Parallel Smith-Waterman Algorithm Hardware Implementation for Ancestors and Offspring Gene Tracer

Asmaa G. Seliem, Wael Abou El-Wafa, A. Galal, H. Hamed
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引用次数: 5

Abstract

Computational Molecular Biology, Bioinformatics and Genomics are the most intriguing sciences to understand the human genome and diseases. These sciences use algorithms to analyze, and predict a hypothesis for the huge collected biological data in gene banks. Sequence alignment is the most important topics in bioinformatics which we are concerned with it in this paper. In comparison of bimolecular sequence (i.e.,DNA, RNA, and protein), regions of sequence similarity indicate functional and structural similarity. As a result, Sequence comparison is the main problem in computational biology, such as evolutionary tree reconstruction, genome analysis, and classification of viruses that belong to specific family of viru+ses. So, it is very important to relate common matched parts between DNA or Protein sequences or to determining ancestors sequences of an offspring before it is used in any process. Due to database was large, parallel smith waterman algorithm (SW) we implemented on Field Programmable Gate Arrays (FPGA) to decrease execution time of algorithm, Which gives significant reduction of execution time and sequential (SW) Execution time by half for two parallel process and decrease the FPGA utilization by 65%.
祖先和后代基因追踪器的并行Smith-Waterman算法硬件实现
计算分子生物学、生物信息学和基因组学是了解人类基因组和疾病的最有趣的科学。这些科学使用算法对基因库中收集的大量生物数据进行分析和预测。序列比对是生物信息学中最重要的课题,本文对其进行了研究。与双分子序列(即DNA、RNA和蛋白质)相比,序列相似性区域表示功能和结构相似性。因此,序列比较是计算生物学中的主要问题,例如进化树重建、基因组分析以及属于特定病毒家族的病毒分类。因此,将DNA或蛋白质序列之间的共同匹配部分或确定后代的祖先序列在任何过程中使用之前都是非常重要的。由于数据库规模较大,为了减少算法的执行时间,我们在FPGA上实现了并行smith waterman算法(SW),使两个并行进程的执行时间和顺序(SW)执行时间显著减少一半,FPGA利用率降低65%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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