rocco 1.7.0

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 samples (e.g., ATAC-seq), where an enrichment in read counts/densities is observed in a nontrivial subset of samples.

Input/Output

• Input: Samples' BAM alignments or BigWig tracks
• Output: BED file of consensus peak regions (Default format is BED3: chrom,start,end)

Note, if BigWig input is used, no preprocessing options can be applied at the alignment level.

How

ROCCO models consensus peak calling as a constrained optimization problem with an upper-bound on the total proportion of the genome selected as open/accessible and a fragmentation penalty 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+) with an asymptotic time complexity independent of sample size
3. Unsupervised Does not require training data or a heuristically determined set of initial candidate peak regions
4. No rigid thresholds on the minimum number/width of supporting samples/replicates
5. Mathematically tractable model permitting worst-case analysis of runtime and performance

Example Behavior

Input

• ENCODE lymphoblastoid data (BEST5, WORST5): 10 real ATAC-seq alignments of varying TSS enrichment (SNR-like quality measure for ATAC-seq)
• Synthetic noisy data (NOISY5)

We run twice under two conditions -- with noisy samples and without for comparison (blue)

Ideally, the resulting consensus peaks should be largely unaffected by the Noisy5 input samples. Likewise, both broad and narrow peaks should be captured.

rocco -i *.BEST5.bam *.WORST5.bam -g hg38 -o rocco_output_without_noise.bed
rocco -i *.BEST5.bam *.WORST5.bam *.NOISY5.bam -g hg38 -o rocco_output_with_noise.bed

• Note, users may run ROCCO with flag --narrowPeak to generate 10-column output with various statistics for comparison of peaks and supplemental validation independent of ROCCO's optimality criterion.
  • As a byproduct, --narrowPeak will likewise produce a 'raw' peak-by-count matrix (one row per peak, one column per sample) that can be used in downstream analyses such as differential accessibility testing.

Output

See also: Running Consenrich+ROCCO.

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

Documentation

For additional details, usage examples, etc. please see ROCCO's documentation: https://nolan-h-hamilton.github.io/ROCCO/

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 .