BACKGROUND: With the maturation of next-generation DNA sequencing (NGS) technologies, the throughput of DNA sequencing reads has soared to over 600 gigabases from a single instrument run. General purpose computing on graphics processing units (GPGPU), extracts the computing power from hundreds of parallel stream processors within graphics processing cores and provides a cost-effective and energy efficient alternative to traditional high-performance computing (HPC) clusters. In this article, we describe the implementation of BarraCUDA, a GPGPU sequence alignment software that is based on BWA, to accelerate the alignment of sequencing reads generated by these instruments to a reference DNA sequence. FINDINGS: Using the NVIDIA Compute Unified Device Architecture (CUDA) software development environment, we ported the most computational-intensive alignment component of BWA to GPU to take advantage of the massive parallelism. As a result, BarraCUDA offers a magnitude of performance boost in alignment throughput when compared to a CPU core while delivering the same level of alignment fidelity. The software is also capable of supporting multiple CUDA devices in parallel to further accelerate the alignment throughput. CONCLUSIONS: BarraCUDA is designed to take advantage of the parallelism of GPU to accelerate the alignment of millions of sequencing reads generated by NGS instruments. By doing this, we could, at least in part streamline the current bioinformatics pipeline such that the wider scientific community could benefit from the sequencing technology.BarraCUDA is currently available from http://seqbarracuda.sf.net.
BACKGROUND: With the maturation of next-generation DNA sequencing (NGS) technologies, the throughput of DNA sequencing reads has soared to over 600 gigabases from a single instrument run. General purpose computing on graphics processing units (GPGPU), extracts the computing power from hundreds of parallel stream processors within graphics processing cores and provides a cost-effective and energy efficient alternative to traditional high-performance computing (HPC) clusters. In this article, we describe the implementation of BarraCUDA, a GPGPU sequence alignment software that is based on BWA, to accelerate the alignment of sequencing reads generated by these instruments to a reference DNA sequence. FINDINGS: Using the NVIDIA Compute Unified Device Architecture (CUDA) software development environment, we ported the most computational-intensive alignment component of BWA to GPU to take advantage of the massive parallelism. As a result, BarraCUDA offers a magnitude of performance boost in alignment throughput when compared to a CPU core while delivering the same level of alignment fidelity. The software is also capable of supporting multiple CUDA devices in parallel to further accelerate the alignment throughput. CONCLUSIONS: BarraCUDA is designed to take advantage of the parallelism of GPU to accelerate the alignment of millions of sequencing reads generated by NGS instruments. By doing this, we could, at least in part streamline the current bioinformatics pipeline such that the wider scientific community could benefit from the sequencing technology.BarraCUDA is currently available from http://seqbarracuda.sf.net.
Authors: David A Wheeler; Maithreyan Srinivasan; Michael Egholm; Yufeng Shen; Lei Chen; Amy McGuire; Wen He; Yi-Ju Chen; Vinod Makhijani; G Thomas Roth; Xavier Gomes; Karrie Tartaro; Faheem Niazi; Cynthia L Turcotte; Gerard P Irzyk; James R Lupski; Craig Chinault; Xing-zhi Song; Yue Liu; Ye Yuan; Lynne Nazareth; Xiang Qin; Donna M Muzny; Marcel Margulies; George M Weinstock; Richard A Gibbs; Jonathan M Rothberg Journal: Nature Date: 2008-04-17 Impact factor: 49.962
Authors: Tarjei S Mikkelsen; Manching Ku; David B Jaffe; Biju Issac; Erez Lieberman; Georgia Giannoukos; Pablo Alvarez; William Brockman; Tae-Kyung Kim; Richard P Koche; William Lee; Eric Mendenhall; Aisling O'Donovan; Aviva Presser; Carsten Russ; Xiaohui Xie; Alexander Meissner; Marius Wernig; Rudolf Jaenisch; Chad Nusbaum; Eric S Lander; Bradley E Bernstein Journal: Nature Date: 2007-07-01 Impact factor: 49.962
Authors: Dustin R Masser; Niran Hadad; Hunter Porter; Michael B Stout; Archana Unnikrishnan; David R Stanford; Willard M Freeman Journal: Geroscience Date: 2018-01-11 Impact factor: 7.713
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