GPU-Based Cloud Service for Smith-Waterman Algorithm Using Frequency Distance Filtration Scheme.
As the conventional means of analyzing the similarity between a query sequence and database sequences, the Smith-Waterman algorithm is feasible for a database search owing to its high sensitivity. However, this algorithm is still quite time consuming. CUDA programming can improve computations effici...
| Publicado en: | BioMed Research International Vol. 2013; pp. 721738 - 721739 |
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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=109856640&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109856640 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: 109856640 2012116902 NLM23653898 PMC3638642 109856640 ppf: 721738 ppct: 1 formats: fmt: @attributes: type: P tig: atl: GPU-Based Cloud Service for Smith-Waterman Algorithm Using Frequency Distance Filtration Scheme. aug: au: Lee, Sheng-Ta Lin, Chun-Yuan Hung, Che Lun affil: Department of Computer Science and Information Engineering, Chang Gung University, No. 259 Sanmin Road, Guishan, Taoyuan 33302, Taiwan. sug: subj: Algorithms Computer Graphics Internet Resource Databases Time Factors ab: As the conventional means of analyzing the similarity between a query sequence and database sequences, the Smith-Waterman algorithm is feasible for a database search owing to its high sensitivity. However, this algorithm is still quite time consuming. CUDA programming can improve computations efficiently by using the computational power of massive computing hardware as graphics processing units (GPUs). This work presents a novel Smith-Waterman algorithm with a frequency-based filtration method on GPUs rather than merely accelerating the comparisons yet expending computational resources to handle such unnecessary comparisons. A user friendly interface is also designed for potential cloud server applications with GPUs. Additionally, two data sets, H1N1 protein sequences (query sequence set) and human protein database (database set), are selected, followed by a comparison of CUDA-SW and CUDA-SW with the filtration method, referred to herein as CUDA-SWf. Experimental results indicate that reducing unnecessary sequence alignments can improve the computational time by up to 41%. Importantly, by using CUDA-SWf as a cloud service, this application can be accessed from any computing environment of a device with an Internet connection without time constraints. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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