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首页> 外文期刊>Journal of Bioinformatics and Sequence Analysis >Evaluating the computing efficiencies (specificity and sensitivity) of graphics processing unit (GPU)-accelerated DNA sequence alignment tools against central processing unit (CPU) alignment tool
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Evaluating the computing efficiencies (specificity and sensitivity) of graphics processing unit (GPU)-accelerated DNA sequence alignment tools against central processing unit (CPU) alignment tool

机译：针对中央处理器（CPU）对齐工具评估图形处理单元（GPU）加速的DNA序列比对工具的计算效率（特异性和灵敏度）

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Bioinformatics is an emerging field, where information technology usage can significantly accelerate life science research. It is a relatively new field and the scope of exploring new tools and techniques seems immense. One major field where bioinformatics plays important role is next generation sequence analysis (NGS), in which an unknown genome is shuttered into pieces and tried to align it to a reference known genome to decipher its functions using sequence comparison. The first well known application of this technology is the human genome project which took nearly 10 years to finish. With advancements in central processing units (CPUs), the alignment time has improved, but has not reached optimal. There seems a constant need to improve this computing time, which made the scope for using graphics processing units (GPUs) and parallel programming tasks to replace CPUs. With access to high performance multi-thread, multi-core parallel computing supercomputers, several GPU based sequence alignment tools have been published recently, some of the major tools are BarraCUDA, CUSHAW, GPU-BWT, SOAP3, and SARUMAN, which claim to speed up the processes anywhere between 2x and 10x times. Most of these tools can be compiled on GCC 4.3 compilers with CUDA. This paper focuses on compiling the current GPU based alignment tools on 70.7 million read pairs (Illumina HiSeq 2000) to align them on a human genome and check its efficiency (time sensitivity and alignment specificity) compared to traditional CPU based alignment (Bowtie) tool. Resulting observations would help researchers choose the appropriate GPU alignment tool to suffice their computing needs.
机译：生物信息学是一个新兴领域，信息技术的使用可以极大地促进生命科学研究。这是一个相对较新的领域，探索新工具和技术的范围似乎很大。生物信息学在其中发挥重要作用的一个主要领域是下一代序列分析（NGS），其中将未知基因组切成碎片，并尝试将其与参考已知基因组对齐以使用序列比较来破译其功能。这项技术的第一个众所周知的应用是人类基因组计划，该计划耗时近10年完成。随着中央处理器（CPU）的进步，对齐时间有所改善，但尚未达到最佳。似乎一直需要改进这种计算时间，这使得可以使用图形处理单元（GPU）和并行编程任务来代替CPU。随着对高性能多线程，多核并行计算超级计算机的访问，最近已经发布了几种基于GPU的序列比对工具，其中一些主要工具是BarraCUDA，CUSHAW，GPU-BWT，SOAP3和SARUMAN，它们声称可以提高速度。在2到10倍之间的任何地方进行处理。这些工具大多数都可以在带有CUDA的GCC 4.3编译器上进行编译。与传统的基于CPU的比对（Bowtie）工具相比，本文着重于在7070万条读取对（Illumina HiSeq 2000）上编译当前基于GPU的比对工具，以使其与人类基因组比对，并检查其效率（时间敏感性和比对特异性）。所得的观察结果将有助于研究人员选择合适的GPU对准工具来满足其计算需求。

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