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Finding simple sequence repeats (SSRs) within human genome using MapReduce based K-mer algorithm

机译:使用基于MapReduce的K-mer算法在人类基因组中查找简单序列重复(SSR)

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Simple sequence repeats (SSRs) is a collection of the different short and repetitive oligonucleotides sequence that is primarily observed in between any large DNA sequence. In comparison to the standard DNA sequence, these short oligonucleotides may have distinct functions and structural properties. In this article, we have presented a massive parallel processing model for basic operations with k-mers from DNA sequences, based on SSRs function. We introduce a novel approach that finds SSRs using k-mer in the first step. We implement the MapReduce based K-mer algorithm for the advantage of parallel execution, which also enables the analysis of large datasets using of the distributed platform. We have compared the result with KAnalyze, a pipelined K-mer toolkit for finding different sizes of k-mer, and found significant improvement in terms of computational time. Our study shows that MapReduce based k-mer has the potential which reduces memory footprint and accurately identifies every SSR of any specified length. Our proposed MapReduce based k-mer method is useful for finding any Simple sequence repeats (SSRs) in large DNA sequences on any distributed platform.
机译:简单序列重复序列(SSR)是不同的短而重复的寡核苷酸序列的集合,主要在任何大的DNA序列之间观察到。与标准DNA序列相比,这些短寡核苷酸可能具有不同的功能和结构特性。在本文中,我们提出了一个大规模的并行处理模型,用于基于SSR功能的DNA序列k-mer基本操作。我们引入了一种新颖的方法,第一步是使用k-mer查找SSR。为了实现并行执行的优势,我们实现了基于MapReduce的K-mer算法,该算法还可以使用分布式平台来分析大型数据集。我们将结果与KAnalyze(一种用于查找不同大小的k-mer的流水线K-mer工具包)进行了比较,发现计算时间有了显着改善。我们的研究表明,基于MapReduce的k-mer具有减少内存占用并准确识别任何指定长度的每个SSR的潜力。我们提出的基于MapReduce的k-mer方法可用于在任何分布式平台上查找大型DNA序列中的任何简单序列重复(SSR)。

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