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Back Translated Peptide K-mer Search and Local Alignment in Large DNA Sequence Databases Using BoND-SD-tree Indexing

机译:使用Bond-SD-Tree索引,在大型DNA序列数据库中返回翻译的肽K-MER搜索和局部对齐

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In the past, genome sequence databases had used main memory indexing, such as the suffix tree, for fast sequence searches. With next generation sequencing technologies, the amount of sequence data being generated is huge and main memory indexing is limited by the amount of memory available. K-mer based techniques are being more used for various genome sequence database applications such as local alignment. K-mer can also provide an excellent basis for creating efficient disk based indexing. In this paper, we have proposed a k-mer based database searching and local alignment tool using box queries on BoND-SD-tree indexing. BoND-tree is quite efficient for indexing and searching in Non-Ordered Discrete Data Space (NDDS). We have conducted experiments on searching DNA sequence databases using back translated protein query sequences and have compared with existing methods. We have also implemented local alignment of back translated protein query sequences with large DNA sequence databases using this index based k-mer search. Performances of this local alignment approach have been compared with that of Tblastn of NCBI. The results are quite promising and justify significance of the proposed approach.
机译:在过去,基因组序列数据库使用了主要内存索引,例如后缀树,以便快速序列搜索。利用下一代测序技术,生成的序列数据量是巨大的,主内存索引受可用的内存量的限制。基于K-MER的技术更适用于诸如局部对准的各种基因组序列数据库应用程序。 K-MER还可以为创建基于磁盘的索引提供优异的基础。在本文中,我们已经提出了一种基于K-MEC基于数据库搜索和本地对齐工具,使用Box SD-Tree索引上的框查询。邦德树对索引和搜索不订购的离散数据空间(NDDS)非常有效。我们在使用后翻译蛋白质查询序列进行了搜索DNA序列数据库的实验,并与现有方法进行了比较。我们还通过基于该索引基于索引的K-MER搜索实现了与大DNA序列数据库的后翻译蛋白质查询序列的局部对齐。将这种局部对准方法的性能与NCBI的TBLASTN进行了比较。结果非常有希望,并证明了提出的方法的重要性。

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