A Bayesian Framework for the Classification of Microbial Gene Activity States

Numerous methods for classifying gene activity states based on gene expression data have been proposed for use in downstream applications, such as incorporating transcriptomics data into metabolic models in order to improve resulting flux predictions. These methods often attempt to classify gene act...

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Published inFrontiers in microbiology Vol. 7; p. 1191
Main Authors Disselkoen, Craig, Greco, Brian, Cook, Kaitlyn, Koch, Kristin, Lerebours, Reginald, Viss, Chase, Cape, Joshua, Held, Elizabeth, Ashenafi, Yonatan, Fischer, Karen, Acosta, Allyson, Cunningham, Mark, Best, Aaron A, DeJongh, Matthew, Tintle, Nathan
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 09.08.2016
Subjects
Bacteria
Bayesian model
Gene activity
Gene Expression
Metabolic Modelling
Microbiology
bacteria
gene activity
Bayesian model
gene expression
metabolic modeling
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Summary:Numerous methods for classifying gene activity states based on gene expression data have been proposed for use in downstream applications, such as incorporating transcriptomics data into metabolic models in order to improve resulting flux predictions. These methods often attempt to classify gene activity for each gene in each experimental condition as belonging to one of two states: active (the gene product is part of an active cellular mechanism) or inactive (the cellular mechanism is not active). These existing methods of classifying gene activity states suffer from multiple limitations, including enforcing unrealistic constraints on the overall proportions of active and inactive genes, failing to leverage a priori knowledge of gene co-regulation, failing to account for differences between genes, and failing to provide statistically meaningful confidence estimates. We propose a flexible Bayesian approach to classifying gene activity states based on a Gaussian mixture model. The model integrates genome-wide transcriptomics data from multiple conditions and information about gene co-regulation to provide activity state confidence estimates for each gene in each condition. We compare the performance of our novel method to existing methods on both simulated data and real data from 907 E. coli gene expression arrays, as well as a comparison with experimentally measured flux values in 29 conditions, demonstrating that our method provides more consistent and accurate results than existing methods across a variety of metrics.
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Reviewed by: Christoph Kaleta, University of Kiel, Germany; Jeremy Zucker, Pacific Northwest National Laboratory, USA
This article was submitted to Systems Microbiology, a section of the journal Frontiers in Microbiology
These authors have contributed equally to this work.
Edited by: Steve Lindemann, Pacific Northwest National Laboratory, USA
ISSN:1664-302X
1664-302X
DOI:10.3389/fmicb.2016.01191