title: HMCan: a method for detecting chromatin modifications in cancer samples using ChIP-seq data.
abstract: BACKGROUND
  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.
authors.affiliation: Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia, Institut Curie, 75248 Paris Cedex 05, France, INSERM, U900, Bioinformatics and Computational Systems Biology of Cancer, Mines ParisTech, Fontainebleau 77300, France and UMR 144 CNRS, Subcellular Structure and Cellular Dynamics.
authors.forename: Haitham
authors.initials: H
authors.lastname: Ashoor
authors.forename: Aurélie
authors.initials: A
authors.lastname: Hérault
authors.forename: Aurélie
authors.initials: A
authors.lastname: Kamoun
authors.forename: François
authors.initials: F
authors.lastname: Radvanyi
authors.forename: Vladimir B
authors.initials: VB
authors.lastname: Bajic
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Barillot
authors.forename: Valentina
authors.initials: V
authors.lastname: Boeva
mesh_terms: D001482:Base Composition; D042002:Chromatin Assembly and Disassembly / Q000235:genetics*; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D003198:Computer Simulation; D056915:DNA Copy Number Variations / Q000235:genetics; D044127:Epigenesis, Genetic; D015894:Genome, Human; D006657:Histones / Q000235:genetics* / Q000378:metabolism; D006801:Humans; D008390:Markov Chains; D020411:Oligonucleotide Array Sequence Analysis; D011401:Promoter Regions, Genetic / Q000235:genetics; D011499:Protein Processing, Post-Translational; D012984:Software; D001749:Urinary Bladder Neoplasms / Q000175:diagnosis / Q000235:genetics*
chemical_list: D006657:Histones
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