Authors
Doderer, Mark S.Anguiano, Zachry
Suresh, Uthra
Dashnamoorthy, Ravi
Bishop, Alexander J. R.
Chen, Yidong
UMass Chan Affiliations
Department of Medicine, Division of Hematology/OncologyDocument Type
Journal ArticlePublication Date
2012-10-26Keywords
Cluster AnalysisDatabases, Genetic
Gene Expression Profiling
Internet
Software
User-Computer Interface
Bioinformatics
Genetics and Genomics
Metadata
Show full item recordAbstract
BACKGROUND: One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. METHODS: After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. RESULTS: We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. CONCLUSIONS: By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments.Source
BMC Genomics. 2012;13 Suppl 6:S18. doi: 10.1186/1471-2164-13-S6-S18. Link to article on publisher's siteDOI
10.1186/1471-2164-13-S6-S18Permanent Link to this Item
http://hdl.handle.net/20.500.14038/39593PubMed ID
23134636Related Resources
Link to Article in PubMedRights
© 2012 Doderer et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ae974a485f413a2113503eed53cd6c53
10.1186/1471-2164-13-S6-S18