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MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure
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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4173015/ https://www.ncbi.nlm.nih.gov/pubmed/24907367 http://dx.doi.org/10.1093/bioinformatics/btu377 |
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author | Kim, Jihoon Levy, Eric Ferbrache, Alex Stepanowsky, Petra Farcas, Claudiu Wang, Shuang Brunner, Stefan Bath, Tyler Wu, Yuan Ohno-Machado, Lucila |
author_facet | Kim, Jihoon Levy, Eric Ferbrache, Alex Stepanowsky, Petra Farcas, Claudiu Wang, Shuang Brunner, Stefan Bath, Tyler Wu, Yuan Ohno-Machado, Lucila |
author_sort | Kim, Jihoon |
collection | PubMed |
description | 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@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-4173015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-41730152014-09-25 MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure Kim, Jihoon Levy, Eric Ferbrache, Alex Stepanowsky, Petra Farcas, Claudiu Wang, Shuang Brunner, Stefan Bath, Tyler Wu, Yuan Ohno-Machado, Lucila Bioinformatics Applications Notes 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@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2014-10 2014-06-06 /pmc/articles/PMC4173015/ /pubmed/24907367 http://dx.doi.org/10.1093/bioinformatics/btu377 Text en © The Author 2014. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ 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 |
spellingShingle | Applications Notes Kim, Jihoon Levy, Eric Ferbrache, Alex Stepanowsky, Petra Farcas, Claudiu Wang, Shuang Brunner, Stefan Bath, Tyler Wu, Yuan Ohno-Machado, Lucila MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure |
title | MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure |
title_full | MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure |
title_fullStr | MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure |
title_full_unstemmed | MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure |
title_short | MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure |
title_sort | magi: a node.js web service for fast microrna-seq analysis in a gpu infrastructure |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4173015/ https://www.ncbi.nlm.nih.gov/pubmed/24907367 http://dx.doi.org/10.1093/bioinformatics/btu377 |
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