Abstract
Comprehensive knowledge of genomic variants in a biological context is key for precision medicine. As next-generation sequencing technologies improve, the amount of literature containing genomic variant data, such as new functions or related phenotypes, rapidly increases. Because numerous articles are published every day, it is almost impossible to manually curate all the variant information from the literature. Many researchers focus on creating an improved automated biomedical natural language processing (BioNLP) method that extracts useful variants and their functional information from the literature. However, there is no gold-standard data set that contains texts annotated with variants and their related functions. To overcome these limitations, we introduce a Biomedical entity Relation ONcology COrpus (BRONCO) that contains more than 400 variants and their relations with genes, diseases, drugs and cell lines in the context of cancer and anti-tumor drug screening research. The variants and their relations were manually extracted from 108 full-text articles. BRONCO can be utilized to evaluate and train new methods used for extracting biomedical entity relations from full-text publications, and thus be a valuable resource to the biomedical text mining research community. Using BRONCO, we quantitatively and qualitatively evaluated the performance of three state-of-the-art BioNLP methods. We also identified their shortcomings, and suggested remedies for each method. We implemented post-processing modules for the three BioNLP methods, which improved their performance.Database URL: http://infos.korea.ac.kr/bronco
Highlights
Modern next-generation sequencing (NGS) technologies have revolutionized modern biomedical research
We developed BRONCO—a Biomedical entity Relation ONcology COrpus—which is a variant-centric data set with related genes, diseases, drugs and cell lines
We attached the guidelines for the manual curation and the curation example file that we provided to the curators as supplementary files
Summary
Modern next-generation sequencing (NGS) technologies have revolutionized modern biomedical research. Cancer genomics studies that use NGS have identified novel somatic alterations such as single-nucleotide variants, insertions and deletions, copy number aberrations, structural variants and gene fusions as actionable targets in cancer. Variant annotation is a key step in the analysis of cancer genomics data. Many thousands of cancer genomes and exomes have been sequenced; the efforts in variant annotation have not been able to keep up with the identified variants. The functional annotation of variants can profoundly impact the conclusions of disease studies. Incorrect or incomplete annotations could cause researchers to overlook disease-relevant variants or label interesting variants as false positives
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