Graphics processing units in bioinformatics, computational biology and systems biology

Abstract Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide inte...

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Veröffentlicht in:Briefings in bioinformatics Jg. 18; H. 5; S. 870 - 885
Hauptverfasser: Nobile, Marco S, Cazzaniga, Paolo, Tangherloni, Andrea, Besozzi, Daniela
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 01.09.2017
Oxford Publishing Limited (England)
Schlagworte:
Algorithms
Bioinformatics
Biological models (mathematics)
Biology
Central processing units
Computer applications
Computer Graphics
Computer programs
CPUs
Genomes
Graphics processing units
Mathematical models
Organic chemistry
Software
Software development tools
Software Review
Systems Biology
CUDA
computational biology
systems biology
graphics processing units
high-performance computing
bioinformatics
ISSN:1467-5463, 1477-4054
Online-Zugang:Volltext
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Zusammenfassung:Abstract Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools.
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ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbw058