Reprints from my posting to SAN-Tech Mailing List and ...


[san-tech][02630] "Cloud computing method improves gene analysis", September 13, 2010 , JHU - The Gazette

Date: Thu, 16 Sep 2010 02:36:13 +0900
Amazon Web Servicesを利用しての科学的成果の実例です

"Cloud computing method improves gene analysis"
 September 13, 2010

  "Researchers at the Johns Hopkins Bloomberg School of Public Health
   have developed software that greatly improves the speed at which
   scientists can analyze RNA sequencing data. Known as Myrna, the new
   software - which is available for free download at "cloud computing,"
   an Internet-based method of sharing computer resources."

  "To test Myrna, Langmead and colleagues," ... "used the software to
   process a large collection of publicly available RNA sequencing data.
   Processing time and storage space were rented from Amazon Web Services.
   According to the study, Myrna calculated differential expression from
   1.1 billion RNA sequencing reads in less than two hours at a cost of
   about $66."

Myrna: Cloud-scale differential gene expression for RNA-seq

"Myrna is a cloud computing tool for calculating differential gene
 expression in large RNA-seq datasets. Myrna uses Bowtie for short read
 alignment and R/Bioconductor for interval calculations, normalization,
 and statistical testing. These tools are combined in an automatic,
 parallel pipeline that runs in the cloud (Elastic MapReduce in this case)
 on a local Hadoop cluster, or on a single computer, exploiting multiple
 computers and CPUs wherever possible."

  Version 1.0.9 - September 13, 2010


"Bioconductor provides tools for the analysis and comprehension of
 high-throughput genomic data. Bioconductor uses the R statistical
 programming language, and is open source and open development. It has
 two releases each year, more than 380 packages, and an active user

学術論文 (?):
"Cloud-scale RNA-sequencing differential expression analysis with Myrna"
 Ben Langmead, Kasper D Hansen and Jeffrey T Leek
 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
 Genome Biology 2010, 11:R83
 Received: 17 May 2010, Revisions received: 7 July 2010
 Accepted: 11 August 2010, Published: 11 August 2010

※一般公開されています (Open Access)。

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