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

Abstract

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/.

MicroRNAs in the Pineal Gland

“…Alignment of Small RNAs to Known miRNAs-The FASTQ sequence files were parsed (using the supplied perl script in the miRanalyzer package (23)) to reduce the read length to 26 bases, removing most of the adaptor introduced during library preparation. Unique reads were counted, and reads that did not meet a median phred quality score of 20 and occurred less than 10 times were discarded (23).…”

“…Unique reads were counted, and reads that did not meet a median phred quality score of 20 and occurred less than 10 times were discarded (23). The parsed file was searched against miRBase and transcript databases, including RefSeq and Rfam, and against the rat genome (rn4) using miRanalyzer (23).…”

“…Candidate miRNAs-Candidate miRNAs were identified by miRanalyzer using the following criteria: absence in miRBase, location of the small RNA in a predicted hairpin-like configuration, and agreement with miRNA prediction models (23). A total of 34 candidate miRNAs were identified (Table 1), of which 12 were found to be homologous to mouse or human miRNAs, containing no more than one mismatch.…”

Analysis of microRNAs in a knock-in hESC line expressing epitope-tagged AGO2

“…To distinguish miRNAs from other small RNAs or degradation products, computational methods based on certain criteria, such as characteristic structures and strong evolutionary conservation of sequences, have been employed (Friedländer et al 2008;Hackenberg et al 2009;Hendrix et al 2010;Mathelier & Carbone 2010;Friedländer et al 2012). Currently, 2588 and 1915 miRNAs are annotated in humans and mice, respectively (miRBase Release 21, 2014; www.mirbase.org).…”

Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression

“…The emergence of next-generation sequencing (NGS) technologies led to development of prediction methods using read mapping in genomes. The earliest of these was miRDeep (Friedländer et al, 2008), followed by miRanalyzer (Hackenberg et al, 2009), MIReNA (Mathelier and Carbone, 2010) and others.…”