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RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine
BACKGROUND: Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Tran...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520835/ https://www.ncbi.nlm.nih.gov/pubmed/36175836 http://dx.doi.org/10.1186/s12859-022-04932-3 |
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author | Wood, E. C. Glen, Amy K. Kvarfordt, Lindsey G. Womack, Finn Acevedo, Liliana Yoon, Timothy S. Ma, Chunyu Flores, Veronica Sinha, Meghamala Chodpathumwan, Yodsawalai Termehchy, Arash Roach, Jared C. Mendoza, Luis Hoffman, Andrew S. Deutsch, Eric W. Koslicki, David Ramsey, Stephen A. |
author_facet | Wood, E. C. Glen, Amy K. Kvarfordt, Lindsey G. Womack, Finn Acevedo, Liliana Yoon, Timothy S. Ma, Chunyu Flores, Veronica Sinha, Meghamala Chodpathumwan, Yodsawalai Termehchy, Arash Roach, Jared C. Mendoza, Luis Hoffman, Andrew S. Deutsch, Eric W. Koslicki, David Ramsey, Stephen A. |
author_sort | Wood, E. C. |
collection | PubMed |
description | BACKGROUND: Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API). RESULTS: To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building—and hosting a web API for querying—a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink. CONCLUSION: RTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json. The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04932-3. |
format | Online Article Text |
id | pubmed-9520835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-95208352022-09-30 RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine Wood, E. C. Glen, Amy K. Kvarfordt, Lindsey G. Womack, Finn Acevedo, Liliana Yoon, Timothy S. Ma, Chunyu Flores, Veronica Sinha, Meghamala Chodpathumwan, Yodsawalai Termehchy, Arash Roach, Jared C. Mendoza, Luis Hoffman, Andrew S. Deutsch, Eric W. Koslicki, David Ramsey, Stephen A. BMC Bioinformatics Database BACKGROUND: Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API). RESULTS: To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building—and hosting a web API for querying—a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink. CONCLUSION: RTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json. The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04932-3. BioMed Central 2022-09-29 /pmc/articles/PMC9520835/ /pubmed/36175836 http://dx.doi.org/10.1186/s12859-022-04932-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Database Wood, E. C. Glen, Amy K. Kvarfordt, Lindsey G. Womack, Finn Acevedo, Liliana Yoon, Timothy S. Ma, Chunyu Flores, Veronica Sinha, Meghamala Chodpathumwan, Yodsawalai Termehchy, Arash Roach, Jared C. Mendoza, Luis Hoffman, Andrew S. Deutsch, Eric W. Koslicki, David Ramsey, Stephen A. RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine |
title | RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine |
title_full | RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine |
title_fullStr | RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine |
title_full_unstemmed | RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine |
title_short | RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine |
title_sort | rtx-kg2: a system for building a semantically standardized knowledge graph for translational biomedicine |
topic | Database |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520835/ https://www.ncbi.nlm.nih.gov/pubmed/36175836 http://dx.doi.org/10.1186/s12859-022-04932-3 |
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