Chrom-Sig: de-noising 1D genomic profiles by signal processing methods.

ABSTRACT:

Motivation

Modern genomic research is driven by next-generation sequencing experiments such as ChIP-seq, CUT&Tag, and CUT&RUN that generate coverage files for transcription factor binding, as well as ATAC-seq that yield coverage files for chromatin accessibility. Due to the inherent technical noise present in the experimental protocols, researchers need statistically rigorous and computationally efficient methods to extract true biological signal from a mixture of signal and noise. However, existing approaches are often computationally demanding or require input or spike-in controls.

Results

We developed Chrom-Sig, a Python package to quickly de-noise 1D genomic coverage tracks by computing the empirical null distribution without prior assumptions or experimental controls. W

SUBMITTER: Gupta NJ 

PROVIDER: S-EPMC12715305 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature