title: HMCan-diff: a method to detect changes in histone modifications in cells with different genetic characteristics.
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.
authors.affiliation: King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal 23955-6900, Saudi Arabia.
authors.forename: Haitham
authors.initials: H
authors.lastname: Ashoor
authors.affiliation: Institut Curie, Inserm U830, PSL Research University, F-75005, Paris, France.
authors.forename: Caroline
authors.initials: C
authors.lastname: Louis-Brennetot
authors.affiliation: Institut Curie, Inserm U830, PSL Research University, F-75005, Paris, France.
authors.forename: Isabelle
authors.initials: I
authors.lastname: Janoueix-Lerosey
authors.affiliation: King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal 23955-6900, Saudi Arabia.
authors.forename: Vladimir B
authors.initials: VB
authors.lastname: Bajic
authors.affiliation: Institut Curie, Inserm U900, Mines ParisTech, PSL Research University, F-75005 Paris, France.|Institut Cochin, Inserm U1016, CNRS UMR 8104, Université Paris Descartes UMR-S1016, F-75014 Paris, France.
authors.forename: Valentina
authors.initials: V
authors.lastname: Boeva
mesh_terms: D000465:Algorithms; D047369:Chromatin Immunoprecipitation; D066264:Datasets as Topic; D018450:Disease Progression; D018628:Gene Dosage; D015972:Gene Expression Regulation, Neoplastic; D042421:Histone Code; D006657:Histones / Q000235:genetics* / Q000378:metabolism; D006801:Humans; D008390:Markov Chains; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D012984:Software
chemical_list: D006657:Histones