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Memory efficient assembly of human genome

机译：人类基因组的记忆有效组装

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The ability to detect the genetic variations between two individuals is an essential component for genetic studies. In these studies, obtaining the genome sequence of both individuals is the first step toward variation detection problem. The emergence of high-throughput sequencing (HTS) technology has made DNA sequencing practical, and is widely used by diagnosticians to increase their knowledge about the casual factor in genetic related diseases. As HTS advances, more data are generated every day than the amount that scientists can process. Genome assembly is one of the existing methods to tackle the variation detection problem. The de Bruijn graph formulation of the assembly problem is widely used in the field. Furthermore, it is the only method which can assemble any genome in linear time. However, it requires an enormous amount of memory in order to assemble any mammalian size genome. The high demands of sequencing more individuals and the urge to assemble them are the driving forces for a memory efficient assembler. In this work, we propose a novel method which builds the de Bruijn graph while consuming lower memory. Moreover, our proposed method can reduce the memory usage by 37% compared to the existing methods. In addition, we used a real data set (chromosome 17 of A/J strain) to illustrate the performance of our method.
机译：检测两个人之间的遗传变异的能力是遗传研究的重要组成部分。在这些研究中，获得两个个体的基因组序列是解决变异检测问题的第一步。高通量测序（HTS）技术的出现使DNA测序变得可行，并被诊断医生广泛使用，以增加他们对遗传相关疾病中偶然因素的认识。随着HTS的发展，每天生成的数据量超过科学家可以处理的数据量。基因组组装是解决变异检测问题的现有方法之一。组装问题的de Bruijn图公式在该领域得到了广泛使用。此外，这是唯一可以在线性时间内组装任何基因组的方法。然而，为了组装任何哺乳动物大小的基因组，它需要大量的记忆。对更多个人进行排序的高要求以及对它们进行组装的渴望是内存高效汇编程序的驱动力。在这项工作中，我们提出了一种新颖的方法，该方法在消耗更少内存的同时构建了de Bruijn图。此外，与现有方法相比，我们提出的方法可以将内存使用量减少37％。另外，我们使用了一个真实的数据集（A / J菌株的17号染色​​体）来说明我们方法的性能。

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