diff --git a/docs/cost_data/cost_curve_1000bp.json b/docs/cost_data/cost_curve_1000bp.json
index 56d93ca..5d0ab7d 100644
--- a/docs/cost_data/cost_curve_1000bp.json
+++ b/docs/cost_data/cost_curve_1000bp.json
@@ -1,1793 +1,2191 @@
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diff --git a/docs/cost_data/cost_curve_100bp.json b/docs/cost_data/cost_curve_100bp.json
index 11a9b9b..177b429 100644
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diff --git a/docs/cost_data/cost_curve_1050bp.json b/docs/cost_data/cost_curve_1050bp.json
index 1728550..fd35aec 100644
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diff --git a/docs/cost_data/cost_curve_1100bp.json b/docs/cost_data/cost_curve_1100bp.json
index e0c301c..995d88d 100644
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diff --git a/docs/cost_data/cost_curve_1150bp.json b/docs/cost_data/cost_curve_1150bp.json
index 569261a..3122171 100644
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diff --git a/docs/cost_data/cost_curve_1200bp.json b/docs/cost_data/cost_curve_1200bp.json
index 1c7b596..d69284a 100644
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diff --git a/docs/cost_data/cost_curve_1250bp.json b/docs/cost_data/cost_curve_1250bp.json
index 8b0bc70..df916fb 100644
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diff --git a/docs/cost_data/cost_curve_1300bp.json b/docs/cost_data/cost_curve_1300bp.json
index 250688f..1760fc6 100644
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diff --git a/docs/cost_data/cost_curve_1350bp.json b/docs/cost_data/cost_curve_1350bp.json
index 7d51b91..80a0df3 100644
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diff --git a/docs/cost_data/cost_curve_1400bp.json b/docs/cost_data/cost_curve_1400bp.json
index 72715c2..de70f8a 100644
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diff --git a/docs/cost_data/cost_curve_1450bp.json b/docs/cost_data/cost_curve_1450bp.json
index 31ab5d3..8a39acf 100644
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diff --git a/docs/cost_data/cost_curve_1500bp.json b/docs/cost_data/cost_curve_1500bp.json
index cc7c759..a000f08 100644
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diff --git a/docs/cost_data/cost_curve_150bp.json b/docs/cost_data/cost_curve_150bp.json
index 7279a28..46d741e 100644
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diff --git a/docs/cost_data/cost_curve_200bp.json b/docs/cost_data/cost_curve_200bp.json
index df248f6..a0c610d 100644
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diff --git a/docs/cost_data/cost_curve_250bp.json b/docs/cost_data/cost_curve_250bp.json
index 71c6f85..5420d8b 100644
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diff --git a/docs/cost_data/cost_curve_300bp.json b/docs/cost_data/cost_curve_300bp.json
index f96cc26..81b0015 100644
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diff --git a/docs/cost_data/cost_curve_350bp.json b/docs/cost_data/cost_curve_350bp.json
index e9545be..a17ebc9 100644
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diff --git a/docs/cost_data/cost_curve_400bp.json b/docs/cost_data/cost_curve_400bp.json
index d560247..3405d9f 100644
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diff --git a/docs/cost_data/cost_curve_450bp.json b/docs/cost_data/cost_curve_450bp.json
index c646990..8a48cdc 100644
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diff --git a/docs/cost_data/cost_curve_500bp.json b/docs/cost_data/cost_curve_500bp.json
index 0684f21..a3b4130 100644
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diff --git a/docs/cost_data/cost_curve_550bp.json b/docs/cost_data/cost_curve_550bp.json
index 42ea871..1f40edf 100644
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diff --git a/docs/cost_data/cost_curve_600bp.json b/docs/cost_data/cost_curve_600bp.json
index 3367eb3..c6cd5a7 100644
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diff --git a/docs/cost_data/cost_curve_650bp.json b/docs/cost_data/cost_curve_650bp.json
index 7f6fef6..e24e4d8 100644
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diff --git a/docs/cost_data/cost_curve_700bp.json b/docs/cost_data/cost_curve_700bp.json
index bfaabbd..77c170d 100644
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diff --git a/docs/cost_data/cost_curve_750bp.json b/docs/cost_data/cost_curve_750bp.json
index b46714c..bf21f27 100644
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diff --git a/docs/cost_data/cost_curve_800bp.json b/docs/cost_data/cost_curve_800bp.json
index aaf54f3..11588f2 100644
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diff --git a/docs/cost_data/cost_curve_850bp.json b/docs/cost_data/cost_curve_850bp.json
index e514097..38eaa65 100644
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diff --git a/docs/cost_data/cost_curve_900bp.json b/docs/cost_data/cost_curve_900bp.json
index a7f7cd2..57806e4 100644
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diff --git a/docs/cost_data/cost_curve_950bp.json b/docs/cost_data/cost_curve_950bp.json
index 5bcf6f7..5d48921 100644
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+ "sdm_cost_max": 100718.7,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4275,
- "usortm_cost": 6274.0,
+ "usortm_cost": 48020.06,
"traditional_cost": 337581.63,
"traditional_min": 337581.63,
"traditional_max": 418806.63,
"sdm_cost": 89004.1,
- "sdm_cost_max": 101107.1
+ "sdm_cost_max": 101107.1,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4300,
- "usortm_cost": 6277.2,
+ "usortm_cost": 48134.49,
"traditional_cost": 339552.87,
"traditional_min": 339552.87,
"traditional_max": 421252.87,
"sdm_cost": 89831.0,
- "sdm_cost_max": 102181.0
+ "sdm_cost_max": 102181.0,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4325,
- "usortm_cost": 6391.63,
+ "usortm_cost": 48137.69,
"traditional_cost": 341524.11,
"traditional_min": 341524.11,
"traditional_max": 423699.11,
"sdm_cost": 90233.8,
- "sdm_cost_max": 102583.8
+ "sdm_cost_max": 102583.8,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4350,
- "usortm_cost": 6394.83,
+ "usortm_cost": 48140.89,
"traditional_cost": 343495.35,
"traditional_min": 343495.35,
"traditional_max": 426145.35,
"sdm_cost": 90621.7,
- "sdm_cost_max": 102971.7
+ "sdm_cost_max": 102971.7,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4375,
- "usortm_cost": 6398.03,
+ "usortm_cost": 48255.32,
"traditional_cost": 345466.59,
"traditional_min": 345466.59,
"traditional_max": 428591.59,
"sdm_cost": 91449.1,
- "sdm_cost_max": 104046.1
+ "sdm_cost_max": 104046.1,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4400,
- "usortm_cost": 6401.23,
+ "usortm_cost": 48258.52,
"traditional_cost": 347437.82,
"traditional_min": 347437.82,
"traditional_max": 431037.82,
"sdm_cost": 91837.0,
- "sdm_cost_max": 104434.0
+ "sdm_cost_max": 104434.0,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4425,
- "usortm_cost": 6515.66,
+ "usortm_cost": 48372.95,
"traditional_cost": 349409.06,
"traditional_min": 349409.06,
"traditional_max": 433484.06,
"sdm_cost": 92239.8,
- "sdm_cost_max": 104836.8
+ "sdm_cost_max": 104836.8,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4450,
- "usortm_cost": 6518.86,
+ "usortm_cost": 48376.15,
"traditional_cost": 351380.3,
"traditional_min": 351380.3,
"traditional_max": 435930.3,
"sdm_cost": 92627.7,
- "sdm_cost_max": 105224.7
+ "sdm_cost_max": 105224.7,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4475,
- "usortm_cost": 6522.06,
+ "usortm_cost": 48379.35,
"traditional_cost": 353351.54,
"traditional_min": 353351.54,
"traditional_max": 438376.54,
"sdm_cost": 93455.1,
- "sdm_cost_max": 106299.1
+ "sdm_cost_max": 106299.1,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4500,
- "usortm_cost": 6525.26,
+ "usortm_cost": 48493.78,
"traditional_cost": 355322.77,
"traditional_min": 355322.77,
"traditional_max": 440822.77,
"sdm_cost": 93843.0,
- "sdm_cost_max": 106687.0
+ "sdm_cost_max": 106687.0,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4525,
- "usortm_cost": 6639.69,
+ "usortm_cost": 48496.98,
"traditional_cost": 357294.01,
"traditional_min": 357294.01,
"traditional_max": 443269.01,
"sdm_cost": 94245.8,
- "sdm_cost_max": 107089.8
+ "sdm_cost_max": 107089.8,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4550,
- "usortm_cost": 6642.89,
+ "usortm_cost": 48611.41,
"traditional_cost": 359265.25,
"traditional_min": 359265.25,
"traditional_max": 445715.25,
"sdm_cost": 95072.7,
- "sdm_cost_max": 108163.7
+ "sdm_cost_max": 108163.7,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4575,
- "usortm_cost": 6646.09,
+ "usortm_cost": 48614.61,
"traditional_cost": 361236.49,
"traditional_min": 361236.49,
"traditional_max": 448161.49,
"sdm_cost": 95461.1,
- "sdm_cost_max": 108552.1
+ "sdm_cost_max": 108552.1,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4600,
- "usortm_cost": 6649.29,
+ "usortm_cost": 48617.81,
"traditional_cost": 363207.72,
"traditional_min": 363207.72,
"traditional_max": 450607.72,
"sdm_cost": 95849.0,
- "sdm_cost_max": 108940.0
+ "sdm_cost_max": 108940.0,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4625,
- "usortm_cost": 6771.56,
+ "usortm_cost": 48740.08,
"traditional_cost": 365178.96,
"traditional_min": 365178.96,
"traditional_max": 453053.96,
"sdm_cost": 96251.8,
- "sdm_cost_max": 109342.8
+ "sdm_cost_max": 109342.8,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4650,
- "usortm_cost": 6774.76,
+ "usortm_cost": 48743.28,
"traditional_cost": 367150.2,
"traditional_min": 367150.2,
"traditional_max": 455500.2,
"sdm_cost": 97078.7,
- "sdm_cost_max": 110416.7
+ "sdm_cost_max": 110416.7,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4675,
- "usortm_cost": 6777.96,
+ "usortm_cost": 48857.71,
"traditional_cost": 369121.44,
"traditional_min": 369121.44,
"traditional_max": 457946.44,
"sdm_cost": 97467.1,
- "sdm_cost_max": 110805.1
+ "sdm_cost_max": 110805.1,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4700,
- "usortm_cost": 6781.16,
+ "usortm_cost": 48860.91,
"traditional_cost": 371092.67,
"traditional_min": 371092.67,
"traditional_max": 460392.67,
"sdm_cost": 97855.0,
- "sdm_cost_max": 111193.0
+ "sdm_cost_max": 111193.0,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4725,
- "usortm_cost": 6895.59,
+ "usortm_cost": 48864.11,
"traditional_cost": 373063.91,
"traditional_min": 373063.91,
"traditional_max": 462838.91,
"sdm_cost": 98696.8,
- "sdm_cost_max": 112281.8
+ "sdm_cost_max": 112281.8,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4750,
- "usortm_cost": 6898.79,
+ "usortm_cost": 48978.54,
"traditional_cost": 375035.15,
"traditional_min": 375035.15,
"traditional_max": 465285.15,
"sdm_cost": 99084.7,
- "sdm_cost_max": 112669.7
+ "sdm_cost_max": 112669.7,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4775,
- "usortm_cost": 6901.99,
+ "usortm_cost": 48981.74,
"traditional_cost": 377006.39,
"traditional_min": 377006.39,
"traditional_max": 467731.39,
"sdm_cost": 99473.1,
- "sdm_cost_max": 113058.1
+ "sdm_cost_max": 113058.1,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4800,
- "usortm_cost": 7016.42,
+ "usortm_cost": 49096.17,
"traditional_cost": 378977.63,
"traditional_min": 378977.63,
"traditional_max": 470177.62,
"sdm_cost": 99861.0,
- "sdm_cost_max": 113446.0
+ "sdm_cost_max": 113446.0,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4825,
- "usortm_cost": 7019.62,
+ "usortm_cost": 49099.37,
"traditional_cost": 380948.86,
"traditional_min": 380948.86,
"traditional_max": 472623.86,
"sdm_cost": 100702.8,
- "sdm_cost_max": 114534.8
+ "sdm_cost_max": 114534.8,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4850,
- "usortm_cost": 7022.82,
+ "usortm_cost": 49102.57,
"traditional_cost": 382920.1,
"traditional_min": 382920.1,
"traditional_max": 475070.1,
"sdm_cost": 101090.7,
- "sdm_cost_max": 114922.7
+ "sdm_cost_max": 114922.7,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4875,
- "usortm_cost": 7026.02,
+ "usortm_cost": 49217.0,
"traditional_cost": 384891.34,
"traditional_min": 384891.34,
"traditional_max": 477516.34,
"sdm_cost": 101479.1,
- "sdm_cost_max": 115311.1
+ "sdm_cost_max": 115311.1,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4900,
- "usortm_cost": 7140.45,
+ "usortm_cost": 49220.2,
"traditional_cost": 386862.58,
"traditional_min": 386862.58,
"traditional_max": 479962.58,
"sdm_cost": 102306.0,
- "sdm_cost_max": 116385.0
+ "sdm_cost_max": 116385.0,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4925,
- "usortm_cost": 7143.65,
+ "usortm_cost": 49223.4,
"traditional_cost": 388833.81,
"traditional_min": 388833.81,
"traditional_max": 482408.81,
"sdm_cost": 102708.8,
- "sdm_cost_max": 116787.8
+ "sdm_cost_max": 116787.8,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4950,
- "usortm_cost": 7146.85,
+ "usortm_cost": 49337.83,
"traditional_cost": 390805.05,
"traditional_min": 390805.05,
"traditional_max": 484855.05,
"sdm_cost": 103096.7,
- "sdm_cost_max": 117175.7
+ "sdm_cost_max": 117175.7,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 4975,
- "usortm_cost": 7150.05,
+ "usortm_cost": 49341.03,
"traditional_cost": 392776.29,
"traditional_min": 392776.29,
"traditional_max": 487301.29,
"sdm_cost": 103924.1,
- "sdm_cost_max": 118250.1
+ "sdm_cost_max": 118250.1,
+ "fold_sampling": 6.0,
+ "skew": 1.5
},
{
"library_size": 5000,
- "usortm_cost": 7272.32,
+ "usortm_cost": 49463.3,
"traditional_cost": 394747.53,
"traditional_min": 394747.53,
"traditional_max": 489747.53,
"sdm_cost": 104312.0,
- "sdm_cost_max": 118638.0
+ "sdm_cost_max": 118638.0,
+ "fold_sampling": 6.0,
+ "skew": 1.5
}
]
\ No newline at end of file
diff --git a/docs/cost_data/default_costs.json b/docs/cost_data/default_costs.json
index 7ca4d24..87d86a4 100644
--- a/docs/cost_data/default_costs.json
+++ b/docs/cost_data/default_costs.json
@@ -2,11 +2,11 @@
"usortm": {
"synthesis": 1374.02,
"cloning": 53.6,
- "sorting": 202.5,
- "barcoding": 488.65,
+ "sorting": 283.5,
+ "barcoding": 1075.03,
"sequencing": 500,
"hitpicking": 79.68,
- "total": 2698.45
+ "total": 3365.83
},
"traditional": {
"synthesis": 10500.0,
@@ -23,19 +23,20 @@
"sequencing": 500,
"total": 10969.0
},
- "savings": 6.36,
- "per_variant_usortm": 5.4,
+ "savings": 5.1,
+ "per_variant_usortm": 6.73,
"per_variant_traditional": 34.35,
"per_variant_sdm": 21.94,
- "wells": 2000,
- "plates": 6,
+ "wells": 4250.0,
+ "plates": 12.0,
+ "fold_sampling": 8.5,
"timeline": {
"assembly_days": 2,
"sort_days": 1,
- "barcode_days": 1,
+ "barcode_days": 2,
"seq_days": 3,
"demux_days": 1,
- "total_days": 8,
+ "total_days": 9,
"timeline": [
{
"step": "Library Assembly",
@@ -52,19 +53,19 @@
{
"step": "PCR Barcoding",
"start": 3,
- "end": 4,
- "days": 1
+ "end": 5,
+ "days": 2
},
{
"step": "Sequencing",
- "start": 4,
- "end": 7,
+ "start": 5,
+ "end": 8,
"days": 3
},
{
"step": "Demux & Analysis",
- "start": 7,
- "end": 8,
+ "start": 8,
+ "end": 9,
"days": 1
}
]
diff --git a/docs/generate_cost_data.py b/docs/generate_cost_data.py
index 74890f6..507c50f 100644
--- a/docs/generate_cost_data.py
+++ b/docs/generate_cost_data.py
@@ -28,17 +28,55 @@
sdm_transformation_cost,
sdm_consumables_cost,
)
+from usortm.costs.method_loader import load_all_methods, compute_cost
+from usortm.simulate.sortm import find_fold_sampling
from usortm.costs.time_functions import calculate_total_timeline
+_methods_cache = None
-def calculate_usortm_cost(lib_size, seq_length):
- """Calculate total uSort-M cost with 4x fold sampling."""
- foldSampling = 4
- wells = lib_size * foldSampling
+def _get_methods():
+ global _methods_cache
+ if _methods_cache is None:
+ _methods_cache = load_all_methods()
+ return _methods_cache
- cost = usortm_synthesis_cost(lib_size, seq_length)
+
+def _gene_pools_synthesis_cost(lib_size, seq_length):
+ """Twist Gene Pools synthesis cost for sequences > 350 bp."""
+ m = _get_methods().get("twist_gene_pools")
+ if m is None:
+ return 0
+ cost = compute_cost(m, lib_size, seq_length)
+ return cost if cost is not None else 0
+
+
+def _compute_fold_sampling(skew, ref_lib_size=500):
+ """Simulate fold-sampling required for 90% coverage at a given skew."""
+ fold, _ = find_fold_sampling(
+ target_coverage=0.90,
+ lib_size=ref_lib_size,
+ skew=skew,
+ n_sims=100,
+ seed=42,
+ )
+ return fold
+
+
+def calculate_usortm_cost(lib_size, seq_length, fold_sampling):
+ """Calculate total uSort-M cost using the given fold-sampling.
+
+ Uses Twist Oligo Pools synthesis for <=350 bp and Twist Gene Pools for longer sequences.
+ """
+ wells = lib_size * fold_sampling
+
+ if seq_length <= 350:
+ synthesis = usortm_synthesis_cost(lib_size, seq_length)
+ else:
+ synthesis = _gene_pools_synthesis_cost(lib_size, seq_length)
+
+ cost = synthesis
cost += usortm_cloning_cost(lib_size)
- cost += usortm_sorting_cost(lib_size, fold_sampling=foldSampling)
+ cost += usortm_sorting_cost(lib_size, fold_sampling=fold_sampling)
cost += usortm_barcoding_cost(n_wells=wells)
cost += usortm_sequencing_cost(n_wells=wells, seq_length=seq_length)
cost += usortm_hitpicking_cost(lib_size, seq_length)
@@ -71,12 +109,12 @@ def calculate_traditional_range(lib_size, seq_length):
return min_cost + common, max_cost + common
-def generate_cost_curves(seq_length, max_lib_size=5000, step=25):
+def generate_cost_curves(seq_length, fold_sampling, skew, max_lib_size=5000, step=25):
"""Generate cost curves for plotting."""
data = []
for lib_size in range(50, max_lib_size + 1, step):
- usortm_cost = calculate_usortm_cost(lib_size, seq_length)
+ usortm_cost = calculate_usortm_cost(lib_size, seq_length, fold_sampling)
trad_cost = calculate_traditional_cost(lib_size, seq_length)
trad_min, trad_max = calculate_traditional_range(lib_size, seq_length)
@@ -91,23 +129,28 @@ def generate_cost_curves(seq_length, max_lib_size=5000, step=25):
'traditional_max': round(trad_max, 2),
'sdm_cost': round(sdm_cost, 2),
'sdm_cost_max': round(sdm_cost_max, 2),
+ 'fold_sampling': round(fold_sampling, 1),
+ 'skew': skew,
})
return data
-def generate_detailed_costs(lib_size, seq_length):
+def generate_detailed_costs(lib_size, seq_length, fold_sampling):
"""Generate detailed breakdown of costs for a specific configuration."""
- # Calculate derived values first (needed for cost functions)
- foldSampling = 4
- wells = lib_size * foldSampling
+ wells = lib_size * fold_sampling
plates = max(1, -(-wells // 384)) # Ceiling division
# uSort-M breakdown
+ if seq_length <= 350:
+ synthesis_cost = usortm_synthesis_cost(lib_size, seq_length)
+ else:
+ synthesis_cost = _gene_pools_synthesis_cost(lib_size, seq_length)
+
usortm_breakdown = {
- 'synthesis': usortm_synthesis_cost(lib_size, seq_length),
+ 'synthesis': synthesis_cost,
'cloning': usortm_cloning_cost(lib_size),
- 'sorting': usortm_sorting_cost(lib_size, fold_sampling=foldSampling),
+ 'sorting': usortm_sorting_cost(lib_size, fold_sampling=fold_sampling),
'barcoding': usortm_barcoding_cost(n_wells=wells),
'sequencing': usortm_sequencing_cost(n_wells=wells, seq_length=seq_length),
'hitpicking': usortm_hitpicking_cost(lib_size, seq_length),
@@ -124,7 +167,7 @@ def generate_detailed_costs(lib_size, seq_length):
trad_breakdown['total'] = sum(trad_breakdown.values())
# Calculate timeline
- timeline = calculate_total_timeline(lib_size, seq_length, fold_sampling=foldSampling)
+ timeline = calculate_total_timeline(lib_size, seq_length, fold_sampling=fold_sampling)
# SDM breakdown
sdm_breakdown = {
@@ -146,6 +189,7 @@ def generate_detailed_costs(lib_size, seq_length):
'per_variant_sdm': round(sdm_breakdown['total'] / lib_size, 2),
'wells': wells,
'plates': plates,
+ 'fold_sampling': round(fold_sampling, 1),
'timeline': timeline,
}
@@ -155,11 +199,30 @@ def main():
output_dir = os.path.join(os.path.dirname(__file__), 'cost_data')
os.makedirs(output_dir, exist_ok=True)
+ methods = _get_methods()
+
+ # Simulate fold-sampling required for 90% coverage for each synthesis method.
+ # Twist Oligo Pools (<=350 bp) and Twist Gene Pools (>350 bp) have different skews.
+ m_short = methods.get("twist_oligo_pools")
+ m_long = methods.get("twist_gene_pools")
+ skew_short = m_short.skew_q90_q10 if m_short else 4.0
+ skew_long = m_long.skew_q90_q10 if m_long else 1.5
+
+ print(f"Simulating fold-sampling for short libraries (skew={skew_short:.1f}×, Twist Oligo Pools)...")
+ fold_short = _compute_fold_sampling(skew_short)
+ print(f" → {fold_short:.1f}× fold-sampling for 90% coverage")
+
+ print(f"Simulating fold-sampling for long libraries (skew={skew_long:.1f}×, Twist Gene Pools)...")
+ fold_long = _compute_fold_sampling(skew_long)
+ print(f" → {fold_long:.1f}× fold-sampling for 90% coverage")
+
# Generate curves for all sequence lengths from 100-1500 bp in 50 bp steps
seq_lengths = list(range(100, 1501, 50))
for seq_len in seq_lengths:
- curve_data = generate_cost_curves(seq_len)
+ fold = fold_short if seq_len <= 350 else fold_long
+ skew = skew_short if seq_len <= 350 else skew_long
+ curve_data = generate_cost_curves(seq_len, fold, skew)
filename = f'cost_curve_{seq_len}bp.json'
with open(os.path.join(output_dir, filename), 'w') as f:
@@ -167,8 +230,8 @@ def main():
print(f"✓ Generated {filename}")
- # Generate detailed costs for default configuration
- default_config = generate_detailed_costs(500, 300)
+ # Generate detailed costs for default configuration (300 bp, short library)
+ default_config = generate_detailed_costs(500, 300, fold_short)
with open(os.path.join(output_dir, 'default_costs.json'), 'w') as f:
json.dump(default_config, f, indent=2)
diff --git a/docs/index.html b/docs/index.html
index c3305b9..65a7a37 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -858,7 +858,11 @@ Citation
container.innerHTML = "";
container.appendChild(plot);
- return currentCosts;
+ // Read simulation parameters from the first data point (constant per seq_length)
+ const foldSampling = costData[0]?.fold_sampling ?? 4;
+ const skew = costData[0]?.skew ?? 4;
+
+ return { ...currentCosts, fold_sampling: foldSampling, skew };
}
async function updateUI() {
@@ -895,13 +899,17 @@ Citation
document.getElementById("cost-per-variant-usortm").textContent = `$${cpvUsortm.toFixed(2)}`;
document.getElementById("cost-per-variant-comparison").textContent = `vs $${cpvComp.toFixed(2)} ${isSDM ? 'SDM' : 'direct'}`;
- // Calculate plates for timeline
- const foldSampling = 4;
+ // Update simulation parameter display
+ document.getElementById("param-fold-sampling").textContent = `${costs.fold_sampling}×`;
+ document.getElementById("param-skew").textContent = `${costs.skew}× (Q90/Q10)`;
+
+ // Calculate plates for timeline using the simulated fold-sampling
+ const foldSampling = costs.fold_sampling ?? 4;
const wells = libSize * foldSampling;
const plates = Math.max(1, Math.ceil(wells / 384));
// Calculate and update total days from timeline
- const totalDays = updateTimeline(plates, libSize, seqLen);
+ const totalDays = updateTimeline(plates, libSize, seqLen, foldSampling);
document.getElementById("total-days").textContent = totalDays;
// Update synthesis method recommendation
@@ -934,7 +942,7 @@ Citation
} else if (seqLen <= 500) {
method = "Twist Multiplex Gene Fragments (300-500 bp, $35/fragment)";
} else {
- method = "Instance Bio or custom synthesis (>500 bp, note: may have high skew)";
+ method = "Twist Gene Pools (300–1800 bp)";
}
}
@@ -952,8 +960,10 @@ Citation
// Update uSort-M assumption based on length
if (seqLen <= 300) {
usortmAssumption.innerHTML = `pool of ${seqLen} bp oligos from commercial vendor`;
+ } else if (seqLen <= 350) {
+ usortmAssumption.innerHTML = `pool of ${seqLen} bp oligos from commercial vendor`;
} else {
- usortmAssumption.innerHTML = `pool of 30 bp inserts from commercial vendor assembled into ${seqLen} bp gene to generate substitution library`;
+ usortmAssumption.innerHTML = `pooled ${seqLen} bp gene synthesis via Twist Gene Pools`;
}
// Update comparison assumption based on mode
@@ -967,9 +977,9 @@ Citation
}
}
-function updateTimeline(plates, libSize, seqLen) {
+function updateTimeline(plates, libSize, seqLen, foldSampling) {
// Calculate timing
- const foldSampling = 4;
+ foldSampling = foldSampling ?? 4;
const wells = libSize * foldSampling;
const sortMins = plates * 8 + 30; // 8 min/plate + 30 min setup (per protocol)
const sortHours = sortMins / 60;
diff --git a/src/usortm/costs/methods/twist_cloned_mgf.toml b/src/usortm/costs/methods/twist_cloned_mgf.toml
index 76c4563..41aac83 100644
--- a/src/usortm/costs/methods/twist_cloned_mgf.toml
+++ b/src/usortm/costs/methods/twist_cloned_mgf.toml
@@ -13,7 +13,7 @@ library_size_max = 12000
[simulation]
error_rate = [5e-5, 2e-4]
-skew_q90_q10 = 3.0 # working estimate; measured range is 1.5–5.0
+skew_q90_q10 = 2.0 # working estimate; measured range is 1.5–5.0
[pricing]
model = "lookup"
diff --git a/src/usortm/costs/methods/twist_gene_pools.toml b/src/usortm/costs/methods/twist_gene_pools.toml
index e2cc1ff..371e608 100644
--- a/src/usortm/costs/methods/twist_gene_pools.toml
+++ b/src/usortm/costs/methods/twist_gene_pools.toml
@@ -13,7 +13,7 @@ library_size_max = 696000
[simulation]
error_rate = [1e-4, 3e-4]
-skew_q90_q10 = 6.0 # 95th/5th <= 10 implies 90th/10th <= 6
+skew_q90_q10 = 1.5 # 95th/5th <= 10 implies 90th/10th <= 6
[pricing]
model = "lookup"
diff --git a/src/usortm/costs/methods/twist_multiplexed_gene_fragments.toml b/src/usortm/costs/methods/twist_multiplexed_gene_fragments.toml
index 05f22e8..16c33aa 100644
--- a/src/usortm/costs/methods/twist_multiplexed_gene_fragments.toml
+++ b/src/usortm/costs/methods/twist_multiplexed_gene_fragments.toml
@@ -13,7 +13,7 @@ library_size_max = 696000
[simulation]
error_rate = [5e-5, 3e-4]
-skew_q90_q10 = 5.0 # TODO: confirm working estimate (no vendor data available)
+skew_q90_q10 = 2.0 # TODO: confirm working estimate (no vendor data available)
[pricing]
model = "lookup"