Nucleic Acids Research Advance Access originally published online on May 11, 2009
Nucleic Acids Research 2009 37(Web Server issue):W68-W76; doi:10.1093/nar/gkp347
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Nucleic Acids Research, 2009, Vol. 37, No. suppl_2 W68-W76
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Articles |
miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments
1Functional Genomics Unit, CIC bioGUNE, CIBERehd, Technology Park of Bizkaia, 48160 Derio, Bizkaia, Spain,2Institute for Bioinformatics and Systems Biology, German Research Center for Environmental Health, Ingolstädter Landstrasse 1, D-85764 Neuherberg,3Department of Genome-Oriented Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universitat München, 85350 Freising,4Bioinformatics Group, Department of Computer Science, University of Leipzig, Haertelstr. 16-18, D-04107 Leipzig, Germany and 5Metabolomics Unit, CIC bioGUNE, CIBERehd, Technology Park of Bizkaia, 48160 Derio, Bizkaia, Spain
*To whom correspondence should be addressed. Tel: +34 944 061 325; Fax: +34 944 061 324; Email: amaransay{at}cicbiogune.es
Received February 28, 2009. Revised April 13, 2009. Accepted April 22, 2009.
Next-generation sequencing allows now the sequencing of small RNA molecules and the estimation of their expression levels. Consequently, there will be a high demand of bioinformatics tools to cope with the several gigabytes of sequence data generated in each single deep-sequencing experiment. Given this scene, we developed miRanalyzer, a web server tool for the analysis of deep-sequencing experiments for small RNAs. The web server tool requires a simple input file containing a list of unique reads and its copy numbers (expression levels). Using these data, miRanalyzer (i) detects all known microRNA sequences annotated in miRBase, (ii) finds all perfect matches against other libraries of transcribed sequences and (iii) predicts new microRNAs. The prediction of new microRNAs is an especially important point as there are many species with very few known microRNAs. Therefore, we implemented a highly accurate machine learning algorithm for the prediction of new microRNAs that reaches AUC values of 97.9% and recall values of up to 75% on unseen data. The web tool summarizes all the described steps in a single output page, which provides a comprehensive overview of the analysis, adding links to more detailed output pages for each analysis module. miRanalyzer is available at http://web.bioinformatics.cicbiogune.es/microRNA/.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.