pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment

• Published in: PloS one Vol. 13; no. 1; p. e0190279
• Main Authors: Warris, Sven, Timal, N. Roshan N., Kempenaar, Marcel, Poortinga, Arne M., van de Geest, Henri, Varbanescu, Ana L., Nap, Jan-Peter
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.01.2018; Public Library of Science (PLoS)
• Subjects: Algorithms; Alignment; Applied research; Bioinformatics; Biology and Life Sciences; BIOS Applied Bioinformatics; Central processing units; Computational biology; Computer and Information Sciences; CPUs; Engineering and Technology; Genomes; Hardware; Internet; Languages; Life sciences; Molecular systematics; Nucleotide sequence; Platforms; PRI BIOS Applied Bioinformatics; Proteins; Python; Research and Analysis Methods; Scientific software; Social Sciences; Studies
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0190279

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. 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. 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.