pyPaSWAS: Een op Python-gebaseerde multi-core CPU en GPU sequence aligner

Sven Warris, N Roshan N Timal, Marcel Kempenaar, Arne M Poortinga, Henri van de Geest, Ana L Varbanescu, Jan-Peter Nap

Onderzoeksoutput: ArticleAcademicpeer review

Uittreksel

BACKGROUND: Our previously published CUDA-only application PaSWAS for Smith-Waterman (SW) sequence alignment of any type of sequence on NVIDIA-based GPUs is platform-specific and therefore adopted less than could be. The OpenCL language is supported more widely and allows use on a variety of hardware platforms. Moreover, there is a need to promote the adoption of parallel computing in bioinformatics by making its use and extension more simple through more and better application of high-level languages commonly used in bioinformatics, such as Python.

RESULTS: The novel application pyPaSWAS presents the parallel SW sequence alignment code fully packed in Python. It is a generic SW implementation running on several hardware platforms with multi-core systems and/or GPUs that provides accurate sequence alignments that also can be inspected for alignment details. Additionally, pyPaSWAS support the affine gap penalty. Python libraries are used for automated system configuration, I/O and logging. This way, the Python environment will stimulate further extension and use of pyPaSWAS.

CONCLUSIONS: pyPaSWAS presents an easy Python-based environment for accurate and retrievable parallel SW sequence alignments on GPUs and multi-core systems. The strategy of integrating Python with high-performance parallel compute languages to create a developer- and user-friendly environment should be considered for other computationally intensive bioinformatics algorithms.

Vertaalde titel van de bijdragepyPaSWAS: Een op Python-gebaseerde multi-core CPU en GPU sequence aligner
Originele taal-2English
Artikelnummere0190279
Aantal pagina's9
TijdschriftPLOS one
Volume13
Nummer van het tijdschrift1
DOI's
StatusPublished - 1 jan 2018

Keywords

  • bioinformatica
  • software ontwikkeling

Citeer dit

Warris, S., Timal, N. R. N., Kempenaar, M., Poortinga, A. M., van de Geest, H., Varbanescu, A. L., & Nap, J-P. (2018). pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment. PLOS one, 13(1), [e0190279]. https://doi.org/10.1371/journal.pone.0190279
Warris, Sven ; Timal, N Roshan N ; Kempenaar, Marcel ; Poortinga, Arne M ; van de Geest, Henri ; Varbanescu, Ana L ; Nap, Jan-Peter. / pyPaSWAS : Python-based multi-core CPU and GPU sequence alignment. In: PLOS one. 2018 ; Vol. 13, Nr. 1.
@article{9810d466747f49799bed5246aee6554d,
title = "pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment",
abstract = "BACKGROUND: Our previously published CUDA-only application PaSWAS for Smith-Waterman (SW) sequence alignment of any type of sequence on NVIDIA-based GPUs is platform-specific and therefore adopted less than could be. The OpenCL language is supported more widely and allows use on a variety of hardware platforms. Moreover, there is a need to promote the adoption of parallel computing in bioinformatics by making its use and extension more simple through more and better application of high-level languages commonly used in bioinformatics, such as Python.RESULTS: The novel application pyPaSWAS presents the parallel SW sequence alignment code fully packed in Python. It is a generic SW implementation running on several hardware platforms with multi-core systems and/or GPUs that provides accurate sequence alignments that also can be inspected for alignment details. Additionally, pyPaSWAS support the affine gap penalty. Python libraries are used for automated system configuration, I/O and logging. This way, the Python environment will stimulate further extension and use of pyPaSWAS.CONCLUSIONS: pyPaSWAS presents an easy Python-based environment for accurate and retrievable parallel SW sequence alignments on GPUs and multi-core systems. The strategy of integrating Python with high-performance parallel compute languages to create a developer- and user-friendly environment should be considered for other computationally intensive bioinformatics algorithms.",
keywords = "bioinformatics, software development, performance analysis, bioinformatica, software ontwikkeling",
author = "Sven Warris and Timal, {N Roshan N} and Marcel Kempenaar and Poortinga, {Arne M} and {van de Geest}, Henri and Varbanescu, {Ana L} and Jan-Peter Nap",
year = "2018",
month = "1",
day = "1",
doi = "10.1371/journal.pone.0190279",
language = "English",
volume = "13",
journal = "PLOS one",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "1",

}

Warris, S, Timal, NRN, Kempenaar, M, Poortinga, AM, van de Geest, H, Varbanescu, AL & Nap, J-P 2018, 'pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment' PLOS one, vol. 13, nr. 1, e0190279. https://doi.org/10.1371/journal.pone.0190279

pyPaSWAS : Python-based multi-core CPU and GPU sequence alignment. / Warris, Sven; Timal, N Roshan N; Kempenaar, Marcel; Poortinga, Arne M; van de Geest, Henri; Varbanescu, Ana L; Nap, Jan-Peter.

In: PLOS one, Vol. 13, Nr. 1, e0190279, 01.01.2018.

Onderzoeksoutput: ArticleAcademicpeer review

TY - JOUR

T1 - pyPaSWAS

T2 - Python-based multi-core CPU and GPU sequence alignment

AU - Warris, Sven

AU - Timal, N Roshan N

AU - Kempenaar, Marcel

AU - Poortinga, Arne M

AU - van de Geest, Henri

AU - Varbanescu, Ana L

AU - Nap, Jan-Peter

PY - 2018/1/1

Y1 - 2018/1/1

N2 - BACKGROUND: Our previously published CUDA-only application PaSWAS for Smith-Waterman (SW) sequence alignment of any type of sequence on NVIDIA-based GPUs is platform-specific and therefore adopted less than could be. The OpenCL language is supported more widely and allows use on a variety of hardware platforms. Moreover, there is a need to promote the adoption of parallel computing in bioinformatics by making its use and extension more simple through more and better application of high-level languages commonly used in bioinformatics, such as Python.RESULTS: The novel application pyPaSWAS presents the parallel SW sequence alignment code fully packed in Python. It is a generic SW implementation running on several hardware platforms with multi-core systems and/or GPUs that provides accurate sequence alignments that also can be inspected for alignment details. Additionally, pyPaSWAS support the affine gap penalty. Python libraries are used for automated system configuration, I/O and logging. This way, the Python environment will stimulate further extension and use of pyPaSWAS.CONCLUSIONS: pyPaSWAS presents an easy Python-based environment for accurate and retrievable parallel SW sequence alignments on GPUs and multi-core systems. The strategy of integrating Python with high-performance parallel compute languages to create a developer- and user-friendly environment should be considered for other computationally intensive bioinformatics algorithms.

AB - BACKGROUND: Our previously published CUDA-only application PaSWAS for Smith-Waterman (SW) sequence alignment of any type of sequence on NVIDIA-based GPUs is platform-specific and therefore adopted less than could be. The OpenCL language is supported more widely and allows use on a variety of hardware platforms. Moreover, there is a need to promote the adoption of parallel computing in bioinformatics by making its use and extension more simple through more and better application of high-level languages commonly used in bioinformatics, such as Python.RESULTS: The novel application pyPaSWAS presents the parallel SW sequence alignment code fully packed in Python. It is a generic SW implementation running on several hardware platforms with multi-core systems and/or GPUs that provides accurate sequence alignments that also can be inspected for alignment details. Additionally, pyPaSWAS support the affine gap penalty. Python libraries are used for automated system configuration, I/O and logging. This way, the Python environment will stimulate further extension and use of pyPaSWAS.CONCLUSIONS: pyPaSWAS presents an easy Python-based environment for accurate and retrievable parallel SW sequence alignments on GPUs and multi-core systems. The strategy of integrating Python with high-performance parallel compute languages to create a developer- and user-friendly environment should be considered for other computationally intensive bioinformatics algorithms.

KW - bioinformatics

KW - software development

KW - performance analysis

KW - bioinformatica

KW - software ontwikkeling

UR - http://www.mendeley.com/research/pypaswas-pythonbased-multicore-cpu-gpu-sequence-alignment

U2 - 10.1371/journal.pone.0190279

DO - 10.1371/journal.pone.0190279

M3 - Article

VL - 13

JO - PLOS one

JF - PLOS one

SN - 1932-6203

IS - 1

M1 - e0190279

ER -

Warris S, Timal NRN, Kempenaar M, Poortinga AM, van de Geest H, Varbanescu AL et al. pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment. PLOS one. 2018 jan 1;13(1). e0190279. https://doi.org/10.1371/journal.pone.0190279