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

TY - JOUR

T1 - Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models

AU - Klein, Hans-Ulrich

AU - Schäfer, Martin

AU - Porse, Bo T

AU - Hasemann, Marie S

AU - Ickstadt, Katja

AU - Dugas, Martin

PY - 2014/1/22

Y1 - 2014/1/22

N2 - Motivation: 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. Results: 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 ChIPseq 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 mixturemodels 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.

AB - Motivation: 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. Results: 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 ChIPseq 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 mixturemodels 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.

U2 - 10.1093/bioinformatics/btu003

DO - 10.1093/bioinformatics/btu003

M3 - Journal article

C2 - 24403540

SN - 1367-4803

JO - Bioinformatics

JF - Bioinformatics

ER -