Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes.

Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.

ReactomeGSA: new features to simplify public data reuse.

MOTIVATION: ReactomeGSA is part of the Reactome knowledgebase and one of the leading multi-omics pathway analysis platforms. ReactomeGSA provides access to quantitative pathway analysis methods supporting different 'omics data types. Additionally, ReactomeGSA can process different datasets simultaneously, leading to a comparative pathway analysis that can also be performed across different species. RESULTS: We present a major update to the ReactomeGSA analysis platforms that greatly simplifies the reuse and direct integration of public data. In order to increase the number of available datasets, we developed the new grein_loader Python application that can directly fetch experiments from the GREIN resource. This enabled us to support both EMBL-EBI's Expression Atlas and GEO RNA-seq Experiments Interactive Navigator within ReactomeGSA. To further increase the visibility and simplify the reuse of public datasets, we integrated a novel search function into ReactomeGSA that enables users to search for public datasets across both supported resources. Finally, we completely re-developed ReactomeGSA's web-frontend and R/Bioconductor package to support the new search and loading features, and greatly simplify the use of ReactomeGSA. AVAILABILITY AND IMPLEMENTATION: The new ReactomeGSA web frontend is available at https://www.reactome.org/gsa with an built-in, interactive tutorial. The ReactomeGSA R package (https://bioconductor.org/packages/release/bioc/html/ReactomeGSA.html) is available through Bioconductor and shipped with detailed documentation and vignettes. The grein_loader Python application is available through the Python Package Index (pypi). The complete source code for all applications is available on GitHub at https://github.com/grisslab/grein_loader and https://github.com/reactome.

The Reactome Pathway Knowledgebase 2024.

The Reactome Knowledgebase (https://reactome.org), an Elixir and GCBR core biological data resource, provides manually curated molecular details of a broad range of normal and disease-related biological processes. Processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Here we review progress towards annotation of the entire human proteome, targeted annotation of disease-causing genetic variants of proteins and of small-molecule drugs in a pathway context, and towards supporting explicit annotation of cell- and tissue-specific pathways. Finally, we briefly discuss issues involved in making Reactome more fully interoperable with other related resources such as the Gene Ontology and maintaining the resulting community resource network.

ChatGPT usage in the Reactome curation process.

Appreciating the rapid advancement and ubiquity of generative AI, particularly ChatGPT, a chatbot using large language models like GPT, we endeavour to explore the potential application of ChatGPT in the data collection and annotation stages within the Reactome curation process. This exploration aimed to create an automated or semi-automated framework to mitigate the extensive manual effort traditionally required for gathering and annotating information pertaining to biological pathways, adopting a Reactome "reaction-centric" approach. In this pilot study, we used ChatGPT/GPT4 to address gaps in the pathway annotation and enrichment in parallel with the conventional manual curation process. This approach facilitated a comparative analysis, where we assessed the outputs generated by ChatGPT against manually extracted information. The primary objective of this comparison was to ascertain the efficiency of integrating ChatGPT or other large language models into the Reactome curation workflow and helping plan our annotation pipeline, ultimately improving our protein-to-pathway association in a reliable and automated or semi-automated way. In the process, we identified some promising capabilities and inherent challenges associated with the utilisation of ChatGPT/GPT4 in general and also specifically in the context of Reactome curation processes. We describe approaches and tools for refining the output given by ChatGPT/GPT4 that aid in generating more accurate and detailed output.

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes.

Disease variant annotation in the context of biological reactions and pathways can provide a standardized overview of molecular phenotypes of pathogenic mutations that is amenable to computational mining and mathematical modeling. Reactome, an open source, manually curated, peer-reviewed database of human biological pathways, provides annotations for over 4000 disease variants of close to 400 genes in the context of ∼800 disease reactions constituting ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics (ACMG). Reactome's pathway-based, reaction-specific disease variant dataset and data model provide a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity.

Using the Reactome Database.

Pathway databases provide descriptions of the roles of proteins, nucleic acids, lipids, carbohydrates, and other molecular entities within their biological cellular contexts. Pathway-centric views of these roles may allow for the discovery of unexpected functional relationships in data such as gene expression profiles and somatic mutation catalogues from tumor cells. For this reason, there is a high demand for high-quality pathway databases and their associated tools. The Reactome project (a collaboration between the Ontario Institute for Cancer Research, New York University Langone Health, the European Bioinformatics Institute, and Oregon Health & Science University) is one such pathway database. Reactome collects detailed information on biological pathways and processes in humans from the primary literature. Reactome content is manually curated, expert-authored, and peer-reviewed and spans the gamut from simple intermediate metabolism to signaling pathways and complex cellular events. This information is supplemented with likely orthologous molecular reactions in mouse, rat, zebrafish, worm, and other model organisms. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Browsing a Reactome pathway Basic Protocol 2: Exploring Reactome annotations of disease and drugs Basic Protocol 3: Finding the pathways involving a gene or protein Alternate Protocol 1: Finding the pathways involving a gene or protein using UniProtKB (SwissProt), Ensembl, or Entrez gene identifier Alternate Protocol 2: Using advanced search Basic Protocol 4: Using the Reactome pathway analysis tool to identify statistically overrepresented pathways Basic Protocol 5: Using the Reactome pathway analysis tool to overlay expression data onto Reactome pathway diagrams Basic Protocol 6: Comparing inferred model organism and human pathways using the Species Comparison tool Basic Protocol 7: Comparing tissue-specific expression using the Tissue Distribution tool.

The IntAct database: efficient access to fine-grained molecular interaction data.

The IntAct molecular interaction database (https://www.ebi.ac.uk/intact) is a curated resource of molecular interactions, derived from the scientific literature and from direct data depositions. As of August 2021, IntAct provides more than one million binary interactions, curated by twelve global partners of the International Molecular Exchange consortium, for which the IntAct database provides a shared curation and dissemination platform. The IMEx curation policy has always emphasised a fine-grained data and curation model, aiming to capture the relevant experimental detail essential for the interpretation of the provided molecular interaction data. Here, we present recent curation focus and progress, as well as a completely redeveloped website which presents IntAct data in a much more user-friendly and detailed way.

The reactome pathway knowledgebase 2022.

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied ('dark') proteins from analyzed datasets in the context of Reactome's manually curated pathways.

IntAct App: a Cytoscape application for molecular interaction network visualization and analysis.

SUMMARY: IntAct App is a Cytoscape 3 application that grants in-depth access to IntAct's molecular interaction data. It build networks where nodes are interacting molecules (mainly proteins, but also genes, RNA, chemicals…) and edges represent evidence of interaction. Users can query a network by providing its molecules, identified by different fields and optionally include all their interacting partners in the resulting network. The app offers three visualizations: one only displaying interactions, another representing every evidence and the last one emphasizing evidence where mutated versions of proteins were used. Users can also filter networks and click on nodes and edges to access all their related details. Finally, the application supports automation of its main features via Cytoscape commands. AVAILABILITY AND IMPLEMENTATION: Implementation available at https://apps.cytoscape.org/apps/intactapp, while the source code is available at https://github.com/EBI-IntAct/IntactApp.