Pathway Distiller - multisource biological pathway consolidation

Abstract

BackgroundOne 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.MethodsAfter 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.ResultsWe 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.ConclusionsBy 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.

PathCards: multi-source consolidation of human biological pathways

“…In turn, hiPathDB ( 10 ) integrates protein interactions from four pathway sources (1661 pathways) and creates ad hoc unified superpathways for a query gene, without globally generating consolidated pathway sets. Finally, a fourth methodology is employed by Pathway Distiller ( 11 ), which mines 2462 pathways from six pathway databases, and subsequently unifies them into clusters of several predecided sizes between 5 and 500, using hierarchical clustering. The third method of interaction mapping taken by ConsensusPathDB and HiPathDB differs conceptually from the fourth method of clustering, where the interaction mapping method provides information on the specific commonalities and discrepancies in protein interactions among sources with regard to specific keywords or genes, while the clustering method suggests which of the pathways are similar enough to be considered for the same cluster.…”

“…Previous attempts to unify pathways from several sources include NCBI’s Biosystems ( 5 ), PathwayCommons ( 6 ), PathJam ( 7 ), HPD ( 8 ), ConsensusPathDB ( 9 ), hiPathDB ( 10 ) and Pathway Distiller ( 11 ). But none of these efforts entail a standardized method to unify numerous sources into a consolidated global repository.…”

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

“…Our developed mapping strategy between different graph representations of analogous pathways enabled us to objectively compare pathway enrichment results that otherwise would have been conducted manually and subjectively. Furthermore, they allowed us to generate super pathways inspired by previous approaches that have shown the benefit of merging similar pathway representations (Stoney et al, 2018;Vivar et al, 2013;Doderer et al, 2012;Belinky et al, 2015). In this case, this was made possible by the fully harmonized gene sets and networks generated by our previous work, ComPath and PathMe.…”

Bioinformatics approaches for the functional interpretation of protein lists: From ontology term enrichment to network analysis

“…metabolic enzymes), the overlay of experimental proteome data has potentially more explanatory value about the dynamics of the metabolome than the overlay of data from the transcriptional level. A variety of tools, some of which commercial, have been developed for the analysis of enrichments of pathways in protein lists (See Table ) , sometimes in combination with GO‐information or protein interaction networks . The knowledge bases at the core of these analyses are often extensively curated, e.g.…”