CoMRI:用于序列相似性查询的压缩多分辨率索引结构

Hong Sun, Ozgur Ozturk, H. Ferhatosmanoğlu
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引用次数: 7

摘要

本文提出了一种基于压缩多分辨率索引(CoMRI)的DNA序列相似性快速检索系统。我们采用虚拟边界矩形(VBR)的概念来构建一个压缩的网格样式索引结构。网格格式相对于树格式的一个优点是子序列位置信息是由VBR列表中相应VBR的顺序给出的。利用vbr,我们的索引结构可以很容易地适应合理大小的内存。结合一种新的优化的多分辨率搜索算法,大大提高了查询速度。对人类染色体序列数据的广泛性能评估表明,与最小边界矩形(mbr)相比,vbr节省了80%-93%的索引存储空间,并且新的搜索算法几乎删除了所有不必要的vbr,从而保证了高效的磁盘I/O和CPU成本。根据我们的实验结果,CoMRI的性能比最近引入的另一种网格索引结构MRS快至少100倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CoMRI: a compressed multiresolution index structure for sequence similarity queries
In this paper, we present CoMRI, compressed multiresolution index, our system for fast sequence similarity search in DNA sequence databases. We employ virtual bounding rectangle (VBR) concept to build a compressed, grid style index structure. An advantage of grid format over trees is subsequence location information is given by the order of corresponding VBR in the VBR list. Taking advantage of VBRs, our index structure fits into a reasonable size of memory easily. Together with a new optimized multiresolution search algorithm, the query speed is improved significantly. Extensive performance evaluations on human chromosome sequence data show that VBRs save 80%-93% index storage size compared to MBRs (minimum bounding rectangles) and new search algorithm prunes almost all unnecessary VBRs which guarantees efficient disk I/O and CPU cost. According to the results of our experiments, the performance of CoMRI is at least 100 times faster than MRS which is another grid index structure introduced very recently.
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