rocco 0.11.0

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 a scalable consensus peak calling algorithm for open chromatin data on multiple samples, e.g., ATAC-seq.

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

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

Input

ROCCO accepts samples' BAM alignments or BigWig coverage tracks and a genome sizes file as input.

Output

A BED file containing peak regions and scores.

Minimal Example

Run ROCCO on the test data included with this repository (BigWig files).

rocco -i tests/data/*.bw --genome_file tests/test_hg38.sizes

Default output BED file named as rocco_peaks_[timestamp].bed. Because no --chrom_param_file is supplied, the default genome-wide budget (0.035), gamma (1.0), etc. are used for all chromosomes. See the Documentation for additional examples and details.

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.