HMCan-diff: a method to detect changes in histone modifications in cells with different genetic characteristics

Haitham Ashoor, Caroline Louis-Brennetot, Isabelle Janoueix-Lerosey, Vladimir B. Bajic, Valentina Boeva

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Comparing histone modification profiles between cancer and normal states, or across different tumor samples, can provide insights into understanding cancer initiation, progression and response to therapy. ChIP-seq histone modification data of cancer samples are distorted by copy number variation innate to any cancer cell. We present HMCan-diff, the first method designed to analyze ChIP-seq data to detect changes in histone modifications between two cancer samples of different genetic backgrounds, or between a cancer sample and a normal control. HMCan-diff explicitly corrects for copy number bias, and for other biases in the ChIP-seq data, which significantly improves prediction accuracy compared to methods that do not consider such corrections. On in silico simulated ChIP-seq data generated using genomes with differences in copy number profiles, HMCan-diff shows a much better performance compared to other methods that have no correction for copy number bias. Additionally, we benchmarked HMCan-diff on four experimental datasets, characterizing two histone marks in two different scenarios. We correlated changes in histone modifications between a cancer and a normal control sample with changes in gene expression. On all experimental datasets, HMCan-diff demonstrated better performance compared to the other methods.
Original languageEnglish (US)
Pages (from-to)gkw1319
JournalNucleic Acids Research
Volume45
Issue number8
DOIs
StatePublished - Jan 3 2017

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