Enhancing Precision Medicine: An Automatic Pipeline Approach for Exploring Genetic Variant-Disease Literature

[EN] Advancements in genomics have generated vast amounts of data, requiring efficient methods for exploring the relationships between genetic variants and diseases. This paper presents a pipeline approach that automatically integrates diverse biomedical databases, including NCBI Gene, MeSH, LitVar2...

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Hauptverfasser: Contreras-Ochando, Lidia, Marco-García, Pere, León-Palacio, Ana, Hurtado Oliver, Lluis Felip, Pla Santamaría, Ferran, Segarra Soriano, Encarnación
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Zusammenfassung:[EN] Advancements in genomics have generated vast amounts of data, requiring efficient methods for exploring the relationships between genetic variants and diseases. This paper presents a pipeline approach that automatically integrates diverse biomedical databases, including NCBI Gene, MeSH, LitVar2, PubTator, and SynVar, for retrieving comprehensive information about genes, variants, diseases, and associated literature. The pipeline consists of multiple stages: querying and searching across the different databases, extracting relevant data, and applying filters to refine the results. Its goal is to bridge the gap in information retrieval related to genetic variants and diseases by providing a systematic framework for discovering relevant literature. The pipeline uses open-access sources to uncover additional articles not referenced in expert reports that mention the genetic variants of interest. In this paper, we present the methodology of the pipeline, discuss its limitations and highlight its potential for advancing information systems, data management, and interoperability in the domains of genomics and precision medicine. This work is partially supported by MCIN/AEI/10.13039/501100011033, by the 'European Union' and 'NextGenerationEU/MRR', and by 'ERDF A way of making Europe' under grants PDC2021-120846-C44 and PID2021-126061OB-C41. It is also partially supported by the Generalitat Valenciana underproject CIPROM/2021/023. We would like to thank the authors of LitVar2 for their valuable assistance. Contreras-Ochando, L.; Marco-García, P.; León-Palacio, A.; Hurtado Oliver, LF.; Pla Santamaría, F.; Segarra Soriano, E. (2023). Enhancing Precision Medicine: An Automatic Pipeline Approach for Exploring Genetic Variant-Disease Literature. Springer Cham. 35-43. https://doi.org/10.1007/978-3-031-47112-4_4 Allot, A., Peng, Y., Wei, C.H., Lee, K., Phan, L., Lu, Z.: LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC. Nucleic Acids Res. 46(W1), W530–W536 (2018) Allot, A., et al.: Tracking genetic variants in the biomedical literature using LitVar 2.0. Nat. Genet. 55, 901–903 (2023) Cano-Gamez, E., Trynka, G.: From GWAS to function: using functional genomics to identify the mechanisms underlying complex diseases. Front. Genet. 11, 424 (2020) Chunn, L.M., et al.: Mastermind: a comprehensive genomic association search engine for empirical evidence curation and genetic variant interpretation. Front. Genet. 11, 577152 (2020) Den Dunnen,