miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments

By Michael Hackenberg, Martin Sturm, David Langenberger, Juan Manuel Falcón-Pérez, Ana María Aransay. Nucleic Acids Res., 2009

314 citations recorded by the source corpus.

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/.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f00dbd2969eed9ed0a5419eee9e19a3de5fbec70

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Citations in the corpus

miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades (2011, methodology): “MIReNA uses combinatorial rules to identify miRNAs (12). miRanalyzer uses a support vector machine (SVM) trained on miRNA features to classify miRNA transcripts from non-miRNA transcripts (13,14). miRTRAP identiﬁes gene loci where many sequenced RNAs map to few deﬁned positions (15).”
Identifying miRNAs, targets and functions (2012, methodology): “Many algorithms, for example, miRScan [25], miRSeeker [26], miRank [27], miRDeep [28], miRDeep2 [29] and miRanalyzer [30], have been proposed.”