Department of Computer Science and Engineering, Sejong University, Seoul, 05006, South Korea.
Department of Software, Korea Aerospace University, Goyang, 10540, South Korea.
Comput Biol Med. 2019 Oct;113:103421. doi: 10.1016/j.compbiomed.2019.103421. Epub 2019 Aug 29.
Most bioinformatic tools for next generation sequencing (NGS) data are computationally intensive, requiring a large amount of computational power for processing and analysis. Here the utility of graphic processing units (GPUs) for NGS data computation is assessed.
In a previous study, we developed a probabilistic evolutionary algorithm with toggling for haplotyping (PEATH) method based on the estimation of distribution algorithm and toggling heuristic. Here, we parallelized the PEATH method (PEATH/G) using general-purpose computing on GPU (GPGPU).
The PEATH/G runs approximately 46.8 times and 25.4 times faster than PEATH on the NA12878 fosmid-sequencing dataset and the HuRef dataset, respectively, with an NVIDIA GeForce GTX 1660Ti. Moreover, the PEATH/G is approximately 13.3 times faster on the fosmid-sequencing dataset, even with an inexpensive conventional GPGPU (NVIDIA GeForce GTX 950).
PEATH/G can be a practical single individual haplotyping tool in terms of both its accuracy and speed. GPGPU can help reduce the running time of NGS analysis tools.
大多数用于下一代测序（NGS）数据的生物信息学工具计算量都很大，处理和分析都需要大量的计算能力。在此，评估了图形处理单元（GPU）在 NGS 数据计算中的效用。
在之前的研究中，我们基于分布估计算法和切换启发式开发了一种具有切换功能的概率进化算法进行单体型分析（PEATH）方法。在这里，我们使用通用图形处理单元（GPGPU）对 PEATH 方法（PEATH/G）进行了并行化。
与 PEATH 相比，在 NVIDIA GeForce GTX 1660Ti 上，PEATH/G 在 NA12878 纤维测序数据集和 HuRef 数据集上的运行速度分别快约 46.8 倍和 25.4 倍。此外，即使使用便宜的传统 GPGPU（NVIDIA GeForce GTX 950），PEATH/G 在纤维测序数据集中的运行速度也快约 13.3 倍。
PEATH/G 在准确性和速度方面都可以成为一种实用的单个体单体型分析工具。GPGPU 可以帮助减少 NGS 分析工具的运行时间。
