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

Tests PyPI - Version

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) and a reference genome assembly (-g) for chromosome sizes
  • Output: BED file of consensus peak regions (Default format is BED3: chrom,start,end). narrowPeak and gappedPeak outputs are supported for BAM inputs. Use --peak_mode both.

How

ROCCO models consensus peak calling as an optimization problem: select the most enriched genomic regions as peaks while controlling total genomic coverage and discouraging fragmented calls.

  • Enrichment is measured by a read density score in the selected genomic regions
  • Budget proportions calibrate selection penalties that control total genomic coverage
  • Fragmentation is controlled via a total variation penalty, multiplied by a penalty parameter $\gamma$ ($\gamma \sum_i |x_{i+1} - x_i|$).

Why

  1. Consideration of enrichment magnitude 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. Less rigid thresholds with respect to the minimum number/width of supporting samples/replicates.
  5. Mathematically tractable model permitting worst-case analysis of runtime and performance

Basic Usage

Run rocco --help for the complete command-line reference.

ROCCO accepts either one or more BAM files, separated by spaces.

rocco -i <BAM> [<BAM> ...] \
  -g <GENOME> \
  -o <OUTPUT.bed>

Genome assemblies hg38, hg19, mm10, mm39, and dm6 can be used with -g, to supply a built-in chromosome sizes file (canonical chromosomes only) and effective genome size.

If using ROCCO with another assembly, just provide the chromosome sizes file and effective genome size as described explicitly:

  • -s, --chrom_sizes_file <FILE>: chromosome names and sizes.
  • --effective_genome_size <BASES>: effective genome size, only required if using RPGC normalization.

For example:

rocco \
  -i sample1.bam sample2.bam sample3.bam \
  -s assemblyName.chrom.sizes \
  --effective_genome_size <BASES> \
  -o consensus_peaks.bed

When troubleshooting, consider processing a subset of the genome for faster evaluation:

rocco \
  -i sample1.bam sample2.bam sample3.bam \
  -g hg38 \
  -o consensus_peaks.bed \
  --chroms chr11 chr20 chr21 chr22 # at least four chroms

Example

Call consensus peaks from three BAM files and generate both narrowPeak and gappedPeak outputs:

rocco \
  -i sample1.bam sample2.bam sample3.bam \
  -g hg38 \
  -o consensus_peaks.bed \
  --peak_mode both

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 .

Project links

Data verified by PyPI on Sep 3, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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Latest release

1 maintainer

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Avatar for nolan-h-hamilton from gravatar.com nolan-h-hamilton

Credits

Author: Nolan Holt Hamilton

GitHub Statistics

Data verified by PyPI on Sep 3, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT

Requires

Python <4, >=3.10

Provides Extra

pytest bigwig

Tags

genomics functional genomics epigenomics epigenetics peak calling chromatin accessibility chromatin consensus peak DNase ATAC ChIP-seq

Classifiers

Report project as malware

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This release

1.14.0 This release

31 files

1.13.0

31 files

1.12.0

31 files

1.11.0

31 files

1.10

31 files

1.7.0

2 files

1.6.3

2 files

1.6.1

2 files

1.6.0.post1

2 files

1.6.0

2 files

1.4.1

2 files

1.3.4

2 files

1.2.0

2 files

1.1.1

2 files

1.1.0

2 files

1.0.3

2 files

1.0.2

2 files

1.0.1

2 files

Pre-release

1.0.0rc1

2 files

0.13.0

2 files

0.12.1

2 files

0.12.0

2 files

0.11.8

2 files

0.11.7

2 files

0.11.6.1

2 files

0.11.6

2 files

0.11.4

2 files

0.11.1

2 files

0.11.0

2 files

0.9.5

2 files

0.9.4

2 files

0.9.3

2 files

0.9.1

2 files

0.9.0

2 files

0.8.4

2 files

0.8.3.1

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0.8.3

2 files

0.8.2

2 files

0.8.0

2 files

0.6.15

2 files

0.6.14

2 files

0.6.13

2 files

0.6.12

2 files

0.6.10

2 files

0.6.7.1

2 files

0.6.7

2 files

0.6.5.1

2 files

0.6.5

2 files

0.6.4

2 files

0.6.3

2 files

0.6.0

2 files

0.5.2.2

2 files

0.5.2.1

2 files

0.5.2

2 files

0.5.1

2 files

0.4.6.1

2 files

0.4.5

2 files

0.4.4

2 files

0.4.3

2 files

0.4.2

2 files

0.4.1

2 files

0.4.0

2 files

0.3.12

2 files

0.3.11

2 files

0.3.10

2 files

0.3.9

2 files

0.3.8

2 files

0.3.7

2 files

0.3.6

2 files

0.3.4

2 files

0.3.3

2 files

0.3.2

2 files

0.3.1

2 files

0.3.0

2 files

0.2.0

2 files

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