hmm序列分析的现代计算技术。

ISRN bioinformatics Pub Date : 2013-09-03 eCollection Date: 2013-01-01 DOI:10.1155/2013/252183
Xiandong Meng, Yanqing Ji
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引用次数: 21

摘要

本文重点介绍了生物信息学数据分析的现代计算体系结构、软件和硬件加速算法的最新研究和评述,重点介绍了序列分析中最重要的应用之一——隐马尔可夫模型(HMM)。我们展示了在生物信息学社会最近开发的各种计算平台上的序列分析工具的详细性能比较。序列分析的特点,如数据和计算密集型的性质,使得它非常有吸引力的优化和并行利用传统的软件方法和创新的硬件加速技术。
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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Modern Computational Techniques for the HMMER Sequence Analysis.

This paper focuses on the latest research and critical reviews on modern computing architectures, software and hardware accelerated algorithms for bioinformatics data analysis with an emphasis on one of the most important sequence analysis applications-hidden Markov models (HMM). We show the detailed performance comparison of sequence analysis tools on various computing platforms recently developed in the bioinformatics society. The characteristics of the sequence analysis, such as data and compute-intensive natures, make it very attractive to optimize and parallelize by using both traditional software approach and innovated hardware acceleration technologies.

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