An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease

BMC Bioinformatics
Ryohei EguchiMd Altaf-Ul-Amin

Abstract

There are different and complicated associations between genes and diseases. Finding the causal associations between genes and specific diseases is still challenging. In this work we present a method to predict novel associations of genes and pathways with inflammatory bowel disease (IBD) by integrating information of differential gene expression, protein-protein interaction and known disease genes related to IBD. We downloaded IBD gene expression data from NCBI's Gene Expression Omnibus, performed statistical analysis to determine differentially expressed genes, collected known IBD genes from DisGeNet database, which were used to construct a IBD related PPI network with HIPPIE database. We adapted our graph-based clustering algorithm DPClusO to cluster the disease PPI network. We evaluated the statistical significance of the identified clusters in the context of determining the richness of IBD genes using Fisher's exact test and predicted novel genes related to IBD. We showed 93.8% of our predictions are correct in the context of other databases and published literatures related to IBD. Finding disease-causing genes is necessary for developing drugs with synergistic effect targeting many genes simultaneously. Here we present a...Continue Reading

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Methods Mentioned

BETA
ubiquitination

Software Mentioned

R package edgeR
HuGeNet
PROSPECTOR
DPClusO
SScore
ENDEAVOUR
edgeR
DisGeNet
DPClus
ToppGene

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