DISMIRA: Prioritization of disease candidates in miRNA-disease associations based on maximum weighted matching inference model and motif-based analysis

Authors

Joseph J. Nalluri, Virginia Commonwealth UniversityFollow
Bhanu K. Kamapantula, Virginia Commonwealth UniversityFollow
Debmalya Barh, Institute of Integrative Omics and Applied Biotechnology
Neha Jain, Institute of Integrative Omics and Applied Biotechnology, Devi Ahilya University
Antaripa Bhattacharya, Institute of Integrative Omics and Applied Biotechnology
Sintia Silva de Almeida, Universidade Federal de Minas Gerais
Rommel Thiago Juca Ramos, Universidade Federal do Pará
Artur Silva, Universidade Federal do Pará
Vasco Azevedo, Universidade Federal de Minas Gerais
Preetam Ghosh, Virginia Commonwealth UniversityFollow

Document Type

Article

Original Publication Date

2015

Journal/Book/Conference Title

BMC Genomics

Volume

16

Issue

Sppl 5

DOI of Original Publication

10.1186/1471-2164-16-S5-S12

Comments

Originally published at http://dx.doi.org/10.1186/1471-2164-16-S5-S12

Date of Submission

December 2015

Abstract

Background

MicroRNAs (miRNAs) have increasingly been found to regulate diseases at a significant level. The interaction of miRNA and diseases is a complex web of multilevel interactions, given the fact that a miRNA regulates upto 50 or more diseases and miRNAs/diseases work in clusters. The clear patterns of miRNA regulations in a disease are still elusive.

Methods

In this work, we approach the miRNA-disease interactions from a network scientific perspective and devise two approaches - maximum weighted matching model (a graph theoretical algorithm which provides the result by solving an optimization equation of selecting the most prominent set of diseases) and motif-based analyses (which investigates the motifs of the miRNA-disease network and selects the most prominent set of diseases based on their maximum number of participation in motifs, thereby revealing the miRNA-disease interaction dynamics) to determine and prioritize the set of diseases which are most certainly impacted upon the activation of a group of queried miRNAs, in a miRNA-disease network.

Results and Conclusion

Our tool, DISMIRA implements the above mentioned approaches and presents an interactive visualization which helps the user in exploring the networking dynamics of miRNAs and diseases by analyzing their neighbors, paths and topological features. A set of miRNAs can be used in this analysis to get the associated diseases for the input group of miRs with ranks and also further analysis can be done to find key miRs or diseases, shortest paths etc.

Rights

Copyright © 2015 Nalluri et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Is Part Of

VCU Computer Science Publications