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.