Exploiting sparseness in de novo genome assembly.
Exploiting sparseness in de novo genome assembly.
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DOI：
10.1186/1471-2105-13-s6-s1
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发表时间：
2012-04-19
影响因子：
3
通讯作者：
Yu DW
中科院分区：
文献类型：
--
作者：
Ye C;Ma ZS;Cannon CH;Pop M;Yu DW
对于从头基因组组装的组装图的构建，非常大的内存需求限制了当前算法的超级计算环境。在本文中，我们证明了构建一个稀疏组装图，该图仅存储一小部分观察到的k-mers作为节点，并且这些节点之间的链接允许在典型的笔记本电脑上重新组装中等大小的基因组（~500 M）。我们在原理验证软件包SparseAssembler中实现了这个稀疏图概念，利用了从de Bruijn图演变而来的新的稀疏k-mer图结构。我们用模拟和真实数据测试了我们的SparseAssembler，与现有的de novo汇编器相比，实现了约90%的内存节省，并保持了较高的汇编精度，而不会牺牲速度。
The very large memory requirements for the construction of assembly graphs for de novo genome assembly limit current algorithms to super-computing environments. In this paper, we demonstrate that constructing a sparse assembly graph which stores only a small fraction of the observed k-mers as nodes and the links between these nodes allows the de novo assembly of even moderately-sized genomes (~500 M) on a typical laptop computer. We implement this sparse graph concept in a proof-of-principle software package, SparseAssembler, utilizing a new sparse k-mer graph structure evolved from the de Bruijn graph. We test our SparseAssembler with both simulated and real data, achieving ~90% memory savings and retaining high assembly accuracy, without sacrificing speed in comparison to existing de novo assemblers.