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A novel miRNA analysis framework to analyze differential biological networks
Ankush Bansal1, Tiratha Raj Singh1, Rajinder Singh Chauhan2
1Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat-, 173234, Solan, H.P., India.
Scientific Reports
|November 4, 2017
Summary
This study presents a network analysis framework for biological systems using graph theory. It helps researchers efficiently analyze transcriptome data and understand complex biological networks.
Area of Science:
- Systems biology
- Bioinformatics
- Genomics
Background:
- Understanding complex biological systems requires integrating top-down and bottom-up analyses.
- Biological components and interactions are often represented as networks (graphs).
- Inefficient network visualization is a challenge with transcriptomic and genomic data.
Purpose of the Study:
- To demonstrate an miRNA analysis framework using graph theory.
- To explore network theory and gene ontology (GO) analysis for inferring biological properties.
- To aid experimental and computational biologists in data analysis.
Main Methods:
- Developed a pipeline from graph theory for miRNA analysis.
- Utilized Jatropha curcas healthy and disease transcriptome datasets.
- Integrated network profiling with GO, correlation, and co-expression analyses.
Main Results:
- The framework effectively visualizes and analyzes biological networks.
- Network profiling combined with GO analysis aids in understanding biological significance.
- The approach facilitates inference of biological properties from network data.
Conclusions:
- The proposed framework enhances the analysis of complex biological systems.
- It provides a valuable tool for interpreting transcriptome and genomic data.
- Facilitates deeper understanding of pathways and biological networks.
