Bioinformatic detection of RiPP Recognition Elements (RRE) in protein sequences.
RREFinder is a specialized tool for identifying RRE domains, which are crucial for the biosynthesis of many Ribosomally synthesized and Post-translationally modified Peptides (RiPPs). It supports both high-speed precision scanning using HMMER and sensitive exploratory discovery using HH-suite.
- Web Interface: A modern, responsive web dashboard for easy analysis and visualization.
- Batch Processing: Upload and analyze multiple GenBank (.gbk) or FASTA files simultaneously.
- Domain Architecture Visualization: Interactive diagrams showing the full domain profile of proteins containing RRE hits.
- Session History: Persistent storage of past analyses in your browser's local storage.
- One-Click Sample: Quickly test the tool with the included Nisin sample data.
- Clone the repository:
git clone https://github.com/QuercusCode/RREFinder-based-analysis.git cd RREFinder-based-analysis - Create the environment:
conda env create -f RREfinder.yml conda activate RREfinder
- (Optional) Setup Exploratory Mode (HHSuite):
python setup_RRE_exploratory.py
If you have Docker installed, you can build and run RREFinder without worrying about dependencies:
- Build the image:
docker build -t rrefinder . - Run the container:
docker run -p 5000:5000 rrefinder
This will automatically set up the environment, binaries, and download the necessary databases.
- Activate the environment:
conda activate RREfinder - Start the Flask server:
python app.py
- Open your browser and navigate to
http://127.0.0.1:5000
RREFinder can also be used directly from the terminal:
# Analyze a GenBank file in precision mode
python RRE.py -i my_sequence.gbk -m precision my_project
# Analyze a FASTA file
python RRE.py -i my_proteins.fasta -t fasta my_projectOutput results are stored in the output/ directory.
If you use RREFinder in your research, please cite:
- RREFinder: Kloosterman AM, Cimermancic P, Elsayed SS, et al. (2020). Expansion of GenBank and the RRE-Finder Tool for the Discovery of Novel RiPPs. (See paper for full citation).
- HMMER (Precision Mode): Eddy SR. (2011). Accelerated Profile HMM Searches. PLoS Computational Biology, 7(10), e1002195.
- HH-suite (Exploratory Mode): Steinegger M, et al. (2019). HH-suite3 for homology detection and structure prediction. BMC Bioinformatics, 20(1), 473.
This project is licensed under the terms included in the LICENSE file.