Shape-based peak identification for ChIP-Seq

TL;DR

This paper introduces T-PIC, a novel non-parametric algorithm inspired by topological data analysis for identifying significant peaks in ChIP-seq data, improving accuracy and robustness over existing methods.

Contribution

The paper presents a new peak calling algorithm based on topological data analysis that enhances detection of binding regions in noisy ChIP-seq data.

Findings

Accurately identifies known binding regions

Discovers previously missed peaks

Better discriminates multiple binding events

Abstract

We present a new algorithm for the identification of bound regions from ChIP-seq experiments. Our method for identifying statistically significant peaks from read coverage is inspired by the notion of persistence in topological data analysis and provides a non-parametric approach that is robust to noise in experiments. Specifically, our method reduces the peak calling problem to the study of tree-based statistics derived from the data. We demonstrate the accuracy of our method on existing datasets, and we show that it can discover previously missed regions and can more clearly discriminate between multiple binding events. The software T-PIC (Tree shape Peak Identification for ChIP-Seq) is available at http://math.berkeley.edu/~vhower/tpic.html