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1.
Curr Protoc ; 3(7): e845, 2023 Jul.
Article in English | MEDLINE | ID: mdl-37467006

ABSTRACT

Understudied or dark proteins have the potential to shed light on as-yet undiscovered molecular mechanisms that underlie phenotypes and suggest innovative therapeutic approaches for many diseases. The Reactome-IDG (Illuminating the Druggable Genome) project aims to place dark proteins in the context of manually curated, highly reliable pathways in Reactome, the most comprehensive, open-source biological pathway knowledgebase, facilitating the understanding functions and predicting therapeutic potentials of dark proteins. The Reactome-IDG web portal, deployed at https://idg.reactome.org, provides a simple, interactive web page for users to search pathways that may functionally interact with dark proteins, enabling the prediction of functions of dark proteins in the context of Reactome pathways. 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. The protocols in this chapter describe step-by-step procedures to use the web portal to learn biological functions of dark proteins in the context of Reactome pathways. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Search for interacting pathways of a protein Support Protocol: Interacting pathway results for an annotated protein Alternate Protocol: Use individual pairwise relationships to predict interacting pathways of a protein Basic Protocol 2: Using the IDG pathway browser to study interacting pathways Basic Protocol 3: Overlaying tissue-specific expression data Basic Protocol 4: Overlaying protein/gene pairwise relationships in the pathway context Basic Protocol 5: Visualizing drug/target interactions.


Subject(s)
Metabolic Networks and Pathways , Signal Transduction , Systems Biology/methods , Proteomics , Proteins/metabolism
2.
bioRxiv ; 2023 Jun 05.
Article in English | MEDLINE | ID: mdl-37333417

ABSTRACT

Limited knowledge about a substantial portion of protein coding genes, known as "dark" proteins, hinders our understanding of their functions and potential therapeutic applications. To address this, we leveraged Reactome, the most comprehensive, open source, open-access pathway knowledgebase, to contextualize dark proteins within biological pathways. By integrating multiple resources and employing a random forest classifier trained on 106 protein/gene pairwise features, we predicted functional interactions between dark proteins and Reactome-annotated proteins. We then developed three scores to measure the interactions between dark proteins and Reactome pathways, utilizing enrichment analysis and fuzzy logic simulations. Correlation analysis of these scores with an independent single-cell RNA sequencing dataset provided supporting evidence for this approach. Furthermore, systematic natural language processing (NLP) analysis of over 22 million PubMed abstracts and manual checking of the literature associated with 20 randomly selected dark proteins reinforced the predicted interactions between proteins and pathways. To enhance the visualization and exploration of dark proteins within Reactome pathways, we developed the Reactome IDG portal, deployed at https://idg.reactome.org, a web application featuring tissue-specific protein and gene expression overlay, as well as drug interactions. Our integrated computational approach, together with the user-friendly web platform, offers a valuable resource for uncovering potential biological functions and therapeutic implications of dark proteins.

3.
Nucleic Acids Res ; 50(D1): D687-D692, 2022 01 07.
Article in English | MEDLINE | ID: mdl-34788843

ABSTRACT

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.


Subject(s)
Antiviral Agents/pharmacology , Knowledge Bases , Proteins/metabolism , COVID-19/metabolism , Data Curation , Genome, Human , Host-Pathogen Interactions , Humans , Proteins/genetics , Signal Transduction , Software
4.
Nucleic Acids Res ; 48(D1): D498-D503, 2020 01 08.
Article in English | MEDLINE | ID: mdl-31691815

ABSTRACT

The Reactome Knowledgebase (https://reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations in a single consistent data model, an extended version of a classic metabolic map. Reactome functions both as an archive of biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. To extend our ability to annotate human disease processes, we have implemented a new drug class and have used it initially to annotate drugs relevant to cardiovascular disease. Our annotation model depends on external domain experts to identify new areas for annotation and to review new content. New web pages facilitate recruitment of community experts and allow those who have contributed to Reactome to identify their contributions and link them to their ORCID records. To improve visualization of our content, we have implemented a new tool to automatically lay out the components of individual reactions with multiple options for downloading the reaction diagrams and associated data, and a new display of our event hierarchy that will facilitate visual interpretation of pathway analysis results.


Subject(s)
Databases, Chemical , Databases, Pharmaceutical , Knowledge Bases , Software , Genome, Human , Humans , Metabolic Networks and Pathways , Protein Interaction Maps , Signal Transduction
5.
Database (Oxford) ; 20192019 01 01.
Article in English | MEDLINE | ID: mdl-31802127

ABSTRACT

Reactome is a manually curated, open-source, open-data knowledge base of biomolecular pathways. Reactome has always provided clear credit attribution for authors, curators and reviewers through fine-grained annotation of all three roles at the reaction and pathway level. These data are visible in the web interface and provided through the various data download formats. To enhance visibility and credit attribution for the work of authors, curators and reviewers, and to provide additional opportunities for Reactome community engagement, we have implemented key changes to Reactome: contributor names are now fully searchable in the web interface, and contributors can 'claim' their contributions to their ORCID profile with a few clicks. In addition, we are reaching out to domain experts to request their help in reviewing and editing Reactome pathways through a new 'Contribution' section, highlighting pathways which are awaiting community review. Database URL: https://reactome.org.


Subject(s)
Data Curation , Signal Transduction , User-Computer Interface
6.
Nucleic Acids Res ; 46(D1): D649-D655, 2018 01 04.
Article in English | MEDLINE | ID: mdl-29145629

ABSTRACT

The Reactome Knowledgebase (https://reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism, and other cellular processes as an ordered network of molecular transformations-an extended version of a classic metabolic map, in a single consistent data model. Reactome functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data such as gene expression profiles or somatic mutation catalogues from tumor cells. To support the continued brisk growth in the size and complexity of Reactome, we have implemented a graph database, improved performance of data analysis tools, and designed new data structures and strategies to boost diagram viewer performance. To make our website more accessible to human users, we have improved pathway display and navigation by implementing interactive Enhanced High Level Diagrams (EHLDs) with an associated icon library, and subpathway highlighting and zooming, in a simplified and reorganized web site with adaptive design. To encourage re-use of our content, we have enabled export of pathway diagrams as 'PowerPoint' files.


Subject(s)
Knowledge Bases , Metabolic Networks and Pathways , Computer Graphics , Databases, Chemical , Databases, Protein , Humans , Internet , Molecular Sequence Annotation , Signal Transduction , User-Computer Interface
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