rocco 1.11.0

Multisample Consensus Peak Calling via Convex Optimization

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

What

ROCCO is an efficient algorithm for detection of "consensus peaks" in large datasets with multiple HTS data samples, where an enrichment in read counts/densities is observed in a nontrivial subset of samples.

Input/Output

• Input: Samples' BAM alignments (-i) or a bigWig file, and a reference genome assembly (-g) for chromosome sizes and annotation of blacklisted regions (if available)
• Output: BED file of consensus peak regions (Default format is BED3: chrom,start,end), optional narrowPeak format (--narrowPeak) with additional columns for peak scores, p-values, q-values, etc.

How

ROCCO models consensus peak calling as a constrained optimization problem with an upper-bound on the total proportion of the genome selected as enriched and a fragmentation penalty (TV) to promote spatial consistency in active regions and sparsity elsewhere.

Why

1. Consideration of enrichment and spatial characteristics of open chromatin signals
2. Scaling to large sample sizes (100+)
3. Unsupervised Does not require training data or a heuristically determined set of initial candidate peak regions
4. No rigid thresholds + less manual tuning with respect to the minimum number/width of supporting samples/replicates.
5. Mathematically tractable model permitting worst-case analysis of runtime and performance

Usage

rocco -i <bam files, Consenrich-generated bigWig file> -g <hg38, hg19, mm10, mm39, dm6, ...> -o <output_file.bed>

for example:

rocco -i sample1.bam sample2.bam sample3.bam -g hg38 -o consensus_peaks.bed --narrowPeak

See rocco --help for more options and details.

Paper/Citation

If using ROCCO in your research, please cite the original paper in Bioinformatics (DOI: btad725)

 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

Installation

PyPI (pip)

python -m pip install rocco --upgrade

If lacking administrative control, you may need to append --user to the above.

Build from Source

If preferred, ROCCO can easily be built from source:

• Clone or download this repository

  git clone https://github.com/nolan-h-hamilton/ROCCO.git
  cd ROCCO
  python setup.py sdist bdist_wheel
  python -m pip install -e .