Using biological networks to integrate, visualize and analyze genomics data

Genetics Selection Evolution

Review

Kenneth Bryan¹  David J. Lynn²  Theodosia Charitou³ 

[1] EMBL Australia Group, Infection and Immunity, South Australian Health and Medical Research Institute (SAHMRI), 5000, North Terrace, Adelaide, SA, Australia;EMBL Australia Group, Infection and Immunity, South Australian Health and Medical Research Institute (SAHMRI), 5000, North Terrace, Adelaide, SA, Australia;School of Medicine, Flinders University, 5042, Bedford Park, SA, Australia;EMBL Australia Group, Infection and Immunity, South Australian Health and Medical Research Institute (SAHMRI), 5000, North Terrace, Adelaide, SA, Australia;Systems Biology Ireland, University College Dublin, Belfield 4, Ireland;Teagasc, The Agriculture and Food Development Authority, Co Meath, Ireland;

关键词: Betweenness Centrality;    Network Visualization;    Random Walk Algorithm;    Molecular Interaction Network;    Bottleneck Node;   

DOI  :  10.1186/s12711-016-0205-1

 received in 2015-11-25, accepted in 2016-03-16,  发布年份 2016

来源: Springer

【 摘 要 】

Network biology is a rapidly developing area of biomedical research and reflects the current view that complex phenotypes, such as disease susceptibility, are not the result of single gene mutations that act in isolation but are rather due to the perturbation of a gene’s network context. Understanding the topology of these molecular interaction networks and identifying the molecules that play central roles in their structure and regulation is a key to understanding complex systems. The falling cost of next-generation sequencing is now enabling researchers to routinely catalogue the molecular components of these networks at a genome-wide scale and over a large number of different conditions. In this review, we describe how to use publicly available bioinformatics tools to integrate genome-wide ‘omics’ data into a network of experimentally-supported molecular interactions. In addition, we describe how to visualize and analyze these networks to identify topological features of likely functional relevance, including network hubs, bottlenecks and modules. We show that network biology provides a powerful conceptual approach to integrate and find patterns in genome-wide genomic data but we also discuss the limitations and caveats of these methods, of which researchers adopting these methods must remain aware.

【 授权许可】

CC BY   
© Charitou et al. 2016