Dataset Information

Annotating genes and genomes with DNA sequences extracted from biomedical articles.

ABSTRACT:

Motivation

Increasing rates of publication and DNA sequencing make the problem of finding relevant articles for a particular gene or genomic region more challenging than ever. Existing text-mining approaches focus on finding gene names or identifiers in English text. These are often not unique and do not identify the exact genomic location of a study.

Results

Here, we report the results of a novel text-mining approach that extracts DNA sequences from biomedical articles and automatically maps them to genomic databases. We find that ?20% of open access articles in PubMed central (PMC) have extractable DNA sequences that can be accurately mapped to the correct gene (91%) and genome (96%). We illustrate the utility of data extracted by text2genome from more than 150 000 PMC articles for the interpretation of ChIP-seq data and the design of quantitative reverse transcriptase (RT)-PCR experiments.

Conclusion

Our approach links articles to genes and organisms without relying on gene names or identifiers. It also produces genome annotation tracks of the biomedical literature, thereby allowing researchers to use the power of modern genome browsers to access and analyze publications in the context of genomic data.

Availability and implementation

Source code is available under a BSD license from http://sourceforge.net/projects/text2genome/ and results can be browsed and downloaded at http://text2genome.org.

SUBMITTER: Haeussler M

PROVIDER: S-EPMC3065681 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Annotating genes and genomes with DNA sequences extracted from biomedical articles.

Haeussler Maximilian M Gerner Martin M Bergman Casey M CM

Bioinformatics (Oxford, England) 20110216 7

<h4>Motivation</h4>Increasing rates of publication and DNA sequencing make the problem of finding relevant articles for a particular gene or genomic region more challenging than ever. Existing text-mining approaches focus on finding gene names or identifiers in English text. These are often not unique and do not identify the exact genomic location of a study.<h4>Results</h4>Here, we report the results of a novel text-mining approach that extracts DNA sequences from biomedical articles and automa ...[more]

PMID: 21325301

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Dataset Information

Annotating genes and genomes with DNA sequences extracted from biomedical articles.

Motivation

Results

Conclusion

Availability and implementation

Publications

Annotating genes and genomes with DNA sequences extracted from biomedical articles.

Similar Datasets

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