Multiple testing of local maxima for detection of peaks in ChIP-Seq data

TL;DR

This paper introduces a topological multiple testing method for detecting peaks in ChIP-Seq data, improving identification of transcription factor binding sites by using kernel smoothing and Monte Carlo p-value calculations.

Contribution

It presents a novel approach combining topological testing and Monte Carlo methods for more accurate peak detection in genomic data.

Findings

Identifies binding sites missed by other methods.

Controls false discovery rate effectively.

Detects nearby binding sites more accurately.

Abstract

A topological multiple testing approach to peak detection is proposed for the problem of detecting transcription factor binding sites in ChIP-Seq data. After kernel smoothing of the tag counts over the genome, the presence of a peak is tested at each observed local maximum, followed by multiple testing correction at the desired false discovery rate level. Valid p-values for candidate peaks are computed via Monte Carlo simulations of smoothed Poisson sequences, whose background Poisson rates are obtained via linear regression from a Control sample at two different scales. The proposed method identifies nearby binding sites that other methods do not.