Literature mining of genetic variants for curation: quantifying the importance of supplementary material

Jimeno-Yepes, Antonio; Verspoor, Karin

doi:10.1093/database/bau003

Cited by 32 publications

(34 citation statements)

References 32 publications

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“…Natural language processing (NLP) approaches attempt to extract this knowledge in the form of structured concepts and relationships such that it can be used for a variety of computational tasks. Just a few of many examples include identifying functional genetic variants [1], identifying biomarkers and phenotypes related to disease [2], and drug repositioning [3]. …”

Section: Introductionmentioning

confidence: 99%

Microtask Crowdsourcing for Disease Mention Annotation in Pubmed Abstracts

Good

Nanis

et al. 2014

Biocomputing 2015

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Identifying concepts and relationships in biomedical text enables knowledge to be applied in computational analyses. Many biological natural language process (BioNLP) projects attempt to address this challenge, but the state of the art in BioNLP still leaves much room for improvement. Progress in BioNLP research depends on large, annotated corpora for evaluating information extraction systems and training machine learning models. Traditionally, such corpora are created by small numbers of expert annotators often working over extended periods of time. Recent studies have shown that workers on microtask crowdsourcing platforms such as Amazon's Mechanical Turk (AMT) can, in aggregate, generate high-quality annotations of biomedical text. Here, we investigated the use of the AMT in capturing disease mentions in PubMed abstracts. We used the NCBI Disease corpus as a gold standard for refining and benchmarking our crowdsourcing protocol. After several iterations, we arrived at a protocol that reproduced the annotations of the 593 documents in the 'training set' of this gold standard with an overall F measure of 0.872 (precision 0.862, recall 0.883). The output can also be tuned to optimize for precision (max = 0.984 when recall = 0.269) or recall (max = 0.980 when precision = 0.436). Each document was examined by 15 workers, and their annotations were merged based on a simple voting method. In total 145 workers combined to complete all 593 documents in the span of 1 week at a cost of $.06 per abstract per worker. The quality of the annotations, as judged with the F measure, increases with the number of workers assigned to each task such that the system can be tuned to balance cost against quality. These results demonstrate that microtask crowdsourcing can be a valuable tool for generating well-annotated corpora in BioNLP. Data produced for this analysis are available at

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Section: Introductionmentioning

confidence: 99%

Microtask Crowdsourcing for Disease Mention Annotation in Pubmed Abstracts

Good

Nanis

et al. 2014

Biocomputing 2015

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“…COSMIC focuses on somatic mutations while InSiGHT collects germline mutations related to Lynch Syndrome for just four genes. In addition, some of the extracted mentions are not functional or significant for the disease, as previously described 11 . For instance in PMID:10469011, the mutation Ala140Thr is extracted but the article states this mutation … is known to be functionally silent , and hence was excluded from the database.…”

Section: Discussionmentioning

confidence: 95%

Mutation extraction tools can be combined for robust recognition of genetic variants in the literature

Jimeno-Yepes

Verspoor

2014

F1000Res

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As the cost of genomic sequencing continues to fall, the amount of data being collected and studied for the purpose of understanding the genetic basis of disease is increasing dramatically. Much of the source information relevant to such efforts is available only from unstructured sources such as the scientific literature, and significant resources are expended in manually curating and structuring the information in the literature. As such, there have been a number of systems developed to target automatic extraction of mutations and other genetic variation from the literature using text mining tools. We have performed a broad survey of the existing publicly available tools for extraction of genetic variants from the scientific literature. We consider not just one tool but a number of different tools, individually and in combination, and apply the tools in two scenarios. First, they are compared in an intrinsic evaluation context, where the tools are tested for their ability to identify specific mentions of genetic variants in a corpus of manually annotated papers, the Variome corpus. Second, they are compared in an extrinsic evaluation context based on our previous study of text mining support for curation of the COSMIC and InSiGHT databases. Our results demonstrate that no single tool covers the full range of genetic variants mentioned in the literature. Rather, several tools have complementary coverage and can be used together effectively. In the intrinsic evaluation on the Variome corpus, the combined performance is above 0.95 in F-measure, while in the extrinsic evaluation the combined recall performance is above 0.71 for COSMIC and above 0.62 for InSiGHT, a substantial improvement over the performance of any individual tool. Based on the analysis of these results, we suggest several directions for the improvement of text mining tools for genetic variant extraction from the literature.

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“…This approach is clearly tied to the availability of abstract-level details about an SNP existing in PubMed; this data is very sparse, in part due to the fact that specific details about genetic variants are only rarely available in published abstracts (Jimeno Yepes & Verspoor, 2014a). Unfortunately, the access to the full content of the article is limited, with only around 600k articles available from PubMed Central (a small proportion of the over 23M citations available from PubMed).…”

Section: Methodsmentioning

confidence: 99%

Associating disease-related genetic variants in intergenic regions to the genes they impact

Macintyre

Jimeno-Yepes

Ong

et al. 2014

PeerJ

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We present a method to assist in interpretation of the functional impact of intergenic disease-associated SNPs that is not limited to search strategies proximal to the SNP. The method builds on two sources of external knowledge: the growing understanding of three-dimensional spatial relationships in the genome, and the substantial repository of information about relationships among genetic variants, genes, and diseases captured in the published biomedical literature. We integrate chromatin conformation capture data (HiC) with literature support to rank putative target genes of intergenic disease-associated SNPs. We demonstrate that this hybrid method outperforms a genomic distance baseline on a small test set of expression quantitative trait loci, as well as either method individually. In addition, we show the potential for this method to uncover relationships between intergenic SNPs and target genes across chromosomes. With more extensive chromatin conformation capture data becoming readily available, this method provides a way forward towards functional interpretation of SNPs in the context of the three dimensional structure of the genome in the nucleus.

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Literature mining of genetic variants for curation: quantifying the importance of supplementary material

Cited by 32 publications

References 32 publications

Microtask Crowdsourcing for Disease Mention Annotation in Pubmed Abstracts

Microtask Crowdsourcing for Disease Mention Annotation in Pubmed Abstracts

Mutation extraction tools can be combined for robust recognition of genetic variants in the literature

Associating disease-related genetic variants in intergenic regions to the genes they impact

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