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

ABSTRACT:

Background

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 respond collectively to specific triggers. Here we formalize a Bayesian inference framework to analyze the composition of transcriptional regulatory networks in metagenomes by determining the probability of regulation of orthologous gene sequences. We assess the performance of this approach on synthetic datasets and we validate it by analyzing the copper-homeostasis network of Firmicutes species in the human gut microbio

SUBMITTER: Hobbs ET 

PROVIDER: S-EPMC4938975 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature