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.

Example Behavior

Input

• ENCODE lymphoblastoid data (BEST5, WORST5)

  • 10 real ATAC-seq alignment tracks of varying quality (TSS enrichment)

• Synthetic noisy data (NOISY5)

  • 5 random alignments

Output

• ROCCO consensus peaks (blue)
  • Effectively separates true signal from noise across multiple samples
  • Robust to noisy samples (e.g., NOISY5)
  • High precision separation of enriched regions

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

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)

pip install rocco # most recent version

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
  pip install -e .

Input/Output

• Input: Samples' BAM alignments or BigWig tracks
  • Note, if BigWig input is used, no preprocessing options can be applied at the alignment level.
• Output: BED file of consensus peak regions