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BioMed Central, Genome Biology, 2(9), p. R31

DOI: 10.1186/gb-2008-9-2-r31

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Text-mining assisted regulatory annotation

Journal article published in 2008 by Stein Aerts, Maximilian Haeussler, Steven van Vooren, Obi L. Griffith ORCID, Paco Hulpiau, Steven Jm Jones, Stephen B. Montgomery, Casey M. Bergman, The Open Regulatory Annotation Consortium
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Abstract Background Decoding transcriptional regulatory networks and the genomic cis -regulatory logic implemented in their control nodes is a fundamental challenge in genome biology. High-throughput computational and experimental analyses of regulatory networks and sequences rely heavily on positive control data from prior small-scale experiments, but the vast majority of previously discovered regulatory data remains locked in the biomedical literature. Results We develop text-mining strategies to identify relevant publications and extract sequence information to assist the regulatory annotation process. Using a vector space model to identify Medline abstracts from papers likely to have high cis -regulatory content, we demonstrate that document relevance ranking can assist the curation of transcriptional regulatory networks and estimate that, minimally, 30,000 papers harbor unannotated cis -regulatory data. In addition, we show that DNA sequences can be extracted from primary text with high cis -regulatory content and mapped to genome sequences as a means of identifying the location, organism and target gene information that is critical to the cis -regulatory annotation process. Conclusion Our results demonstrate that text-mining technologies can be successfully integrated with genome annotation systems, thereby increasing the availability of annotated cis -regulatory data needed to catalyze advances in the field of gene regulation.