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

Jihoon Kim; Eric Levy; Alex Ferbrache; Petra Stepanowsky; Claudiu Farcas; Shuang Wang; Stefan Brunner; Tyler Bath; Yuan Wu; Lucila Ohno-Machado

doi:10.1093/bioinformatics/btu377

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

Bioinformatics. 2014 Oct;30(19):2826-7. doi: 10.1093/bioinformatics/btu377. Epub 2014 Jun 6.

Authors

Affiliations

¹ Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA.
² Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA Division of Biomedical Informatics, University of California at San Diego, Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA, Biomedical Informatics Program, School of Informatics, University of Applied Sciences Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria and Department of Biostatistics and Biomedical Informatics, Duke University, Durham, NC 27710, USA.

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.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Computational Biology / methods*
Computer Graphics*
Internet
MicroRNAs / analysis*
Programming Languages
Sequence Analysis, RNA*
Software

Substances

MicroRNAs

Abstract

Publication types

MeSH terms

Substances

Grants and funding