Abstract
BackgroundThe 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.ResultsHere, 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.ConclusionsThis 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.
Highlights
The complexity of representing biological systems is compounded by an ever-expanding body of knowledge emerging from multi-omics experiments
Three case scenarios applied at increasingly granular scales of pathway knowledge are presented to illustrate the usability of the framework in database integration from a global, database-wide perspective to a detailed, path way level one
PathMe functions The PathMe package offers a set of functionalities for the set of databases incorporated far: i) download the raw pathway files, ii) generate Biological Expression Language (BEL) networks and export them as binary data, iii) summarize the transformed content, and iv) calculate detailed network statistics (Table 1)
Summary
The complexity of representing biological systems is compounded by an ever-expanding body of knowledge emerging from multi-omics experiments. Parallel to the development of novel data-driven approaches, pathway databases emerged as comprehensive resources that could be used to complement analyses with prior knowledge These resources have embraced standard file formats and schemata in order to facilitate the exchange of pathway knowledge. ComPath, the precursor of this work, harmonized pathway information at the gene level in order to conduct extensive manual curation that mapped and cross-referenced pathway representations across databases. This mapping catalog reveals which pathways are covered by which database (e.g., pathway in resource X is equivalent to pathway in resource Y) and facilitates comparing the results of pathway enrichment methods
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