Fast single individual haplotyping method using GPGPU
- Authors
- Na, J.C.; Lee, I.; Rhee, J.-K.; Shin, S.-Y.
- Issue Date
- Oct-2019
- Publisher
- Elsevier Ltd
- Keywords
- CUDA; GPGPU; Next generation sequencing; PEATH/G; Single individual haplotyping
- Citation
- Computers in Biology and Medicine, v.113, pp.103421
- Journal Title
- Computers in Biology and Medicine
- Volume
- 113
- Start Page
- 103421
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/38910
- DOI
- 10.1016/j.compbiomed.2019.103421
- ISSN
- 0010-4825
- 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. © 2019 Elsevier Ltd
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