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Reactome

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Reactome is a free online database of biological pathways.[1][2][3] It is manually curated and authored by PhD-level biologists, in collaboration with Reactome editorial staff. The content is cross-referenced to many bioinformatics databases. The rationale behind Reactome is to visually represent biological pathways in full mechanistic detail, while making the source data available in a computationally accessible format.

Reactome is maintained by an international multidisciplinary team from OICR, OHSU, EMBL-EBI and NYULMC, with expertise in pathway curation and annotation, software development, and training and outreach, dedicated to providing the research community with openly accessible biological pathway knowledge. The Reactome team is led by Lincoln Stein (OICR). Peter D'Eustachio (NYULMC), Henning Hermjakob (EMBL-EBI), Guanming Wu (OHSU). The Reactome helpdesk can be reached via email.

Reactome: a database of reactions, pathways and biological processes.
The Reactome logo
Content
DescriptionReactome: a database of reactions, pathways and biological processes.
Contact
Primary citationPMID 37941124
Access
Data formatBioPAX
SBML
Websitehttps://reactome.org
Download URLhttps://reactome.org/download-data
Web service URLhttps://reactome.org/ContentService/
Miscellaneous
Licensehttps://reactome.org/license
Data release
frequency		https://reactome.org/about/release-calendar

The website can be used to browse pathways and submit data to a suite of data analysis tools. The underlying data is fully downloadable in a number of standard formats including PDF, SBML, Neo4j GraphDB, MySQL, PSI-MITAB, and BioPAX. Pathway diagrams use a Process Description (PD) Systems Biology Graphical Notation (SBGN)-based style.

The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways. Examples of biological pathways in Reactome include signal transduction, innate and acquired immune function, transcriptional regulation, programmed cell death and classical intermediary metabolism.

The pathways represented in Reactome are species-specific, with each pathway step supported by literature citations that contain an experimental verification of the process represented. If no experimental verification using human reagents exists, pathways may contain steps manually inferred from non-human experimental details, but only if an expert biologist, named as Author of the pathway, and a second biologist, names as Reviewer, agree that this is a valid inference to make. The human pathways are used to computationally generate by an orthology-based process derived pathways in other organisms.

Database organization

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Reactome database releases occur quarterly.

In Reactome, human biological processes are annotated by breaking them down into series of molecular events. Like classical chemistry reactions each Reactome event has input physical entities (substrates) which interact, possibly facilitated by enzymes or other molecular catalysts, to generate output physical entities (products).

Reactions include the classical chemical interconversions of intermediary metabolism, binding events, complex formation, transport events that direct molecules between cellular compartments, and events such as the activation of a protein by cleavage of one or more of its peptide bonds. Individual events can be grouped together into pathways.

Physical entities can be small molecules like glucose or ATP, or large molecules like DNA, RNA, and proteins, encoded directly or indirectly in the human genome. Physical entities are cross-referenced to relevant external databases, such as UniProt for proteins and ChEBI for small molecules. Localization of molecules to subcellular compartments is a key feature of the regulation of human biological processes, so molecules in the Reactome database are associated with specific locations. Thus in Reactome instances of the same chemical entity in different locations (e.g., extracellular glucose and cytosolic glucose) are treated as distinct chemical entities.

The Gene Ontology controlled vocabularies are used to describe the subcellular locations of molecules and reactions, molecular functions, and the larger biological processes that a specific reaction is part of.

Database content

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The database contains curated annotations that cover a diverse set of topics in molecular and cellular biology. Details of annotation topics can be found in the table of contents. Details of current and future annotation projects can be found in the calendar of annotation projects.

Reactome invites biological experts as reviewers for completed pathways that are ready for external review. Reviewers will be credited with authorship or reviewership for contributions. Each pathway is associated with a DOI and can be cited as a publication. Reactome contributions in can be easily claimed using the ORCID claiming feature.

The pathway content at Reactome is freely available for download in several data and image formats. Reactome is completely open access and open source. Usage of Reactome material is covered by two Creative Commons licenses. The terms of the Creative Commons Public Domain (CC0) License apply to all Reactome annotation files, e.g. identifier mapping data, specialized data files, and interaction data derived from Reactome. The terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License apply to all software and code, e.g. relating to the functionality of the reactome.org, derived websites and webservices, the Curator Tool, the Functional Interaction application, SQL and Graph Database data dumps, and Pathway Illustrations (Enhanced High-Level Diagrams), Icon Library, Art and Branding Materials. Reactome can be cited using their major publications or by individual pathways or images.

Tools

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There are tools on the website for viewing an interactive pathway diagram, performing pathway mapping and pathway over-representation analysis and for overlaying expression data onto Reactome pathways. The pathway mapping and over-representation tools take a single column of protein/compound identifiers, Uniprot and ChEBI accessions are preferred but the interface will accept and interpret many other identifiers or symbols. Mixed identifiers can be used. Over-representation results are presented as a list of statistically over-represented pathways.

Expression data is submitted in a multi-column format, the first column identifying the protein, additional columns are expected to be numeric expression values, they can in fact be any numeric value, e.g. differential expression, quantitative proteomics, GWAS scores. The expression data is represented as colouring of the corresponding proteins in pathway diagrams, using the colours of the visible spectrum so 'hot' red colours represent high values. If multiple columns of numeric data are submitted the overlay tool can display them as separate 'experiments', e.g. timepoints or a disease progression.

The database can be browsed and searched as an on-line textbook.[4] An online users guide is available. Users can also download the current data set or individual pathways and reactions in a variety of formats including PDF, BioPAX, and SBML[5]

Reactome also has a ReactomeGSA[6] tool, integrated into the Reactome Analysis Tools that allows comparative pathway analyses of multi-omics datasets, with compatibility with single-cell RNA-seq data. Public data from EBI Expression Atlas, Single Cell Expression Atlas, and NCBI GREIN GEO data can be integrated into the analysis. ReactomeGSA is also available as a R Bioconductor package.

Reactome also has a ReactomeIDG[7] web portal, since 2023, aimed to place dark proteins in the context of manually curated, highly reliable Reactome pathways, to facilitate the understanding of functions and predicting therapeutic potential of dark or understudied proteins. Enhanced visualization features implemented at the portal allow users to investigate the functional contexts for dark proteins based on tissue-specific gene or protein expression, drug-target interactions, or protein or gene pairwise relationships in the original Reactome's systems biology graph notation (SBGN) diagrams or the new simplified functional interaction (FI) network view of pathways.

ReactomeFIViz is a Cytoscape app designed to find pathways and network patterns related to diseases. The app accesses Reactome pathways, perform pathway enrichment analysis for a set of genes, visualize hit pathways, and investigate functional relationships among genes in hit pathways. The app also accesses the Reactome Functional Interaction (FI) network.[8]

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See also

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There are several Reactomes that concentrate on specific organisms, the largest of these is focused on human biology, described on this page.

See Plant Reactome.

References

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  1. ^ Croft, D.; O'Kelly, G.; Wu, G.; Haw, R.; Gillespie, M.; Matthews, L.; Caudy, M.; Garapati, P.; Gopinath, G.; Jassal, B.; Jupe, S.; Kalatskaya, I.; Mahajan, S.; May, B.; Ndegwa, N.; Schmidt, E.; Shamovsky, V.; Yung, C.; Birney, E.; Hermjakob, H.; d'Eustachio, P.; Stein, L. (2010). "Reactome: A database of reactions, pathways and biological processes". Nucleic Acids Research. 39 (Database issue): D691–D697. doi:10.1093/nar/gkq1018. PMC 3013646. PMID 21067998.
  2. ^ Joshi-Tope, G.; Gillespie, M.; Vastrik, I.; d'Eustachio, P.; Schmidt, E.; De Bono, B.; Jassal, B.; Gopinath, G.; Wu, G.; Matthews, L.; Lewis, S.; Birney, E.; Stein, L. (2004). "Reactome: A knowledgebase of biological pathways". Nucleic Acids Research. 33 (Database issue): D428–D432. doi:10.1093/nar/gki072. PMC 540026. PMID 15608231.
  3. ^ Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, Caudy M, Garapati P, Gillespie M, Kamdar MR, Jassal B, Jupe S, Matthews L, May B, Palatnik S, Rothfels K, Shamovsky V, Song H, Williams M, Birney E, Hermjakob H, Stein L, D'Eustachio P (2014). "The Reactome pathway knowledgebase". Nucleic Acids Res. 42 (Database issue): D472–7. doi:10.1093/nar/gkt1102. PMC 3965010. PMID 24243840.
  4. ^ Haw, R; Stein, L (Jun 2012). "Using the reactome database". Current Protocols in Bioinformatics. Chapter 8: 8.7.1–8.7.23. doi:10.1002/0471250953.bi0807s38. PMC 3427849. PMID 22700314.
  5. ^ Croft, D (2013). "Building Models Using Reactome Pathways as Templates". In Silico Systems Biology. Methods in Molecular Biology. Vol. 1021. pp. 273–83. doi:10.1007/978-1-62703-450-0_14. ISBN 978-1-62703-449-4. PMC 11184635. PMID 23715990.
  6. ^ Griss, Johannes; Viteri, Guilherme; Sidiropoulos, Konstantinos; Nguyen, Vy; Fabregat, Antonio; Hermjakob, Henning (December 2020). "ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis". Molecular & Cellular Proteomics. 19 (12): 2115–2125. doi:10.1074/mcp.TIR120.002155. PMC 7710148. PMID 32907876.
  7. ^ Beavers, Deidre; Brunson, Timothy; Sanati, Nasim; Matthews, Lisa; Haw, Robin; Shorser, Solomon; Sevilla, Cristoffer; Viteri, Guilherme; Conley, Patrick; Rothfels, Karen; Hermjakob, Henning; Stein, Lincoln; D'Eustachio, Peter; Wu, Guanming (July 2023). "Illuminate the Functions of Dark Proteins Using the Reactome-IDG Web Portal". Current Protocols. 3 (7): e845. doi:10.1002/cpz1.845. ISSN 2691-1299. PMC 10399304. PMID 37467006.
  8. ^ Wu, Guanming; Feng, Xin; Stein, Lincoln (2010). "A human functional protein interaction network and its application to cancer data analysis". Genome Biology. 11 (5): R53. doi:10.1186/gb-2010-11-5-r53. ISSN 1474-760X. PMC 2898064. PMID 20482850.
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Other molecular pathway databases

  1. ^ Bohler, Anwesha; Wu, Guanming; Kutmon, Martina; Pradhana, Leontius Adhika; Coort, Susan L.; Hanspers, Kristina; Haw, Robin; Pico, Alexander R.; Evelo, Chris T.; Blackwell, Kim T. (20 May 2016). "Reactome from a WikiPathways Perspective". PLOS Computational Biology. 12 (5): e1004941. Bibcode:2016PLSCB..12E4941B. doi:10.1371/journal.pcbi.1004941. PMC 4874630. PMID 27203685.
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