NeuroDNet - an open source platform for constructing and analyzing neurodegenerative disease networks

BMC Neuroscience

James Gomes²  Bhyravabhotla Jayaram¹  Aditya K Padhi²  Suhas V Vasaikar² 

[1] Supercomputing Facility for Bioinformatics & Computational Biology, New Delhi, 110016, India;Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, Block 1A, Room No. 307, Hauz Khas, New Delhi 110016, India

关键词: Systems analysis;    Boolean network;    Protein-Protein interaction network;    Disease gene network;    Neurodegenerative diseases;   

Others  :  1140614
DOI  :  10.1186/1471-2202-14-3

 received in 2012-08-17, accepted in 2012-12-13,  发布年份 2013

【 摘 要 】

Background

Genetic networks control cellular functions. Aberrations in normal cellular function are caused by mutations in genes that disrupt the fine tuning of genetic networks and cause disease or disorder. However, the large number of signalling molecules, genes and proteins that constitute such networks, and the consequent complexity of interactions, has restrained progress in research elucidating disease mechanisms. Hence, carrying out a systematic analysis of how diseases alter the character of these networks is important. We illustrate this through our work on neurodegenerative disease networks. We created a database, NeuroDNet, which brings together relevant information about signalling molecules, genes and proteins, and their interactions, for constructing neurodegenerative disease networks.

Description

NeuroDNet is a database with interactive tools that enables the creation of interaction networks for twelve neurodegenerative diseases under one portal for interrogation and analyses. It is the first of its kind, which enables the construction and analysis of neurodegenerative diseases through protein interaction networks, regulatory networks and Boolean networks. The database has a three-tier architecture - foundation, function and interface. The foundation tier contains the human genome data with 23857 protein-coding genes linked to more than 300 genes reported in clinical studies of neurodegenerative diseases. The database architecture was designed to retrieve neurodegenerative disease information seamlessly through the interface tier using specific functional information. Features of this database enable users to extract, analyze and display information related to a disease in many different ways.

Conclusions

The application of NeuroDNet was illustrated using three case studies. Through these case studies, the construction and analyses of a PPI network for angiogenin protein in amyotrophic lateral sclerosis, a signal-gene-protein interaction network for presenilin protein in Alzheimer's disease and a Boolean network for a mammalian cell cycle was demonstrated. NeuroDNet is accessible at http://bioschool.iitd.ac.in/NeuroDNet/ webcite.

【 授权许可】

   
2013 Vasaikar et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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