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Transcriptomics to Metabolomics: A Network Perspective for Big Data

Transcriptomics to Metabolomics: A Network Perspective for Big Data
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Author(s): Ankush Bansal (Jaypee University of Information Technology, India)and Pulkit Anupam Srivastava (Jaypee University of Information Technology, India)
Copyright: 2018
Pages: 19
Source title: Applying Big Data Analytics in Bioinformatics and Medicine
Source Author(s)/Editor(s): Miltiadis D. Lytras (Deree - The American College of Greece, Greece)and Paraskevi Papadopoulou (Deree - The American College of Greece, Greece)
DOI: 10.4018/978-1-5225-2607-0.ch008


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A lot of omics data is generated in a recent decade which flooded the internet with transcriptomic, genomics, proteomics and metabolomics data. A number of software, tools, and web-servers have developed to analyze the big data omics. This review integrates the various methods that have been employed over the years to interpret the gene regulatory and metabolic networks. It illustrates random networks, scale-free networks, small world network, bipartite networks and other topological analysis which fits in biological networks. Transcriptome to metabolome network is of interest because of key enzymes identification and regulatory hub genes prediction. It also provides an insight into the understanding of omics technologies, generation of data and impact of in-silico analysis on the scientific community.

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