Streaming support for data intensive cloud-based sequence analysis.
Cloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of "resources-on-demand" and "pay-as-you-go", scientists with no or limited infrastructure can have access to scalable and...
| Publicado en: | BioMed Research International Vol. 2013; pp. 791051 - 791052 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104074750&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104074750 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104074750 2012133659 NLM23710461 PMC3655485 104074750 ppf: 791051 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Streaming support for data intensive cloud-based sequence analysis. aug: au: Issa, Shadi A Kienzler, Romeo El-Kalioby, Mohamed Tonellato, Peter J Wall, Dennis Bruggmann, Rémy Abouelhoda, Mohamed affil: Center for Informatics Sciences, Nile University, Giza, Egypt. sug: subj: Bioinformatics Sequence Analysis Software Genomics Methods Internet Systems Analysis ab: Cloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of "resources-on-demand" and "pay-as-you-go", scientists with no or limited infrastructure can have access to scalable and cost-effective computational resources. However, the large size of NGS data causes a significant data transfer latency from the client's site to the cloud, which presents a bottleneck for using cloud computing services. In this paper, we provide a streaming-based scheme to overcome this problem, where the NGS data is processed while being transferred to the cloud. Our scheme targets the wide class of NGS data analysis tasks, where the NGS sequences can be processed independently from one another. We also provide the elastream package that supports the use of this scheme with individual analysis programs or with workflow systems. Experiments presented in this paper show that our solution mitigates the effect of data transfer latency and saves both time and cost of computation. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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