Peak-calling and Prioritization pipeline for ChIP-Seq data
Project description
ChIP-seq is a standard method to identify genome-wide protein-DNA interaction or histone modification sites. A substantial number of peak-calling programs exist to automatically identify binding sites from ChIP-Seq data; PePr differs in that it takes into account variance among biological replicates. Even with transcription factor experiments, accounting for variation has the potential to identify binding sites more consistently functionally relevant in the context under study.
Here we offer a novel ChIP-Seq Peak-calling and Prioritization pipeline (PePr) that uses a sliding window approach and models read counts across replicates and between groups with a negative binomial distribution. PePr empirically estimates the optimal shift/fragment size and sliding window width, and estimates dispersion from the local genomic area. Regions with less variability across replicates are ranked more favorably than regions with greater variability. Optional post-processing steps are also made available to filter out peaks not exhibiting the expected shift size and/or to narrow the width of peaks.
