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.
Quick Start Demo
The quick start demo is an interactive Jupyter Notebook showcasing ROCCO's functionality.
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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