BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes

Ines De Santiago,Chandra Sekhar Reddy Chilamakuri,Ke Yuan,Kerstin B Meyer,Wei Liu,Martin O'Reilly,Bruce A J Ponder,Florian Markowetz

doi:10.1186/s13059-017-1165-7

Ines De Santiago, Chandra Sekhar Reddy Chilamakuri + Show 6 more

Open Access

https://doi.org/10.1186/s13059-017-1165-7

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Abstract

Allele-specific measurements of transcription factor binding from ChIP-seq data are key to dissecting the allelic effects of non-coding variants and their contribution to phenotypic diversity. However, most methods of detecting an allelic imbalance assume diploid genomes. This assumption severely limits their applicability to cancer samples with frequent DNA copy-number changes. Here we present a Bayesian statistical approach called BaalChIP to correct for the effect of background allele frequency on the observed ChIP-seq read counts. BaalChIP allows the joint analysis of multiple ChIP-seq samples across a single variant and outperforms competing approaches in simulations. Using 548 ENCODE ChIP-seq and six targeted FAIRE-seq samples, we show that BaalChIP effectively corrects allele-specific analysis for copy-number variation and increases the power to detect putative cis-acting regulatory variants in cancer genomes.

Highlights

Allele-specific measurements of transcription factor (TF) binding from ChIP-seq data have provided important insights into the allelic effects of non-coding variants and their contribution to phenotypic diversity [1,2,3,4,5]
Overview of BaalChIP work flow In this study, we aim to devise a method that allows us to correct for copy-number changes and other biases in the analysis of allelic-specific binding (ASB) from ChIP-seq or similar data
The BaalChIP work flow requires three different sets of input data (Fig. 1): the single-nucleotide polymorphisms (SNPs) variants file, ChIP-seq data sets, and a corresponding set of genomic DNA (gDNA) files for each individual sample

Summary

Introduction

Allele-specific measurements of transcription factor (TF) binding from ChIP-seq data have provided important insights into the allelic effects of non-coding variants and their contribution to phenotypic diversity [1,2,3,4,5]. Existing approaches to infer allelic imbalance Previous studies have used ChIP-seq and RNA-seq to identify ASB and allele-specific expression (ASE) These studies have described methods to address technical and methodological biases such as the sequence context of a SNP [6], alignment biases to the reference allele [7, 8], and the issue of increasing detection power by combining multiple SNPs in the same gene [9] or across multiple ChIP-seq samples [10].

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