PathMe: merging and exploring mechanistic pathway knowledge

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.

[1]  Lincoln D. Stein,et al.  Impact of outdated gene annotations on pathway enrichment analysis , 2016, Nature Methods.

[2]  Martin Hofmann-Apitius,et al.  BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language , 2018, bioRxiv.

[3]  Edoardo Saccenti,et al.  Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling , 2018, bioRxiv.

[4]  Gary D Bader,et al.  BioPAX – A community standard for pathway data sharing , 2010, Nature Biotechnology.

[5]  Pooja Mittal,et al.  A novel signaling pathway impact analysis , 2009, Bioinform..

[6]  Andreas Zell,et al.  KEGGtranslator: visualizing and converting the KEGG PATHWAY database to various formats , 2011, Bioinform..

[7]  Gary D. Bader,et al.  Cytoscape.js: a graph theory library for visualisation and analysis , 2015, Bioinform..

[8]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[9]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[10]  Chris T. A. Evelo,et al.  The BridgeDb framework: standardized access to gene, protein and metabolite identifier mapping services , 2010, BMC Bioinformatics.

[11]  Charles Tapley Hoyt,et al.  PyBEL: a computational framework for Biological Expression Language , 2017, Bioinform..

[12]  Ram Rup Sarkar,et al.  Comparison of human cell signaling pathway databases—evolution, drawbacks and challenges , 2015, Database J. Biol. Databases Curation.

[13]  Mathew W. Wright,et al.  The HUGO Gene Nomenclature Committee (HGNC) , 2001, Human Genetics.

[14]  Ryan Miller,et al.  Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources , 2016, PLoS Comput. Biol..

[15]  Bofei Zhang,et al.  RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites , 2018, Metabolites.

[16]  Philip Lijnzaad,et al.  The Ensembl genome database project , 2002, Nucleic Acids Res..

[17]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[18]  Benjamin M. Gyori,et al.  FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining , 2018, bioRxiv.

[19]  Doron Lancet,et al.  PathCards: multi-source consolidation of human biological pathways , 2015, Database J. Biol. Databases Curation.

[20]  Ralf Herwig,et al.  ConsensusPathDB—a database for integrating human functional interaction networks , 2008, Nucleic Acids Res..

[21]  Robert D. Finn,et al.  The Pfam protein families database: towards a more sustainable future , 2015, Nucleic Acids Res..

[22]  Sang Gyun Kim,et al.  Rapamycin differentially inhibits S6Ks and 4E-BP1 to mediate cell-type-specific repression of mRNA translation , 2008, Proceedings of the National Academy of Sciences.

[23]  Emmanuel Barillot,et al.  BiNoM 2.0, a Cytoscape plugin for accessing and analyzing pathways using standard systems biology formats , 2013, BMC Systems Biology.

[24]  Tatiana A. Tatusova,et al.  Entrez Gene: gene-centered information at NCBI , 2004, Nucleic Acids Res..

[25]  R. Memmott,et al.  Akt-dependent and -independent mechanisms of mTOR regulation in cancer. , 2009, Cellular signalling.

[26]  Gabriele Sales,et al.  metaGraphite–a new layer of pathway annotation to get metabolite networks , 2018, Bioinform..

[27]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[28]  Martin Hofmann-Apitius,et al.  ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases , 2018, npj Systems Biology and Applications.

[29]  Gary D. Bader,et al.  Pathway Commons, a web resource for biological pathway data , 2010, Nucleic Acids Res..

[30]  Nuno Nunes,et al.  PathVisio 3: An Extendable Pathway Analysis Toolbox , 2015, PLoS Comput. Biol..

[31]  The Gene Ontology Consortium,et al.  Expansion of the Gene Ontology knowledgebase and resources , 2016, Nucleic Acids Res..

[32]  Sarala M. Wimalaratne,et al.  The Systems Biology Graphical Notation , 2009, Nature Biotechnology.

[33]  F. Pattou,et al.  mTORC1 and mTORC2 regulate insulin secretion through Akt in INS-1 cells. , 2013, The Journal of endocrinology.

[34]  Minoru Kanehisa,et al.  KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..

[35]  Chris T. A. Evelo,et al.  Reactome from a WikiPathways Perspective , 2016, PLoS Comput. Biol..

[36]  Christoph Steinbeck,et al.  ChEBI in 2016: Improved services and an expanding collection of metabolites , 2015, Nucleic Acids Res..

[37]  Gabriele Sales,et al.  graphite - a Bioconductor package to convert pathway topology to gene network , 2012, BMC Bioinformatics.

[38]  Gary D. Bader,et al.  Using Biological Pathway Data with Paxtools , 2013, PLoS Comput. Biol..

[39]  Julio Saez-Rodriguez,et al.  OmniPath: guidelines and gateway for literature-curated signaling pathway resources , 2016, Nature Methods.

[40]  Yanli Wang,et al.  PubChem: Integrated Platform of Small Molecules and Biological Activities , 2008 .

[41]  Ryan Miller,et al.  WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research , 2017, Nucleic Acids Res..

[42]  Gerbert A. Jansen,et al.  Critical assessment of human metabolic pathway databases: a stepping stone for future integration , 2011, BMC Systems Biology.

[43]  Jean-Baptiste Poline,et al.  Experimenting with reproducibility: a case study of robustness in bioinformatics , 2018, GigaScience.

[44]  Davide Altomare,et al.  Homeostasis and the Importance for a Balance Between AKT/mTOR Activity and Intracellular Signaling , 2012, Current medicinal chemistry.

[45]  S K Burley,et al.  Hierarchical phosphorylation of the translation inhibitor 4E-BP1. , 2001, Genes & development.

[46]  Benjamin M. Gyori,et al.  From word models to executable models of signaling networks using automated assembly , 2017, bioRxiv.

[47]  Jing Chen,et al.  NDEx, the Network Data Exchange. , 2015, Cell systems.