CUDA-enabled hierarchical ward clustering of protein structures based on the nearest neighbour chain algorithm

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • Hoang-Vu Dang
  • Bertil Schmidt
  • Andreas Hildebrandt
  • Tuan Tu Tran
  • Anna Katharina Hildebrandt
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000374772700005&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1177/1094342015597988
eISSN
1741-2846
Externe Identifier
  • Clarivate Analytics Document Solution ID: DK2VO
ISSN
1094-3420
Ausgabe der Veröffentlichung
2
Zeitschrift
INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS
Schlüsselwörter
  • CUDA
  • protein structures
  • clustering
  • bioinformatics
  • protein docking
Paginierung
200 - 211
Datum der Veröffentlichung
2016
Status
Published
Titel
CUDA-enabled hierarchical ward clustering of protein structures based on the nearest neighbour chain algorithm
Sub types
  • Article
Ausgabe der Zeitschrift
30

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Abstract
<jats:p> Clustering of molecular systems according to their three-dimensional structure is an important step in many bioinformatics workflows. In applications such as docking or structure prediction, many algorithms initially generate large numbers of candidate poses (or decoys), which are then clustered to allow for subsequent computationally expensive evaluations of reasonable representatives. Since the number of such candidates can easily range from thousands to millions, performing the clustering on standard central processing units (CPUs) is highly time consuming. In this paper, we analyse and evaluate different approaches to parallelize the nearest neighbour chain algorithm to perform hierarchical Ward clustering of protein structures, using both atom-based root mean square deviation (RMSD) and rigid-body RMSD molecular distances on a graphics processing unit (GPU). This leads to a speedup of around one order of magnitude of our CUDA implementation on a GeForce Titan GPU compared to a multi-threaded CPU implementation on a Core-i7 2700. Furthermore, the runtimes compare favourably with ClusCo, another state-of-the-art CUDA-enabled protein structure clustering method, while achieving similar accuracy on the iTasser benchmark dataset. Our implementation has also been incorporated into the Biochemical Algorithms library to allow easy integration into biologists’ workflows. </jats:p>
Autoren
  • Hoang-Vu Dang
  • Bertil Schmidt
  • Andreas Hildebrandt
  • Tuan Tu Tran
  • Anna Katharina Hildebrandt
DOI
10.1177/1094342015597988
eISSN
1741-2846
ISSN
1094-3420
Ausgabe der Veröffentlichung
2
Zeitschrift
The International Journal of High Performance Computing Applications
Sprache
en
Online publication date
2015
Paginierung
200 - 211
Datum der Veröffentlichung
2016
Status
Published
Herausgeber
SAGE Publications
Herausgeber URL
http://dx.doi.org/10.1177/1094342015597988
Datum der Datenerfassung
2021
Titel
CUDA-enabled hierarchical ward clustering of protein structures based on the nearest neighbour chain algorithm
Ausgabe der Zeitschrift
30

Datenquelle: Crossref

Autoren
  • Hoang-Vu Dang
  • Bertil Schmidt
  • Andreas Hildebrandt
  • Tuan Tu Tran
  • Anna Katharina Hildebrandt
Zeitschrift
Int. J. High Perform. Comput. Appl.
Artikelnummer
2
Paginierung
200 - 211
Datum der Veröffentlichung
2016
Titel
CUDA-enabled hierarchical ward clustering of protein structures based on the nearest neighbour chain algorithm.
Ausgabe der Zeitschrift
30

Datenquelle: DBLP

Autoren
  • Hoang-Vu Dang
  • Bertil Schmidt
  • Andreas Hildebrandt
  • Tuan Tu Tran
  • Anna Katharina Hildebrandt
Zeitschrift
The International Journal of High Performance Computing Applications
Artikelnummer
2
Paginierung
200 - 211
Datum der Veröffentlichung
2016
Herausgeber
SAGE Publications Sage UK: London, England
Datum der Datenerfassung
2020
Titel
CUDA-enabled hierarchical ward clustering of protein structures based on the nearest neighbour chain algorithm
Sub types
  • article
Ausgabe der Zeitschrift
30

Datenquelle: Manual

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