开发平铺阵列的性能

M. Abbas
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引用次数: 3

摘要

基因组平铺阵列能够检查序列已知的随机物种的基因组。如果考虑到诸如寡核苷酸质量和重复区域等特征,为这种阵列设计合适的寡核苷酸探针在计算上是困难的。先前的工作已经开发了最小平铺路径问题,用于选择寡核苷酸,使用Dijkstra最短路径算法在计算机上从数百万个候选寡核苷酸中计算通用最优平铺路径。虽然Dijkstra的算法效果很好,但它比较复杂,路由器处理时间可能比较长,导致网络效率下降。本文讨论了一种能够降低耕作阵列平均复杂度的搜索方法。这种愿望是通过使用更快的算法搜索到探针的最短路径来实现的。本文对A*算法进行了改进,并利用改进后的A**算法来代替Dijkstra算法。增强后的版本效率更高,可以降低平均时间复杂度,从而提高平铺阵列的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing the Performance of Tiling Arrays
Genomic tiling arrays are able to inspect the genome of haphazard species for which the sequence is known. The plan of proper oligonucleotide probes for such arrays is computationally difficult if features such as oligonucleotide quality and recurring regions are considered. Prior works have developed the minimal tiling path problem for the choice of oligonucleotides using Dijkstra’s shortest path algorithm to compute universal finest tiling paths from millions of candidate oligonucleotides on computers. Although Dijkstra’s algorithm works well, it is complicated and may take a long time for routers to process it and the efficiency of the network fails. In this paper, the author discusses a search approach that can decrease the average complexity time of tilling arrays. This aspiration is realized by searching for the shortest path to the probes using a faster algorithm. This paper enhances A* Algorithm and exploits the enhanced version, called A**, instead of Dijkstra’s algorithm. The enhanced version is more efficient and can decrease the average time complexity, thus increasing the performance of tiling array.
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