ROCCO: A Robust Method for Detection of Open Chromatin via Convex Optimization.

ABSTRACT:

Motivation

Analysis of open chromatin regions across multiple samples from two or more distinct conditions can determine altered gene regulatory patterns associated with biological phenotypes and complex traits.The ATAC-seq assay allows for tractable genome-wide open chromatin profiling of large numbers of samples. Stable, broadly applicable genomic annotations of open chromatin regions are not available. Thus, most studies first identify open regions using peak calling methods for each sample independently. These are then heuristically combined to obtain a consensus peak set. Reconciling sample-specific peak results post hoc from larger cohorts is particularly challenging, and informative spatial features specific to open chromatin signals are not leveraged effectively.

Results

We propose a novel method, ROCCO, that determines consensus open chromatin regions across multiple samples simultaneously. ROCCO employs robust summary statistics and solves a constrained optimization problem formulated to account for both enrichment and spatial dependence of open chromatin signal data. We show this formulation admits attractive theoretical and conceptual properties as well as superior empirical performance compared to current methodology.

Availability

Source code, documentation, and usage demos for ROCCO are available on GitHub at: https://github.com/nolan-h-hamilton/ROCCO. ROCCO can also be installed as a standalone binary utility using pip/PyPI.

Supplementary information

A supplement to the manuscript is included with this submission. Additional resources of potential interest are available at https://github.com/nolan-h-hamilton/ROCCO.

SUBMITTER: Hamilton NH 

PROVIDER: S-EPMC10715771 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature