Robust ATAC-seq Peak Calling for Many Samples via Convex Optimization
Project description
[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
- Explicitly accounts for both enrichment and spatial characteristics of open chromatin signals to capture the full extent of peaks;
- No arbitrary thresholds on the minimum number of supporting samples/replicates;
- Is efficient for large numbers of samples with an asymptotic time complexity independent of sample count;
- 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;
- 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
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