A novel miRNA analysis framework to analyze differential biological networks

Scientific Reports
Ankush BansalRajinder Singh Chauhan

Abstract

For understanding complex biological systems, a systems biology approach, involving both the top-down and bottom-up analyses, is often required. Numerous system components and their connections are best characterised as networks, which are primarily represented as graphs, with several nodes connected at multiple edges. Inefficient network visualisation is a common problem related to transcriptomic and genomic datasets. In this article, we demonstrate an miRNA analysis framework with the help of Jatropha curcas healthy and disease transcriptome datasets, functioning as a pipeline derived from the graph theory universe, and discuss how the network theory, along with gene ontology (GO) analysis, can be used to infer biological properties and other important features of a network. Network profiling, combined with GO, correlation, and co-expression analyses, can aid in efficiently understanding the biological significance of pathways, networks, as well as a studied system. The proposed framework may help experimental and computational biologists to analyse their own data and infer meaningful biological information.

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Citations

Oct 29, 2019·Annual Review of Pathology·Sonia MondinoCarmen Buchrieser
Dec 27, 2021·Genomics·Behzad Hajieghrari, Naser Farrokhi

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Methods Mentioned

BETA
RNA-seq

Software Mentioned

WebGestalt GSAT
BLAST2GO
SAMtools
Trimmomatic
SM2miR
miRNA
BWA
house Perl scripts
mfold
miR

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