rocco 0.3.1

Robust ATAC-seq Peak Calling for Many Samples via Convex Optimization

[R]obust [O]pen [C]hromatin Dection via [C]onvex [O]ptimization

Underlying ROCCO is a constrained optimization problem that can be solved efficiently to predict consensus regions of open chromatin across multiple samples.

Features

1. Explicitly accounts for both enrichment and spatial characteristics of open chromatin signals to capture the full extent of peaks;
2. No arbitrary thresholds on the minimum number of supporting samples/replicates;
3. Is efficient for large numbers of samples with an asymptotic time complexity independent of sample count;
4. Does not require training data or initial candidate peak regions which are hard to define given the lack of a priori sets of open chromatin regions;
5. Employs a mathematically tractable model permitting guarantees of performance and efficiency.

Getting Started

Install Dependencies with Conda

A ROCCO-specific conda environment with all dependencies installed can be created using rocco_conda.yml:

conda env create -n rocco --file docs/CONDA/rocco_conda.yml

load via: conda activate rocco.

Alternatively, dependencies (standard bioinformatics tools listed in docs) can be installed manually.

Install ROCCO with pip (PyPi page)

pip install rocco

Quick Start Demo

To see ROCCO in action, refer to the Jupyter notebook: demo.ipynb.

This demonstration offers an interactive overview of the ROCCO pipeline that can be executed by running the commands in each cell. Output from a previous session is included if you do not wish to run the pipeline yourself.

Citation

ROCCO: A Robust Method for Detection of Open Chromatin via Convex Optimization
Nolan H. Hamilton, Terrence S. Furey
bioRxiv 2023.05.24.542132; doi: https://doi.org/10.1101/2023.05.24.542132