Fast single individual haplotyping method using GPGPU. (October 2019)

• Record Type: Journal Article
• Authors: Na, Joong Chae; Lee, Inbok; Rhee, Je-Keun; Shin, Soo-Yong
• Is Part Of: Computers in biology and medicine. Volume 113(2019)
• Publication Date: 2019-10
• Subjects: Single individual haplotyping -- PEATH/G -- GPGPU -- CUDA -- Next generation sequencing; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2019.103421
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: 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. Method: 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). Results: 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). Conclusions: 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. Highlights: PEATH method was developed for single individual haplotyping (SIH). PEATH/G, the parallel version of PEATH, uses GPGPU to achieve fast SIH results. PEATH/G runs more than 25 times faster than PEATH on NA12878 and HuRef datasets. CUDA implementation can reduce the running time of NGS analysis tools.