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

Similar Datasets

Project description:BackgroundBiomedical corpora annotated with event-level information represent an important resource for domain-specific information extraction (IE) systems. However, bio-event annotation alone cannot cater for all the needs of biologists. Unlike work on relation and event extraction, most of which focusses on specific events and named entities, we aim to build a comprehensive resource, covering all statements of causal association present in discourse. Causality lies at the heart of biomedical knowledge, such as diagnosis, pathology or systems biology, and, thus, automatic causality recognition can greatly reduce the human workload by suggesting possible causal connections and aiding in the curation of pathway models. A biomedical text corpus annotated with such relations is, hence, crucial for developing and evaluating biomedical text mining.ResultsWe have defined an annotation scheme for enriching biomedical domain corpora with causality relations. This schema has subsequently been used to annotate 851 causal relations to form BioCause, a collection of 19 open-access full-text biomedical journal articles belonging to the subdomain of infectious diseases. These documents have been pre-annotated with named entity and event information in the context of previous shared tasks. We report an inter-annotator agreement rate of over 60% for triggers and of over 80% for arguments using an exact match constraint. These increase significantly using a relaxed match setting. Moreover, we analyse and describe the causality relations in BioCause from various points of view. This information can then be leveraged for the training of automatic causality detection systems.ConclusionAugmenting named entity and event annotations with information about causal discourse relations could benefit the development of more sophisticated IE systems. These will further influence the development of multiple tasks, such as enabling textual inference to detect entailments, discovering new facts and providing new hypotheses for experimental work.

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

OmicsDI is part of the ELIXIR infrastructure

Tweets