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. 2017 Nov 6;7(1):14604.
doi: 10.1038/s41598-017-14973-x.

A novel miRNA analysis framework to analyze differential biological networks

Ankush Bansal  1 , Tiratha Raj Singh  1 , Rajinder Singh Chauhan  2
Affiliations

A novel miRNA analysis framework to analyze differential biological networks

Ankush Bansal et al. Sci Rep. .

Erratum in

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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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
miRNA target distribution (A) miRNA target unique to healthy and diseased condition (B) miRNA targets common in both healthy and diseased conditions. miRNA targets in healthy are represented in green color while diseased in red color (C) Pearson Correlation Coefficient (PCC) analysis; green and red color represents healthy (JH) and diseased (JV) respectively (D) Bipartite network showing two different subsets, namely miRNA and miRNA target with directed connection network.
Figure 2
Figure 2
Bipartite network for common miRNA targets in (A) healthy (JH) condition (B) diseased (JV) condition.
Figure 3
Figure 3
miRNA targets involved in biological processes in (A) healthy (JH) condition (B) diseased (JV) condition; score based node prioritization shown using filter parameter.
Figure 4
Figure 4
miRNA targets associated molecular functions in healthy (A) healthy (JH) condition (B) diseased (JV) condition; score based node prioritization shown using filter parameter.
Figure 5
Figure 5
miRNA targets ontology on the basis of cellular components in (A) healthy (JH) condition (B) diseased (JV) condition; score based node prioritization shown using filter parameter.
Figure 6
Figure 6
miRNA analysis framework workflow consisting 6 modules; Transcriptome Data Annotation, miRNA identification, miRNA-mRNA target prediction, gene ontology enrichment inferred network construction, correlation analysis - PCC scoring function and co-expression network construction.
Figure 7
Figure 7
Workflow diagram for co-expression network construction.
See this image and copyright information in PMC

References

    1. Barabási A-L, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 2004;5:101–113. doi: 10.1038/nrg1272. - DOI - PubMed
    1. Kumar, V., Bansal, A. & Chauhan, R. S. Modular Design of Picroside-II Biosynthesis Deciphered through NGS Transcriptomes and Metabolic Intermediates Analysis in Naturally Variant Chemotypes of a Medicinal Herb, Picrorhiza kurroa. Front. Plant Sci. 8 (2017). - PMC - PubMed
    1. Bansal, A. & Srivastava, P. A. Transcriptomics to Metabolomics: A Network Perspective for BigData. IGI Glob. 188–206, 10.4018/978-1-5225-2607-0.ch008 (2018).
    1. Jindal K, Bansal A. APOEε2 is Associated with Milder Clinical and Pathological Alzheimer’s Disease. Ann. Neurosci. 2016;23:112–112. doi: 10.1159/000443572. - DOI - PMC - PubMed
    1. Bansal, A. & Ramana, J. TCGDB: A Compendium of Molecular Signatures of Thyroid Cancer and Disorders. J. Cancer Sci. Ther. 7 (2015).

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