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

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
Externally published	Yes

Keywords

16S rRNA targeted probes
Genetic affinity
Parallel algorithm

ASJC Scopus subject areas

Software
Theoretical Computer Science
Hardware and Architecture
Computer Networks and Communications
Artificial Intelligence

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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.",

keywords = "16S rRNA targeted probes, Genetic affinity, Parallel algorithm",

author = "Dianhui Zhu and Yuriy Fofanov and Willson, \{Richard C.\} and Fox, \{George E.\}",

note = "Funding Information: This research was funded in part by Grants from the Institute of Space Systems Operations to GEF, the National Aeronautics and Space Administration Office of Biological and Physical Research (NNJ04HF43G) to GEF and RCW and the Texas Learning and Computational Center (TLC2) to YF, GEF and RCW. We thank the system administrators in TLC2 for their technical supports.",

year = "2006",

month = dec,

doi = "10.1016/j.jpdc.2006.03.002",

language = "English (US)",

volume = "66",

pages = "1546--1551",

journal = "Journal of Parallel and Distributed Computing",

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T1 - A parallel computing algorithm for 16S rRNA probe design

AU - Zhu, Dianhui

AU - Fofanov, Yuriy

AU - Willson, Richard C.

AU - Fox, George E.

N1 - Funding Information: This research was funded in part by Grants from the Institute of Space Systems Operations to GEF, the National Aeronautics and Space Administration Office of Biological and Physical Research (NNJ04HF43G) to GEF and RCW and the Texas Learning and Computational Center (TLC2) to YF, GEF and RCW. We thank the system administrators in TLC2 for their technical supports.

PY - 2006/12

Y1 - 2006/12

N2 - 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.

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.

KW - 16S rRNA targeted probes

KW - Genetic affinity

KW - Parallel algorithm

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UR - https://www.scopus.com/pages/publications/33750995533#tab=citedBy

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DO - 10.1016/j.jpdc.2006.03.002

M3 - Article

AN - SCOPUS:33750995533

SN - 0743-7315

VL - 66

SP - 1546

EP - 1551

JO - Journal of Parallel and Distributed Computing

JF - Journal of Parallel and Distributed Computing

IS - 12

ER -

A parallel computing algorithm for 16S rRNA probe design

Abstract

Keywords

ASJC Scopus subject areas

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