Co-Expression Networks for Causal Gene Identification Based on RNA-Seq Data of Corynebacterium pseudotuberculosis.

Genes
Edian F. FrancoRommel T J Ramos

Abstract

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-Seq are one of the most commonly used techniques to perform transcriptional experiments. Computational analysis of such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-Seq datasets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsigand next identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50% of the genes identified as influential had some essential cellular functions in the genomes. In the strains analyzed, most o...Continue Reading

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Citations

Mar 7, 2021·Microorganisms·Doglas PariseJan Baumbach

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Datasets Mentioned

BETA
E-MTAB-2017
E-MTAB-9217
GSE114125

Methods Mentioned

BETA
RNA-Seq

Software Mentioned

Trimmomatic
miRsigpipeline
OGEE
CutAdapt
edgeR
Salmon
FastQC
SOLID
ClusterMaker
miRsig

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