Toward an automatic method for extracting cancer- and other disease-related point mutations from the biomedical literature

ABCMdb: a database for the comparative analysis of protein mutations in ABC transporters, and a potential framework for a general application

To overcome the pathological phenomena caused by altered function of ABC (ATP Binding Cassette) proteins, their mechanisms of action are extensively investigated, often involving the design of mutant constructs for experiments. Designing mutagenetic constructs, interpreting the result of mutagenetic experiments, and finding individual genetic variants require an extensive knowledge of previously published mutations. To aid the recapitulation of mutations described in the literature, we set up a database of ABC protein mutations (ABCMdb) extracted from full-text papers using an automatic mining approach. We have also developed a Web application interface to compare mutations in different ABC proteins using sequence alignments and to interactively map the mutations to 3D structural models. Currently our database contains protein mutations published for ABCB1, ABCB11, ABCC1, ABCC6, ABCC7, and the proteins of the ABCG subfamily. The database will be extended to include other members and subfamilies, and to provide information on whether or not a mutation is disease causing, represents a high-incidence polymorphism, or was generated only in vitro. The ABCMdb database should already help to compare the effects of mutations at homologous positions in different ABC proteins, and its interactive tools aim to advance the design of experiments for a wider range of proteins.

A mutation-centric approach to identifying pharmacogenomic relations in text

OBJECTIVES

To explore the notion of mutation-centric pharmacogenomic relation extraction and to evaluate our approach against reference pharmacogenomic relations.

METHODS

From a corpus of MEDLINE abstracts relevant to genetic variation, we identify co-occurrences between drug mentions extracted using MetaMap and RxNorm, and genetic variants extracted by EMU. The recall of our approach is evaluated against reference relations curated manually in PharmGKB. We also reviewed a random sample of 180 relations in order to evaluate its precision.

RESULTS

One crucial aspect of our strategy is the use of biological knowledge for identifying specific genetic variants in text, not simply gene mentions. On the 104 reference abstracts from PharmGKB, the recall of our mutation-centric approach is 33-46%. Applied to 282,000 abstracts from MEDLINE, our approach identifies pharmacogenomic relations in 4534 abstracts, with a precision of 65%.

CONCLUSIONS

Compared to a relation-centric approach, our mutation-centric approach shows similar recall, but slightly lower precision. We show that both approaches have limited overlap in their results, but are complementary and can be used in combination. Rather than a solution for the automatic curation of pharmacogenomic knowledge, we see these high-throughput approaches as tools to assist biocurators in the identification of pharmacogenomic relations of interest from the published literature. This investigation also identified three challenging aspects of the extraction of pharmacogenomic relations, namely processing full-text articles, sequence validation of DNA variants and resolution of genetic variants to reference databases, such as dbSNP.

Incorporating molecular and functional context into the analysis and prioritization of human variants associated with cancer

BACKGROUND AND OBJECTIVE

With recent breakthroughs in high-throughput sequencing, identifying deleterious mutations is one of the key challenges for personalized medicine. At the gene and protein level, it has proven difficult to determine the impact of previously unknown variants. A statistical method has been developed to assess the significance of disease mutation clusters on protein domains by incorporating domain functional annotations to assist in the functional characterization of novel variants.

METHODS

Disease mutations aggregated from multiple databases were mapped to domains, and were classified as either cancer- or non-cancer-related. The statistical method for identifying significantly disease-associated domain positions was applied to both sets of mutations and to randomly generated mutation sets for comparison. To leverage the known function of protein domain regions, the method optionally distributes significant scores to associated functional feature positions.

RESULTS

Most disease mutations are localized within protein domains and display a tendency to cluster at individual domain positions. The method identified significant disease mutation hotspots in both the cancer and non-cancer datasets. The domain significance scores (DS-scores) for cancer form a bimodal distribution with hotspots in oncogenes forming a second peak at higher DS-scores than non-cancer, and hotspots in tumor suppressors have scores more similar to non-cancers. In addition, on an independent mutation benchmarking set, the DS-score method identified mutations known to alter protein function with very high precision.

CONCLUSION

By aggregating mutations with known disease association at the domain level, the method was able to discover domain positions enriched with multiple occurrences of deleterious mutations while incorporating relevant functional annotations. The method can be incorporated into translational bioinformatics tools to characterize rare and novel variants within large-scale sequencing studies.

A SNPshot of PubMed to associate genetic variants with drugs, diseases, and adverse reactions

MOTIVATION

Genetic factors determine differences in pharmacokinetics, drug efficacy, and drug responses between individuals and sub-populations. Wrong dosages of drugs can lead to severe adverse drug reactions in individuals whose drug metabolism drastically differs from the "assumed average". Databases such as PharmGKB are excellent sources of pharmacogenetic information on enzymes, genetic variants, and drug response affected by changes in enzymatic activity. Here, we seek to aid researchers, database curators, and clinicians in their search for relevant information by automatically extracting these data from literature.

APPROACH

We automatically populate a repository of information on genetic variants, relations to drugs, occurrence in sub-populations, and associations with disease. We mine textual data from PubMed abstracts to discover such genotype-phenotype associations, focusing on SNPs that can be associated with variations in drug response. The overall repository covers relations found between genes, variants, alleles, drugs, diseases, adverse drug reactions, populations, and allele frequencies. We cross-reference these data to EntrezGene, PharmGKB, PubChem, and others.

RESULTS

The performance regarding entity recognition and relation extraction yields a precision of 90-92% for the major entity types (gene, drug, disease), and 76-84% for relations involving these types. Comparison of our repository to PharmGKB reveals a coverage of 93% of gene-drug associations in PharmGKB and 97% of the gene-variant mappings based on 180,000 PubMed abstracts.

AVAILABILITY

http://bioai4core.fulton.asu.edu/snpshot.