Robust ATAC-seq Peak Calling for Many Samples via Convex Optimization
Project description
ROCCO: [R]obust [O]pen [C]hromatin Detection via [C]onvex [O]ptimization
ROCCO is a scalable consensus peak calling algorithm for open chromatin count signals in large sample sizes.
Features
- Consideration of enrichment and spatial characteristics of open chromatin signals to capture the full extent of peaks;
- Mathematically tractable model that permits performance and efficiency guarantees.
- Efficient for large numbers of samples with an asymptotic time complexity independent of sample size;
- No arbitrary thresholds on the minimum number of supporting samples/replicates;
- No required training data or a heuristically determined set of initial candidate peak regions;
Paper/Citation
If using ROCCO in your research, please cite the original paper in Bioinformatics
Nolan H Hamilton, Terrence S Furey, ROCCO: a robust method for detection of open chromatin via convex optimization,
Bioinformatics, Volume 39, Issue 12, December 2023
DOI: 10.1093/bioinformatics/btad725
Documentation
Documentation and example usage are available at https://nolan-h-hamilton.github.io/ROCCO/
Installation
pip install rocco
Demo
https://github.com/nolan-h-hamilton/ROCCO/tree/main/docs/demo/demo.ipynb
Input
ROCCO requires BAM alignments or BigWig coverage tracks and a genome sizes file as input.
rocco -i sample1.bam sample2.bam sample3.bam [...] --genome_file hg38.sizes --chrom_param_file hg38
or with a wildcard:
rocco -i *.bam --genome_file hg38.sizes --chrom_param_file hg38
BigWig input:
rocco -i *.bw --genome_file hg38.sizes --chrom_param_file hg38
Output
A BED file containing peak regions and scores.
Testing ROCCO
cd tests
pytest -v -rPA -l -k "regular" test_rocco.py
Version History
Previous releases can be found at https://github.com/nolan-h-hamilton/ROCCO/tags
Additional dependencies for optional features:
- 'mosek': Commercial grade solver. Users can instantly obtain a free academic license or generous trial commericial license at https://www.mosek.com/products/academic-licenses/.
- 'ortools': includes the first-order solver, PDLP.
- 'pytest': allows local execution of the Tests workflow.
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