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Genome Biol. 2008;9(2):R31. doi: 10.1186/gb-2008-9-2-r31. Epub 2008 Feb 13.

Text-mining assisted regulatory annotation.

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Laboratory of Neurogenetics, Department of Molecular and Developmental Genetics, VIB, Leuven, B-3000, Belgium.



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.


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.


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.

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