ArticleBioinformatics (Oxford, England)2018
tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed, 99 citations in OpenAlex.
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Despite significant efforts in expert curation, clinical relevance about most of the 154 million dbSNP reference variants (RS) remains unknown. However, a wealth of knowledge about the variant biological function/disease impact is buried in unstructured literature data. Previous studies have attempted to harvest and unlock such information with text-mining techniques but are of limited use because their mutation extraction results are not standardized or integrated with curated data. Results: We propose an automatic method to extract and normalize variant mentions to unique identifiers (dbSNP RSIDs). Our method, in benchmarking results, demonstrates a high F-measure of ∼90% and compared favorably to the state of the art. Next, we applied our approach to the entire PubMed and validated the results by verifying that each extracted variant-gene pair matched the dbSNP annotation based on mapped genomic position, and by analyzing variants curated in ClinVar. We then determined which text-mined variants and genes constituted novel discoveries. Our analysis reveals 41 889 RS numbers (associated with 9151 genes) not found in ClinVar. Moreover, we obtained a rich set worth further review: 12 462 rare variants (MAF ≤ 0.01) in 3849 genes which are presumed to be deleterious and not frequently found in the general population. To our knowledge, this is the first large-scale study to analyze and integrate text-mined variant data with curated knowledge in existing databases. Our results suggest that databases can be significantly enriched by text mining and that the combined information can greatly assist human efforts in evaluating/prioritizing variants in genomic research. Availability and implementation: The tmVar 2.0 source code and corpus are freely available at https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/. Contact: zhiyong.lu@nih.gov.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.