Warris Sven, Timal N Roshan N, Kempenaar Marcel, Poortinga Arne M, van de Geest Henri, Varbanescu Ana L, Nap Jan-Peter
Expertise Centre ALIFE, Institute for Life Science & Technology, Hanze University of Applied Sciences Groningen, Groningen, the Netherlands.
Applied Bioinformatics, Wageningen University and Research, Wageningen, the Netherlands.
PLoS One. 2018 Jan 2;13(1):e0190279. doi: 10.1371/journal.pone.0190279. eCollection 2018.
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.
我们之前发布的仅支持CUDA的应用程序PaSWAS，用于在基于NVIDIA的GPU上对任何类型的序列进行史密斯-沃特曼（SW）序列比对，它是特定于平台的，因此采用率低于预期。OpenCL语言得到更广泛的支持，并允许在各种硬件平台上使用。此外，有必要通过更广泛、更好地应用生物信息学中常用的高级语言（如Python），使并行计算的使用和扩展更加简单，从而促进其在生物信息学中的采用。
新颖的应用程序pyPaSWAS展示了完全用Python编写的并行SW序列比对代码。它是一个通用的SW实现，可在具有多核系统和/或GPU的多个硬件平台上运行，提供准确的序列比对，还可以检查比对细节。此外，pyPaSWAS支持仿射间隙罚分。Python库用于自动系统配置、输入/输出和日志记录。通过这种方式，Python环境将促进pyPaSWAS的进一步扩展和使用。
pyPaSWAS为在GPU和多核系统上进行准确且可检索的并行SW序列比对提供了一个简单的基于Python的环境。对于其他计算密集型的生物信息学算法，应考虑将Python与高性能并行计算语言集成，以创建一个对开发者和用户都友好的环境的策略。
