BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures

Publication type:
Journal article
Metadata:
Autoren
  • Jikai Zhang
  • Haidong Lan
  • Yuandong Chan
  • Yuan Shang
  • Bertil Schmidt
  • Weiguo Liu
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000481416400019&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1093/bioinformatics/bty930
eISSN
1460-2059
Externe Identifier
  • Clarivate Analytics Document Solution ID: IR4PJ
  • PubMed Identifier: 30445566
ISSN
1367-4803
Ausgabe der Veröffentlichung
13
Zeitschrift
BIOINFORMATICS
Paginierung
2306 - 2308
Datum der Veröffentlichung
2019
Status
Published
Titel
BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures
Sub types
  • Article
Ausgabe der Zeitschrift
35

Data source: Web of Science (Lite)

Other metadata sources:
Abstract
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Modern bioinformatics tools for analyzing large-scale NGS datasets often need to include fast implementations of core sequence alignment algorithms in order to achieve reasonable execution times. We address this need by presenting the BGSA toolkit for optimized implementations of popular bit-parallel global pairwise alignment algorithms on modern microprocessors.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>BGSA outperforms Edlib, SeqAn and BitPAl for pairwise edit distance computations and Parasail, SeqAn and BitPAl when using more general scoring schemes for pairwise alignments of a batch of sequence reads on both standard multi-core CPUs and Xeon Phi many-core CPUs. Furthermore, banded edit distance performance of BGSA on a Xeon Phi-7210 outperforms the highly optimized NVBio implementation on a Titan X GPU for the seed verification stage of a read mapper by a factor of 4.4.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>BGSA is open-source and available at https://github.com/sdu-hpcl/BGSA.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec>
Autoren
  • Jikai Zhang
  • Haidong Lan
  • Yuandong Chan
  • Yuan Shang
  • Bertil Schmidt
  • Weiguo Liu
DOI
10.1093/bioinformatics/bty930
Editoren
  • Inanc Birol
eISSN
1367-4811
ISSN
1367-4803
Ausgabe der Veröffentlichung
13
Zeitschrift
Bioinformatics
Sprache
en
Online publication date
2018
Paginierung
2306 - 2308
Datum der Veröffentlichung
2019
Status
Published
Herausgeber
Oxford University Press (OUP)
Herausgeber URL
http://dx.doi.org/10.1093/bioinformatics/bty930
Datum der Datenerfassung
2023
Titel
BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures
Ausgabe der Zeitschrift
35

Data source: Crossref

Abstract
<h4>Motivation</h4>Modern bioinformatics tools for analyzing large-scale NGS datasets often need to include fast implementations of core sequence alignment algorithms in order to achieve reasonable execution times. We address this need by presenting the BGSA toolkit for optimized implementations of popular bit-parallel global pairwise alignment algorithms on modern microprocessors.<h4>Results</h4>BGSA outperforms Edlib, SeqAn and BitPAl for pairwise edit distance computations and Parasail, SeqAn and BitPAl when using more general scoring schemes for pairwise alignments of a batch of sequence reads on both standard multi-core CPUs and Xeon Phi many-core CPUs. Furthermore, banded edit distance performance of BGSA on a Xeon Phi-7210 outperforms the highly optimized NVBio implementation on a Titan X GPU for the seed verification stage of a read mapper by a factor of 4.4.<h4>Availability and implementation</h4>BGSA is open-source and available at https://github.com/sdu-hpcl/BGSA.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.
Addresses
  • School of Software, Shandong University, Jinan, China.
Autoren
  • Jikai Zhang
  • Haidong Lan
  • Yuandong Chan
  • Yuan Shang
  • Bertil Schmidt
  • Weiguo Liu
DOI
10.1093/bioinformatics/bty930
eISSN
1367-4811
Externe Identifier
  • PubMed Identifier: 30445566
Funding acknowledgements
  • Engineering Research Center of Digital Media Technology, Ministry of Education:
  • NSFC: U1806205
  • Center for High Performance Computing and System Simulation, Pilot National Laboratory for Marine Science and Technology:
Open access
false
ISSN
1367-4803
Ausgabe der Veröffentlichung
13
Zeitschrift
Bioinformatics (Oxford, England)
Schlüsselwörter
  • Sequence Alignment
  • Sequence Analysis, DNA
  • Algorithms
  • Software
Sprache
eng
Medium
Print
Paginierung
2306 - 2308
Datum der Veröffentlichung
2019
Status
Published
Datum der Datenerfassung
2018
Titel
BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures.
Sub types
  • Research Support, Non-U.S. Gov't
  • Journal Article
Ausgabe der Zeitschrift
35

Data source: Europe PubMed Central

Abstract
MOTIVATION: Modern bioinformatics tools for analyzing large-scale NGS datasets often need to include fast implementations of core sequence alignment algorithms in order to achieve reasonable execution times. We address this need by presenting the BGSA toolkit for optimized implementations of popular bit-parallel global pairwise alignment algorithms on modern microprocessors. RESULTS: BGSA outperforms Edlib, SeqAn and BitPAl for pairwise edit distance computations and Parasail, SeqAn and BitPAl when using more general scoring schemes for pairwise alignments of a batch of sequence reads on both standard multi-core CPUs and Xeon Phi many-core CPUs. Furthermore, banded edit distance performance of BGSA on a Xeon Phi-7210 outperforms the highly optimized NVBio implementation on a Titan X GPU for the seed verification stage of a read mapper by a factor of 4.4. AVAILABILITY AND IMPLEMENTATION: BGSA is open-source and available at https://github.com/sdu-hpcl/BGSA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Date of acceptance
2018
Autoren
  • Jikai Zhang
  • Haidong Lan
  • Yuandong Chan
  • Yuan Shang
  • Bertil Schmidt
  • Weiguo Liu
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/30445566
DOI
10.1093/bioinformatics/bty930
eISSN
1367-4811
Ausgabe der Veröffentlichung
13
Zeitschrift
Bioinformatics
Schlüsselwörter
  • Algorithms
  • Sequence Alignment
  • Sequence Analysis, DNA
  • Software
Sprache
eng
Country
England
Paginierung
2306 - 2308
PII
5184957
Datum der Veröffentlichung
2019
Status
Published
Datum, an dem der Datensatz öffentlich gemacht wurde
2020
Titel
BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures.
Sub types
  • Journal Article
  • Research Support, Non-U.S. Gov't
Ausgabe der Zeitschrift
35

Data source: PubMed

Autoren
  • Jikai Zhang
  • Haidong Lan
  • Yuandong Chan
  • Yuan Shang
  • Bertil Schmidt
  • Weiguo Liu
Zeitschrift
Bioinform.
Artikelnummer
13
Paginierung
2306 - 2308
Datum der Veröffentlichung
2019
Titel
BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures.
Ausgabe der Zeitschrift
35

Data source: DBLP

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