This bioinformatics software allows integration of heterogeneous data from high-throughput genomic, transcriptomic, proteomic and metabolomic studies such that functional interactions between genes, proteins and metabolites can be elucidated. It combines multiple sources of omics data and incorporates pathway information available in Gene Ontology (GO) terms and knowledge bases such as KEGG to produce gene regulatory networks by enrichment analysis. New connections between genes, transcripts, proteins, and metabolites can be identified based on a priori given sets of genes. The networks that result can identify new biomarkers for predicting disease progression and also suggest drug targets for therapeutic intervention. Currently available analytical tools lack the flexibility to combine various omics data sources for enrichment analysis which results in loss of valuable information. Additionally, as each pathway is allowed to interact, the cross-talk between pathways that is highlighted by network analysis is uniquely identified using this new analytical tool.
Significant pathways are identified from gene expression and metabolic profiles
Researchers in the Statistics Department at the University of Michigan developed a network-based approach for integrative analysis of biological pathways. Biologically relevant pathways along with gene expression and metabolomic profiles are uploaded, and the algorithm first determines which of the pathways are significant for the two data sets separately. Pathway enrichment results are combined and scored based on statistical models and assembled into a network highlighting pathway cross-talk. The most interactive pathways are proposed as being important in conferring different phenotypes. The methodology was validated using patient-derived prostate cancer samples. This analysis revealed that amino sugar metabolism is important in prostate cancer progression, which was verified using pre-clinical studies.
- Software for analysis of high-throughput genomic, proteomic and metabolomic data
- Identify biomarkers for progression of complex diseases
- Predict and assess treatment efficacy
- Identify drug targets for therapeutic intervention
- Integrates data from various heterogeneous sources
- Accepts and combines data with varying degrees of completeness
- Identifies cross-pathway interactions
- Provides a global perspective without data loss