type: raw_signal\nplatform: ONT\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "POD5 files contain raw Oxford Nanopore electrical signal data (newer format replacing FAST5)", "dataset": "general", "claims": 55, "files": 11, "won": 11, "datasets": {"AnVIL_IGVF_Mouse_R1": 11}, "source_types": {}}, {"id": "reads_base", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 2, "condition": "extensions: [\".fastq\", \".fq\"]", "effect": "data_type: reads\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "FASTQ files contain raw sequencing reads. Reference not applicable until alignment", "dataset": "general", "claims": 85080, "files": 21270, "won": 21270, "datasets": {"ANVIL_T2T": 6404, "AnVIL_IGVF_Mouse_R1": 3959, "ANVIL_HPRC": 3856, "AnVIL_HPRC_R2": 3661, "AnVIL_MAGE": 1558, "AnVIL_ENCORE_RS293": 820, "ANVIL_T2T_CHRY": 558, "AnVIL_ENCORE_293T": 448, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 6}, "source_types": {}}, {"id": "fasta_base", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "format: FASTA", "effect": "data_type: sequence\nstatus:\n  platform: not_applicable\n  instrument_model: not_applicable\n  assay_type: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "FASTA file detected; header inspection needed to determine if de novo assembly, reference genome, or transcript sequences", "dataset": "general", "claims": 5880, "files": 980, "won": 980, "datasets": {"ANVIL_HPRC": 476, "AnVIL_HPRC_R2": 466, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 27, "ANVIL_T2T_CHRY": 6, "ANVIL_T2T": 5}, "source_types": {}}, {"id": "pangenome_graph", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 1, "condition": "extensions: [\".gfa\", \".rgfa\", \".gbz\", \".vg\", \".gbwt\", \".xg\"]", "effect": "data_modality: genomic\ndata_type: pangenome\nstatus:\n  platform: not_applicable\n  instrument_model: not_applicable\n  assay_type: not_applicable", "expanded": [], "rationale": "Sequence-graph formats (GFA/rGFA/GBZ/vg/GBWT/xg) encode a pangenome graph \u2014 a genomic assembly product, so no sequencing platform or assay applies", "dataset": "general", "claims": 1710, "files": 342, "won": 342, "datasets": {"ANVIL_HPRC": 336, "AnVIL_HPRC_R2": 6}, "source_types": {}}, {"id": "isoseq_filename", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bam\", \".cram\"]\nfilename_pattern: \"(?i)\\\\.flnc\\\\.\"", "effect": "data_modality: transcriptomic", "expanded": [], "rationale": "Filename contains IsoSeq indicator (flnc = full-length non-chimeric transcript reads)", "dataset": "general", "claims": 230, "files": 230, "won": 230, "datasets": {"AnVIL_HPRC_R2": 230}, "source_types": {}}, {"id": "hifi_filename", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bam\", \".cram\", \".sam\"]\nfilename_pattern: \"(?i)(^|[._-])hifi|_pb_\"", "effect": "platform: PACBIO", "expanded": [], "rationale": "HiFi names PacBio's read chemistry: it says the platform, not the assay or the modality \u2014 Iso-Seq is HiFi too (#430)", "dataset": "general", "claims": 1240, "files": 1240, "won": 1240, "datasets": {"AnVIL_HPRC_R2": 899, "ANVIL_HPRC": 341}, "source_types": {}}, {"id": "star_filename", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bam\", \".cram\", \".sam\"]\nfilename_pattern: \"(?i)Aligned\\\\.sortedByCoord|_STAR_|\\\\.star\\\\.\"", "effect": "data_modality: transcriptomic\nassay_type: RNA-seq", "expanded": [], "rationale": "STAR aligner output indicates RNA-seq", "dataset": "general", "claims": 2640, "files": 1320, "won": 1320, "datasets": {"ANVIL_nhp_dGTEx_V1": 686, "AnVIL_ENCORE_RS293": 410, "AnVIL_ENCORE_293T": 224}, "source_types": {}}, {"id": "pangenome_reference_mc", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".gfa\", \".rgfa\", \".gbz\", \".vg\", \".gbwt\", \".xg\"]\nfilename_pattern: \"(?i)-mc-\"", "effect": "data_modality: genomic\ndata_type: pangenome.reference", "expanded": [], "rationale": "Minigraph-Cactus (mc) HPRC graphs are published pangenome references used as the alignment coordinate system", "dataset": "general", "claims": 52, "files": 26, "won": 26, "datasets": {"ANVIL_HPRC": 20, "AnVIL_HPRC_R2": 6}, "source_types": {}}, {"id": "filename_ref_grch38", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: &reference_bearing_extensions\n  [\".bam\", \".cram\", \".sam\",\n   \".vcf\", \".bcf\", \".gvcf\", \".g.vcf\",\n   \".bed\", \".narrowpeak\", \".broadpeak\", \".bigwig\", \".bw\", \".bedgraph\",\n   \".fasta\", \".fa\", \".fna\", \".dict\", \".sizes\",\n   \".gfa\", \".rgfa\", \".gbz\", \".vg\", \".gbwt\", \".xg\",\n   \".snarls\", \".dist\", \".min\", \".gg\", \".hapl\", \".trans\",\n   \".chain\", \".paf\", \".delta\", \".hal\",\n   \".gff3\",\n   \".pgen\", \".pvar\",\n   \".bai\", \".crai\", \".tbi\", \".csi\", \".fai\"]\nfilename_pattern: \"(?i)hg38|grch38|hs38\"", "effect": "reference_assembly: GRCh38", "expanded": [], "rationale": "Filename contains GRCh38/hg38 reference indicator", "dataset": "general", "claims": 1582, "files": 1582, "won": 1441, "datasets": {"AnVIL_HPRC_R2": 930, "ANVIL_T2T": 372, "ANVIL_HPRC": 266, "ANVIL_T2T_CHRY": 10, "ANVIL_1000G_PRIMED_data_model": 2, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 2}, "source_types": {}}, {"id": "filename_ref_grch37", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions:\n- .bam\n- .cram\n- .sam\n- .vcf\n- .bcf\n- .gvcf\n- .g.vcf\n- .bed\n- .narrowpeak\n- .broadpeak\n- .bigwig\n- .bw\n- .bedgraph\n- .fasta\n- .fa\n- .fna\n- .dict\n- .sizes\n- .gfa\n- .rgfa\n- .gbz\n- .vg\n- .gbwt\n- .xg\n- .snarls\n- .dist\n- .min\n- .gg\n- .hapl\n- .trans\n- .chain\n- .paf\n- .delta\n- .hal\n- .gff3\n- .pgen\n- .pvar\n- .bai\n- .crai\n- .tbi\n- .csi\n- .fai\nfilename_pattern: (?i)hg19|grch37|hs37|b37", "effect": "reference_assembly: GRCh37", "expanded": ["reference_bearing_extensions"], "rationale": "Filename contains GRCh37/hg19 reference indicator", "dataset": "general", "claims": 1, "files": 1, "won": 1, "datasets": {"ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 1}, "source_types": {}}, {"id": "filename_ref_chm13", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions:\n- .bam\n- .cram\n- .sam\n- .vcf\n- .bcf\n- .gvcf\n- .g.vcf\n- .bed\n- .narrowpeak\n- .broadpeak\n- .bigwig\n- .bw\n- .bedgraph\n- .fasta\n- .fa\n- .fna\n- .dict\n- .sizes\n- .gfa\n- .rgfa\n- .gbz\n- .vg\n- .gbwt\n- .xg\n- .snarls\n- .dist\n- .min\n- .gg\n- .hapl\n- .trans\n- .chain\n- .paf\n- .delta\n- .hal\n- .gff3\n- .pgen\n- .pvar\n- .bai\n- .crai\n- .tbi\n- .csi\n- .fai\nfilename_pattern: (?i)chm13|t2t|CHM13v2|hs1", "effect": "reference_assembly: CHM13", "expanded": ["reference_bearing_extensions"], "rationale": "Filename contains CHM13/T2T reference indicator", "dataset": "general", "claims": 1709, "files": 1709, "won": 1672, "datasets": {"AnVIL_HPRC_R2": 930, "ANVIL_T2T": 501, "ANVIL_HPRC": 266, "ANVIL_T2T_CHRY": 10, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 2}, "source_types": {}}, {"id": "fasta_assembly_filename", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "format: FASTA\nfilename_pattern: \"(?i)(^|[._-])(assembly(?!\\\\d)|hap[12])|[._](pat|mat)(ernal)?[._]\"", "effect": "data_modality: genomic\ndata_type: assembly\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Filename contains assembly/assembler indicator \u2014 de novo assembly", "dataset": "general", "claims": 4680, "files": 936, "won": 936, "datasets": {"ANVIL_HPRC": 472, "AnVIL_HPRC_R2": 464}, "source_types": {}}, {"id": "fasta_haplotype_filename", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "format: FASTA\nfilename_pattern: \"(?i)haplotype|(^|[._-])(phased|hapdup)\"", "effect": "data_modality: genomic\ndata_type: assembly\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Filename contains haplotype/phasing indicator \u2014 de novo assembly", "dataset": "general", "claims": 120, "files": 24, "won": 24, "datasets": {"ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 24}, "source_types": {}}, {"id": "bed_methylation", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bed\"]\nfilename_pattern: \"(?i)modbam2bed|(^|[._-])cpg\"", "effect": "data_modality: epigenomic.methylation\ndata_type: annotations\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "BED file is modbam2bed output or names CpG methylation calls; which assay produced them is not in the name", "dataset": "general", "claims": 112, "files": 28, "won": 28, "datasets": {"ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 28}, "source_types": {}}, {"id": "bed_expression", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bed\"]\nfilename_pattern: \"(?i)(^|[._-])(TMM|counts)|leafcutter|\\\\.TSS\\\\.\"", "effect": "data_modality: transcriptomic\ndata_type: annotations\nassay_type: RNA-seq\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "BED file contains gene expression quantification or splicing data", "dataset": "general", "claims": 60, "files": 12, "won": 12, "datasets": {"AnVIL_MAGE": 12}, "source_types": {}}, {"id": "bed_assembly_qc", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bed\"]\nfilename_pattern: \"(?i)(\\\\.hap[12]\\\\.|[\\\\./_]paternal[\\\\./_]|[\\\\./_]maternal[\\\\./_]|\\\\.dip\\\\.bed|\\\\.switch\\\\.|(^|[._-])flagger|\\\\.lowQ\\\\.|unreliable|issues\\\\.bed|_genbank\\\\.)\"", "effect": "data_modality: genomic\ndata_type: annotations\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "BED file is assembly QC output (haplotype regions, error flags) on de novo genomic assemblies", "dataset": "general", "claims": 6860, "files": 1715, "won": 1715, "datasets": {"ANVIL_HPRC": 1713, "ANVIL_T2T_CHRY": 2}, "source_types": {}}, {"id": "bed_mosdepth_regions", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bed\"]\nfilename_pattern: \"\\\\.regions\\\\.bed\\\\.gz$\"", "effect": "data_type: annotations.coverage\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "mosdepth's `\u003cprefix>.regions.bed.gz`: mean read depth per genome window, measured from an alignment", "dataset": "general", "claims": 20991, "files": 6997, "won": 6997, "datasets": {"ANVIL_T2T_CHRY": 3760, "ANVIL_T2T": 3237}, "source_types": {}}, {"id": "star_signal_coverage", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".bw\", \".bigwig\"]\nfilename_pattern: \"[._-]Signal\\\\.Unique(Multiple)?\\\\.strand[+-]\\\\.\"", "effect": "data_modality: transcriptomic\ndata_type: annotations.coverage\nassay_type: RNA-seq\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "A `Signal.Unique[Multiple].strand\u00b1` bigWig: STAR's per-base read depth, and STAR is an RNA-seq aligner", "dataset": "general", "claims": 12680, "files": 2536, "won": 2536, "datasets": {"AnVIL_ENCORE_RS293": 1640, "AnVIL_ENCORE_293T": 896}, "source_types": {}}, {"id": "intervals_targets", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 1, "condition": "extensions: [\".bed\"]\n# Not bare `regions`: it names mosdepth's coverage tracks and assembly QC\n# regions as often as a target list (#541).\nfilename_pattern: \"(?i)(^|[._-])(targets?|capture|exome)\"", "effect": "status:\n  data_modality: not_applicable\n  data_type: not_applicable\n  assay_type: not_applicable\n  platform: not_applicable\n  instrument_model: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Target/capture region file \u2014 reference data, not primary experimental data", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "text_stats", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".txt\", \".tsv\"]\nfilename_pattern: \"(?i)\\\\.stats\\\\.txt$|samtools\\\\.stats|(^|[._-])mosdepth\"", "effect": "data_type: qc_report\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "QC report (samtools stats, mosdepth, bcftools stats) about another file", "dataset": "general", "claims": 112644, "files": 37548, "won": 37548, "datasets": {"ANVIL_T2T_CHRY": 19937, "ANVIL_T2T": 17566, "ANVIL_HPRC": 45}, "source_types": {}}, {"id": "text_expression", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".txt\", \".tsv\", \".csv\"]\nfilename_pattern: \"(?i)expression|tpm\\\\.txt|fpkm\\\\.txt\"", "effect": "data_modality: transcriptomic\ndata_type: expression_matrix\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Filename contains `expression`, `tpm.txt` or `fpkm.txt`", "dataset": "general", "claims": 20, "files": 5, "won": 5, "datasets": {"AnVIL_MAGE": 5}, "source_types": {}}, {"id": "salmon_quant", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "file_name", "tier": 2, "condition": "extensions: [\".sf\"]\nfilename_pattern: \"(?i)(^|[._])quant\\\\.sf$\"", "effect": "data_modality: transcriptomic\ndata_type: quantification\nassay_type: RNA-seq\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Salmon/Sailfish transcript-level quantification (quant.sf)", "dataset": "general", "claims": 3170, "files": 634, "won": 634, "datasets": {"AnVIL_ENCORE_RS293": 410, "AnVIL_ENCORE_293T": 224}, "source_types": {}}, {"id": "platform_pacbio", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@RG\"\nheader_field: \"PL\"\nheader_pattern: \"PACBIO\"", "effect": "platform: PACBIO", "expanded": [], "rationale": "PL:PACBIO indicates PacBio long-read sequencing", "dataset": "general", "claims": 1810, "files": 1810, "won": 1810, "datasets": {"AnVIL_HPRC_R2": 1260, "ANVIL_HPRC": 550}, "source_types": {}}, {"id": "platform_illumina", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@RG\"\nheader_field: \"PL\"\nheader_pattern: \"ILLUMINA\"", "effect": "platform: ILLUMINA", "expanded": [], "rationale": "PL:ILLUMINA in @RG header \u2014 definitive Illumina platform tag", "dataset": "general", "claims": 10623, "files": 10623, "won": 10623, "datasets": {"ANVIL_T2T_CHRY": 3760, "ANVIL_T2T": 3237, "ANVIL_1000G_high_coverage_2019": 3202, "AnVIL_HPRC_R2": 200, "ANVIL_HPRC": 156, "ANVIL_nhp_dGTEx_V1": 68}, "source_types": {}}, {"id": "platform_ont", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@RG\"\nheader_field: \"PL\"\nheader_pattern: \"ONT\"", "effect": "platform: ONT", "expanded": [], "rationale": "PL:ONT indicates Oxford Nanopore long-read sequencing", "dataset": "general", "claims": 1300, "files": 1300, "won": 1300, "datasets": {"AnVIL_HPRC_R2": 947, "ANVIL_HPRC": 350, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 3}, "source_types": {}}, {"id": "instrument_model_revio", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@RG\"\nheader_field: \"PM\"\nheader_pattern: \"^REVIO$\"\nheader_match_all: true", "effect": "instrument_model: Revio\nplatform: PACBIO", "expanded": [], "rationale": "@RG PM is REVIO, which names the PacBio Revio; the model implies the platform", "dataset": "general", "claims": 1368, "files": 684, "won": 684, "datasets": {"AnVIL_HPRC_R2": 684}, "source_types": {}}, {"id": "instrument_model_novaseq_x", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@RG\"\nheader_field: \"PM\"\nheader_pattern: \"^NovaSeq X$\"\nheader_match_all: true", "effect": "instrument_model: Illumina NovaSeq X\nplatform: ILLUMINA", "expanded": [], "rationale": "PM:NovaSeq X names the Illumina NovaSeq X, and so the platform; `NovaSeq X Plus` is a different value and not matched", "dataset": "general", "claims": 136, "files": 68, "won": 68, "datasets": {"ANVIL_nhp_dGTEx_V1": 68}, "source_types": {}}, {"id": "ont_basecall_dna", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@RG\"\nheader_field: \"DS\"\nheader_pattern: \"basecall_model=[^;\\\\s]*dna_\"", "effect": "data_modality: genomic", "expanded": [], "rationale": "ONT basecall model containing dna_ indicates DNA sequencing (genomic data). Handles both modern (dna_r10...) and legacy date-prefixed (2021-05-05_dna_r9...) formats", "dataset": "general", "claims": 1300, "files": 1300, "won": 1300, "datasets": {"AnVIL_HPRC_R2": 947, "ANVIL_HPRC": 350, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 3}, "source_types": {}}, {"id": "program_star", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@PG\"\nheader_field: \"PN\"\nheader_pattern: \"STAR\"", "effect": "data_modality: transcriptomic\nassay_type: RNA-seq", "expanded": [], "rationale": "STAR (Spliced Transcripts Alignment to a Reference) is the most widely used RNA-seq aligner. Presence strongly indicates RNA-seq data", "dataset": "general", "claims": 4198, "files": 2099, "won": 2099, "datasets": {"AnVIL_MAGE": 779, "ANVIL_nhp_dGTEx_V1": 686, "AnVIL_ENCORE_RS293": 410, "AnVIL_ENCORE_293T": 224}, "source_types": {}}, {"id": "program_bwa", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@PG\"\nheader_field: \"PN\"\nheader_pattern: \"bwa\"", "effect": "data_modality: genomic", "expanded": [], "rationale": "BWA (Burrows-Wheeler Aligner) is the standard short-read aligner for DNA sequencing. Commonly used for WGS, WES, and ChIP-seq", "dataset": "general", "claims": 10555, "files": 10555, "won": 10555, "datasets": {"ANVIL_T2T_CHRY": 3760, "ANVIL_T2T": 3237, "ANVIL_1000G_high_coverage_2019": 3202, "AnVIL_HPRC_R2": 200, "ANVIL_HPRC": 156}, "source_types": {}}, {"id": "program_minimap2", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@PG\"\nheader_field: \"PN\"\nheader_pattern: \"minimap2\"", "effect": "data_modality: genomic", "expanded": [], "rationale": "Minimap2 is a versatile aligner for long reads (PacBio, ONT) and assemblies. Can also be used for direct RNA with -ax splice preset", "dataset": "general", "claims": 1400, "files": 1400, "won": 1400, "datasets": {"ANVIL_HPRC": 738, "AnVIL_HPRC_R2": 659, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 3}, "source_types": {}}, {"id": "program_ccs", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@PG\"\nheader_field: \"PN\"\nheader_pattern: \"ccs\"", "effect": "platform: PACBIO", "expanded": [], "rationale": "The 'ccs' program generates HiFi reads from PacBio subreads. Confirms PacBio platform (used for both WGS and IsoSeq)", "dataset": "general", "claims": 1810, "files": 1810, "won": 1810, "datasets": {"AnVIL_HPRC_R2": 1260, "ANVIL_HPRC": 550}, "source_types": {}}, {"id": "aligned_has_sq", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@SQ\"\nheader_present: true", "effect": "data_type: alignments\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "@SQ lines declare a reference, so the file is aligned to it. The reads themselves are not read (#538)", "dataset": "general", "claims": 42852, "files": 14284, "won": 14284, "datasets": {"ANVIL_T2T_CHRY": 3760, "ANVIL_T2T": 3299, "ANVIL_1000G_high_coverage_2019": 3202, "AnVIL_HPRC_R2": 1124, "AnVIL_MAGE": 779, "ANVIL_nhp_dGTEx_V1": 754, "ANVIL_HPRC": 730, "AnVIL_ENCORE_RS293": 410, "AnVIL_ENCORE_293T": 224, "AnVIL_IGVF_Mouse_R1": 2}, "source_types": {}}, {"id": "unaligned_no_sq", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".bam\", \".cram\"]\nheader_section: \"@SQ\"\nheader_absent: true", "effect": "data_type: reads\nstatus:\n  reference_assembly: not_applicable\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "No @SQ lines: the header declares no reference, so the file holds unaligned reads. Common for PacBio HiFi and ONT mod-base deliverables", "dataset": "general", "claims": 12452, "files": 3113, "won": 3113, "datasets": {"AnVIL_HPRC_R2": 2207, "ANVIL_HPRC": 900, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 6}, "source_types": {}}, {"id": "vcf_ref_grch38", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".vcf\", \".bcf\", \".gvcf\", \".g.vcf\"]\nvcf_header_type: \"##reference\"\nvcf_pattern: \"(?i)(grch38|hg38|hs38|GCA_000001405\\\\.(1[5-9]|[2-9]\\\\d|\\\\d{3,}))\"", "effect": "reference_assembly: GRCh38", "expanded": [], "rationale": "##reference line containing GRCh38, hg38, or the GRCh38 GenBank accession indicates variants called against GRCh38", "dataset": "general", "claims": 37811, "files": 37811, "won": 4658, "datasets": {"ANVIL_T2T": 34339, "ANVIL_1000G_high_coverage_2019": 3252, "ANVIL_HPRC": 148, "ANVIL_T2T_CHRY": 72}, "source_types": {}}, {"id": "vcf_ref_chm13", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".vcf\", \".bcf\", \".gvcf\", \".g.vcf\"]\nvcf_header_type: \"##reference\"\nvcf_pattern: \"(?i)(chm13|t2t|hs1)\"", "effect": "reference_assembly: CHM13", "expanded": [], "rationale": "##reference line containing CHM13 or T2T indicates variants called against T2T-CHM13", "dataset": "general", "claims": 33449, "files": 33449, "won": 33449, "datasets": {"ANVIL_T2T": 33281, "ANVIL_T2T_CHRY": 168}, "source_types": {}}, {"id": "vcf_contig_grch38", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".vcf\", \".bcf\", \".gvcf\", \".g.vcf\"]\nvcf_header_type: \"##contig\"\nvcf_pattern: \"(?i)(grch38|hg38|hs38|GCA_000001405\\\\.(1[5-9]|[2-9]\\\\d|\\\\d{3,}))\"", "effect": "reference_assembly: GRCh38", "expanded": [], "rationale": "##contig assembly subfield naming GRCh38, hg38, hs38, or the GRCh38 GenBank accession declares the reference genome", "dataset": "general", "claims": 33147, "files": 33147, "won": 0, "datasets": {"ANVIL_T2T": 33147}, "source_types": {}}, {"id": "vcf_contig_chm13", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".vcf\", \".bcf\", \".gvcf\", \".g.vcf\"]\nvcf_header_type: \"##contig\"\nvcf_pattern: \"(?i)(chm13|t2t|hs1)\"", "effect": "reference_assembly: CHM13", "expanded": [], "rationale": "##contig assembly subfield naming CHM13, T2T, or hs1 declares the reference genome", "dataset": "general", "claims": 33315, "files": 33315, "won": 33315, "datasets": {"ANVIL_T2T": 33147, "ANVIL_T2T_CHRY": 168}, "source_types": {}}, {"id": "fastq_illumina_modern", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \"^@[A-Z0-9-]+:\\\\d+:[A-Z0-9]+:\\\\d+:\\\\d+:\\\\d+:\\\\d+\"", "effect": "platform: ILLUMINA", "expanded": [], "rationale": "Modern Illumina read names (Casava 1.8+) follow the format @instrument:run:flowcell:lane:tile:x:y", "dataset": "general", "claims": 19608, "files": 19608, "won": 19608, "datasets": {"ANVIL_T2T": 6404, "AnVIL_IGVF_Mouse_R1": 3948, "ANVIL_HPRC": 3428, "AnVIL_HPRC_R2": 3002, "AnVIL_MAGE": 1558, "AnVIL_ENCORE_RS293": 820, "AnVIL_ENCORE_293T": 448}, "source_types": {}}, {"id": "fastq_illumina_hiseq_desc", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \" HS2[05]00[-_]\"", "effect": "platform: ILLUMINA", "expanded": [], "rationale": "HiSeq 2000/2500 instrument identifier found in read description", "dataset": "general", "claims": 558, "files": 558, "won": 558, "datasets": {"ANVIL_T2T_CHRY": 558}, "source_types": {}}, {"id": "fastq_illumina_ena_hiseq", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \"^@[EDS]RR\\\\d+\\\\.\\\\d+ HS2[05]00\"", "effect": "platform: ILLUMINA", "expanded": [], "rationale": "ENA/SRA-reformatted read names with HiSeq 2000/2500 instrument ID in description", "dataset": "general", "claims": 558, "files": 558, "won": 558, "datasets": {"ANVIL_T2T_CHRY": 558}, "source_types": {}}, {"id": "fastq_pacbio_ccs", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \"^@m\\\\d+[A-Za-z]*_\\\\d+_\\\\d+/\\\\d+/ccs\"", "effect": "platform: PACBIO\ndata_type: reads\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "PacBio CCS (HiFi) read names follow @movie/zmw/ccs format. The 'ccs' suffix indicates high-accuracy long reads (>Q20)", "dataset": "general", "claims": 3868, "files": 967, "won": 967, "datasets": {"AnVIL_HPRC_R2": 659, "ANVIL_HPRC": 308}, "source_types": {}}, {"id": "fastq_pacbio_generic", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \"^@m\\\\d+[A-Za-z]*_\\\\d+_\\\\d+/\\\\d+\"", "effect": "platform: PACBIO\ndata_type: reads\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "Generic PacBio read names follow @movie/zmw format", "dataset": "general", "claims": 3868, "files": 967, "won": 967, "datasets": {"AnVIL_HPRC_R2": 659, "ANVIL_HPRC": 308}, "source_types": {}}, {"id": "fastq_ont_uuid", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \"^@[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{12}\"", "effect": "platform: ONT\ndata_type: reads\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "ONT read names are UUIDs (format: 8-4-4-4-12 hex characters). Uniquely identifies Oxford Nanopore data", "dataset": "general", "claims": 548, "files": 137, "won": 137, "datasets": {"ANVIL_HPRC": 120, "AnVIL_IGVF_Mouse_R1": 11, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 6}, "source_types": {}}, {"id": "fastq_ont_metadata", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "content", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]\nfastq_pattern: \"runid=[a-f0-9]+\"", "effect": "platform: ONT\ndata_type: reads\nstatus:\n  variant_kind: not_applicable\n  variant_origin: not_applicable", "expanded": [], "rationale": "ONT reads often include 'runid=' metadata in the header line", "dataset": "general", "claims": 504, "files": 126, "won": 126, "datasets": {"ANVIL_HPRC": 120, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 6}, "source_types": {}}, {"id": "fastq_modality_unknown", "kind": "yaml", "defined_in": "src/meta_disco/rules/unified_rules.yaml", "basis": "extension", "tier": 3, "condition": "extensions: [\".fastq\", \".fq\"]", "effect": "status:\n  data_modality: not_classified", "expanded": [], "rationale": "FASTQ modality cannot be determined from reads alone \u2014 could be genomic, transcriptomic, or epigenomic depending on assay", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "contig_length_detection", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "BAM/CRAM/SAM header: the @SQ contig names and lengths for the family; for the build within it, chr1 and chrY's lengths and M5 checksums, and the reference name @SQ UR or an @PG command line declares", "expanded": [], "effect": "reference_assembly", "rationale": "An alignment's @SQ lines list the reference it was aligned to, contig by contig, and a primary chromosome's length differs between the reference_contig_lengths assemblies that share its name, so matching them identifies the reference family. Where the build within it has a term of its own (#473), chr1 and chrY's lengths and checksums pick it, and a declared reference name only breaks a tie among the builds those allow.", "dataset": "general", "claims": 14282, "files": 14282, "won": 14282, "datasets": {"ANVIL_T2T_CHRY": 3760, "ANVIL_T2T": 3299, "ANVIL_1000G_high_coverage_2019": 3202, "AnVIL_HPRC_R2": 1124, "AnVIL_MAGE": 779, "ANVIL_nhp_dGTEx_V1": 754, "ANVIL_HPRC": 730, "AnVIL_ENCORE_RS293": 410, "AnVIL_ENCORE_293T": 224}, "source_types": {}}, {"id": "vcf_contig_length", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header: the ##contig names and lengths for the family; for the build within it, chr1 and chrY's lengths, and the reference name ##reference or a command line declares (not for a lifted-over VCF)", "expanded": [], "effect": "reference_assembly", "rationale": "A VCF's ##contig lines declare the reference its positions are on. Their lengths identify the reference family as @SQ lengths do. ##contig carries no checksum, so builds that differ only in sequence are told apart only by a declared reference name, and without one the value is the family.", "dataset": "general", "claims": 206840, "files": 206840, "won": 206840, "datasets": {"ANVIL_T2T": 109708, "ANVIL_T2T_CHRY": 90618, "ANVIL_1000G_high_coverage_2019": 3252, "ANVIL_1000G_PRIMED_data_model": 2900, "ANVIL_HPRC": 299, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 59, "AnVIL_HPRC_R2": 2, "ANVIL_nhp_dGTEx_V1": 2}, "source_types": {}}, {"id": "vcf_gvcf", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header: the reference-confidence mode (-ERC) on the command line of the step that made the file (the end of its data flow), where that step is a GATK HaplotypeCaller; and the ALT column of the first records the head read", "expanded": [], "effect": "data_type", "rationale": "A gVCF represents every site, variant or not, each in a record of its own or in a block record covering a run of sites with no variant, and carries the symbolic \u003cNON_REF> allele in every record's ALT (GATK, 'GVCF - Genomic Variant Call Format', https://gatk.broadinstitute.org/hc/en-us/articles/360035531812). HaplotypeCaller writes one when run with -ERC GVCF (non-variant sites condensed into blocks) or -ERC BP_RESOLUTION (a record per site); NONE, the default, is regular calling (GATK, 'HaplotypeCaller', https://gatk.broadinstitute.org/hc/en-us/articles/360037225632). Both must say so: the step that made the file is a HaplotypeCaller in one of those modes; and every record read has \u003cNON_REF> among its ALT alleles, at least one with it alone, a site with no variant (#607). The step alone is not enough: a tool that writes no command line leaves the header's last step in place, so a gVCF filtered to its variant sites would still read as made by HaplotypeCaller; its records then have no site with \u003cNON_REF> alone. The records alone are not either: GenotypeGVCFs drops \u003cNON_REF> in a normal run, but keeps it at some sites under --include-non-variant-sites (GATK forum, https://gatk.broadinstitute.org/hc/en-us/community/posts/360056352871). The ##ALT=\u003cID=NON_REF> and ##GVCFBlock header lines, and an earlier step's -ERC GVCF line, carry over into the joint-called VCFs and PLINK .pvar files made from gVCFs, so none of them is read.", "dataset": "general", "claims": 168825, "files": 168825, "won": 168825, "datasets": {"ANVIL_T2T_CHRY": 90378, "ANVIL_T2T": 75245, "ANVIL_1000G_high_coverage_2019": 3202}, "source_types": {}}, {"id": "vcf_step_caller_kind", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header: its command lines, as steps; the producing step (the end of their data flow), walked back through steps that keep the kind (SelectVariants, GenotypeGVCFs, FilterMutectCalls, ApplyVQSR, ApplyRecalibration, bcftools view, norm, annotate) to the step that called the variants", "expanded": [], "effect": "variant_kind", "rationale": "The caller calls one kind of variant, so its kind is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no kind (#654). The records cannot settle it: a mixed callset shows a structural variant in its first records only by chance (#630). Where the chain names no caller (no one producing step, or a kept step's input written by no step the header records), the header's steps that do not keep the kind name it: a merge among them (vcf_merge_small_callers), else the one tool they are, if they are exactly one. A command line the reader does not know gives no kind.", "dataset": "general", "claims": 168840, "files": 168840, "won": 168840, "datasets": {"ANVIL_T2T_CHRY": 90378, "ANVIL_T2T": 75245, "ANVIL_1000G_high_coverage_2019": 3202, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 15}, "source_types": {}}, {"id": "vcf_source_caller_kind", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header whose command lines name no caller (or that has none): its ##source lines and a ##DeepVariant_version line", "expanded": [], "effect": "variant_kind", "rationale": "The caller calls one kind of variant, so its kind is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no kind (#654). The records cannot settle it: a mixed callset shows a structural variant in its first records only by chance (#630). Structural-variant callers (Sniffles, SVIM) and DeepVariant record themselves only so. Taken only where they name exactly one tool, spellings of one caller counted once: a header naming two tools gives no kind.", "dataset": "general", "claims": 157, "files": 157, "won": 157, "datasets": {"ANVIL_T2T": 134, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 23}, "source_types": {}}, {"id": "vcf_merge_small_callers", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header whose caller is a merge (bcftools concat, merge; GATK GenomicsDBImport, CombineGVCFs): every caller its command lines and ##source lines name, and its ##INFO and ##ALT declarations of structural variants", "expanded": [], "effect": "variant_kind", "rationale": "A merge holds what its inputs held. It is small variants where every caller the header names calls small variants and the header declares no structural-variant field: a merged header can lose an input's ##source line, but the VCF spec requires a structural variant to be declared, so an input's structural variants leave their declarations behind (#654).", "dataset": "general", "claims": 51, "files": 51, "won": 51, "datasets": {"ANVIL_1000G_high_coverage_2019": 50, "ANVIL_T2T": 1}, "source_types": {}}, {"id": "vcf_step_caller_origin", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header: its command lines, as steps; the caller vcf_step_caller_kind finds behind the producing step, and that caller's own command line for a somatic mode's flags; its ##source spellings; and its SOMATIC INFO, ##tumor_sample and ##normal_sample declarations", "expanded": [], "effect": "variant_origin", "rationale": "Each caller the table lists calls variants of one origin, germline or somatic, or, for one listed with a somatic mode, of one origin unless a mode flag is set (a caller that mixes them, as Mutect2 can, is not listed), so its origin is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no origin (#658). A caller with a somatic mode (Sniffles2's --non-germline, --mosaic and two more) gives its origin only where the header records its command line without the mode's flags, or names it by a ##source spelling of a version without the mode (Sniffles 1). A germline reading is refused where the header declares the SOMATIC INFO flag or ##tumor_sample / ##normal_sample. The caller is found as for the kind; the header's one other step, taken where the chain names none, is refused where the header names itself a tool of another origin, or a caller whose somatic mode it leaves open.", "dataset": "general", "claims": 168840, "files": 168840, "won": 168840, "datasets": {"ANVIL_T2T_CHRY": 90378, "ANVIL_T2T": 75245, "ANVIL_1000G_high_coverage_2019": 3202, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 15}, "source_types": {}}, {"id": "vcf_source_caller_origin", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header whose command lines name no caller (or that has none): its ##source lines and a ##DeepVariant_version line, and its SOMATIC INFO, ##tumor_sample and ##normal_sample declarations", "expanded": [], "effect": "variant_origin", "rationale": "Each caller the table lists calls variants of one origin, germline or somatic, or, for one listed with a somatic mode, of one origin unless a mode flag is set (a caller that mixes them, as Mutect2 can, is not listed), so its origin is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no origin (#658). A caller with a somatic mode (Sniffles2's --non-germline, --mosaic and two more) gives its origin only where the header records its command line without the mode's flags, or names it by a ##source spelling of a version without the mode (Sniffles 1). A germline reading is refused where the header declares the SOMATIC INFO flag or ##tumor_sample / ##normal_sample. Taken only where they name exactly one tool, as for vcf_source_caller_kind.", "dataset": "general", "claims": 157, "files": 157, "won": 157, "datasets": {"ANVIL_T2T": 134, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 23}, "source_types": {}}, {"id": "vcf_merge_germline_callers", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "VCF header whose caller is a merge (bcftools concat, merge; GATK GenomicsDBImport, CombineGVCFs): every caller its command lines and ##source lines name, those callers' command lines for a somatic mode's flags, and its declarations of somatic calls", "expanded": [], "effect": "variant_origin", "rationale": "A merge holds what its inputs held. It is germline where every caller the header names calls germline variants, none in a somatic mode, and the header declares neither the SOMATIC INFO flag nor a tumour or normal sample: a merged header can lose an input's ##source line, so a somatic input is also looked for in what the header declares (#658).", "dataset": "general", "claims": 63, "files": 63, "won": 63, "datasets": {"ANVIL_1000G_high_coverage_2019": 50, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 12, "ANVIL_T2T": 1}, "source_types": {}}, {"id": "tar_step_caller_kind", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "GenomicsDB workspace tar: its vcfheader.vcf, the header GenomicsDBImport writes, which records the import's command line beside command lines carried over from its gVCFs' headers, read as a VCF's is: VCF header: its command lines, as steps; the producing step (the end of their data flow), walked back through steps that keep the kind (SelectVariants, GenotypeGVCFs, FilterMutectCalls, ApplyVQSR, ApplyRecalibration, bcftools view, norm, annotate) to the step that called the variants", "expanded": [], "effect": "variant_kind", "rationale": "A workspace holds the calls it imported, and its header records the steps behind it, so its caller is read as a VCF's (#678), the workspace's name (the tar's, less .tar and any .gz) standing for the file's, since the import's output is the directory the tar was made from; a tar named otherwise reads the same, through the header's import, a merge. The caller calls one kind of variant, so its kind is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no kind (#654). The records cannot settle it: a mixed callset shows a structural variant in its first records only by chance (#630). Where the chain names no caller (no one producing step, or a kept step's input written by no step the header records), the header's steps that do not keep the kind name it: a merge among them (tar_merge_small_callers), else the one tool they are, if they are exactly one. A command line the reader does not know gives no kind. A workspace whose header was not read gives no kind.", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "tar_source_caller_kind", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "GenomicsDB workspace tar: its vcfheader.vcf, the header GenomicsDBImport writes, which records the import's command line beside command lines carried over from its gVCFs' headers, read as a VCF's is: VCF header whose command lines name no caller (or that has none): its ##source lines and a ##DeepVariant_version line", "expanded": [], "effect": "variant_kind", "rationale": "A workspace holds the calls it imported, and its header records the steps behind it, so its caller is read as a VCF's (#678), the workspace's name (the tar's, less .tar and any .gz) standing for the file's, since the import's output is the directory the tar was made from; a tar named otherwise reads the same, through the header's import, a merge. The caller calls one kind of variant, so its kind is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no kind (#654). The records cannot settle it: a mixed callset shows a structural variant in its first records only by chance (#630). Structural-variant callers (Sniffles, SVIM) and DeepVariant record themselves only so. Taken only where they name exactly one tool, spellings of one caller counted once: a header naming two tools gives no kind.", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "tar_merge_small_callers", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "GenomicsDB workspace tar: its vcfheader.vcf, the header GenomicsDBImport writes, which records the import's command line beside command lines carried over from its gVCFs' headers, read as a VCF's is: VCF header whose caller is a merge (bcftools concat, merge; GATK GenomicsDBImport, CombineGVCFs): every caller its command lines and ##source lines name, and its ##INFO and ##ALT declarations of structural variants", "expanded": [], "effect": "variant_kind", "rationale": "A workspace holds the calls it imported, and its header records the steps behind it, so its caller is read as a VCF's (#678), the workspace's name (the tar's, less .tar and any .gz) standing for the file's, since the import's output is the directory the tar was made from; a tar named otherwise reads the same, through the header's import, a merge. A merge holds what its inputs held. It is small variants where every caller the header names calls small variants and the header declares no structural-variant field: a merged header can lose an input's ##source line, but the VCF spec requires a structural variant to be declared, so an input's structural variants leave their declarations behind (#654).", "dataset": "general", "claims": 124335, "files": 124335, "won": 124335, "datasets": {"ANVIL_T2T_CHRY": 93180, "ANVIL_T2T": 31155}, "source_types": {}}, {"id": "tar_step_caller_origin", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "GenomicsDB workspace tar: its vcfheader.vcf, the header GenomicsDBImport writes, which records the import's command line beside command lines carried over from its gVCFs' headers, read as a VCF's is: VCF header: its command lines, as steps; the caller tar_step_caller_kind finds behind the producing step, and that caller's own command line for a somatic mode's flags; its ##source spellings; and its SOMATIC INFO, ##tumor_sample and ##normal_sample declarations", "expanded": [], "effect": "variant_origin", "rationale": "A workspace holds the calls it imported, and its header records the steps behind it, so its caller is read as a VCF's (#678), the workspace's name (the tar's, less .tar and any .gz) standing for the file's, since the import's output is the directory the tar was made from; a tar named otherwise reads the same, through the header's import, a merge. Each caller the table lists calls variants of one origin, germline or somatic, or, for one listed with a somatic mode, of one origin unless a mode flag is set (a caller that mixes them, as Mutect2 can, is not listed), so its origin is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no origin (#658). A caller with a somatic mode (Sniffles2's --non-germline, --mosaic and two more) gives its origin only where the header records its command line without the mode's flags, or names it by a ##source spelling of a version without the mode (Sniffles 1). A germline reading is refused where the header declares the SOMATIC INFO flag or ##tumor_sample / ##normal_sample. The caller is found as for the kind; the header's one other step, taken where the chain names none, is refused where the header names itself a tool of another origin, or a caller whose somatic mode it leaves open. A workspace whose header was not read gives no origin.", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "tar_source_caller_origin", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "GenomicsDB workspace tar: its vcfheader.vcf, the header GenomicsDBImport writes, which records the import's command line beside command lines carried over from its gVCFs' headers, read as a VCF's is: VCF header whose command lines name no caller (or that has none): its ##source lines and a ##DeepVariant_version line, and its SOMATIC INFO, ##tumor_sample and ##normal_sample declarations", "expanded": [], "effect": "variant_origin", "rationale": "A workspace holds the calls it imported, and its header records the steps behind it, so its caller is read as a VCF's (#678), the workspace's name (the tar's, less .tar and any .gz) standing for the file's, since the import's output is the directory the tar was made from; a tar named otherwise reads the same, through the header's import, a merge. Each caller the table lists calls variants of one origin, germline or somatic, or, for one listed with a somatic mode, of one origin unless a mode flag is set (a caller that mixes them, as Mutect2 can, is not listed), so its origin is the file's, looked up in rules/callers.yaml; a tool the table does not list gives no origin (#658). A caller with a somatic mode (Sniffles2's --non-germline, --mosaic and two more) gives its origin only where the header records its command line without the mode's flags, or names it by a ##source spelling of a version without the mode (Sniffles 1). A germline reading is refused where the header declares the SOMATIC INFO flag or ##tumor_sample / ##normal_sample. Taken only where they name exactly one tool, as for tar_source_caller_kind.", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "tar_merge_germline_callers", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "GenomicsDB workspace tar: its vcfheader.vcf, the header GenomicsDBImport writes, which records the import's command line beside command lines carried over from its gVCFs' headers, read as a VCF's is: VCF header whose caller is a merge (bcftools concat, merge; GATK GenomicsDBImport, CombineGVCFs): every caller its command lines and ##source lines name, those callers' command lines for a somatic mode's flags, and its declarations of somatic calls", "expanded": [], "effect": "variant_origin", "rationale": "A workspace holds the calls it imported, and its header records the steps behind it, so its caller is read as a VCF's (#678), the workspace's name (the tar's, less .tar and any .gz) standing for the file's, since the import's output is the directory the tar was made from; a tar named otherwise reads the same, through the header's import, a merge. A merge holds what its inputs held. It is germline where every caller the header names calls germline variants, none in a somatic mode, and the header declares neither the SOMATIC INFO flag nor a tumour or normal sample: a merged header can lose an input's ##source line, so a somatic input is also looked for in what the header declares (#658).", "dataset": "general", "claims": 124335, "files": 124335, "won": 124335, "datasets": {"ANVIL_T2T_CHRY": 93180, "ANVIL_T2T": 31155}, "source_types": {}}, {"id": "bed_coordinate_reference", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "BED body: the largest coordinate seen per chromosome, and whether names carry a chr prefix", "expanded": [], "effect": "reference_assembly", "rationale": "A coordinate past a chromosome's end in a human reference rules that reference out, whether the names carry a chr prefix or not; where more than one is left, a chr prefix also rules out GRCh37; and a reference is claimed only when exactly one is left.", "dataset": "general", "claims": 7542, "files": 7542, "won": 7542, "datasets": {"ANVIL_T2T_CHRY": 3763, "ANVIL_T2T": 3259, "ANVIL_HPRC": 509, "AnVIL_MAGE": 8, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 3}, "source_types": {}}, {"id": "bed_nonstandard_contigs", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "BED body: chromosome names", "expanded": [], "effect": "reference_assembly", "rationale": "When no chromosome name is a standard one (1-22, X, Y, M or MT, with or without chr), the intervals are on an assembly's own contigs, not a standard reference, so reference_assembly does not apply.", "dataset": "general", "claims": 3922, "files": 3922, "won": 3922, "datasets": {"AnVIL_HPRC_R2": 2310, "ANVIL_HPRC": 1612}, "source_types": {}}, {"id": "fasta_transcript_contigs", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "FASTA header lines: sequence names", "expanded": [], "effect": "data_modality, data_type", "rationale": "Sequence names in transcript id form (ENST, NM_, NR_, XM_, rna-) outnumbering reference chromosome names mean the file holds transcript sequences: transcriptomic, data_type sequence.", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "fasta_reference_contigs", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "FASTA header lines: sequence names", "expanded": [], "effect": "reference_assembly, data_modality, data_type", "rationale": "At least 20 sequence names that are one known reference's chromosomes mean the file is that reference genome. A tie between references names none (#88).", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "fasta_assembler_contigs", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "FASTA header lines: sequence names", "expanded": [], "effect": "data_modality, data_type, reference_assembly", "rationale": "Sequence names in the form assemblers write (h1tg, ptg, utg, ctg, scaffold_, contig_) mean a de novo assembly: genomic, data_type assembly, and no reference applies, since an assembly is its own.", "dataset": "general", "claims": 1134, "files": 378, "won": 378, "datasets": {"ANVIL_HPRC": 378}, "source_types": {}}, {"id": "fasta_many_contigs", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "FASTA header lines: how many sequences, and whether any is a reference chromosome", "expanded": [], "effect": "data_modality, data_type, reference_assembly", "rationale": "More than 50 sequences, none of them a reference chromosome and none named as a transcript or by an assembler, are read as a de novo assembly: genomic, data_type assembly, no reference applies.", "dataset": "general", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "rgfa_stable_rank_reference", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "rGFA segments: the SR (stable rank) and SN (stable name) tags", "expanded": [], "effect": "data_type", "rationale": "Segments at stable rank 0 are the backbone that defines the graph's coordinate system, so a graph that has them is a reference pangenome (pangenome.reference).", "dataset": "general", "claims": 10, "files": 10, "won": 10, "datasets": {"ANVIL_HPRC": 6, "AnVIL_HPRC_R2": 4}, "source_types": {}}, {"id": "tar_inner_format", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "tar archive head: its member names", "expanded": [], "effect": "data_modality, data_type, assay_type", "rationale": "A container has no format of its own (#245), so it takes its contents'. A GenomicsDB member layout is a variant store (genomic, variants); otherwise the archive takes what the rules resolve for its dominant recognized member extension.", "dataset": "general", "claims": 249162, "files": 124564, "won": 124564, "datasets": {"ANVIL_T2T_CHRY": 93180, "ANVIL_T2T": 31155, "ANVIL_HPRC": 163, "AnVIL_IGVF_Mouse_R1": 64, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 2}, "source_types": {}}, {"id": "sample_map_content", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "a GATK sample-name map's rows: two or three tab-separated columns, the second a VCF path", "expanded": [], "effect": "data_type, data_modality, reference_assembly, assay_type, platform, instrument_model, variant_kind, variant_origin", "rationale": "A file whose rows are a sample name and a VCF path is a list of files, not their data (#621): its data_type is sample_map, and the seven dimensions of the data do not apply to it, as for a checksum (#596). Read from the content, so a file that is only named like a map keeps what its name says.", "dataset": "general", "claims": 192, "files": 24, "won": 24, "datasets": {"ANVIL_T2T": 24}, "source_types": {}}, {"id": "idat_chip_type", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "an Illumina IDAT's header: its chip type (field 403) and its number of probes (field 1000)", "expanded": [], "effect": "data_modality, assay_type", "rationale": "Genotyping and methylation BeadChips both write IDAT, so the extension cannot say which (#603). A chip is recognised only as the exact chip type and probe count read from real files: 1-95um_multi-swath_for_8x2-5M with 2,522,340 probes is the HPRC's Infinium Omni2.5-8 v1.3 genotyping BeadChip, as its methods name it (Liao et al. 2023, PMC10172123). Any other chip, a methylation chip included, is left not_classified with what was read.", "dataset": "general", "claims": 320, "files": 160, "won": 160, "datasets": {"ANVIL_HPRC": 160}, "source_types": {}}, {"id": "idat_scanner", "kind": "code", "defined_in": "src/meta_disco/header_classifier.py", "basis": "content", "tier": 4, "condition": "an Illumina IDAT's run log (field 300): the software each Scan row names", "expanded": [], "effect": "platform, instrument_model", "rationale": "The run log records the software that scanned the chip, not the scanner's model, so the model is inferred from the software: iScan Control Software is the software of Illumina's iScan System, the scanner the HPRC's methods name for its arrays (#603). If another Illumina scanner ran the same software, its files would be called iScan; a source naming the scanner would settle it. Every Scan row must name the same recognised software; otherwise platform and instrument are left not_classified with what was read.", "dataset": "general", "claims": 320, "files": 160, "won": 160, "datasets": {"ANVIL_HPRC": 160}, "source_types": {}}, {"id": "index_by_extension", "kind": "code", "defined_in": "scripts/classify_index_files.py", "basis": "extension", "tier": null, "condition": "file extension: .bai, .crai, .tbi, .csi, .pbi and the others INDEX_TO_PARENT declares", "expanded": [], "effect": "data_type", "rationale": "An index file is an index whatever it indexes, and its extension says so, with or without a parent (#437). The YAML rule index_file says the same for an index this producer misses.", "dataset": "general", "claims": 224726, "files": 224726, "won": 224726, "datasets": {"ANVIL_T2T": 116300, "ANVIL_T2T_CHRY": 98144, "ANVIL_1000G_high_coverage_2019": 6454, "AnVIL_HPRC_R2": 1858, "AnVIL_MAGE": 831, "ANVIL_nhp_dGTEx_V1": 756, "ANVIL_HPRC": 356, "ANVIL_1000G_PRIMED_data_model": 23, "AnVIL_IGVF_Mouse_R1": 4}, "source_types": {}}, {"id": "inherited_from_parent", "kind": "code", "defined_in": "src/meta_disco/reconcile_inherit.py", "basis": "parent_file", "tier": null, "condition": "the settled answers, at reconcile, of the parents in one input role of the file's generated_by", "expanded": [], "effect": "data_modality, platform, reference_assembly, assay_type, instrument_model, variant_kind, variant_origin", "rationale": "A file's step passes what its activity declares for each input role (rules/activities.yaml), from the parents in that role. Parents that agree give their value; parents that differ give mixed; a parent not_classified or in conflict gives nothing (contract 4.9). The claim is weighed with the file's own (4.2-4.6).", "dataset": "general", "claims": 3266662, "files": 534193, "won": 534193, "datasets": {"ANVIL_T2T": 273092, "ANVIL_T2T_CHRY": 247041, "ANVIL_1000G_high_coverage_2019": 6454, "AnVIL_ENCORE_RS293": 2460, "AnVIL_HPRC_R2": 1858, "AnVIL_ENCORE_293T": 1352, "AnVIL_MAGE": 795, "ANVIL_nhp_dGTEx_V1": 756, "ANVIL_HPRC": 356, "ANVIL_1000G_PRIMED_data_model": 23, "AnVIL_IGVF_Mouse_R1": 6}, "source_types": {}}, {"id": "phenotypic_sex.female", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: phenotypic_sex, value: \"female\"}", "effect": "{phenotypic_sex: Female}", "expanded": [], "rationale": "The term itself, as T2T, HPRC and NIA_CARD write it lower case and PRIMED and AnVIL capitalised.", "dataset": "general", "seeded": false, "claims": 5293, "files": 1705, "won": 1704, "datasets": {"(donor records)": 1705}, "source_types": {"repository_metadata": 3448, "published_value": 1845}}, {"id": "phenotypic_sex.male", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: phenotypic_sex, value: \"male\"}", "effect": "{phenotypic_sex: Male}", "expanded": [], "rationale": "The term itself, as T2T, HPRC and NIA_CARD write it lower case and PRIMED and AnVIL capitalised.", "dataset": "general", "seeded": false, "claims": 5243, "files": 1690, "won": 1689, "datasets": {"(donor records)": 1690}, "source_types": {"repository_metadata": 3421, "published_value": 1822}}, {"id": "phenotypic_sex.pedigree_1", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: phenotypic_sex, value: \"1\"}", "effect": "{phenotypic_sex: Male}", "expanded": [], "rationale": "1000 Genomes' `pedigree.sex` is a PED file's sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `1` means this only in a PED file.", "dataset": "source anvil, dataset ANVIL_1000G_high_coverage_2019", "seeded": false, "claims": 1599, "files": 1599, "won": 1598, "datasets": {"(donor records)": 1599}, "source_types": {"repository_metadata": 1599}}, {"id": "phenotypic_sex.pedigree_2", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: phenotypic_sex, value: \"2\"}", "effect": "{phenotypic_sex: Female}", "expanded": [], "rationale": "1000 Genomes' `pedigree.sex` is a PED file's sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `2` means this only in a PED file.", "dataset": "source anvil, dataset ANVIL_1000G_high_coverage_2019", "seeded": false, "claims": 1603, "files": 1603, "won": 1603, "datasets": {"(donor records)": 1603}, "source_types": {"repository_metadata": 1603}}, {"id": "phenotypic_sex.sgdp_m", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: phenotypic_sex, value: \"M\"}", "effect": "{phenotypic_sex: Male}", "expanded": [], "rationale": "SGDP's metadata file writes a genome's sex in `Gender` as M, F or U (not known); scoped to SGDP, since a bare `M` means male only there (#559). Its U has no row and makes no claim.", "dataset": "source sgdp", "seeded": false, "claims": 174, "files": 174, "won": 174, "datasets": {"(donor records)": 174}, "source_types": {"external_ground_truth": 174}}, {"id": "phenotypic_sex.sgdp_f", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: phenotypic_sex, value: \"F\"}", "effect": "{phenotypic_sex: Female}", "expanded": [], "rationale": "SGDP's metadata file writes a genome's sex in `Gender` as M, F or U (not known); scoped to SGDP, since a bare `F` means female only there (#559). Its U has no row and makes no claim.", "dataset": "source sgdp", "seeded": false, "claims": 101, "files": 101, "won": 101, "datasets": {"(donor records)": 101}, "source_types": {"external_ground_truth": 101}}, {"id": "genetic_ancestry.afr", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"AFR\"}", "effect": "{genetic_ancestry: AFR}", "expanded": [], "rationale": "1000 Genomes' superpopulation code, the term itself (HANCESTRO:2000).", "dataset": "general", "seeded": false, "claims": 893, "files": 893, "won": 893, "datasets": {"(donor records)": 893}, "source_types": {"repository_metadata": 893}}, {"id": "genetic_ancestry.amr", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"AMR\"}", "effect": "{genetic_ancestry: AMR}", "expanded": [], "rationale": "1000 Genomes' superpopulation code, the term itself (HANCESTRO:2001).", "dataset": "general", "seeded": false, "claims": 490, "files": 490, "won": 490, "datasets": {"(donor records)": 490}, "source_types": {"repository_metadata": 490}}, {"id": "genetic_ancestry.eas", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"EAS\"}", "effect": "{genetic_ancestry: EAS}", "expanded": [], "rationale": "1000 Genomes' superpopulation code, the term itself (HANCESTRO:2002).", "dataset": "general", "seeded": false, "claims": 585, "files": 585, "won": 585, "datasets": {"(donor records)": 585}, "source_types": {"repository_metadata": 585}}, {"id": "genetic_ancestry.eur", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"EUR\"}", "effect": "{genetic_ancestry: EUR}", "expanded": [], "rationale": "1000 Genomes' superpopulation code, the term itself (HANCESTRO:2003).", "dataset": "general", "seeded": false, "claims": 633, "files": 633, "won": 633, "datasets": {"(donor records)": 633}, "source_types": {"repository_metadata": 633}}, {"id": "genetic_ancestry.sas", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"SAS\"}", "effect": "{genetic_ancestry: SAS}", "expanded": [], "rationale": "1000 Genomes' superpopulation code, the term itself (HANCESTRO:2004).", "dataset": "general", "seeded": false, "claims": 601, "files": 601, "won": 601, "datasets": {"(donor records)": 601}, "source_types": {"repository_metadata": 601}}, {"id": "genetic_ancestry.acb", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"ACB\", alternates: [\"ACB | AFR\"]}", "effect": "{genetic_ancestry: ACB}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0660); PRIMED's `population_label` writes it with its superpopulation, `ACB | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 511, "files": 119, "won": 119, "datasets": {"(donor records)": 119}, "source_types": {"repository_metadata": 511}}, {"id": "genetic_ancestry.asw", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"ASW\", alternates: [\"ASW | AFR\"]}", "effect": "{genetic_ancestry: ASW}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0659); PRIMED's `population_label` writes it with its superpopulation, `ASW | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 331, "files": 74, "won": 74, "datasets": {"(donor records)": 74}, "source_types": {"repository_metadata": 331}}, {"id": "genetic_ancestry.beb", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"BEB\", alternates: [\"BEB | SAS\"]}", "effect": "{genetic_ancestry: BEB}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0802); PRIMED's `population_label` writes it with its superpopulation, `BEB | SAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 562, "files": 131, "won": 131, "datasets": {"(donor records)": 131}, "source_types": {"repository_metadata": 562}}, {"id": "genetic_ancestry.cdx", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"CDX\", alternates: [\"CDX | EAS\"]}", "effect": "{genetic_ancestry: CDX}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0752); PRIMED's `population_label` writes it with its superpopulation, `CDX | EAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 406, "files": 93, "won": 93, "datasets": {"(donor records)": 93}, "source_types": {"repository_metadata": 406}}, {"id": "genetic_ancestry.ceu", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"CEU\", alternates: [\"CEU | EUR\"]}", "effect": "{genetic_ancestry: CEU}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0766); PRIMED's `population_label` writes it with its superpopulation, `CEU | EUR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 746, "files": 179, "won": 179, "datasets": {"(donor records)": 179}, "source_types": {"repository_metadata": 746}}, {"id": "genetic_ancestry.chb", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"CHB\", alternates: [\"CHB | EAS\"]}", "effect": "{genetic_ancestry: CHB}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0753); PRIMED's `population_label` writes it with its superpopulation, `CHB | EAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 445, "files": 103, "won": 103, "datasets": {"(donor records)": 103}, "source_types": {"repository_metadata": 445}}, {"id": "genetic_ancestry.chs", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"CHS\", alternates: [\"CHS | EAS\"]}", "effect": "{genetic_ancestry: CHS}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0756); PRIMED's `population_label` writes it with its superpopulation, `CHS | EAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 694, "files": 163, "won": 163, "datasets": {"(donor records)": 163}, "source_types": {"repository_metadata": 694}}, {"id": "genetic_ancestry.clm", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"CLM\", alternates: [\"CLM | AMR\"]}", "effect": "{genetic_ancestry: CLM}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0684); PRIMED's `population_label` writes it with its superpopulation, `CLM | AMR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 571, "files": 133, "won": 133, "datasets": {"(donor records)": 133}, "source_types": {"repository_metadata": 571}}, {"id": "genetic_ancestry.esn", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"ESN\", alternates: [\"ESN | AFR\"]}", "effect": "{genetic_ancestry: ESN}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0661); PRIMED's `population_label` writes it with its superpopulation, `ESN | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 635, "files": 149, "won": 149, "datasets": {"(donor records)": 149}, "source_types": {"repository_metadata": 635}}, {"id": "genetic_ancestry.fin", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"FIN\", alternates: [\"FIN | EUR\"]}", "effect": "{genetic_ancestry: FIN}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0767); PRIMED's `population_label` writes it with its superpopulation, `FIN | EUR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 433, "files": 99, "won": 99, "datasets": {"(donor records)": 99}, "source_types": {"repository_metadata": 433}}, {"id": "genetic_ancestry.gbr", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"GBR\", alternates: [\"GBR | EUR\"]}", "effect": "{genetic_ancestry: GBR}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0765); PRIMED's `population_label` writes it with its superpopulation, `GBR | EUR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 402, "files": 91, "won": 91, "datasets": {"(donor records)": 91}, "source_types": {"repository_metadata": 402}}, {"id": "genetic_ancestry.gih", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"GIH\", alternates: [\"GIH | SAS\"]}", "effect": "{genetic_ancestry: GIH}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0803); PRIMED's `population_label` writes it with its superpopulation, `GIH | SAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 447, "files": 103, "won": 103, "datasets": {"(donor records)": 103}, "source_types": {"repository_metadata": 447}}, {"id": "genetic_ancestry.gwd", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"GWD\", alternates: [\"GWD | AFR\"]}", "effect": "{genetic_ancestry: GWD}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0664); PRIMED's `population_label` writes it with its superpopulation, `GWD | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 765, "files": 178, "won": 178, "datasets": {"(donor records)": 178}, "source_types": {"repository_metadata": 765}}, {"id": "genetic_ancestry.ibs", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"IBS\", alternates: [\"IBS | EUR\"]}", "effect": "{genetic_ancestry: IBS}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0768); PRIMED's `population_label` writes it with its superpopulation, `IBS | EUR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 659, "files": 157, "won": 157, "datasets": {"(donor records)": 157}, "source_types": {"repository_metadata": 659}}, {"id": "genetic_ancestry.itu", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"ITU\", alternates: [\"ITU | SAS\"]}", "effect": "{genetic_ancestry: ITU}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0806); PRIMED's `population_label` writes it with its superpopulation, `ITU | SAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 459, "files": 107, "won": 107, "datasets": {"(donor records)": 107}, "source_types": {"repository_metadata": 459}}, {"id": "genetic_ancestry.jpt", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"JPT\", alternates: [\"JPT | EAS\"]}", "effect": "{genetic_ancestry: JPT}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0754); PRIMED's `population_label` writes it with its superpopulation, `JPT | EAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 460, "files": 104, "won": 104, "datasets": {"(donor records)": 104}, "source_types": {"repository_metadata": 460}}, {"id": "genetic_ancestry.khv", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"KHV\", alternates: [\"KHV | EAS\"]}", "effect": "{genetic_ancestry: KHV}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0755); PRIMED's `population_label` writes it with its superpopulation, `KHV | EAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 530, "files": 122, "won": 122, "datasets": {"(donor records)": 122}, "source_types": {"repository_metadata": 530}}, {"id": "genetic_ancestry.lwk", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"LWK\", alternates: [\"LWK | AFR\"]}", "effect": "{genetic_ancestry: LWK}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0666); PRIMED's `population_label` writes it with its superpopulation, `LWK | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 430, "files": 99, "won": 99, "datasets": {"(donor records)": 99}, "source_types": {"repository_metadata": 430}}, {"id": "genetic_ancestry.msl", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"MSL\", alternates: [\"MSL | AFR\"]}", "effect": "{genetic_ancestry: MSL}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0667); PRIMED's `population_label` writes it with its superpopulation, `MSL | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 437, "files": 100, "won": 100, "datasets": {"(donor records)": 100}, "source_types": {"repository_metadata": 437}}, {"id": "genetic_ancestry.mxl", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"MXL\", alternates: [\"MXL | AMR\"]}", "effect": "{genetic_ancestry: MXL}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0685); PRIMED's `population_label` writes it with its superpopulation, `MXL | AMR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 419, "files": 97, "won": 97, "datasets": {"(donor records)": 97}, "source_types": {"repository_metadata": 419}}, {"id": "genetic_ancestry.pel", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"PEL\", alternates: [\"PEL | AMR\"]}", "effect": "{genetic_ancestry: PEL}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0686); PRIMED's `population_label` writes it with its superpopulation, `PEL | AMR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 535, "files": 122, "won": 122, "datasets": {"(donor records)": 122}, "source_types": {"repository_metadata": 535}}, {"id": "genetic_ancestry.pjl", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"PJL\", alternates: [\"PJL | SAS\"]}", "effect": "{genetic_ancestry: PJL}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0804); PRIMED's `population_label` writes it with its superpopulation, `PJL | SAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 623, "files": 146, "won": 146, "datasets": {"(donor records)": 146}, "source_types": {"repository_metadata": 623}}, {"id": "genetic_ancestry.pur", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"PUR\", alternates: [\"PUR | AMR\"]}", "effect": "{genetic_ancestry: PUR}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0687); PRIMED's `population_label` writes it with its superpopulation, `PUR | AMR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 608, "files": 139, "won": 139, "datasets": {"(donor records)": 139}, "source_types": {"repository_metadata": 608}}, {"id": "genetic_ancestry.stu", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"STU\", alternates: [\"STU | SAS\"]}", "effect": "{genetic_ancestry: STU}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0805); PRIMED's `population_label` writes it with its superpopulation, `STU | SAS`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 489, "files": 114, "won": 114, "datasets": {"(donor records)": 114}, "source_types": {"repository_metadata": 489}}, {"id": "genetic_ancestry.tsi", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"TSI\", alternates: [\"TSI | EUR\"]}", "effect": "{genetic_ancestry: TSI}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0769); PRIMED's `population_label` writes it with its superpopulation, `TSI | EUR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 464, "files": 107, "won": 107, "datasets": {"(donor records)": 107}, "source_types": {"repository_metadata": 464}}, {"id": "genetic_ancestry.yri", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: genetic_ancestry, value: \"YRI\", alternates: [\"YRI | AFR\"]}", "effect": "{genetic_ancestry: YRI}", "expanded": [], "rationale": "1000 Genomes' population code, the term itself (HANCESTRO:0668); PRIMED's `population_label` writes it with its superpopulation, `YRI | AFR`, which the term sits under.", "dataset": "general", "seeded": false, "claims": 750, "files": 178, "won": 178, "datasets": {"(donor records)": 178}, "source_types": {"repository_metadata": 750}}, {"id": "organism_type.human", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: organism_type, value: \"Human\"}", "effect": "{organism_type: Human}", "expanded": [], "rationale": "The term itself, as AnVIL's `anvil_donor.organism_type` writes it.", "dataset": "general", "seeded": false, "claims": 9656, "files": 3209, "won": 3209, "datasets": {"(donor records)": 3209}, "source_types": {"published_value": 9656}}, {"id": "organism_type.mouse", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: organism_type, value: \"Mouse\"}", "effect": "{organism_type: Mouse}", "expanded": [], "rationale": "The term itself, as AnVIL's `anvil_donor.organism_type` writes it; every Mouse donor in anvil15 is IGVF's, whose donors are laboratory mice (Mus musculus).", "dataset": "general", "seeded": false, "claims": 183, "files": 183, "won": 183, "datasets": {"(donor records)": 183}, "source_types": {"published_value": 183}}, {"id": "reported_ethnicity.black_or_african_american", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: reported_ethnicity, value: \"Black or African American\"}", "effect": "{reported_ethnicity: Black or African American}", "expanded": [], "rationale": "The term itself, as AnVIL's `anvil_donor.reported_ethnicity` writes it, and NIA_CARD's `subject.race_ethnicity` it is made from.", "dataset": "general", "seeded": false, "claims": 2, "files": 1, "won": 1, "datasets": {"(donor records)": 1}, "source_types": {"repository_metadata": 1, "published_value": 1}}, {"id": "reported_ethnicity.hispanic_or_latino", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: reported_ethnicity, value: \"Hispanic or Latino\"}", "effect": "{reported_ethnicity: Hispanic or Latino}", "expanded": [], "rationale": "The term itself, as AnVIL's `anvil_donor.reported_ethnicity` writes it, and NIA_CARD's `subject.race_ethnicity` it is made from.", "dataset": "general", "seeded": false, "claims": 2, "files": 1, "won": 1, "datasets": {"(donor records)": 1}, "source_types": {"repository_metadata": 1, "published_value": 1}}, {"id": "reported_ethnicity.white", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: reported_ethnicity, value: \"White\"}", "effect": "{reported_ethnicity: White}", "expanded": [], "rationale": "The term itself, as AnVIL's `anvil_donor.reported_ethnicity` writes it, and NIA_CARD's `subject.race_ethnicity` it is made from.", "dataset": "general", "seeded": false, "claims": 2, "files": 1, "won": 1, "datasets": {"(donor records)": 1}, "source_types": {"repository_metadata": 1, "published_value": 1}}, {"id": "biosample_type.tissue", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: biosample_type, value: \"Tissue\"}", "effect": "{biosample_type: Tissue}", "expanded": [], "rationale": "The term itself, as AnVIL's `anvil_biosample.biosample_type` writes it.", "dataset": "general", "seeded": false, "claims": 1518, "files": 1518, "won": 1518, "datasets": {"(sample records)": 1518}, "source_types": {"published_value": 1518}}, {"id": "biosample_type.unknown", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: biosample_type, value: \"Unknown\"}", "effect": "{}", "expanded": [], "rationale": "AnVIL's `Unknown` names no type: reviewed, and no claim. On anvil15 it is every filled HPRC_R2 biosample's (230 of 234; 4 are null) and 135 of IGVF's.", "dataset": "general", "seeded": false, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "assay_type.hi_c", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: assay_type, value: \"Hi-C\", alternates: [\"hic\"]}", "effect": "{data_modality: epigenomic.3d_contact_maps}", "expanded": [], "rationale": "Hi-C measures chromatin contacts, which MODAL's `3D contact maps` names (#563); the modality is the information measured, not the use HPRC puts it to (scaffolding). The vocabulary has no assay term for it yet (#399), so nothing is declared for assay_type. `hic` is the table-name span of the same data.", "dataset": "general", "seeded": false, "claims": 6772, "files": 3770, "won": 3770, "datasets": {"AnVIL_HPRC_R2": 3002, "ANVIL_HPRC": 768}, "source_types": {"repository_metadata": 6772}}, {"id": "assay_type.isoseq", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: assay_type, value: \"isoseq\"}", "effect": null, "expanded": [], "rationale": null, "dataset": "general", "seeded": true, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "assay_type.single_nucleus_atac_seq", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: assay_type, value: [\"single-nucleus ATAC-seq\"]}", "effect": "{assay_type: snATAC-seq, data_modality: epigenomic.chromatin_accessibility}", "expanded": [], "rationale": "IGVF's `file.assay_titles`. Single-nucleus ATAC-seq: our own term, under EFO's sc/snATAC-seq, which has no single-nucleus child (#533). ATAC-seq measures open chromatin. In AnVIL_IGVF_Mouse_R1 the value is on the reads (FASTQ) and seqspec files of the ATAC half of a SHARE-seq experiment.", "dataset": "general", "seeded": false, "claims": 20, "files": 10, "won": 10, "datasets": {"AnVIL_IGVF_Mouse_R1": 10}, "source_types": {"repository_metadata": 20}}, {"id": "assay_type.single_nucleus_atac_seq+single_nucleus_rna_sequencing_assay", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: assay_type, value: [\"single-nucleus ATAC-seq\", \"single-nucleus RNA sequencing assay\"]}", "effect": "{assay_type: SHARE-seq}", "expanded": [], "rationale": "IGVF's `file.assay_titles`. Both assays of one SHARE-seq experiment: IGVF's analysis sets that carry the pair (IGVFDS5339UKYM, IGVFDS5530HYKI) give `preferred_assay_titles: [\"SHARE-seq\"]`, EFO:0022962. Nothing is declared for data_modality. The value is the analysis set's label, on every file of the set, and each file holds one half: the count matrices and kallisto outputs are RNA, the fragment files ATAC. Any one modality would be wrong for some of them, and the vocabulary holds one per file, so each file's own evidence decides. Scoped to this dataset: the two titles alone do not name the protocol, since another joint assay (10x Multiome for one) could carry the same pair, so elsewhere the value selects no row and is queued.", "dataset": "source anvil, dataset AnVIL_IGVF_Mouse_R1", "seeded": false, "claims": 12, "files": 12, "won": 10, "datasets": {"AnVIL_IGVF_Mouse_R1": 12}, "source_types": {"repository_metadata": 12}}, {"id": "assay_type.single_nucleus_rna_sequencing_assay", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: assay_type, value: [\"single-nucleus RNA sequencing assay\"]}", "effect": "{assay_type: snRNA-seq, data_modality: transcriptomic}", "expanded": [], "rationale": "IGVF's `file.assay_titles`. Single-nucleus RNA sequencing, EFO:0009809 (#533); RNA-seq measures the transcriptome.", "dataset": "general", "seeded": false, "claims": 13466, "files": 6733, "won": 6733, "datasets": {"AnVIL_IGVF_Mouse_R1": 6733}, "source_types": {"repository_metadata": 13466}}, {"id": "assay_type.wgs", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: assay_type, value: \"WGS\"}", "effect": "{assay_type: WGS, data_modality: genomic}", "expanded": [], "rationale": "Whole-genome sequencing, spelled as our term; a WGS library is genomic DNA.", "dataset": "general", "seeded": false, "claims": 38776, "files": 19388, "won": 19388, "datasets": {"ANVIL_1000G_high_coverage_2019": 9603, "ANVIL_T2T": 6404, "AnVIL_HPRC_R2": 2817, "ANVIL_T2T_CHRY": 558, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 6}, "source_types": {"repository_metadata": 24852, "external_ground_truth": 13924}}, {"id": "data_modality.genomic_hprc_r2", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: \"GENOMIC\"}", "effect": "{}", "expanded": [], "rationale": "The `library_source` column's SRA value. It names the molecule sequenced, not the information measured: SRA files WGS, bisulfite, ATAC, ChIP and Hi-C alike under GENOMIC, because each sequences DNA, so it is no evidence of a MODAL modality (#563). Hi-C arrived under it here (the `hic` table). Scoped to the datasets whose library_source columns carry it: elsewhere the value selects no row, and so enters the review queue rather than being declined unreviewed. Measured on run 20260927_225823: none of the 5,619 files it reached here depended on it alone for data_modality.", "dataset": "source anvil, dataset AnVIL_HPRC_R2", "seeded": false, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_modality.genomic_1000g_high_coverage", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: \"GENOMIC\"}", "effect": "{}", "expanded": [], "rationale": "The `sample` table's `library_source` column, the same SRA value as `data_modality.genomic_hprc_r2`, for the same reason. Measured on run 20260927_225823: none of the 9,603 files it reached depended on it alone for data_modality.", "dataset": "source anvil, dataset ANVIL_1000G_high_coverage_2019", "seeded": false, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_modality.genomic_ena", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: \"GENOMIC\"}", "effect": "{}", "expanded": [], "rationale": "ENA's `library_source` for a run, the same SRA value as `data_modality.genomic_hprc_r2`, for the same reason: it names the molecule sequenced, not a MODAL modality (#563). A WGS run's modality comes from its `library_strategy` instead (`assay_type.wgs`).", "dataset": "source ena", "seeded": false, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_modality.methylation", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: \"methylation\"}", "effect": "{data_modality: epigenomic.methylation}", "expanded": [], "rationale": "The `ont_methylation` table-name span; its files are bigwig methylation tracks.", "dataset": "general", "seeded": false, "claims": 462, "files": 462, "won": 462, "datasets": {"AnVIL_HPRC_R2": 462}, "source_types": {"repository_metadata": 462}}, {"id": "data_modality.single_nucleus_atac_seq", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: [\"single-nucleus ATAC-seq\"]}", "effect": "{assay_type: snATAC-seq, data_modality: epigenomic.chromatin_accessibility}", "expanded": [], "rationale": "AnVIL's published `anvil_file.data_modality`, which carries IGVF's assay titles. Single-nucleus ATAC-seq: our own term, under EFO's sc/snATAC-seq, which has no single-nucleus child (#533). ATAC-seq measures open chromatin. In AnVIL_IGVF_Mouse_R1 the value is on the reads (FASTQ) and seqspec files of the ATAC half of a SHARE-seq experiment.", "dataset": "general", "seeded": false, "claims": 20, "files": 10, "won": 10, "datasets": {"AnVIL_IGVF_Mouse_R1": 10}, "source_types": {"published_value": 20}}, {"id": "data_modality.single_nucleus_atac_seq+single_nucleus_rna_sequencing_assay", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: [\"single-nucleus ATAC-seq\", \"single-nucleus RNA sequencing assay\"]}", "effect": "{assay_type: SHARE-seq}", "expanded": [], "rationale": "AnVIL's published `anvil_file.data_modality`, which carries IGVF's assay titles. Both assays of one SHARE-seq experiment: IGVF's analysis sets that carry the pair (IGVFDS5339UKYM, IGVFDS5530HYKI) give `preferred_assay_titles: [\"SHARE-seq\"]`, EFO:0022962. Nothing is declared for data_modality. The value is the analysis set's label, on every file of the set, and each file holds one half: the count matrices and kallisto outputs are RNA, the fragment files ATAC. Any one modality would be wrong for some of them, and the vocabulary holds one per file, so each file's own evidence decides. Scoped to this dataset: the two titles alone do not name the protocol, since another joint assay (10x Multiome for one) could carry the same pair, so elsewhere the value selects no row and is queued.", "dataset": "source anvil, dataset AnVIL_IGVF_Mouse_R1", "seeded": false, "claims": 12, "files": 12, "won": 10, "datasets": {"AnVIL_IGVF_Mouse_R1": 12}, "source_types": {"published_value": 12}}, {"id": "data_modality.single_nucleus_rna_sequencing_assay", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: [\"single-nucleus RNA sequencing assay\"]}", "effect": "{assay_type: snRNA-seq, data_modality: transcriptomic}", "expanded": [], "rationale": "AnVIL's published `anvil_file.data_modality`, which carries IGVF's assay titles. Single-nucleus RNA sequencing, EFO:0009809 (#533); RNA-seq measures the transcriptome.", "dataset": "general", "seeded": false, "claims": 13466, "files": 6733, "won": 6733, "datasets": {"AnVIL_IGVF_Mouse_R1": 6733}, "source_types": {"published_value": 13466}}, {"id": "data_modality.transcriptomic", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_modality, value: \"transcriptomic\"}", "effect": null, "expanded": [], "rationale": null, "dataset": "general", "seeded": true, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_type.alignments", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"alignments\"}", "effect": null, "expanded": [], "rationale": null, "dataset": "general", "seeded": true, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_type.annotation", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"annotation\"}", "effect": "{data_type: annotations}", "expanded": [], "rationale": "The `assembly_annotation` table-name span; its files are feature annotations of an assembly.", "dataset": "general", "seeded": false, "claims": 881, "files": 881, "won": 881, "datasets": {"ANVIL_HPRC": 881}, "source_types": {"repository_metadata": 881}}, {"id": "data_type.assembly", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"assembly\"}", "effect": "{data_type: assembly}", "expanded": [], "rationale": "The `assembly` table-name span; its files are the de-novo assemblies (.fa.gz).", "dataset": "general", "seeded": false, "claims": 466, "files": 466, "won": 464, "datasets": {"AnVIL_HPRC_R2": 466}, "source_types": {"repository_metadata": 466}}, {"id": "data_type.bam", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"bam\"}", "effect": "{}", "expanded": [], "rationale": "A file format, not a data type: the kinnex table's data_type column holds what its filetype column should. Ruled to mean nothing here rather than left queued.", "dataset": "general", "seeded": false, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_type.censat", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"censat\"}", "effect": "{data_type: annotations}", "expanded": [], "rationale": "Centromeric satellite annotation tracks (BED) of an assembly.", "dataset": "general", "seeded": false, "claims": 462, "files": 462, "won": 462, "datasets": {"AnVIL_HPRC_R2": 462}, "source_types": {"repository_metadata": 462}}, {"id": "data_type.censat_centromeres", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"censat_centromeres\"}", "effect": "{data_type: annotations}", "expanded": [], "rationale": "Centromere annotation tracks (BED) of an assembly.", "dataset": "general", "seeded": false, "claims": 462, "files": 462, "won": 462, "datasets": {"AnVIL_HPRC_R2": 462}, "source_types": {"repository_metadata": 462}}, {"id": "data_type.chains", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"chains\"}", "effect": null, "expanded": [], "rationale": null, "dataset": "general", "seeded": true, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_type.fragments", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"fragments\"}", "effect": "{data_modality: epigenomic.chromatin_accessibility}", "expanded": [], "rationale": "IGVF's `file.content_type`. A fragment file: one line per transposase-cut fragment, with its cell barcode. In AnVIL_IGVF_Mouse_R1 the two such files are the ATAC half of a SHARE-seq experiment, whose assay_titles pair declares no modality (#570), and ATAC-seq measures open chromatin. Scoped to this dataset: the word names a file layout, which other chromatin assays (CUT&Tag for one) write too, so elsewhere it does not say accessibility.", "dataset": "source anvil, dataset AnVIL_IGVF_Mouse_R1", "seeded": false, "claims": 2, "files": 2, "won": 2, "datasets": {"AnVIL_IGVF_Mouse_R1": 2}, "source_types": {"repository_metadata": 2}}, {"id": "data_type.gaps", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"gaps\"}", "effect": null, "expanded": [], "rationale": null, "dataset": "general", "seeded": true, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_type.kallisto_single_cell_rnaseq_output", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"kallisto single cell RNAseq output\"}", "effect": "{data_modality: transcriptomic}", "expanded": [], "rationale": "IGVF's `file.content_type`. kallisto's single-cell RNA-seq output, as IGVF names it, so RNA. Most of these files are titled snRNA-seq alone, which already says transcriptomic; on the two of the SHARE-seq pair it is what says which half the file holds (#570). Scoped to this dataset, the one whose vocabulary it is. No data_type: the tar is a bundle of kallisto's outputs \u2014 the cached tar heads hold, variously, BUS files, `matrix.ec`, `kb_info.json` and cells \u00d7 genes `.mtx` matrices \u2014 not one data type, though IGVF files it as a cell-by-gene `MatrixFile`.", "dataset": "source anvil, dataset AnVIL_IGVF_Mouse_R1", "seeded": false, "claims": 304, "files": 304, "won": 304, "datasets": {"AnVIL_IGVF_Mouse_R1": 304}, "source_types": {"repository_metadata": 304}}, {"id": "data_type.liftoff", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"liftoff\"}", "effect": "{data_type: annotations}", "expanded": [], "rationale": "Gene annotations lifted onto an assembly with Liftoff (GFF3).", "dataset": "general", "seeded": false, "claims": 462, "files": 462, "won": 462, "datasets": {"AnVIL_HPRC_R2": 462}, "source_types": {"repository_metadata": 462}}, {"id": "data_type.repeat_masker", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"repeat_masker\"}", "effect": "{data_type: annotations}", "expanded": [], "rationale": "RepeatMasker repeat annotations of an assembly (BED and .out).", "dataset": "general", "seeded": false, "claims": 1018, "files": 1018, "won": 1018, "datasets": {"AnVIL_HPRC_R2": 924, "ANVIL_HPRC": 94}, "source_types": {"repository_metadata": 1018}}, {"id": "data_type.segdups", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"segdups\"}", "effect": "{data_type: annotations}", "expanded": [], "rationale": "Segmental duplication tracks (BED) of an assembly.", "dataset": "general", "seeded": false, "claims": 462, "files": 462, "won": 462, "datasets": {"AnVIL_HPRC_R2": 462}, "source_types": {"repository_metadata": 462}}, {"id": "data_type.sequences", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"sequences\"}", "effect": null, "expanded": [], "rationale": null, "dataset": "general", "seeded": true, "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "data_type.sparse_gene_count_matrix", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"sparse gene count matrix\"}", "effect": "{data_type: expression_matrix, data_modality: transcriptomic}", "expanded": [], "rationale": "IGVF's `file.content_type`. A cell-by-gene count matrix, as IGVF's `summary` on these files says. In AnVIL_IGVF_Mouse_R1 each is RNA: the files titled with snRNA-seq alone, and the two of the SHARE-seq pair (#570), which IGVF derives from the same reads as that experiment's kallisto RNA output. Scoped to this dataset: a gene matrix alone does not say RNA, since one can hold ATAC gene-activity scores.", "dataset": "source anvil, dataset AnVIL_IGVF_Mouse_R1", "seeded": false, "claims": 1032, "files": 516, "won": 516, "datasets": {"AnVIL_IGVF_Mouse_R1": 516}, "source_types": {"repository_metadata": 1032}}, {"id": "data_type.transcript_quantifications", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"transcript quantifications\"}", "effect": "{data_type: quantification, data_modality: transcriptomic}", "expanded": [], "rationale": "IGVF's `file.content_type`. Per-transcript abundances quantify RNA. Scoped to this dataset, the one whose vocabulary it is.", "dataset": "source anvil, dataset AnVIL_IGVF_Mouse_R1", "seeded": false, "claims": 16, "files": 8, "won": 8, "datasets": {"AnVIL_IGVF_Mouse_R1": 8}, "source_types": {"repository_metadata": 16}}, {"id": "data_type.unaligned_reads", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"unaligned reads\"}", "effect": "{data_type: reads}", "expanded": [], "rationale": "Reads, as the source says; whether aligned is not the data type.", "dataset": "general", "seeded": false, "claims": 4690, "files": 4690, "won": 4690, "datasets": {"AnVIL_HPRC_R2": 4690}, "source_types": {"repository_metadata": 4690}}, {"id": "data_type.unaligned_reads_with_5mc_mods", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"unaligned reads with 5mC mods\"}", "effect": "{data_type: reads}", "expanded": [], "rationale": "Reads carrying base-modification calls; the modification tags do not change the data type.", "dataset": "general", "seeded": false, "claims": 641, "files": 641, "won": 641, "datasets": {"AnVIL_HPRC_R2": 641}, "source_types": {"repository_metadata": 641}}, {"id": "data_type.unaligned_reads_with_5mcg_5hmcg_mods", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: data_type, value: \"unaligned reads with 5mCG_5hmCG mods\"}", "effect": "{data_type: reads}", "expanded": [], "rationale": "Reads carrying base-modification calls; the modification tags do not change the data type.", "dataset": "general", "seeded": false, "claims": 288, "files": 288, "won": 288, "datasets": {"AnVIL_HPRC_R2": 288}, "source_types": {"repository_metadata": 288}}, {"id": "platform.deepconsensus", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"deepconsensus\"}", "effect": "{platform: PACBIO}", "expanded": [], "rationale": "DeepConsensus is PacBio's HiFi read-polishing method; the span names PacBio data. Platform only.", "dataset": "general", "seeded": false, "claims": 619, "files": 619, "won": 619, "datasets": {"AnVIL_HPRC_R2": 619}, "source_types": {"repository_metadata": 619}}, {"id": "platform.gridion", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"GridION\"}", "effect": "{platform: ONT, instrument_model: GridION}", "expanded": [], "rationale": "An Oxford Nanopore instrument, spelled as ENA/SRA's model.", "dataset": "general", "seeded": false, "claims": 4, "files": 2, "won": 2, "datasets": {"AnVIL_HPRC_R2": 2}, "source_types": {"repository_metadata": 4}}, {"id": "platform.hifi", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"hifi\"}", "effect": "{platform: PACBIO}", "expanded": [], "rationale": "HiFi reads are PacBio's; the span says platform only and implies no assay (#430).", "dataset": "general", "seeded": false, "claims": 1292, "files": 1292, "won": 1292, "datasets": {"AnVIL_HPRC_R2": 1069, "ANVIL_HPRC": 223}, "source_types": {"repository_metadata": 1292}}, {"id": "platform.illumina", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"ILLUMINA\", alternates: [\"illumina\"]}", "effect": "{platform: ILLUMINA}", "expanded": [], "rationale": "Spelled as our term, as SRA's platform value and as a table-name span. Identity by ruling.", "dataset": "general", "seeded": false, "claims": 16762, "files": 16562, "won": 16562, "datasets": {"ANVIL_T2T": 6404, "ANVIL_1000G_high_coverage_2019": 6398, "AnVIL_HPRC_R2": 3202, "ANVIL_T2T_CHRY": 558}, "source_types": {"repository_metadata": 9800, "external_ground_truth": 6962}}, {"id": "platform.illumina_hiseq_2000", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"Illumina HiSeq 2000\"}", "effect": "{platform: ILLUMINA, instrument_model: Illumina HiSeq 2000}", "expanded": [], "rationale": "An Illumina instrument model, spelled as ENA/SRA's; the model implies the platform.", "dataset": "general", "seeded": false, "claims": 1116, "files": 558, "won": 558, "datasets": {"ANVIL_T2T_CHRY": 558}, "source_types": {"external_ground_truth": 1116}}, {"id": "platform.illumina_novaseq_6000", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"Illumina NovaSeq 6000\"}", "effect": "{platform: ILLUMINA, instrument_model: Illumina NovaSeq 6000}", "expanded": [], "rationale": "An Illumina instrument model, spelled as ENA/SRA's; the model implies the platform.", "dataset": "general", "seeded": false, "claims": 52422, "files": 21209, "won": 21209, "datasets": {"ANVIL_1000G_high_coverage_2019": 9603, "ANVIL_T2T": 6404, "AnVIL_HPRC_R2": 3202, "AnVIL_IGVF_Mouse_R1": 2000}, "source_types": {"repository_metadata": 39614, "external_ground_truth": 12808}}, {"id": "platform.illumina_novaseq_6001_to_6698", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "slot: platform\nvalue: \"Illumina NovaSeq 6001\"\nalternates: [\n    \"Illumina NovaSeq 6002\", \"Illumina NovaSeq 6003\", \"Illumina NovaSeq 6004\", \"Illumina NovaSeq 6005\", \"Illumina NovaSeq 6006\",\n    \"Illumina NovaSeq 6007\", \"Illumina NovaSeq 6008\", \"Illumina NovaSeq 6009\", \"Illumina NovaSeq 6010\", \"Illumina NovaSeq 6011\",\n    \"Illumina NovaSeq 6012\", \"Illumina NovaSeq 6013\", \"Illumina NovaSeq 6014\", \"Illumina NovaSeq 6015\", \"Illumina NovaSeq 6016\",\n    \"Illumina NovaSeq 6017\", \"Illumina NovaSeq 6018\", \"Illumina NovaSeq 6019\", \"Illumina NovaSeq 6020\", \"Illumina NovaSeq 6021\",\n    \"Illumina NovaSeq 6022\", \"Illumina NovaSeq 6023\", \"Illumina NovaSeq 6024\", \"Illumina NovaSeq 6025\", \"Illumina NovaSeq 6026\",\n    \"Illumina NovaSeq 6027\", \"Illumina NovaSeq 6028\", \"Illumina NovaSeq 6029\", \"Illumina NovaSeq 6030\", \"Illumina NovaSeq 6031\",\n    \"Illumina NovaSeq 6032\", \"Illumina NovaSeq 6033\", \"Illumina NovaSeq 6034\", \"Illumina NovaSeq 6035\", \"Illumina NovaSeq 6036\",\n    \"Illumina NovaSeq 6037\", \"Illumina NovaSeq 6038\", \"Illumina NovaSeq 6039\", \"Illumina NovaSeq 6040\", \"Illumina NovaSeq 6041\",\n    \"Illumina NovaSeq 6042\", \"Illumina NovaSeq 6043\", \"Illumina NovaSeq 6044\", \"Illumina NovaSeq 6045\", \"Illumina NovaSeq 6046\",\n    \"Illumina NovaSeq 6047\", \"Illumina NovaSeq 6048\", \"Illumina NovaSeq 6049\", \"Illumina NovaSeq 6050\", \"Illumina NovaSeq 6051\",\n    \"Illumina NovaSeq 6052\", \"Illumina NovaSeq 6053\", \"Illumina NovaSeq 6054\", \"Illumina NovaSeq 6055\", \"Illumina NovaSeq 6056\",\n    \"Illumina NovaSeq 6057\", \"Illumina NovaSeq 6058\", \"Illumina NovaSeq 6059\", \"Illumina NovaSeq 6060\", \"Illumina NovaSeq 6061\",\n    \"Illumina NovaSeq 6062\", \"Illumina NovaSeq 6063\", \"Illumina NovaSeq 6064\", \"Illumina NovaSeq 6065\", \"Illumina NovaSeq 6066\",\n    \"Illumina NovaSeq 6067\", \"Illumina NovaSeq 6068\", \"Illumina NovaSeq 6069\", \"Illumina NovaSeq 6070\", \"Illumina NovaSeq 6071\",\n    \"Illumina NovaSeq 6072\", \"Illumina NovaSeq 6073\", \"Illumina NovaSeq 6074\", \"Illumina NovaSeq 6075\", \"Illumina NovaSeq 6076\",\n    \"Illumina NovaSeq 6077\", \"Illumina NovaSeq 6078\", \"Illumina NovaSeq 6079\", \"Illumina NovaSeq 6080\", \"Illumina NovaSeq 6081\",\n    \"Illumina NovaSeq 6082\", \"Illumina NovaSeq 6083\", \"Illumina NovaSeq 6084\", \"Illumina NovaSeq 6085\", \"Illumina NovaSeq 6086\",\n    \"Illumina NovaSeq 6087\", \"Illumina NovaSeq 6088\", \"Illumina NovaSeq 6089\", \"Illumina NovaSeq 6090\", \"Illumina NovaSeq 6091\",\n    \"Illumina NovaSeq 6092\", \"Illumina NovaSeq 6093\", \"Illumina NovaSeq 6094\", \"Illumina NovaSeq 6095\", \"Illumina NovaSeq 6096\",\n    \"Illumina NovaSeq 6097\", \"Illumina NovaSeq 6098\", \"Illumina NovaSeq 6099\", \"Illumina NovaSeq 6100\", \"Illumina NovaSeq 6101\",\n    \"Illumina NovaSeq 6102\", \"Illumina NovaSeq 6103\", \"Illumina NovaSeq 6104\", \"Illumina NovaSeq 6105\", \"Illumina NovaSeq 6106\",\n    \"Illumina NovaSeq 6107\", \"Illumina NovaSeq 6108\", \"Illumina NovaSeq 6109\", \"Illumina NovaSeq 6110\", \"Illumina NovaSeq 6111\",\n    \"Illumina NovaSeq 6112\", \"Illumina NovaSeq 6113\", \"Illumina NovaSeq 6114\", \"Illumina NovaSeq 6115\", \"Illumina NovaSeq 6116\",\n    \"Illumina NovaSeq 6117\", \"Illumina NovaSeq 6118\", \"Illumina NovaSeq 6119\", \"Illumina NovaSeq 6120\", \"Illumina NovaSeq 6121\",\n    \"Illumina NovaSeq 6122\", \"Illumina NovaSeq 6123\", \"Illumina NovaSeq 6124\", \"Illumina NovaSeq 6125\", \"Illumina NovaSeq 6126\",\n    \"Illumina NovaSeq 6127\", \"Illumina NovaSeq 6128\", \"Illumina NovaSeq 6129\", \"Illumina NovaSeq 6130\", \"Illumina NovaSeq 6131\",\n    \"Illumina NovaSeq 6132\", \"Illumina NovaSeq 6133\", \"Illumina NovaSeq 6134\", \"Illumina NovaSeq 6135\", \"Illumina NovaSeq 6136\",\n    \"Illumina NovaSeq 6137\", \"Illumina NovaSeq 6138\", \"Illumina NovaSeq 6139\", \"Illumina NovaSeq 6140\", \"Illumina NovaSeq 6141\",\n    \"Illumina NovaSeq 6142\", \"Illumina NovaSeq 6143\", \"Illumina NovaSeq 6144\", \"Illumina NovaSeq 6145\", \"Illumina NovaSeq 6146\",\n    \"Illumina NovaSeq 6147\", \"Illumina NovaSeq 6148\", \"Illumina NovaSeq 6149\", \"Illumina NovaSeq 6150\", \"Illumina NovaSeq 6151\",\n    \"Illumina NovaSeq 6152\", \"Illumina NovaSeq 6153\", \"Illumina NovaSeq 6154\", \"Illumina NovaSeq 6155\", \"Illumina NovaSeq 6156\",\n    \"Illumina NovaSeq 6157\", \"Illumina NovaSeq 6158\", \"Illumina NovaSeq 6159\", \"Illumina NovaSeq 6160\", \"Illumina NovaSeq 6161\",\n    \"Illumina NovaSeq 6162\", \"Illumina NovaSeq 6163\", \"Illumina NovaSeq 6164\", \"Illumina NovaSeq 6165\", \"Illumina NovaSeq 6166\",\n    \"Illumina NovaSeq 6167\", \"Illumina NovaSeq 6168\", \"Illumina NovaSeq 6169\", \"Illumina NovaSeq 6170\", \"Illumina NovaSeq 6171\",\n    \"Illumina NovaSeq 6172\", \"Illumina NovaSeq 6173\", \"Illumina NovaSeq 6174\", \"Illumina NovaSeq 6175\", \"Illumina NovaSeq 6176\",\n    \"Illumina NovaSeq 6177\", \"Illumina NovaSeq 6178\", \"Illumina NovaSeq 6179\", \"Illumina NovaSeq 6180\", \"Illumina NovaSeq 6181\",\n    \"Illumina NovaSeq 6182\", \"Illumina NovaSeq 6183\", \"Illumina NovaSeq 6184\", \"Illumina NovaSeq 6185\", \"Illumina NovaSeq 6186\",\n    \"Illumina NovaSeq 6187\", \"Illumina NovaSeq 6188\", \"Illumina NovaSeq 6189\", \"Illumina NovaSeq 6190\", \"Illumina NovaSeq 6191\",\n    \"Illumina NovaSeq 6192\", \"Illumina NovaSeq 6193\", \"Illumina NovaSeq 6194\", \"Illumina NovaSeq 6195\", \"Illumina NovaSeq 6196\",\n    \"Illumina NovaSeq 6197\", \"Illumina NovaSeq 6198\", \"Illumina NovaSeq 6199\", \"Illumina NovaSeq 6200\", \"Illumina NovaSeq 6201\",\n    \"Illumina NovaSeq 6202\", \"Illumina NovaSeq 6203\", \"Illumina NovaSeq 6204\", \"Illumina NovaSeq 6205\", \"Illumina NovaSeq 6206\",\n    \"Illumina NovaSeq 6207\", \"Illumina NovaSeq 6208\", \"Illumina NovaSeq 6209\", \"Illumina NovaSeq 6210\", \"Illumina NovaSeq 6211\",\n    \"Illumina NovaSeq 6212\", \"Illumina NovaSeq 6213\", \"Illumina NovaSeq 6214\", \"Illumina NovaSeq 6215\", \"Illumina NovaSeq 6216\",\n    \"Illumina NovaSeq 6217\", \"Illumina NovaSeq 6218\", \"Illumina NovaSeq 6219\", \"Illumina NovaSeq 6220\", \"Illumina NovaSeq 6221\",\n    \"Illumina NovaSeq 6222\", \"Illumina NovaSeq 6223\", \"Illumina NovaSeq 6224\", \"Illumina NovaSeq 6225\", \"Illumina NovaSeq 6226\",\n    \"Illumina NovaSeq 6227\", \"Illumina NovaSeq 6228\", \"Illumina NovaSeq 6229\", \"Illumina NovaSeq 6230\", \"Illumina NovaSeq 6231\",\n    \"Illumina NovaSeq 6232\", \"Illumina NovaSeq 6233\", \"Illumina NovaSeq 6234\", \"Illumina NovaSeq 6235\", \"Illumina NovaSeq 6236\",\n    \"Illumina NovaSeq 6237\", \"Illumina NovaSeq 6238\", \"Illumina NovaSeq 6239\", \"Illumina NovaSeq 6240\", \"Illumina NovaSeq 6241\",\n    \"Illumina NovaSeq 6242\", \"Illumina NovaSeq 6243\", \"Illumina NovaSeq 6244\", \"Illumina NovaSeq 6245\", \"Illumina NovaSeq 6246\",\n    \"Illumina NovaSeq 6247\", \"Illumina NovaSeq 6248\", \"Illumina NovaSeq 6249\", \"Illumina NovaSeq 6250\", \"Illumina NovaSeq 6251\",\n    \"Illumina NovaSeq 6252\", \"Illumina NovaSeq 6253\", \"Illumina NovaSeq 6254\", \"Illumina NovaSeq 6255\", \"Illumina NovaSeq 6256\",\n    \"Illumina NovaSeq 6257\", \"Illumina NovaSeq 6258\", \"Illumina NovaSeq 6259\", \"Illumina NovaSeq 6260\", \"Illumina NovaSeq 6261\",\n    \"Illumina NovaSeq 6262\", \"Illumina NovaSeq 6263\", \"Illumina NovaSeq 6264\", \"Illumina NovaSeq 6265\", \"Illumina NovaSeq 6266\",\n    \"Illumina NovaSeq 6267\", \"Illumina NovaSeq 6268\", \"Illumina NovaSeq 6269\", \"Illumina NovaSeq 6270\", \"Illumina NovaSeq 6271\",\n    \"Illumina NovaSeq 6272\", \"Illumina NovaSeq 6273\", \"Illumina NovaSeq 6274\", \"Illumina NovaSeq 6275\", \"Illumina NovaSeq 6276\",\n    \"Illumina NovaSeq 6277\", \"Illumina NovaSeq 6278\", \"Illumina NovaSeq 6279\", \"Illumina NovaSeq 6280\", \"Illumina NovaSeq 6281\",\n    \"Illumina NovaSeq 6282\", \"Illumina NovaSeq 6283\", \"Illumina NovaSeq 6284\", \"Illumina NovaSeq 6285\", \"Illumina NovaSeq 6286\",\n    \"Illumina NovaSeq 6287\", \"Illumina NovaSeq 6288\", \"Illumina NovaSeq 6289\", \"Illumina NovaSeq 6290\", \"Illumina NovaSeq 6291\",\n    \"Illumina NovaSeq 6292\", \"Illumina NovaSeq 6293\", \"Illumina NovaSeq 6294\", \"Illumina NovaSeq 6295\", \"Illumina NovaSeq 6296\",\n    \"Illumina NovaSeq 6297\", \"Illumina NovaSeq 6298\", \"Illumina NovaSeq 6299\", \"Illumina NovaSeq 6300\", \"Illumina NovaSeq 6301\",\n    \"Illumina NovaSeq 6302\", \"Illumina NovaSeq 6303\", \"Illumina NovaSeq 6304\", \"Illumina NovaSeq 6305\", \"Illumina NovaSeq 6306\",\n    \"Illumina NovaSeq 6307\", \"Illumina NovaSeq 6308\", \"Illumina NovaSeq 6309\", \"Illumina NovaSeq 6310\", \"Illumina NovaSeq 6311\",\n    \"Illumina NovaSeq 6312\", \"Illumina NovaSeq 6313\", \"Illumina NovaSeq 6314\", \"Illumina NovaSeq 6315\", \"Illumina NovaSeq 6316\",\n    \"Illumina NovaSeq 6317\", \"Illumina NovaSeq 6318\", \"Illumina NovaSeq 6319\", \"Illumina NovaSeq 6320\", \"Illumina NovaSeq 6321\",\n    \"Illumina NovaSeq 6322\", \"Illumina NovaSeq 6323\", \"Illumina NovaSeq 6324\", \"Illumina NovaSeq 6325\", \"Illumina NovaSeq 6326\",\n    \"Illumina NovaSeq 6327\", \"Illumina NovaSeq 6328\", \"Illumina NovaSeq 6329\", \"Illumina NovaSeq 6330\", \"Illumina NovaSeq 6331\",\n    \"Illumina NovaSeq 6332\", \"Illumina NovaSeq 6333\", \"Illumina NovaSeq 6334\", \"Illumina NovaSeq 6335\", \"Illumina NovaSeq 6336\",\n    \"Illumina NovaSeq 6337\", \"Illumina NovaSeq 6338\", \"Illumina NovaSeq 6339\", \"Illumina NovaSeq 6340\", \"Illumina NovaSeq 6341\",\n    \"Illumina NovaSeq 6342\", \"Illumina NovaSeq 6343\", \"Illumina NovaSeq 6344\", \"Illumina NovaSeq 6345\", \"Illumina NovaSeq 6346\",\n    \"Illumina NovaSeq 6347\", \"Illumina NovaSeq 6348\", \"Illumina NovaSeq 6349\", \"Illumina NovaSeq 6350\", \"Illumina NovaSeq 6351\",\n    \"Illumina NovaSeq 6352\", \"Illumina NovaSeq 6353\", \"Illumina NovaSeq 6354\", \"Illumina NovaSeq 6355\", \"Illumina NovaSeq 6356\",\n    \"Illumina NovaSeq 6357\", \"Illumina NovaSeq 6358\", \"Illumina NovaSeq 6359\", \"Illumina NovaSeq 6360\", \"Illumina NovaSeq 6361\",\n    \"Illumina NovaSeq 6362\", \"Illumina NovaSeq 6363\", \"Illumina NovaSeq 6364\", \"Illumina NovaSeq 6365\", \"Illumina NovaSeq 6366\",\n    \"Illumina NovaSeq 6367\", \"Illumina NovaSeq 6368\", \"Illumina NovaSeq 6369\", \"Illumina NovaSeq 6370\", \"Illumina NovaSeq 6371\",\n    \"Illumina NovaSeq 6372\", \"Illumina NovaSeq 6373\", \"Illumina NovaSeq 6374\", \"Illumina NovaSeq 6375\", \"Illumina NovaSeq 6376\",\n    \"Illumina NovaSeq 6377\", \"Illumina NovaSeq 6378\", \"Illumina NovaSeq 6379\", \"Illumina NovaSeq 6380\", \"Illumina NovaSeq 6381\",\n    \"Illumina NovaSeq 6382\", \"Illumina NovaSeq 6383\", \"Illumina NovaSeq 6384\", \"Illumina NovaSeq 6385\", \"Illumina NovaSeq 6386\",\n    \"Illumina NovaSeq 6387\", \"Illumina NovaSeq 6388\", \"Illumina NovaSeq 6389\", \"Illumina NovaSeq 6390\", \"Illumina NovaSeq 6391\",\n    \"Illumina NovaSeq 6392\", \"Illumina NovaSeq 6393\", \"Illumina NovaSeq 6394\", \"Illumina NovaSeq 6395\", \"Illumina NovaSeq 6396\",\n    \"Illumina NovaSeq 6397\", \"Illumina NovaSeq 6398\", \"Illumina NovaSeq 6399\", \"Illumina NovaSeq 6400\", \"Illumina NovaSeq 6401\",\n    \"Illumina NovaSeq 6402\", \"Illumina NovaSeq 6403\", \"Illumina NovaSeq 6404\", \"Illumina NovaSeq 6405\", \"Illumina NovaSeq 6406\",\n    \"Illumina NovaSeq 6407\", \"Illumina NovaSeq 6408\", \"Illumina NovaSeq 6409\", \"Illumina NovaSeq 6410\", \"Illumina NovaSeq 6411\",\n    \"Illumina NovaSeq 6412\", \"Illumina NovaSeq 6413\", \"Illumina NovaSeq 6414\", \"Illumina NovaSeq 6415\", \"Illumina NovaSeq 6416\",\n    \"Illumina NovaSeq 6417\", \"Illumina NovaSeq 6418\", \"Illumina NovaSeq 6419\", \"Illumina NovaSeq 6420\", \"Illumina NovaSeq 6421\",\n    \"Illumina NovaSeq 6422\", \"Illumina NovaSeq 6423\", \"Illumina NovaSeq 6424\", \"Illumina NovaSeq 6425\", \"Illumina NovaSeq 6426\",\n    \"Illumina NovaSeq 6427\", \"Illumina NovaSeq 6428\", \"Illumina NovaSeq 6429\", \"Illumina NovaSeq 6430\", \"Illumina NovaSeq 6431\",\n    \"Illumina NovaSeq 6432\", \"Illumina NovaSeq 6433\", \"Illumina NovaSeq 6434\", \"Illumina NovaSeq 6435\", \"Illumina NovaSeq 6436\",\n    \"Illumina NovaSeq 6437\", \"Illumina NovaSeq 6438\", \"Illumina NovaSeq 6439\", \"Illumina NovaSeq 6440\", \"Illumina NovaSeq 6441\",\n    \"Illumina NovaSeq 6442\", \"Illumina NovaSeq 6443\", \"Illumina NovaSeq 6444\", \"Illumina NovaSeq 6445\", \"Illumina NovaSeq 6446\",\n    \"Illumina NovaSeq 6447\", \"Illumina NovaSeq 6448\", \"Illumina NovaSeq 6449\", \"Illumina NovaSeq 6450\", \"Illumina NovaSeq 6451\",\n    \"Illumina NovaSeq 6452\", \"Illumina NovaSeq 6453\", \"Illumina NovaSeq 6454\", \"Illumina NovaSeq 6455\", \"Illumina NovaSeq 6456\",\n    \"Illumina NovaSeq 6457\", \"Illumina NovaSeq 6458\", \"Illumina NovaSeq 6459\", \"Illumina NovaSeq 6460\", \"Illumina NovaSeq 6461\",\n    \"Illumina NovaSeq 6462\", \"Illumina NovaSeq 6463\", \"Illumina NovaSeq 6464\", \"Illumina NovaSeq 6465\", \"Illumina NovaSeq 6466\",\n    \"Illumina NovaSeq 6467\", \"Illumina NovaSeq 6468\", \"Illumina NovaSeq 6469\", \"Illumina NovaSeq 6470\", \"Illumina NovaSeq 6471\",\n    \"Illumina NovaSeq 6472\", \"Illumina NovaSeq 6473\", \"Illumina NovaSeq 6474\", \"Illumina NovaSeq 6475\", \"Illumina NovaSeq 6476\",\n    \"Illumina NovaSeq 6477\", \"Illumina NovaSeq 6478\", \"Illumina NovaSeq 6479\", \"Illumina NovaSeq 6480\", \"Illumina NovaSeq 6481\",\n    \"Illumina NovaSeq 6482\", \"Illumina NovaSeq 6483\", \"Illumina NovaSeq 6484\", \"Illumina NovaSeq 6485\", \"Illumina NovaSeq 6486\",\n    \"Illumina NovaSeq 6487\", \"Illumina NovaSeq 6488\", \"Illumina NovaSeq 6489\", \"Illumina NovaSeq 6490\", \"Illumina NovaSeq 6491\",\n    \"Illumina NovaSeq 6492\", \"Illumina NovaSeq 6493\", \"Illumina NovaSeq 6494\", \"Illumina NovaSeq 6495\", \"Illumina NovaSeq 6496\",\n    \"Illumina NovaSeq 6497\", \"Illumina NovaSeq 6498\", \"Illumina NovaSeq 6499\", \"Illumina NovaSeq 6500\", \"Illumina NovaSeq 6501\",\n    \"Illumina NovaSeq 6502\", \"Illumina NovaSeq 6503\", \"Illumina NovaSeq 6504\", \"Illumina NovaSeq 6505\", \"Illumina NovaSeq 6506\",\n    \"Illumina NovaSeq 6507\", \"Illumina NovaSeq 6508\", \"Illumina NovaSeq 6509\", \"Illumina NovaSeq 6510\", \"Illumina NovaSeq 6511\",\n    \"Illumina NovaSeq 6512\", \"Illumina NovaSeq 6513\", \"Illumina NovaSeq 6514\", \"Illumina NovaSeq 6515\", \"Illumina NovaSeq 6516\",\n    \"Illumina NovaSeq 6517\", \"Illumina NovaSeq 6518\", \"Illumina NovaSeq 6519\", \"Illumina NovaSeq 6520\", \"Illumina NovaSeq 6521\",\n    \"Illumina NovaSeq 6522\", \"Illumina NovaSeq 6523\", \"Illumina NovaSeq 6524\", \"Illumina NovaSeq 6525\", \"Illumina NovaSeq 6526\",\n    \"Illumina NovaSeq 6527\", \"Illumina NovaSeq 6528\", \"Illumina NovaSeq 6529\", \"Illumina NovaSeq 6530\", \"Illumina NovaSeq 6531\",\n    \"Illumina NovaSeq 6532\", \"Illumina NovaSeq 6533\", \"Illumina NovaSeq 6534\", \"Illumina NovaSeq 6535\", \"Illumina NovaSeq 6536\",\n    \"Illumina NovaSeq 6537\", \"Illumina NovaSeq 6538\", \"Illumina NovaSeq 6539\", \"Illumina NovaSeq 6540\", \"Illumina NovaSeq 6541\",\n    \"Illumina NovaSeq 6542\", \"Illumina NovaSeq 6543\", \"Illumina NovaSeq 6544\", \"Illumina NovaSeq 6545\", \"Illumina NovaSeq 6546\",\n    \"Illumina NovaSeq 6547\", \"Illumina NovaSeq 6548\", \"Illumina NovaSeq 6549\", \"Illumina NovaSeq 6550\", \"Illumina NovaSeq 6551\",\n    \"Illumina NovaSeq 6552\", \"Illumina NovaSeq 6553\", \"Illumina NovaSeq 6554\", \"Illumina NovaSeq 6555\", \"Illumina NovaSeq 6556\",\n    \"Illumina NovaSeq 6557\", \"Illumina NovaSeq 6558\", \"Illumina NovaSeq 6559\", \"Illumina NovaSeq 6560\", \"Illumina NovaSeq 6561\",\n    \"Illumina NovaSeq 6562\", \"Illumina NovaSeq 6563\", \"Illumina NovaSeq 6564\", \"Illumina NovaSeq 6565\", \"Illumina NovaSeq 6566\",\n    \"Illumina NovaSeq 6567\", \"Illumina NovaSeq 6568\", \"Illumina NovaSeq 6569\", \"Illumina NovaSeq 6570\", \"Illumina NovaSeq 6571\",\n    \"Illumina NovaSeq 6572\", \"Illumina NovaSeq 6573\", \"Illumina NovaSeq 6574\", \"Illumina NovaSeq 6575\", \"Illumina NovaSeq 6576\",\n    \"Illumina NovaSeq 6577\", \"Illumina NovaSeq 6578\", \"Illumina NovaSeq 6579\", \"Illumina NovaSeq 6580\", \"Illumina NovaSeq 6581\",\n    \"Illumina NovaSeq 6582\", \"Illumina NovaSeq 6583\", \"Illumina NovaSeq 6584\", \"Illumina NovaSeq 6585\", \"Illumina NovaSeq 6586\",\n    \"Illumina NovaSeq 6587\", \"Illumina NovaSeq 6588\", \"Illumina NovaSeq 6589\", \"Illumina NovaSeq 6590\", \"Illumina NovaSeq 6591\",\n    \"Illumina NovaSeq 6592\", \"Illumina NovaSeq 6593\", \"Illumina NovaSeq 6594\", \"Illumina NovaSeq 6595\", \"Illumina NovaSeq 6596\",\n    \"Illumina NovaSeq 6597\", \"Illumina NovaSeq 6598\", \"Illumina NovaSeq 6599\", \"Illumina NovaSeq 6600\", \"Illumina NovaSeq 6601\",\n    \"Illumina NovaSeq 6602\", \"Illumina NovaSeq 6603\", \"Illumina NovaSeq 6604\", \"Illumina NovaSeq 6605\", \"Illumina NovaSeq 6606\",\n    \"Illumina NovaSeq 6607\", \"Illumina NovaSeq 6608\", \"Illumina NovaSeq 6609\", \"Illumina NovaSeq 6610\", \"Illumina NovaSeq 6611\",\n    \"Illumina NovaSeq 6612\", \"Illumina NovaSeq 6613\", \"Illumina NovaSeq 6614\", \"Illumina NovaSeq 6615\", \"Illumina NovaSeq 6616\",\n    \"Illumina NovaSeq 6617\", \"Illumina NovaSeq 6618\", \"Illumina NovaSeq 6619\", \"Illumina NovaSeq 6620\", \"Illumina NovaSeq 6621\",\n    \"Illumina NovaSeq 6622\", \"Illumina NovaSeq 6623\", \"Illumina NovaSeq 6624\", \"Illumina NovaSeq 6625\", \"Illumina NovaSeq 6626\",\n    \"Illumina NovaSeq 6627\", \"Illumina NovaSeq 6628\", \"Illumina NovaSeq 6629\", \"Illumina NovaSeq 6630\", \"Illumina NovaSeq 6631\",\n    \"Illumina NovaSeq 6632\", \"Illumina NovaSeq 6633\", \"Illumina NovaSeq 6634\", \"Illumina NovaSeq 6635\", \"Illumina NovaSeq 6636\",\n    \"Illumina NovaSeq 6637\", \"Illumina NovaSeq 6638\", \"Illumina NovaSeq 6639\", \"Illumina NovaSeq 6640\", \"Illumina NovaSeq 6641\",\n    \"Illumina NovaSeq 6642\", \"Illumina NovaSeq 6643\", \"Illumina NovaSeq 6644\", \"Illumina NovaSeq 6645\", \"Illumina NovaSeq 6646\",\n    \"Illumina NovaSeq 6647\", \"Illumina NovaSeq 6648\", \"Illumina NovaSeq 6649\", \"Illumina NovaSeq 6650\", \"Illumina NovaSeq 6651\",\n    \"Illumina NovaSeq 6652\", \"Illumina NovaSeq 6653\", \"Illumina NovaSeq 6654\", \"Illumina NovaSeq 6655\", \"Illumina NovaSeq 6656\",\n    \"Illumina NovaSeq 6657\", \"Illumina NovaSeq 6658\", \"Illumina NovaSeq 6659\", \"Illumina NovaSeq 6660\", \"Illumina NovaSeq 6661\",\n    \"Illumina NovaSeq 6662\", \"Illumina NovaSeq 6663\", \"Illumina NovaSeq 6664\", \"Illumina NovaSeq 6665\", \"Illumina NovaSeq 6666\",\n    \"Illumina NovaSeq 6667\", \"Illumina NovaSeq 6668\", \"Illumina NovaSeq 6669\", \"Illumina NovaSeq 6670\", \"Illumina NovaSeq 6671\",\n    \"Illumina NovaSeq 6672\", \"Illumina NovaSeq 6673\", \"Illumina NovaSeq 6674\", \"Illumina NovaSeq 6675\", \"Illumina NovaSeq 6676\",\n    \"Illumina NovaSeq 6677\", \"Illumina NovaSeq 6678\", \"Illumina NovaSeq 6679\", \"Illumina NovaSeq 6680\", \"Illumina NovaSeq 6681\",\n    \"Illumina NovaSeq 6682\", \"Illumina NovaSeq 6683\", \"Illumina NovaSeq 6684\", \"Illumina NovaSeq 6685\", \"Illumina NovaSeq 6686\",\n    \"Illumina NovaSeq 6687\", \"Illumina NovaSeq 6688\", \"Illumina NovaSeq 6689\", \"Illumina NovaSeq 6690\", \"Illumina NovaSeq 6691\",\n    \"Illumina NovaSeq 6692\", \"Illumina NovaSeq 6693\", \"Illumina NovaSeq 6694\", \"Illumina NovaSeq 6695\", \"Illumina NovaSeq 6696\",\n    \"Illumina NovaSeq 6697\", \"Illumina NovaSeq 6698\"\n]", "effect": "{platform: ILLUMINA}", "expanded": [], "rationale": "Every number from 6001 to 6698 in 1000G's participant.instrument_model, two files each, beside 14,605 lines of `Illumina NovaSeq 6000`. They look like a spreadsheet fill series run down from that value; no such models exist. The vendor is certain, so platform is declared. The model most likely is the NovaSeq 6000, but that is a guess, so instrument_model is not (#532). The correction belongs at the source.", "dataset": "general", "seeded": false, "claims": 1396, "files": 1396, "won": 1396, "datasets": {"ANVIL_1000G_high_coverage_2019": 1396}, "source_types": {"repository_metadata": 1396}}, {"id": "platform.illumina_novaseq_x", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"Illumina NovaSeq X\"}", "effect": "{platform: ILLUMINA, instrument_model: Illumina NovaSeq X}", "expanded": [], "rationale": "An Illumina instrument model, spelled as ENA/SRA's; the model implies the platform.", "dataset": "general", "seeded": false, "claims": 3360, "files": 1680, "won": 1680, "datasets": {"AnVIL_IGVF_Mouse_R1": 1680}, "source_types": {"repository_metadata": 3360}}, {"id": "platform.illumina_nextseq_2000", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"Illumina NextSeq 2000\"}", "effect": "{platform: ILLUMINA, instrument_model: NextSeq 2000}", "expanded": [], "rationale": "An Illumina instrument model; ENA/SRA spells it without the vendor, as `NextSeq 2000`.", "dataset": "general", "seeded": false, "claims": 536, "files": 268, "won": 268, "datasets": {"AnVIL_IGVF_Mouse_R1": 268}, "source_types": {"repository_metadata": 536}}, {"id": "platform.kinnex", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"kinnex\"}", "effect": "{platform: PACBIO}", "expanded": [], "rationale": "Kinnex is a PacBio library kit; the span names PacBio data. Platform only.", "dataset": "general", "seeded": false, "claims": 230, "files": 230, "won": 230, "datasets": {"AnVIL_HPRC_R2": 230}, "source_types": {"repository_metadata": 230}}, {"id": "platform.minion", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"MinION\"}", "effect": "{platform: ONT, instrument_model: MinION}", "expanded": [], "rationale": "An Oxford Nanopore instrument, spelled as ENA/SRA's model.", "dataset": "general", "seeded": false, "claims": 4, "files": 2, "won": 2, "datasets": {"AnVIL_HPRC_R2": 2}, "source_types": {"repository_metadata": 4}}, {"id": "platform.nanopore", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"nanopore\"}", "effect": "{platform: ONT}", "expanded": [], "rationale": "A column-name span naming Oxford Nanopore data.", "dataset": "general", "seeded": false, "claims": 120, "files": 120, "won": 120, "datasets": {"ANVIL_HPRC": 120}, "source_types": {"repository_metadata": 120}}, {"id": "platform.ont", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"ont\"}", "effect": "{platform: ONT}", "expanded": [], "rationale": "Spelled as our term, as a table-name span. Identity by ruling.", "dataset": "general", "seeded": false, "claims": 1391, "files": 1391, "won": 1391, "datasets": {"AnVIL_HPRC_R2": 1391}, "source_types": {"repository_metadata": 1391}}, {"id": "platform.ont_gridion_x5", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"ONT GridION X5\"}", "effect": "{platform: ONT, instrument_model: GridION}", "expanded": [], "rationale": "The GridION X5, ONT's GridION with five flow-cell positions. ENA/SRA has one GridION model and no separate X5, so the model is GridION.", "dataset": "general", "seeded": false, "claims": 40, "files": 20, "won": 20, "datasets": {"AnVIL_IGVF_Mouse_R1": 20}, "source_types": {"repository_metadata": 40}}, {"id": "platform.ont_promethion_2_solo", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"ONT PromethION 2 Solo\"}", "effect": "{platform: ONT, instrument_model: PromethION}", "expanded": [], "rationale": "The PromethION 2 Solo, a two-flow-cell PromethION device. ENA/SRA has one PromethION model and no separate P2 Solo, so the model is PromethION.", "dataset": "general", "seeded": false, "claims": 4, "files": 2, "won": 2, "datasets": {"AnVIL_IGVF_Mouse_R1": 2}, "source_types": {"repository_metadata": 4}}, {"id": "platform.oxford_nanopore", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"OXFORD_NANOPORE\"}", "effect": "{platform: ONT}", "expanded": [], "rationale": "SRA's platform value for Oxford Nanopore.", "dataset": "general", "seeded": false, "claims": 929, "files": 929, "won": 929, "datasets": {"AnVIL_HPRC_R2": 929}, "source_types": {"repository_metadata": 929}}, {"id": "platform.pacbio", "kind": "mapping", "defined_in": "src/meta_disco/rules/value_map.yaml", "basis": "mapping", "tier": null, "condition": "{slot: platform, value: \"PACBIO\"}", "effect": "{platform: PACBIO}", "expanded": [], "rationale": "Spelled as our term (the kinnex table's spelling). 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table: 'sample_maps'; raw_activity: 'GenomicsDBImport'; child_column: 'file_id'; parent_column: '--sample-name-map'", "reads_is_catalog": true, "rationale": "A workspace tar's own vcfheader.vcf says GenomicsDBImport read this sample-name map, and the tar's callset.json names exactly the samples the map's published copy lists: the tar merges the gVCFs the map lists, read through it (#710, a workaround until AnVIL holds the map, #624).", "claims": 31158, "files": 31158, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 31158}, "source_types": {}}, {"id": "activity.t2t_variant_call", "activity": "VariantCallActivity / calls_from", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_metadata'; table: '1KGP_CHM13v2_sample' \u00b7 'SGDP_CHM13v2_sample' \u00b7 'SGDP_GRCh38_sample' \u00b7 'participant'; child_column: 'chr10_hcvcf_gz' \u00b7 'chr11_hcvcf_gz' \u00b7 'chr12_hcvcf_gz' \u00b7 'chr13_hcvcf_gz' \u00b7 'chr14_hcvcf_gz' \u00b7 'chr15_hcvcf_gz' \u00b7 'chr16_hcvcf_gz' \u00b7 'chr17_hcvcf_gz' \u00b7 'chr18_hcvcf_gz' \u00b7 'chr19_hcvcf_gz' \u00b7 'chr1_hcvcf_gz' \u00b7 'chr20_hcvcf_gz' \u00b7 'chr21_hcvcf_gz' \u00b7 'chr22_hcvcf_gz' \u00b7 'chr2_hcvcf_gz' \u00b7 'chr3_hcvcf_gz' \u00b7 'chr4_hcvcf_gz' \u00b7 'chr5_hcvcf_gz' \u00b7 'chr6_hcvcf_gz' \u00b7 'chr7_hcvcf_gz' \u00b7 'chr8_hcvcf_gz' \u00b7 'chr9_hcvcf_gz' \u00b7 'xx_x_hcvcf_gz' \u00b7 'xy_x_nonpar_hcvcf_gz' \u00b7 'xy_x_par_hcvcf_gz' \u00b7 'xy_y_nonpar_hcvcf_gz'; parent_column: 'cram'", "reads_is_catalog": true, "rationale": "HaplotypeCaller's per-chromosome VCFs are called from the sample row's CRAM.", "claims": 90378, "files": 90378, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 90378}, "source_types": {}}, {"id": "activity.t2t_index", "activity": "IndexActivity / indexed", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_metadata'; table: '1KGP_CHM13v2_sample' \u00b7 'SGDP_CHM13v2_sample' \u00b7 'SGDP_GRCh38_sample' \u00b7 'participant'; child_column: 'chr10_hcvcf_index' \u00b7 'chr11_hcvcf_index' \u00b7 'chr12_hcvcf_index' \u00b7 'chr13_hcvcf_index' \u00b7 'chr14_hcvcf_index' \u00b7 'chr15_hcvcf_index' \u00b7 'chr16_hcvcf_index' \u00b7 'chr17_hcvcf_index' \u00b7 'chr18_hcvcf_index' \u00b7 'chr19_hcvcf_index' \u00b7 'chr1_hcvcf_index' \u00b7 'chr20_hcvcf_index' \u00b7 'chr21_hcvcf_index' \u00b7 'chr22_hcvcf_index' \u00b7 'chr2_hcvcf_index' \u00b7 'chr3_hcvcf_index' \u00b7 'chr4_hcvcf_index' \u00b7 'chr5_hcvcf_index' \u00b7 'chr6_hcvcf_index' \u00b7 'chr7_hcvcf_index' \u00b7 'chr8_hcvcf_index' \u00b7 'chr9_hcvcf_index' \u00b7 'cram_index' \u00b7 'mosdepth_regions_bed_idx' \u00b7 'mosdepth_regions_index' \u00b7 'xx_x_hcvcf_index' \u00b7 'xy_x_nonpar_hcvcf_index' \u00b7 'xy_x_par_hcvcf_index' \u00b7 'xy_y_nonpar_hcvcf_index'; parent_column: 'chr10_hcvcf_gz' \u00b7 'chr11_hcvcf_gz' \u00b7 'chr12_hcvcf_gz' \u00b7 'chr13_hcvcf_gz' \u00b7 'chr14_hcvcf_gz' \u00b7 'chr15_hcvcf_gz' \u00b7 'chr16_hcvcf_gz' \u00b7 'chr17_hcvcf_gz' \u00b7 'chr18_hcvcf_gz' \u00b7 'chr19_hcvcf_gz' \u00b7 'chr1_hcvcf_gz' \u00b7 'chr20_hcvcf_gz' \u00b7 'chr21_hcvcf_gz' \u00b7 'chr22_hcvcf_gz' \u00b7 'chr2_hcvcf_gz' \u00b7 'chr3_hcvcf_gz' \u00b7 'chr4_hcvcf_gz' \u00b7 'chr5_hcvcf_gz' \u00b7 'chr6_hcvcf_gz' \u00b7 'chr7_hcvcf_gz' \u00b7 'chr8_hcvcf_gz' \u00b7 'chr9_hcvcf_gz' \u00b7 'cram' \u00b7 'mosdepth_regions_bed' \u00b7 'xx_x_hcvcf_gz' \u00b7 'xy_x_nonpar_hcvcf_gz' \u00b7 'xy_x_par_hcvcf_gz' \u00b7 'xy_y_nonpar_hcvcf_gz'", "reads_is_catalog": true, "rationale": "Each index column on a sample row indexes the file in the column it is named after (`cram_index` the `cram`, `chr1_hcvcf_index` the `chr1_hcvcf_gz`). The two lists match every combination, not only those pairs: no other pair occurs, and any index of one of these files is an index of it.", "claims": 104302, "files": 104302, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 97898, "ANVIL_T2T": 6404}, "source_types": {}}, {"id": "activity.t2t_joint_genotype", "activity": "JointGenotypingActivity / genotyped_from", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_metadata'; table: 'interval'; child_column: 'genotyped_bgzip'; parent_column: 'genomics_db_tar'", "reads_is_catalog": true, "rationale": "The interval row's VCF is its GenomicsDB workspace genotyped across every sample (GATK GenotypeGVCFs reads the workspace) and trimmed to the interval (SelectVariants). The VCF's header carries the workspace's GenomicsDBImport line and SelectVariants', but not GenotypeGVCFs', so the row is what states the link (#610), and with it the calls' kind and origin, which the header no longer names (#678). The SelectVariants step selects intervals, not types (no T2T header names --select-type), so the kind passes.", "claims": 31155, "files": 31155, "won": 0, "datasets": {"ANVIL_T2T": 31155}, "source_types": {}}, {"id": "activity.t2t_variant_filter", "activity": "VariantFilterActivity / processed", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_metadata'; table: 'chromosome'; child_column: 'chm13_pass_vcf_gz' \u00b7 'chm13_recalibrated_vcf_gz' \u00b7 'grch38_pass_vcf_gz'; parent_column: 'chm13_raw_vcf_gz' \u00b7 'chm13_recalibrated_vcf_gz' \u00b7 'grch38_recalibrated_vcf_gz'", "reads_is_catalog": true, "rationale": "On a chromosome row, the recalibrated callset is the raw one with every row labelled by GATK's ApplyVQSR (its header records both passes), and the PASS callset is the recalibrated one with only its passing rows kept (`bcftools view -f PASS`). The row holds CHM13's raw, recalibrated and PASS callsets and GRCh38's recalibrated and PASS ones. The lists match every combination, but the lineage map pairs each chain only within itself, so no line pairs a CHM13 file with a GRCh38 one (#610).", "claims": 72, "files": 72, "won": 0, "datasets": {"ANVIL_T2T": 72}, "source_types": {}}, {"id": "activity.igvf_index", "activity": "IndexActivity / indexed", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_metadata'; table: 'file'; child_column: 'file_path'; parent_column: 'derived_from'; child_data_type: 'index'; parent_data_type: 'alignments' \u00b7 'fragments'", "reads_is_catalog": true, "rationale": "An IGVF index is derived from the alignments or fragments file it indexes.", "claims": 4, "files": 4, "won": 0, "datasets": {"AnVIL_IGVF_Mouse_R1": 4}, "source_types": {}}, {"id": "activity.igvf_alignment", "activity": "AlignmentActivity / reads", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_metadata'; table: 'file'; child_column: 'file_path'; parent_column: 'derived_from'; child_data_type: 'alignments'; parent_data_type: 'reads'", "reads_is_catalog": true, "rationale": "IGVF alignments derived from reads are those reads aligned.", "claims": 6, "files": 2, "won": 0, "datasets": {"AnVIL_IGVF_Mouse_R1": 2}, "source_types": {}}, {"id": "activity.track_bedgraphtobigwig", "activity": "CoverageActivity / covered", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_activity'; table: 'anvil_activity'; raw_activity: 'Track: bedGraphToBigWig'; parent_column: 'used_file_id'", "reads_is_catalog": true, "rationale": "Each step used one STAR BAM and generated its four `Signal.{Unique,UniqueMultiple}.strand\u00b1` bigWigs: STAR's per-strand coverage of that BAM, converted by bedGraphToBigWig, so the raw words name the conversion, not what was computed.", "claims": 2536, "files": 2536, "won": 0, "datasets": {"AnVIL_ENCORE_RS293": 1640, "AnVIL_ENCORE_293T": 896}, "source_types": {}}, {"id": "activity.quantification_salmon", "activity": "ExpressionActivity / quantified", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_activity'; table: 'anvil_activity'; raw_activity: 'Quantificatioin: salmon'; parent_column: 'used_file_id'", "reads_is_catalog": true, "rationale": "salmon quantifies the reads it used; every file this step used in anvil15 is a trimmed FASTQ. `Quantificatioin` is AnVIL's spelling, matched as written.", "claims": 1268, "files": 634, "won": 0, "datasets": {"AnVIL_ENCORE_RS293": 410, "AnVIL_ENCORE_293T": 224}, "source_types": {}}, {"id": "activity.merger_fastq", "activity": "MergeActivity / shard", "defined_in": "src/meta_disco/rules/activity_map.yaml", "reads": "source_type: 'repository_activity'; table: 'anvil_activity'; raw_activity: 'Merger: multiple FASTQ files'; parent_column: 'used_file_id'", "reads_is_catalog": true, "rationale": "The step joins the FASTQs it used into one FASTQ.", "claims": 20, "files": 8, "won": 0, "datasets": {"AnVIL_ENCORE_293T": 8}, "source_types": {}}], "sample_rules": [{"id": "bam_read_group_samples", "reads": "a BAM/CRAM header's @RG lines: each distinct SM, every read group having one", "rationale": "SM is the SAM specification's field for the sample a read group's reads come from. A header whose every read group has an SM, each recognised, names the file's samples; one with a read group without SM, or an SM no identifier rule recognises (`Sample`, `BioSample_3`), names none.", "defined_in": "src/meta_disco/sample_identity.py", "claims": 12020, "files": 12020, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 3760, "ANVIL_T2T": 3237, "ANVIL_1000G_high_coverage_2019": 3202, "AnVIL_HPRC_R2": 735, "AnVIL_MAGE": 731, "ANVIL_HPRC": 355}, "source_types": {}}, {"id": "vcf_sample_columns", "reads": "a VCF header's #CHROM line: each column after FORMAT", "rationale": "The VCF specification gives each sample whose genotypes a file holds a column after FORMAT, named for the sample. A header whose every column is recognised names the file's samples; one with a column no identifier rule recognises (a BAM path, `syndip`) names none, and a sites-only file has no sample columns.", "defined_in": "src/meta_disco/sample_identity.py", "claims": 203374, "files": 203374, "won": 0, "datasets": {"ANVIL_T2T": 109478, "ANVIL_T2T_CHRY": 90618, "ANVIL_1000G_high_coverage_2019": 3224, "ANVIL_1000G_PRIMED_data_model": 46, "ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 4, "AnVIL_MAGE": 3, "ANVIL_HPRC": 1}, "source_types": {}}, {"id": "sample_map_rows", "reads": "a GATK sample-name map's rows: the sample name in each row's first column", "rationale": "A sample-name map gives each sample a GenomicsDB import joint-calls a row, the sample's name first and its gVCF second; the import writes that name as the sample's column in what it makes (#668). A map whose every name is recognised names the samples of the cohort it lists; one with a name no identifier rule recognises names none.", "defined_in": "src/meta_disco/sample_identity.py", "claims": 24, "files": 24, "won": 0, "datasets": {"ANVIL_T2T": 24}, "source_types": {}}, {"id": "fast5_tracking_sample", "reads": "a nanopore FAST5's first 64 KiB: each attribute named sample_id (MinKNOW writes it in tracking_id)", "rationale": "MinKNOW writes one sequencing run's reads to a FAST5, and records the sample the run sequenced as every read's tracking_id sample_id; the one NIA_CARD file read whole stores it once, shared by its 4,000 reads (#605). A head holding one recognised value names the file's sample; one holding none, an unrecognised value or two or more values names none. The attribute is found by its name and its message parsed whole, not reached by walking the file, which would read about 7.5 MB.", "defined_in": "src/meta_disco/sample_identity.py", "claims": 12394, "files": 12394, "won": 0, "datasets": {"ANVIL_NIA_CARD_Coriell_Cell_Lines_Open": 12394}, "source_types": {}}, {"id": "samples_inherited_from_parent", "reads": "the settled samples, at reconcile, of the parent in an input role of the file's generated_by that passes samples (rules/activities.yaml's passes_samples)", "rationale": "An index, checksum, QC report or coverage track describes exactly the file it was made from, and a cohort's merge exactly the samples its list names, so the file holds that parent's samples (#668). A parent with none, or parents naming different lists, pass nothing; the file's own sources and the parent's settle as any two sources do.", "defined_in": "src/meta_disco/reconcile_samples.py", "claims": 339074, "files": 339074, "won": 0, "datasets": {"ANVIL_T2T": 163034, "ANVIL_T2T_CHRY": 152897, "ANVIL_1000G_high_coverage_2019": 19278, "AnVIL_HPRC_R2": 2316, "AnVIL_MAGE": 782, "ANVIL_HPRC": 744, "ANVIL_1000G_PRIMED_data_model": 23}, "source_types": {}}, {"id": "external_sample_map_rows", "reads": "the rows, at reconcile, of a GATK sample-name map held outside AnVIL for the dataset (sources/external_files.yaml): the sample name in each row's first column", "rationale": "A sample-name map's first column names the samples its import joint-called, as a catalog map's does (sample_map_rows, #668). A map whose every name is recognised names them; one with a name no identifier rule recognises names none. A workaround until AnVIL holds the map (#624).", "defined_in": "src/meta_disco/sample_identity.py", "claims": 25, "files": 25, "won": 0, "datasets": {"(external sample maps)": 25}, "source_types": {}}, {"id": "family_id_as_written", "reads": "a submitter table's family id column (a PED file's `familyid`), on the row naming the donor", "rationale": "A family id is a name for a group of relatives, with no vocabulary to translate it into, so the string is the answer as the source wrote it. It means something only among datasets sharing a numbering: HPRC and HPRC_R2 reuse 1000 Genomes' ids, and two sources writing different ids for one donor are a conflict, never a choice.", "defined_in": "src/meta_disco/reconcile_entities.py", "claims": 3574, "files": 3210, "won": 0, "datasets": {"(donor records)": 3210}, "source_types": {}}, {"id": "anatomical_site_as_written", "reads": "the repository's own record of a sample: AnVIL's `anvil_biosample.anatomical_site`", "rationale": "AnVIL's sites are not yet mapped to an ontology (#702), so the string is the answer as AnVIL wrote it; its `Unknown` names no site and makes no claim. A pooled sample's site is AnVIL's join of its members' (`Brain, Heart`), kept as one string until #702 makes the slot a list.", "defined_in": "src/meta_disco/reconcile_entities.py", "claims": 1800, "files": 1800, "won": 0, "datasets": {"(sample records)": 1800}, "source_types": {}}, {"id": "coriell_cell_line_or_dna", "reads": "a string matching ^(?:GM|NA)(\\d{5})$; names a sample and its donor (the sample is collected from the donor of the same identifier)", "rationale": "A Coriell repository number names one person's cell line (GM) or the DNA extracted from it (NA); both prefixes with the same number are one sample, written NA as 1000 Genomes writes it. The number also names the person the line came from, as 1000 Genomes' `pedigree` and PRIMED's `subject` key their people by it.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 81664, "files": 80258, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 59995, "ANVIL_T2T": 10440, "AnVIL_HPRC_R2": 3385, "ANVIL_1000G_high_coverage_2019": 3121, "AnVIL_MAGE": 1112, "(donor records)": 1074, "(sample records)": 1049, "ANVIL_HPRC": 82}, "source_types": {}}, {"id": "coriell_hg", "reads": "a string matching ^HG\\d{5}$; names a sample and its donor (the sample is collected from the donor of the same identifier)", "rationale": "The NHGRI repository's HG numbers at Coriell, as 1000 Genomes, HPRC and T2T name their samples: one number, one person, and that person's cell line.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 176235, "files": 172399, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 124110, "ANVIL_T2T": 21580, "AnVIL_HPRC_R2": 10902, "ANVIL_1000G_high_coverage_2019": 6461, "ANVIL_HPRC": 3005, "(donor records)": 2172, "(sample records)": 2165, "AnVIL_MAGE": 2004}, "source_types": {}}, {"id": "giab_name", "reads": "a string matching ^HG00[1-7]$; names a sample and its donor (the sample is collected from the donor of the same identifier)", "rationale": "Genome in a Bottle names its seven reference samples HG001 to HG007, each one person's cell line, and HPRC's `participant` and NIA_CARD's `subject` key those people by the same name. They are the same people as some Coriell numbers, but joining two namespaces by rule is a crosswalk's (#361), so they stay apart; only a source's `same_donor_as` line joins two (#559).", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 1885, "files": 1879, "won": 0, "datasets": {"ANVIL_HPRC": 1785, "AnVIL_HPRC_R2": 82, "(donor records)": 6, "(sample records)": 6}, "source_types": {}}, {"id": "sra_sample_accession", "reads": "a string matching ^[SED]RS\\d+$; names a sample", "rationale": "An INSDC sample accession (SRA, ENA, DDBJ) names one sample.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "biosample_accession", "reads": "a string matching ^SAM(?:N|EA|D)\\d+$; names a sample", "rationale": "An INSDC BioSample accession (NCBI, EBI, DDBJ) names one sample.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 0, "files": 0, "won": 0, "datasets": {}, "source_types": {}}, {"id": "igvf_donor_accession", "reads": "a string matching ^IGVFDO[0-9A-Z]{8}$; names a donor", "rationale": "The IGVF Data Portal issues one donor accession per donor, unique across the portal; its samples have accessions of their own (IGVFSM\u2026), so the accession names no sample.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 183, "files": 183, "won": 0, "datasets": {"(donor records)": 183}, "source_types": {}}, {"id": "igvf_sample_accession", "reads": "a string matching ^IGVFSM[0-9A-Z]{8}$; names a sample", "rationale": "The IGVF Data Portal issues one sample accession per sample, unique across the portal; its donors have accessions of their own (IGVFDO\u2026), so the accession names no donor.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 1935, "files": 1935, "won": 0, "datasets": {"(sample records)": 1935}, "source_types": {}}, {"id": "sgdp_id", "reads": "a string matching ^(?:S|B|T|BR)_[A-Za-z_]+-\\d+$; names a donor", "rationale": "SGDP names each genome it sequenced once, by its panel, its population and a number; one genome is one person, so the name names that person. The sample a dataset sequenced is named by the dataset (`simons_data_sample_N` in T2T_CHRY), not by this name.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 104, "files": 104, "won": 0, "datasets": {"(donor records)": 104}, "source_types": {}}, {"id": "hgdp_cell_line", "reads": "a string matching ^HGDP\\d{5}$; names a donor", "rationale": "The CEPH-HGDP panel numbers each person's cell line once, at the Fondation Jean Dausset-CEPH, so the number names the person it came from, as a Coriell number does.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 130, "files": 130, "won": 0, "datasets": {"(donor records)": 130}, "source_types": {}}, {"id": "sgdp_sample_name", "reads": "a string matching ^simons_data_sample_\\d+$ in ANVIL_T2T_CHRY; names a sample", "rationale": "ANVIL_T2T_CHRY's SGDP sample tables have one row per name, so a name is one sample in that dataset; it is not an identifier anywhere else.", "defined_in": "src/meta_disco/rules/sample_namespaces.yaml", "claims": 32927, "files": 32369, "won": 0, "datasets": {"ANVIL_T2T_CHRY": 32090, "(sample records)": 279}, "source_types": {}}], "callers": [{"name": "HaplotypeCaller", "pattern": "HaplotypeCaller", "kind": "small", "origin": "germline", "somatic_mode": "", "reason": "GATK's germline caller of SNVs and indels by local assembly; structural variants are outside what it calls.", "kind_files": 293212, "origin_files": 293212}, {"name": "DeepVariant", "pattern": "DeepVariant", "kind": "small", "origin": "germline", "somatic_mode": "", "reason": "Google's deep-learning germline caller of SNVs and small indels; it does not call structural variants, and its somatic counterpart is a separate tool, DeepSomatic.", "kind_files": 18, "origin_files": 24}, {"name": "PEPPER-Margin-DeepVariant", "pattern": "PEPPER[-_]?Margin[-_]?DeepVariant", "kind": "small", "origin": "germline", "somatic_mode": "", "reason": "DeepVariant on long reads, after PEPPER's candidate finding and Margin's phasing; a germline caller of SNVs and small indels, as DeepVariant is.", "kind_files": 0, "origin_files": 0}, {"name": "Sniffles", "pattern": "sniffles2?(?:[_-]v?[0-9][0-9.]*)?", "kind": "structural", "origin": "germline", "somatic_mode": "flags --combine-output-filtered, --mosaic, --mosaic-include-germline, --non-germline; predated by sniffles. Sniffles2 calls germline structural variants by default, and rare, somatic or mosaic ones with --non-germline (2.0.x) or --mosaic (2.2 on); --mosaic-include-germline (2.2 on) turns mosaic mode on and reports germline SVs beside the mosaic ones, and --combine-output-filtered (2.0.7 on) includes low-confidence / mosaic SVs in multi-calling. As each version's own options say; it records its command line under ##command. Sniffles 1 has no such mode (1.0.12's options were read on 2026-10-10) and writes its ##source as plain \"Sniffles\".", "reason": "A long-read structural-variant caller (Sniffles and Sniffles2): it calls insertions, deletions, duplications, inversions and breakends.", "kind_files": 136, "origin_files": 136}, {"name": "SVIM", "pattern": "svim(?:-asm)?(?:[_-]v?[0-9][0-9.]*)?", "kind": "structural", "origin": "germline", "somatic_mode": "", "reason": "Structural-variant callers from long reads (SVIM) and from assemblies (SVIM-asm), both calling the germline variants of one sample.", "kind_files": 17, "origin_files": 29}], "undeclared": [], "labels": {"yaml": ["When", "Then"], "code": ["Reads", "Sets"], "mapping": ["Match", "Declares"]}, "text": {"intro": "Every rule meta-disco uses to describe a file, such as \"a .bam file holds alignments\" or \"a CRAM whose chromosome lengths match GRCh38 is aligned to GRCh38\". Each row shows what the rule looks at, what it concludes and why, and how often it applied in the classification named below.", "key": [["Kind", "where the rule is written: yaml, the rules file; code, in Python, for checks a rules file cannot express, such as comparing chromosome lengths; mapping, a translation from a value a submitter wrote (\"Revio\") to our term (\"PACBIO\")."], ["Basis", "what the rule looks at: the file's extension, its size, its name, the dataset it belongs to, its contents (headers, chromosome names and lengths, what a tar archive holds), the answer for the file it was made from (parent_file, for an index), or a submitter's value (mapping)."], ["Files", "how many files the rule gave an answer for."], ["Claims", "how many answers it gave: one rule can answer several fields of one file."], ["Won", "how many of those files ended up with the rule's answer, or a more specific form of it (a rule saying CHM13 on a file found to be T2T-CHM13v2.0). It can be lower when a stronger rule, or the submitter's metadata, gave a different answer. For our own rules, this compares with the answer meta-disco worked out on its own, before the submitter's metadata; for a mapping, with the file's final answer."], ["Highlighted", "a rule that gave no answer for any file this time."]], "markers_note": "Not rules. Each is a reason a field of a file was left without an answer.", "edge_rules_note": "Rules that say which file a file was made from, rather than giving a field an answer. The code rules name the parent from the file's own name, or from the command line in its header that made it (#609), and only where exactly one file of the dataset carries that name. The activity-map rows (#584) translate what a source table says about a step, and reconcile resolves the parent that table names (#577). One code rule is reconcile's: the members of a sample map held outside AnVIL for a dataset (#710). Counted by the inputs of the reconciled `generated_by` that cite them, and of the external lists' steps.", "callers_note": "The rows of `rules/callers.yaml` (#654, #658): each variant caller, the kind of variant it calls and their origin, which the caller rules look up once they have found the caller in a VCF's header, or a GenomicsDB workspace's (#678). A caller with a somatic mode gives its origin only where its command line shows none of the mode's flags, or a `##source` spelling predates the mode. Counted by the files whose kind or origin a caller rule took from the row: a merge counts each of its callers.", "undeclared_head": "Rule names with no rule behind them", "undeclared_note": "The counts name these rules, but the rule files in this version of meta-disco do not contain them. That happens when a rule was renamed or removed after the files were classified, or when a change still under review added it. There is nothing to show for them but the count. Once the files are classified again with the current rules, this list is empty.", "none_note": "None: every rule the counts name is in the rule files."}};
     const LABELS = DATA.labels, TEXT = DATA.text;
     const fmt = n => n.toLocaleString();
     const esc = s => String(s ?? "").replace(/[&<>"]/g, c => ({"&": "&amp;", "<": "&lt;", ">": "&gt;", '"': "&quot;"}[c]));
diff --git a/docs/rules-report.md b/docs/rules-report.md
index 2aa2ded0..61ca8e2a 100644
--- a/docs/rules-report.md
+++ b/docs/rules-report.md
@@ -2,7 +2,7 @@
 
 Every rule meta-disco uses to describe a file, such as "a .bam file holds alignments" or "a CRAM whose chromosome lengths match GRCh38 is aligned to GRCh38". Each row shows what the rule looks at, what it concludes and why, and how often it applied in the classification named below.
 
-188 rules, 23 edge rules and 16 sample and donor rules. Counts from the reconciled records of 708,088 files, in the run started on 19 October 2026 (output folder 20261019_225524), and the run's 3,416 donor records, counted as files under (donor records), and its 5,434 sample records, under (sample records), and the 25 sample maps held outside AnVIL its steps read through (#710), under (external sample maps). Generated by `make rules-report` (#572).
+190 rules, 23 edge rules and 18 sample and donor rules. Counts from the reconciled records of 708,088 files, in the run started on 19 October 2026 (output folder 20261019_225524), and the run's 3,668 donor records, counted as files under (donor records), and its 5,434 sample records, under (sample records), and the 25 sample maps held outside AnVIL its steps read through (#710), under (external sample maps). Generated by `make rules-report` (#572).
 
 - **Kind**: where the rule is written: yaml, the rules file; code, in Python, for checks a rules file cannot express, such as comparing chromosome lengths; mapping, a translation from a value a submitter wrote ("Revio") to our term ("PACBIO").
 - **Basis**: what the rule looks at: the file's extension, its size, its name, the dataset it belongs to, its contents (headers, chromosome names and lengths, what a tar archive holds), the answer for the file it was made from (parent_file, for an index), or a submitter's value (mapping).
@@ -17,7 +17,7 @@ Every rule meta-disco uses to describe a file, such as "a .bam file holds alignm
 | code | content | 26 | 7 |
 | code | extension | 1 | 0 |
 | code | parent_file | 1 | 0 |
-| mapping | mapping | 104 | 14 |
+| mapping | mapping | 106 | 14 |
 | yaml | content | 23 | 0 |
 | yaml | extension | 15 | 3 |
 | yaml | file_name | 18 | 1 |
@@ -119,6 +119,8 @@ Every rule meta-disco uses to describe a file, such as "a .bam file holds alignm
 | `phenotypic_sex.male` | The term itself, as T2T, HPRC and NIA_CARD write it lower case and PRIMED and AnVIL capitalised. | general |  | 1,690 | 5,243 | 1,689 | `(donor records)` 1,690 | repository_metadata 3,421; published_value 1,822 |
 | `phenotypic_sex.pedigree_1` | 1000 Genomes' `pedigree.sex` is a PED file's sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `1` means this only in a PED file. | source anvil, dataset ANVIL_1000G_high_coverage_2019 |  | 1,599 | 1,599 | 1,598 | `(donor records)` 1,599 | repository_metadata 1,599 |
 | `phenotypic_sex.pedigree_2` | 1000 Genomes' `pedigree.sex` is a PED file's sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `2` means this only in a PED file. | source anvil, dataset ANVIL_1000G_high_coverage_2019 |  | 1,603 | 1,603 | 1,603 | `(donor records)` 1,603 | repository_metadata 1,603 |
+| `phenotypic_sex.sgdp_m` | SGDP's metadata file writes a genome's sex in `Gender` as M, F or U (not known); scoped to SGDP, since a bare `M` means male only there (#559). Its U has no row and makes no claim. | source sgdp |  | 174 | 174 | 174 | `(donor records)` 174 | external_ground_truth 174 |
+| `phenotypic_sex.sgdp_f` | SGDP's metadata file writes a genome's sex in `Gender` as M, F or U (not known); scoped to SGDP, since a bare `F` means female only there (#559). Its U has no row and makes no claim. | source sgdp |  | 101 | 101 | 101 | `(donor records)` 101 | external_ground_truth 101 |
 | `genetic_ancestry.afr` | 1000 Genomes' superpopulation code, the term itself (HANCESTRO:2000). | general |  | 893 | 893 | 893 | `(donor records)` 893 | repository_metadata 893 |
 | `genetic_ancestry.amr` | 1000 Genomes' superpopulation code, the term itself (HANCESTRO:2001). | general |  | 490 | 490 | 490 | `(donor records)` 490 | repository_metadata 490 |
 | `genetic_ancestry.eas` | 1000 Genomes' superpopulation code, the term itself (HANCESTRO:2002). | general |  | 585 | 585 | 585 | `(donor records)` 585 | repository_metadata 585 |
@@ -277,13 +279,15 @@ Rules that say which samples a file's data comes from, rather than giving a fiel
 | `external_sample_map_rows` | the rows, at reconcile, of a GATK sample-name map held outside AnVIL for the dataset (sources/external_files.yaml): the sample name in each row's first column | 25 | A sample-name map's first column names the samples its import joint-called, as a catalog map's does (sample_map_rows, #668). A map whose every name is recognised names them; one with a name no identifier rule recognises names none. A workaround until AnVIL holds the map (#624). | `src/meta_disco/sample_identity.py` |
 | `family_id_as_written` | a submitter table's family id column (a PED file's `familyid`), on the row naming the donor | 3,210 | A family id is a name for a group of relatives, with no vocabulary to translate it into, so the string is the answer as the source wrote it. It means something only among datasets sharing a numbering: HPRC and HPRC_R2 reuse 1000 Genomes' ids, and two sources writing different ids for one donor are a conflict, never a choice. | `src/meta_disco/reconcile_entities.py` |
 | `anatomical_site_as_written` | the repository's own record of a sample: AnVIL's `anvil_biosample.anatomical_site` | 1,800 | AnVIL's sites are not yet mapped to an ontology (#702), so the string is the answer as AnVIL wrote it; its `Unknown` names no site and makes no claim. A pooled sample's site is AnVIL's join of its members' (`Brain, Heart`), kept as one string until #702 makes the slot a list. | `src/meta_disco/reconcile_entities.py` |
-| `coriell_cell_line_or_dna` | a string matching ^(?:GM\|NA)(\d{5})$; names a sample and its donor (the sample is collected from the donor of the same identifier) | 80,240 | A Coriell repository number names one person's cell line (GM) or the DNA extracted from it (NA); both prefixes with the same number are one sample, written NA as 1000 Genomes writes it. The number also names the person the line came from, as 1000 Genomes' `pedigree` and PRIMED's `subject` key their people by it. | `src/meta_disco/rules/sample_namespaces.yaml` |
+| `coriell_cell_line_or_dna` | a string matching ^(?:GM\|NA)(\d{5})$; names a sample and its donor (the sample is collected from the donor of the same identifier) | 80,258 | A Coriell repository number names one person's cell line (GM) or the DNA extracted from it (NA); both prefixes with the same number are one sample, written NA as 1000 Genomes writes it. The number also names the person the line came from, as 1000 Genomes' `pedigree` and PRIMED's `subject` key their people by it. | `src/meta_disco/rules/sample_namespaces.yaml` |
 | `coriell_hg` | a string matching ^HG\d{5}$; names a sample and its donor (the sample is collected from the donor of the same identifier) | 172,399 | The NHGRI repository's HG numbers at Coriell, as 1000 Genomes, HPRC and T2T name their samples: one number, one person, and that person's cell line. | `src/meta_disco/rules/sample_namespaces.yaml` |
-| `giab_name` | a string matching ^HG00[1-7]$; names a sample and its donor (the sample is collected from the donor of the same identifier) | 1,879 | Genome in a Bottle names its seven reference samples HG001 to HG007, each one person's cell line, and HPRC's `participant` and NIA_CARD's `subject` key those people by the same name. They are the same people as some Coriell numbers, but joining two namespaces is a crosswalk's (#361), so they stay apart. | `src/meta_disco/rules/sample_namespaces.yaml` |
+| `giab_name` | a string matching ^HG00[1-7]$; names a sample and its donor (the sample is collected from the donor of the same identifier) | 1,879 | Genome in a Bottle names its seven reference samples HG001 to HG007, each one person's cell line, and HPRC's `participant` and NIA_CARD's `subject` key those people by the same name. They are the same people as some Coriell numbers, but joining two namespaces by rule is a crosswalk's (#361), so they stay apart; only a source's `same_donor_as` line joins two (#559). | `src/meta_disco/rules/sample_namespaces.yaml` |
 | `sra_sample_accession` | a string matching ^[SED]RS\d+$; names a sample | 0 | An INSDC sample accession (SRA, ENA, DDBJ) names one sample. | `src/meta_disco/rules/sample_namespaces.yaml` |
 | `biosample_accession` | a string matching ^SAM(?:N\|EA\|D)\d+$; names a sample | 0 | An INSDC BioSample accession (NCBI, EBI, DDBJ) names one sample. | `src/meta_disco/rules/sample_namespaces.yaml` |
 | `igvf_donor_accession` | a string matching ^IGVFDO[0-9A-Z]{8}$; names a donor | 183 | The IGVF Data Portal issues one donor accession per donor, unique across the portal; its samples have accessions of their own (IGVFSM…), so the accession names no sample. | `src/meta_disco/rules/sample_namespaces.yaml` |
 | `igvf_sample_accession` | a string matching ^IGVFSM[0-9A-Z]{8}$; names a sample | 1,935 | The IGVF Data Portal issues one sample accession per sample, unique across the portal; its donors have accessions of their own (IGVFDO…), so the accession names no donor. | `src/meta_disco/rules/sample_namespaces.yaml` |
+| `sgdp_id` | a string matching ^(?:S\|B\|T\|BR)_[A-Za-z_]+-\d+$; names a donor | 104 | SGDP names each genome it sequenced once, by its panel, its population and a number; one genome is one person, so the name names that person. The sample a dataset sequenced is named by the dataset (`simons_data_sample_N` in T2T_CHRY), not by this name. | `src/meta_disco/rules/sample_namespaces.yaml` |
+| `hgdp_cell_line` | a string matching ^HGDP\d{5}$; names a donor | 130 | The CEPH-HGDP panel numbers each person's cell line once, at the Fondation Jean Dausset-CEPH, so the number names the person it came from, as a Coriell number does. | `src/meta_disco/rules/sample_namespaces.yaml` |
 | `sgdp_sample_name` | a string matching ^simons_data_sample_\d+$ in ANVIL_T2T_CHRY; names a sample | 32,369 | ANVIL_T2T_CHRY's SGDP sample tables have one row per name, so a name is one sample in that dataset; it is not an identifier anywhere else. | `src/meta_disco/rules/sample_namespaces.yaml` |
 
 ## Caller table
@@ -2558,6 +2562,50 @@ Rationale:
 1000 Genomes' `pedigree.sex` is a PED file's sex column, where 1 is male and 2 female; scoped to the dataset, since a bare `2` means this only in a PED file.
 ```
 
+### `phenotypic_sex.sgdp_m`
+
+mapping, basis mapping, in `src/meta_disco/rules/value_map.yaml`
+
+Match:
+
+```yaml
+{slot: phenotypic_sex, value: "M"}
+```
+
+Declares:
+
+```yaml
+{phenotypic_sex: Male}
+```
+
+Rationale:
+
+```text
+SGDP's metadata file writes a genome's sex in `Gender` as M, F or U (not known); scoped to SGDP, since a bare `M` means male only there (#559). Its U has no row and makes no claim.
+```
+
+### `phenotypic_sex.sgdp_f`
+
+mapping, basis mapping, in `src/meta_disco/rules/value_map.yaml`
+
+Match:
+
+```yaml
+{slot: phenotypic_sex, value: "F"}
+```
+
+Declares:
+
+```yaml
+{phenotypic_sex: Female}
+```
+
+Rationale:
+
+```text
+SGDP's metadata file writes a genome's sex in `Gender` as M, F or U (not known); scoped to SGDP, since a bare `F` means female only there (#559). Its U has no row and makes no claim.
+```
+
 ### `genetic_ancestry.afr`
 
 mapping, basis mapping, in `src/meta_disco/rules/value_map.yaml`