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      MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure

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          Abstract

          Summary: MAGI is a web service for fast MicroRNA-Seq data analysis in a graphics processing unit (GPU) infrastructure. Using just a browser, users have access to results as web reports in just a few hours—>600% end-to-end performance improvement over state of the art. MAGI’s salient features are (i) transfer of large input files in native FASTA with Qualities (FASTQ) format through drag-and-drop operations, (ii) rapid prediction of microRNA target genes leveraging parallel computing with GPU devices, (iii) all-in-one analytics with novel feature extraction, statistical test for differential expression and diagnostic plot generation for quality control and (iv) interactive visualization and exploration of results in web reports that are readily available for publication.

          Availability and implementation: MAGI relies on the Node.js JavaScript framework, along with NVIDIA CUDA C, PHP: Hypertext Preprocessor (PHP), Perl and R. It is freely available at http://magi.ucsd.edu.

          Contact: j5kim@ 123456ucsd.edu

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          MicroRNA Targets in Drosophila

          Anton Enright, Bino John, Ulrike Gaul … (2003)
          Additional data files Additional data file 1, 2, 3 and 4. Supplementary Material Additional data file 1 Additional data file 1 Click here for additional data file Additional data file 2 Additional data file 2 Click here for additional data file Additional data file 3 Additional data file 3 Click here for additional data file Additional data file 4 Additional data file 4 Click here for additional data file
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            miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments

            Michael Hackenberg, Naiara Rodriguez-Ezpeleta, Ana M. Aransay (2011)
            We present a new version of miRanalyzer, a web server and stand-alone tool for the detection of known and prediction of new microRNAs in high-throughput sequencing experiments. The new version has been notably improved regarding speed, scope and available features. Alignments are now based on the ultrafast short-read aligner Bowtie (granting also colour space support, allowing mismatches and improving speed) and 31 genomes, including 6 plant genomes, can now be analysed (previous version contained only 7). Differences between plant and animal microRNAs have been taken into account for the prediction models and differential expression of both, known and predicted microRNAs, between two conditions can be calculated. Additionally, consensus sequences of predicted mature and precursor microRNAs can be obtained from multiple samples, which increases the reliability of the predicted microRNAs. Finally, a stand-alone version of the miRanalyzer that is based on a local and easily customized database is also available; this allows the user to have more control on certain parameters as well as to use specific data such as unpublished assemblies or other libraries that are not available in the web server. miRanalyzer is available at http://bioinfo2.ugr.es/miRanalyzer/miRanalyzer.php.
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              mirTools: microRNA profiling and discovery based on high-throughput sequencing

              Erle Zhu, Fangqing Zhao, Gang Xu … (2010)
              miRNAs are small, non-coding RNA that negatively regulate gene expression at post-transcriptional level, which play crucial roles in various physiological and pathological processes, such as development and tumorigenesis. Although deep sequencing technologies have been applied to investigate various small RNA transcriptomes, their computational methods are far away from maturation as compared to microarray-based approaches. In this study, a comprehensive web server mirTools was developed to allow researchers to comprehensively characterize small RNA transcriptome. With the aid of mirTools, users can: (i) filter low-quality reads and 3/5′ adapters from raw sequenced data; (ii) align large-scale short reads to the reference genome and explore their length distribution; (iii) classify small RNA candidates into known categories, such as known miRNAs, non-coding RNA, genomic repeats and coding sequences; (iv) provide detailed annotation information for known miRNAs, such as miRNA/miRNA*, absolute/relative reads count and the most abundant tag; (v) predict novel miRNAs that have not been characterized before; and (vi) identify differentially expressed miRNAs between samples based on two different counting strategies: total read tag counts and the most abundant tag counts. We believe that the integration of multiple computational approaches in mirTools will greatly facilitate current microRNA researches in multiple ways. mirTools can be accessed at http://centre.bioinformatics.zj.cn/mirtools/ and http://59.79.168.90/mirtools.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): October 2014
                Publication date (Electronic): 06 June 2014
                Publication date PMC-release: 06 June 2014
                Volume: 30
                Issue: 19
                Pages: 2826-2827
                Affiliations
                1Division of Biomedical Informatics, University of California at San Diego, 2Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, 3Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and 4Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Ziv Bar-Joseph

                Article
                Publisher ID: btu377
                DOI: 10.1093/bioinformatics/btu377
                PMC ID: 4173015
                PubMed ID: 24907367
                SO-VID: 48cc1249-255e-4298-b730-4c7514086b0a
                Copyright © © The Author 2014. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Date received : 25 February 2014
                Date revision received : 14 May 2014
                Date accepted : 2 June 2014
                Page count
                Pages: 2
                Categories
                Subject: Applications Notes
                Subject: Gene Expression

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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