Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models

• Published in: Bioinformatics Vol. 30; no. 8; pp. 1154 - 1162
• Main Authors: Klein, Hans-Ulrich, Schäfer, Martin, Porse, Bo T, Hasemann, Marie S, Ickstadt, Katja, Dugas, Martin
• Format: Journal Article
• Language: English
• Published: England 15.04.2014
• Subjects: Bayes Theorem; Chromatin Immunoprecipitation; Computational Biology - methods; Epigenesis, Genetic; Histones - chemistry; Oligonucleotide Array Sequence Analysis; Sequence Analysis, RNA - methods; Transcription, Genetic
• ISSN: 1367-4803; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btu003

Histone modifications are a key epigenetic mechanism to activate or repress the transcription of genes. Datasets of matched transcription data and histone modification data obtained by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatics approach to detect genes that show different transcript abundances between two conditions putatively caused by alterations in histone modification. We introduce a correlation measure for integrative analysis of ChIP-seq and gene transcription data measured by RNA sequencing or microarrays and demonstrate that a proper normalization of ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different types of distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene transcription and histone modification. The method is applied to different datasets, and its superiority to a naive separate analysis of both data types is demonstrated. R/Bioconductor package epigenomix. h.klein@uni-muenster.de Supplementary information: Supplementary data are available at Bioinformatics online.