A Bayesian inference method for the analysis of transcriptional regulatory networks in metagenomic data

Abstract

Metagenomics enables the analysis of bacterial population composition and the study of emergent population features, such as shared metabolic pathways. Recently, we have shown that metagenomics datasets can be leveraged to characterize population-wide transcriptional regulatory networks, or meta-regulons, providing insights into how bacterial populations… (More)
DOI: 10.1186/s13015-016-0082-8

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Cite this paper

@inproceedings{Hobbs2016ABI, title={A Bayesian inference method for the analysis of transcriptional regulatory networks in metagenomic data}, author={Elizabeth T. Hobbs and Talmo Pereira and Patrick K. O'Neill and Ivan Erill}, booktitle={Algorithms for Molecular Biology}, year={2016} }