ComPath: An ecosystem for exploring, analyzing, and curating mappings across pathway databases

Domingo‐Fernándéz, Daniel; Hoyt, Charles Tapley; Alvarez, Carlos Bobis; Marín-Llaó, Josep; Hofmann-Apitius, Martin

doi:10.1101/353235

Cited by 14 publications

(16 citation statements)

References 46 publications

(57 reference statements)

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“…Thus, the maximum degree of consensus between databases can be constrained by databases which contain fewer entities. The scope of the pathways comprised in each database varies. Each database places a distinct emphasis on discrete aspects or regions of biological pathways which tend to be defined subjectively in the absence of standard nomenclatures, as outlined by [14] who reported only 21 equivalent pathways between the three databases. Therefore, despite the presence of key biological players in all three databases, the majority of biological entities are particular to a single database.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, metadatabases such as Pathway Commons [9] and ConsensusPathDB [26], which incorporate several different primary resources in their data warehouses, and integrative software applications such as graphite [37] and OmniPath [41] have been created with the primary intention to integrate pathway knowledge from multiple databases. Beyond these, other approaches such as those taken in PathCards [4], RaMP [45], and ComPath [14], have focused on integrating gene sets and chemical knowledge related to pathways, but without including their topological information (i.e, relationships were excluded from the network). For instance, 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.…”

Section: Introductionmentioning

confidence: 99%

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PathMe: merging and exploring mechanistic pathway knowledge

et al. 2019

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PathMe: merging and exploring mechanistic pathway knowledge

et al. 2019

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“…Pathways were analyzed in the context of data obtained from both Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, and ontology data was analyzed using the Gene Ontology Consortium database ( Ashburner et al, 2000 ; Kanehisa and Goto, 2000 ). Functional analysis was carried out using both iPathwayGuide and Reactome in view of discrepancies in pathway resources between databases that limit interoperability and might influence results ( Domingo-Fernández et al, 2018 ). DEGs were also evaluated by comparison to published datasets describing the acute effects of scuba diving on the peripheral blood transcriptome ( Eftedal et al, 2013 ).…”

Section: Methodsmentioning

confidence: 99%

Acute Effects on the Human Peripheral Blood Transcriptome of Decompression Sickness Secondary to Scuba Diving

et al. 2021

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Decompression sickness (DCS) develops due to inert gas bubble formation in bodily tissues and in the circulation, leading to a wide range of potentially serious clinical manifestations. Its pathophysiology remains incompletely understood. In this study, we aim to explore changes in the human leukocyte transcriptome in divers with DCS compared to closely matched unaffected controls after uneventful diving. Cases (n = 7) were divers developing the typical cutis marmorata rash after diving with a confirmed clinical diagnosis of DCS. Controls (n = 6) were healthy divers who surfaced from a ≥25 msw dive without decompression violation or evidence of DCS. Blood was sampled at two separate time points—within 8 h of dive completion and 40–44 h later. Transcriptome analysis by RNA-Sequencing followed by bioinformatic analysis was carried out to identify differentially expressed genes and relate their function to biological pathways. In DCS cases, we identified enrichment of transcripts involved in acute inflammation, activation of innate immunity and free radical scavenging pathways, with specific upregulation of transcripts related to neutrophil function and degranulation. DCS-induced transcriptomic events were reversed at the second time point following exposure to hyperbaric oxygen. The observed changes are consistent with findings from animal models of DCS and highlight a continuum between the responses elicited by uneventful diving and diving complicated by DCS. This study sheds light on the inflammatory pathophysiology of DCS and the associated immune response. Such data may potentially be valuable in the search for novel treatments targeting this disease.

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“…We employ a semi-automated procedure to find corresponding pathways from the seven databases. Our approach is similar in spirit to that of ComPath, 6 which generates mappings between pathway annotations by considering the lexical similarity between names and content similarity between genes of each pair of pathways followed by a manual curation. Given two pathway databases A and B , pathway a ∈ A and b ∈ B are corresponding pathways if two conditions hold.…”

Section: Topological Structure Of Corresponding Pathwaysmentioning

confidence: 99%

Stop Bickering! Reconciling Signaling Pathway Databases with Network Topologies

Rubel

Singh

Ritz

2021

Preprint

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A major goal of molecular systems biology is to understand the coordinated function of genes or proteins in response to cellular signals and to understand these dynamics in the context of disease. Signaling pathway databases such as KEGG, NetPath, NCI-PID, and Panther describe the molecular interactions involved in different cellular responses. While the same pathway may be present in different databases, prior work has shown that the particular proteins and interactions differ across database annotations. However, to our knowledge no one has attempted to quantify their structural differences. It is important to characterize artifacts or other biases within pathway databases, which can provide a more informed interpretation for downstream analyses. In this work, we consider signaling pathways as graphs and we use topological measures to study their structure. We find that topological characterization using graphlets (small, connected subgraphs) distinguishes signaling pathways from appropriate null models of interaction networks. Next, we quantify topological similarity across pathway databases. Our analysis reveals that the pathways harbor database-specific characteristics implying that even though these databases describe the same pathways, they tend to be systematically different from one another. We show that pathway-specific topology can be uncovered after accounting for database-specific structure. This work present the first step towards elucidating common pathway structure beyond their specific database annotations.

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ComPath: An ecosystem for exploring, analyzing, and curating mappings across pathway databases

Cited by 14 publications

References 46 publications

PathMe: merging and exploring mechanistic pathway knowledge

PathMe: merging and exploring mechanistic pathway knowledge

Acute Effects on the Human Peripheral Blood Transcriptome of Decompression Sickness Secondary to Scuba Diving

Stop Bickering! Reconciling Signaling Pathway Databases with Network Topologies

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