Using a Whole Genome Co-expression Network to Inform the Functional Characterisation of Predicted Genomic Elements fromMycobacterium tuberculosisTranscriptomic Data

Abstract

A whole genome co-expression network was created using Mycobacterium tuberculosis transcriptomic data from publicly available RNA-sequencing experiments covering a wide variety of experimental conditions. The network includes expressed regions with no formal annotation, including putative short RNAs and untranslated regions of expressed transcripts, along with the protein-coding genes. These unannotated expressed transcripts were among the best-connected members of the module sub-networks, making up more than half of the "hub" elements in modules that include protein-coding genes known to be part of regulatory systems involved in stress response and host adaptation. This dataset provides a valuable resource for investigating the role of non-coding RNA, and conserved hypothetical proteins, in transcriptomic remodelling. Based on their connections to genes with known functional groupings and correlations with replicated host conditions, predicted expressed transcripts can be screened as suitable candidates for further experimental validation.

Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the noncanonical translatome

“…We utilized a dedicated statistical approach which enabled us to uncover expression and coexpression patterns for thousands of nORFs despite their low expression. While previous studies have used coexpression to investigate nORFs and their cellular roles in various species [48][49][50] , our study represents a significant technical advancement in that it is the first to combine thousands of RNA-seq samples with computational methods that account for sparsity and low expression levels when calculating coexpression 55,56 .…”

“…Most coexpression studies have focused on cORFs but a few recent studies suggest that coexpression networks are a useful tool for investigating nORFs as well. For instance, Stiens et al 48 constructed a coexpression network for Mycobacterium tuberculosis to study unannotated transcripts and other studies have employed coexpression networks to study small ORFs in plants 49,50 . Work by Li et al 18 in S. cerevisiae showed that many transcribed nORFs form coexpression clusters.…”