miRSel: automated extraction of associations between microRNAs and genes from the biomedical literature - PubMed (original) (raw)

miRSel: automated extraction of associations between microRNAs and genes from the biomedical literature

Haroon Naeem et al. BMC Bioinformatics. 2010.

Abstract

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.

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Figures

Figure 1

Figure 1

The number of miRNA and gene/protein pair matches with synonym expansion, strictness and post filters in human. No selection all miRNA-gene co-occurrences found in the publication titles and abstracts are displayed. Counts of miRNA-target pairs in the main text refer to this first column. The organism specificity can be increased by the taxonomy filter that requires confirmation of the selected organism. The text-mining results can also be restricted to miRNA gene pairs found within single sentences. The particlular type of association in miRNA-gene pairs can be restricted by the relation filter. Additional filters report pairs only if they are confirmed by target prediction algorithms (e.g. Pita) or manually curated databases (e.g. miRecords, mir2Disease, TarBase).

Figure 2

Figure 2

A web based graphical user interface to the database. miRSel can be queried via different options, including miRNA, target, gene ontology and PubMed keyword queries. If multiple options are selected, the results are AND-combined. Several filters are provided to control recall vs. precision of the mining results. For details see text.

Figure 3

Figure 3

A schematic workflow of miRSel search by miRNA ID. After entering a complete or partial search key (e.g. a miRNA) (A) the user can select a subset of the matching miRNAs (B). Then, corresponding miRNA-target co-occurrences stored in the database are displayed in a tabular format (C). This table enables the navigation to miRNA or gene pages of primary databases (e.g. D = miRBase, E = Entrez Gene, PubMed abstracts that reference particular co-occurrences (F), or to the database sources for which the pair has been integrated (G). Also, details related to each miRNA-target pair e.g. all possible names for a given miRNA or protein in the literature and comparison results of other databases and sequence prediction can be displayed from the table (H). Finally, a miRNA target interaction graph (I) can be displayed that also enables the navigation to miRNA and gene pages (nodes) or PubMed abstracts (edges).

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