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 crosstalks 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.

A Computational Approach for Mapping Heme Biology in the Context of Hemolytic Disorders

“…representation exhibits slight differences. These differences and complementarities between pathways provide us with a more comprehensive view of the studied pathways, as illustrated by our previous work (Domingo-Fernández et al, 2019).…”

“…First, the three different representations of this pathway were downloaded from each database and converted to BEL using PathMe (Domingo-Fernández et al, 2019). Next, the three BEL networks were combined with the HemeKG network highlighting their overlaps (Supplementary Figures S1, S2) in order to specifically analyze these parts of the combined network.…”

Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis

“…While integrative resources such as STRING (Szklarczyk et al, 2019), PathwayCommons (Cerami et al, 2011), ConsensusPathDB (Kamburov et al, 2013), PathMe , and ComPath (Domingo‐Fernández et al, 2019) use mostly the major process description resources (e.g., Reactome (Jassal et al, 2020) and ACSN (Kuperstein et al, 2015)) and resources with undirected interactions (e.g., IntAct (Orchard et al, 2014) and BioGRID (Oughtred et al, 2019)), the network database of OmniPath focuses on activity flow representation, providing a convenient input for multiple analysis techniques (Touré et al, 2020); Appendix 1). OmniPath is not limited to literature curated interactions and it also includes activity flow, kinase–substrate, and ligand–receptor interactions without references as separate datasets, so that the users can decide which ones to use according to their purposes (Dataset EV2).…”

Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms

“…This interactome was comprised of tens of thousands of human protein-protein interactions from six databases including KEGG (Kanehisa et al ., 2021), Reactome (Jassal et al ., 2020), WikiPathways (Martens et al ., 2020), BioGrid (Oughtred et al ., 2019), IntAct (Orchard et al ., 2014), and PathwayCommons (Rodchenkov et al ., 2020). We would like to note that the first three of the six databases were harmonized through PathMe (Domingo-Fernández et al ., 2019). Additionally, for each of the six databases, only proteins that belonged to pathways from MPath (Mubeen et al ., 2019), an integrative resource that combines multiple databases and merges gene sets of equivalent pathways, were included in the interactome, thus ensuring that each protein in the network was minimally assigned to a single pathway.…”