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DASH: localising dynamic programming for order of magnitude faster, accurate sequence alignment

机译:DASH:对动态编程进行本地化以更快的数量级,准确的序列比对

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In this paper we present our genomic and proteomic sequence alignment algorithm, DASH, which results in order of magnitude speed improvement when compared to NCBI-BLAST 2.2.6, with superior sensitivity. Dynamic programming (DP) is the predominant contributor to search time for algorithms such as BLAST and FastA/P. Improving the efficiency of DP provides an opportunity to increase sensitivity, or significantly reduce search times and help offset the effects of the continuing exponential growth in database sizes. Specifically, for nucleotide searching we have demonstrated an order of magnitude speed improvement with significantly improved sensitivity, or alternatively moderate speed up with further sensitivity gains, depending on the parameters selected. Smith-Waterman complete DP is used as the sensitivity benchmark. Similar speed and sensitivity results are presented for protein searching. Since our algorithm is highly parallel, we have developed dedicated hardware which we will present in a companion paper, and a distributed version of our software (DDASH), which we expect to provide linear speedup on a cluster.
机译:在本文中,我们介绍了我们的基因组和蛋白质组序列比对算法DASH,与NCBI-BLAST 2.2.6相比,该算法可提高幅度速度,并具有更高的灵敏度。动态编程(DP)是搜索诸如BLAST和FastA / P之类的算法的时间的主要贡献者。提高DP的效率可提供提高敏感性或大幅减少搜索时间的机会,并有助于抵消数据库大小持续指数增长的影响。具体来说,对于核苷酸搜索,我们已经证明了速度的数量级提高,同时灵敏度显着提高,或者根据选定的参数,速度进一步提高,灵敏度进一步提高。 Smith-Waterman完整DP用作灵敏度基准。提出了相似的速度和灵敏度结果用于蛋白质搜索。由于我们的算法是高度并行的,因此我们开发了专用硬件(将在随附的论文中介绍)和分布式软件(DDASH),希望在集群上提供线性加速。

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