A novel miRNA analysis framework to analyze differential biological networks

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.

Identification and Annotation of Potential Function of Regulatory Antisense Long Non-Coding RNAs Related to Feed Efficiency in Bos taurus Bulls

“…Common practice is to construct co-expression networks and use correlation partners of lncRNAs for gene and pathway enrichment analyses. This guilt-by-association approach has been applied to non-coding elements, such as miRNAs [27][28][29] and lncRNAs [30][31][32][33].…”

An integrative approach to develop computational pipeline for drug-target interaction network analysis

“…In this study, we used an in-house Perl script to calculate gene co-expression; we calculated various scores, assigned weights to each score, and finally generated a combined score. Methodology was adopted from our previous study on miRNA regulatory network analysis 3 .…”

“…Analysis of protein interaction network for targets of FDA approved drugs and genes related to disease in OMIM revealed that most drug targets are not even closer to the genes specifically involved in the disease and hence reflects the lack of selectivity in traditional drugs towards the genetic cause 1 . Besides, biasness of literature-mined interaction sets towards well-known proteins, dependence of current approach on target profile similarity or identification of shortest path between drug targets in the interactome has proved to be less efficient in the analysis of relationship between drugs and disease 2 , 3 .…”

MicroRNAs signatures, bioinformatics analysis of miRNAs, miRNA mimics and antagonists, and miRNA therapeutics in osteosarcoma

“…Furthermore, network analysis is taken into consideration for providing drug target, as well as for planning novel therapeutic and diagnostic approaches. Network biology is developed to inspect components for deducing valuable data from large transcriptomic datasets, by which metabolic networks depend on each other are capable of showing the behavior of the network biology [38][39][40].…”