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BACKGROUND: MicroRNAs have been discovered as important regulators of gene expression. To identify the target genes of microRNAs, several databases and prediction algorithms have been developed. Only few experimentally confirmed microRNA targets are available in databases. Many of the microRNA targets stored in databases were derived from large-scale experiments that are considered not very reliable. We propose to use text mining of publication abstracts for extracting microRNA-gene associations including microRNA-target relations to complement current repositories. RESULTS: The microRNA-gene association database miRSel combines text-mining results with existing databases and computational predictions. Text mining enables the reliable extraction of microRNA, gene and protein occurrences as well as their relationships from texts. Thereby, we increased the number of human, mouse and rat miRNA-gene associations by at least three-fold as compared to e.g. TarBase, a resource for miRNA-gene associations. CONCLUSIONS: Our database miRSel offers the currently largest collection of literature derived miRNA-gene associations. Comprehensive collections of miRNA-gene associations are important for the development of miRNA target prediction tools and the analysis of regulatory networks. miRSel is updated daily and can be queried using a web-based interface via microRNA identifiers, gene and protein names, PubMed queries as well as gene ontology (GO) terms. miRSel is freely available online at http://services.bio.ifi.lmu.de/mirsel.
Naeem et al. (Tue,) studied this question.
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