Chapter 10 - Computational intelligence for genomic data: a network biology approach

Abstract

Advanced scientific community of the 21st century uses comprehensive and complex data structures of Next Generation Sequencing (NGS) datasets to decipher the genome for healthcare, development, and management. In this process the massive amount of datasets refers this context toward “big data” approaches. NGS big data provides genetic information on a large scale. The purpose of this article is to establish a building block between big data approaches with computational biology to identify/target the proteins from the available genomic datasets. NGS data mining and analysis provided the genetic information related with different functions and involvement in various pathways. Network biology approach contributed for the better understanding of functional values and pathway enrichment architecture of cancer encouraging proteins from the genetic datasets. This chapter is an overview of discussion of brief history of advancement of DNA sequencing and associated technologies as well as characteristics features of different sequencing platforms. Finally, total 70 genes were identified on the basis of functional values obtained from PANTHER database and these encoded protein coding gene sequences of selected genes were retrieved from UniProt database and subjected for network analysis. STRING web server was used to establish protein networks between selected Parkinson's disease associated genes, with consideration of medium confidence parameter i.e., 0.4. Highest confidence and lowest confidence parameters for STRING server are 0.9 and 0.1 respectively. We have highlighted network biology followed by NGS data analysis tools and techniques.