RT Journal Article
SR Electronic
T1 PathMe: Merging and exploring mechanistic pathway knowledge
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 451625
DO 10.1101/451625
A1 Daniel Domingo-Fernández
A1 Sarah Mubeen
A1 Josep Marín-Llaó
A1 Charles Tapley Hoyt
A1 Martin Hofmann-Apitius
YR 2018
UL http://biorxiv.org/content/early/2018/10/24/451625.abstract
AB Abstract 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 modelling of biological abstractions. 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 which allows users to comprehensively explore pathway cross-talks and compare areas of consensus and discrepancies.Availability PathMe’s source code is available at https://github.com/ComPath/PathMe under the Apache 2.0 license. We provide a freely accessible deployment of the PathMe Viewer at https://pathme.scai.fraunhofer.de/.