christianuf.blogg.se

Merging Glioblastoma Data for Correlation and Network-Based Analysis Using GeneSpring 13ĭr.

Dipa Roy Choudhury, Agilent Technologies, Inc.Ī demonstration to show how GeneSpring can be used to study correlation of expression of transcriptomic and proteomic data to identify curated signaling pathways that might be deregulated specifically in Glioblastoma (GBM) by clustering across omic data types.

Homologene and BridgeDB are implemented in GeneSpring to facilitate integrative analysis through translation functions, linking probes across data types, array platforms, and organisms that map to the same biological entity. Keywords: correlation of expression of transcriptomic and proteomic data, pathway and network analysisįrom GWAS SNP to Molecular Mechanism: Insights Gained from Promoter Modeling and Network Analysis Since GeneSpring can create literature-derived networks, we then extended our investigation to identify NLP- (Natural Language Processing) derived networks of genes consisting largely of interesting genes from the multi-omic experiment that allow cross-talk between curated pathways that were differentially expressed in GBM datasets. Genome-Wide-Association-Studies (GWAS) have been used to search for genetic clues linked to diseases or population groups. A long-standing problem of GWAS approaches is that the result is a mere statistical correlation of some mutation (usually a SNP) with a specific condition or disease, and in most cases the correlated SNPs are located outside the coding region of any gene (≈ 80% of such SNPs according to ENCODE). In order to locate potential target genes, the next coding gene upstream or downstream of the SNP is taken as a candidate-an approach that often fails because many disease-correlated SNPs are in fact regulatory variants that affect genes distant from the SNP site. We have analyzed a regulatory SNP correlated with diabetic nephropathy in a GWAS study and leveraged Genomatix' unique capabilities in comparative promoter analysis to link that SNP to a complex regulatory network (PMID 23434934). This network in turn affects a complete pathway involving genes located on different chromosomes from the regulatory SNP of interest.