iBMQ: a R/Bioconductor package for integrated Bayesian modeling of eQTL data.
Details
Serval ID
serval:BIB_466705C0405C
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
iBMQ: a R/Bioconductor package for integrated Bayesian modeling of eQTL data.
Journal
Bioinformatics
ISSN
1367-4811 (Electronic)
ISSN-L
1367-4803
Publication state
Published
Issued date
01/11/2013
Peer-reviewed
Oui
Volume
29
Number
21
Pages
2797-2798
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Publication Status: ppublish
Abstract
Recently, mapping studies of expression quantitative loci (eQTL) (where gene expression levels are viewed as quantitative traits) have provided insight into the biology of gene regulation. Bayesian methods provide natural modeling frameworks for analyzing eQTL studies, where information shared across markers and/or genes can increase the power to detect eQTLs. Bayesian approaches tend to be computationally demanding and require specialized software. As a result, most eQTL studies use univariate methods treating each gene independently, leading to suboptimal results.
We present a powerful, computationally optimized and free open-source R package, iBMQ. Our package implements a joint hierarchical Bayesian model where all genes and SNPs are modeled concurrently. Model parameters are estimated using a Markov chain Monte Carlo algorithm. The free and widely used openMP parallel library speeds up computation. Using a mouse cardiac dataset, we show that iBMQ improves the detection of large trans-eQTL hotspots compared with other state-of-the-art packages for eQTL analysis.
The R-package iBMQ is available from the Bioconductor Web site at http://bioconductor.org and runs on Linux, Windows and MAC OS X. It is distributed under the Artistic Licence-2.0 terms.
christian.deschepper@ircm.qc.ca or rgottard@fhcrc.org.
Supplementary data are available at Bioinformatics online.
We present a powerful, computationally optimized and free open-source R package, iBMQ. Our package implements a joint hierarchical Bayesian model where all genes and SNPs are modeled concurrently. Model parameters are estimated using a Markov chain Monte Carlo algorithm. The free and widely used openMP parallel library speeds up computation. Using a mouse cardiac dataset, we show that iBMQ improves the detection of large trans-eQTL hotspots compared with other state-of-the-art packages for eQTL analysis.
The R-package iBMQ is available from the Bioconductor Web site at http://bioconductor.org and runs on Linux, Windows and MAC OS X. It is distributed under the Artistic Licence-2.0 terms.
christian.deschepper@ircm.qc.ca or rgottard@fhcrc.org.
Supplementary data are available at Bioinformatics online.
Keywords
Algorithms, Animals, Bayes Theorem, Gene Expression, Markov Chains, Mice, Monte Carlo Method, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Software
Pubmed
Web of science
Open Access
Yes
Create date
28/02/2022 11:45
Last modification date
27/02/2024 7:19