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PathMe: Merging and exploring mechanistic pathway knowledge

 
: Domingo-Fernandez, D.; Mubeen, S.; Marín-Llaó, J.; Hoyt, C.T.; Hofmann-Apitius, M.

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Volltext ()

BMC bioinformatics. Online journal 20 (2019), Art. 243, 12 S.
http://www.biomedcentral.com/1471-2105/
ISSN: 1471-2105
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer SCAI ()

Abstract
Background: The complexity of representing biological systems is compounded by an ever-expanding body of knowledge emerging from multi-omics experiments. A number of pathway databases have facilitated pathway-centric approaches that assist in the interpretation of molecular signatures yielded by these experiments. However, the lack of interoperability between pathway databases has hindered the ability to harmonize these resources and to exploit their consolidated knowledge. Such a unification of pathway knowledge is imperative in enhancing the comprehension and modeling of biological abstractions.
Results: Here, we present PathMe, a Python package that transforms pathway knowledge from three major pathway databases into a unified abstraction using Biological Expression Language as the pivotal, integrative schema. PathMe is complemented by a novel web application (freely available at https://pathme.scai.fraunhofer.de/) which allows users to comprehensively explore pathway crosstalk and compare areas of consensus and discrepancies.
Conclusions: This work has harmonized three major pathway databases and transformed them into a unified schema in order to gain a holistic picture of pathway knowledge. We demonstrate the utility of the PathMe framework in: i) integrating pathway landscapes at the database level, ii) comparing the degree of consensus at the pathway level, and iii) exploring pathway crosstalk and investigating consensus at the molecular level.

: http://publica.fraunhofer.de/dokumente/N-558606.html