rocco 0.12.0

Robust ATAC-seq Peak Calling for Many Samples via Convex Optimization

ROCCO: [R]obust [O]pen [C]hromatin Detection via [C]onvex [O]ptimization

ROCCO is an optimal consensus peak calling algorithm for open chromatin data that is scalable to large sample sizes.

Features

1. Consideration of enrichment and spatial characteristics of open chromatin signals to capture the full extent of peaks;
2. Mathematically tractable model that permits performance and efficiency guarantees.
3. Efficient for large numbers of samples with an asymptotic time complexity independent of sample size;
4. No arbitrary thresholds on the minimum number of supporting samples/replicates;
5. 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 accepts BAM alignments and a genome sizes file as input.

rocco -i sample1.bam sample2.bam sample3.bam [...] -g hg38.sizes --params hg38

or with a wildcard:

rocco -i *.bam -g hg38.sizes --params hg38

See rocco --help for more details.

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:

• 'ortools': includes the first-order solver, PDLP.
• 'pytest': allows local execution of the Tests workflow.