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Nucleic Acids Res. 2017 May 5;45(8):e58. doi: 10.1093/nar/gkw1319.

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

Author information

1
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.
2
Institut Curie, Inserm U830, PSL Research University, F-75005, Paris, France.
3
Institut Curie, Inserm U900, Mines ParisTech, PSL Research University, F-75005 Paris, France.
4
Institut Cochin, Inserm U1016, CNRS UMR 8104, Université Paris Descartes UMR-S1016, F-75014 Paris, France.

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.

PMID:
28053124
PMCID:
PMC5416852
DOI:
10.1093/nar/gkw1319
[Indexed for MEDLINE]
Free PMC Article

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