HMCan: a method for detecting chromatin modifications in cancer samples using ChIP-seq data

Haitham Ashoor,François Radvanyi,Vladimir B Bajic,Aurélie Hérault,Emmanuel Barillot,Valentina Boeva,Aurélie Kamoun

doi:10.1093/bioinformatics/btt524

Abstract

Motivation: Cancer cells are often characterized by epigenetic changes, which include aberrant histone modifications. In particular, local or regional epigenetic silencing is a common mechanism in cancer for silencing expression of tumor suppressor genes. Though several tools have been created to enable detection of histone marks in ChIP-seq data from normal samples, it is unclear whether these tools can be efficiently applied to ChIP-seq data generated from cancer samples. Indeed, cancer genomes are often characterized by frequent copy number alterations: gains and losses of large regions of chromosomal material. Copy number alterations may create a substantial statistical bias in the evaluation of histone mark signal enrichment and result in underdetection of the signal in the regions of loss and overdetection of the signal in the regions of gain.Results: We present HMCan (Histone modifications in cancer), a tool specially designed to analyze histone modification ChIP-seq data produced from cancer genomes. HMCan corrects for the GC-content and copy number bias and then applies Hidden Markov Models to detect the signal from the corrected data. On simulated data, HMCan outperformed several commonly used tools developed to analyze histone modification data produced from genomes without copy number alterations. HMCan also showed superior results on a ChIP-seq dataset generated for the repressive histone mark H3K27me3 in a bladder cancer cell line. HMCan predictions matched well with experimental data (qPCR validated regions) and included, for example, the previously detected H3K27me3 mark in the promoter of the DLEC1 gene, missed by other tools we tested.Availability: Source code and binaries can be downloaded at http://www.cbrc.kaust.edu.sa/hmcan/, implemented in C++.Contact: haitham.ashoor@kaust.edu.saSupplementary information: Supplementary data are available at Bioinformatics online.

Highlights

ChIP-Seq is a combination of chromatin immunoprecipitation and next-generation sequencing of extracted DNA fragments (Robertson et al, 2007)
Histone marks help partitioning the genome into euchromatin, which is accessible for transcription, and heterochromatin
We set read length 1⁄4 76 bp, fragment length 1⁄4 150 bp, $20% of the reads came from the signal regions and $80% of the reads came from the non-signal regions

Summary

Introduction

ChIP-Seq is a combination of chromatin immunoprecipitation and next-generation sequencing of extracted DNA fragments (Robertson et al, 2007). The ChIP-Seq technique is widely used for identification of epigenetic marks such as histone variants and different covalent modifications of histone tails (Furey, 2012). Histone marks help partitioning the genome into euchromatin, which is accessible for transcription, and heterochromatin. Trimethylation of lysine 9 of histone 3 (H3K9me3) and trimethylation of lysine 27 of histone 3 (H3K27me3) are marks associated with pericentromeric heterochromatin and regions of polycomb-mediated repression (Kharchenko et al, 2011). Trimethylation of lysine 36 of histone 3 (H3K36me3) is a mark of transcription elongation; trimethylation of lysine 4 of histone 3 (H3K4me3) marks active or poised promoters; monomethylation of lysine 4 of histone 3 (H3K4me1) together with acetylation of lysine 27 of histone 3 correlates with active enhancers (Kouzarides, 2007). Some marks are narrow and cover 1–10 consecutive nucleosomes (e.g. H3K4me or H3K4me3), whereas others (e.g. H3K27me and H3K36me3) can cover large genomic regions, from tens to hundreds of kilobases in length

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Discussion

Conclusion