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.

Nobile, M., Cazzaniga, P., Tangherloni, A., Besozzi, D. (2017). Graphics processing units in bioinformatics, computational biology and systems biology. BRIEFINGS IN BIOINFORMATICS, 18(5), 870-885 [10.1093/bib/bbw058].

Graphics processing units in bioinformatics, computational biology and systems biology

NOBILE, MARCO SALVATORE
Primo
;
CAZZANIGA, PAOLO
Secondo
;
TANGHERLONI, ANDREA
Penultimo
;
BESOZZI, DANIELA
Ultimo
2017

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.
Articolo in rivista - Articolo scientifico
Bioinformatics; Computational biology; CUDA; Graphics processing units; High-performance computing; Systems biology;
CUDA; bioinformatics; computational biology; graphics processing units; high-performance computing; systems biology
English
2017
18
5
870
885
reserved
Nobile, M., Cazzaniga, P., Tangherloni, A., Besozzi, D. (2017). Graphics processing units in bioinformatics, computational biology and systems biology. BRIEFINGS IN BIOINFORMATICS, 18(5), 870-885 [10.1093/bib/bbw058].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/130277
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