rocco 1.6.1

Robust ATAC-seq Peak Calling for Many Samples 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 (namely, 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

ROCCO offers several attractive features:

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. No required 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)

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

Comparing each output file:

• ROCCO is unaffected by the Noisy5 samples and effectively identifies true signal across multiple samples
• ROCCO simultaneously detects both wide and narrow consensus peaks

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 .