PathMe: merging and exploring mechanistic pathway knowledge

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. Electronic supplementary material The online version of this article (10.1186/s12859-019-2863-9) contains supplementary material, which is available to authorized users.

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

“…In order to systematically compare results yielded by different databases, we retrieved the contents of KEGG, Reactome, and WikiPathways using ComPath (Domingo-Fernández et al, 2018) and converted it into the Gene Matrix Transposed (GMT) file format. Generated networks encoded in Biological Expression Language (BEL; Slater, 2014) were retrieved using PathMe (Domingo-Fernández et al, 2019).…”

“…In a second step, we merged equivalent pathways by taking the graph union with respect to contained genes and interactions ( Figures 1B, C ). We have also described this step in more detail in our earlier work (Domingo-Fernandez et al, 2019).…”

“…Several integrative resources have been developed, including meta-databases [e.g., Pathway Commons (Cerami et al, 2011), MSigDB (Liberzon et al, 2015), and ConsensusPathDB (Kamburov et al, 2008)] that enable pathway exploration in their corresponding web applications and integrative software tools [e.g., graphite (Sales et al, 2018), PathMe (Domingo-Fernandez et al, 2019), and OmniPath (Türei et al, 2016)] designed to enable bioinformatics analyses. By consolidating pathway databases, these resources have attempted to summarize major reference points in the existing knowledge and demonstrate how data contained in one resource can be complemented by data contained in others.…”

“…As a solution, we propose to integrate different pathway resources via a method where semantically analogous pathways across databases (e.g., “Notch signaling pathway” in KEGG and “Signaling by NOTCH” pathway in Reactome) are combined. This approach exploits the pathway mappings and harmonized pathway representations described in our previous work (Domingo-Fernández et al, 2018; Domingo-Fernandez et al, 2019). We demonstrate that when aided by our integrative pathway database, it is possible to better capture expected disease biology than with individual resources, and to sometimes obtain better predictions of clinical endpoints.…”

Re-curation and rational enrichment of knowledge graphs in Biological Expression Language

“…For situations when the appropriate concept/term is unclear, several tools have been developed and made freely available to the community to help curators build semantically interoperable models including the Ontology Lookup Service [OLS; (28)], the Ontology Mapping Service (OxO; https://www.ebi.ac.uk/spot/oxo), Zooma (https://www.ebi.ac.uk/spot/zooma) and CEDAR Workbench (29). Further, recent work from Domingo-Fernández et al on mapping pathways between major databases (30) and a critical assessment of their overlaps and contradictions (31) has shown that the adoption of standards like MIRIAM has been slow and that while the syntax of the varying formats used by each database may be correct, their semantic interoperability is still lacking.…”