A parallel computing algorithm for 16S rRNA probe design

Dianhui Zhu; Yuriy Fofanov; Richard C. Willson; George E. Fox

doi:10.1016/j.jpdc.2006.03.002

A parallel computing algorithm for 16S rRNA probe design

Dianhui Zhu, Yuriy Fofanov, Richard C. Willson, George E. Fox

Houston Methodist

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

With the continuing rapid increase in the number of available 16S ribosomal RNA (rRNA) sequences, it is a significant computational challenge to efficiently design 16S rRNA targeted probes. In our previous work, we designed a fast software tool called ProkProbePicker (PPP) that takes O (log N) time for a worst-case scenario search. Despite this improvement, it can still take many hours for PPP to extract probes for all the clusters in a phylogenetic tree. Herein, a parallelized version of PPP is described. When run on 80 processors, this version of PPP took only 67 min to extract probes, while some 87 h were needed by the sequential version of PPP. The speedup increased linearly with the increase of CPU numbers, which revealed the outstanding scalability of the parallelized version of PPP.

Original language	English (US)
Pages (from-to)	1546-1551
Number of pages	6
Journal	Journal of Parallel and Distributed Computing
Volume	66
Issue number	12
DOIs	https://doi.org/10.1016/j.jpdc.2006.03.002
State	Published - Dec 2006

Keywords

16S rRNA targeted probes
Genetic affinity
Parallel algorithm

ASJC Scopus subject areas

Computer Science Applications
Hardware and Architecture
Control and Systems Engineering

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10.1016/j.jpdc.2006.03.002

Cite this

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title = "A parallel computing algorithm for 16S rRNA probe design",

abstract = "With the continuing rapid increase in the number of available 16S ribosomal RNA (rRNA) sequences, it is a significant computational challenge to efficiently design 16S rRNA targeted probes. In our previous work, we designed a fast software tool called ProkProbePicker (PPP) that takes O (log N) time for a worst-case scenario search. Despite this improvement, it can still take many hours for PPP to extract probes for all the clusters in a phylogenetic tree. Herein, a parallelized version of PPP is described. When run on 80 processors, this version of PPP took only 67 min to extract probes, while some 87 h were needed by the sequential version of PPP. The speedup increased linearly with the increase of CPU numbers, which revealed the outstanding scalability of the parallelized version of PPP.",

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AU - Zhu, Dianhui

AU - Fofanov, Yuriy

AU - Willson, Richard C.

AU - Fox, George E.

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AB - With the continuing rapid increase in the number of available 16S ribosomal RNA (rRNA) sequences, it is a significant computational challenge to efficiently design 16S rRNA targeted probes. In our previous work, we designed a fast software tool called ProkProbePicker (PPP) that takes O (log N) time for a worst-case scenario search. Despite this improvement, it can still take many hours for PPP to extract probes for all the clusters in a phylogenetic tree. Herein, a parallelized version of PPP is described. When run on 80 processors, this version of PPP took only 67 min to extract probes, while some 87 h were needed by the sequential version of PPP. The speedup increased linearly with the increase of CPU numbers, which revealed the outstanding scalability of the parallelized version of PPP.

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A parallel computing algorithm for 16S rRNA probe design

Abstract

Keywords

ASJC Scopus subject areas

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