SparseAssembler：稀疏de Bruijn图的从头组装
SparseAssembler: de novo Assembly with the Sparse de Bruijn Graph
摘要：
基于de Bruijn图的算法是用于从头基因组组装的两种最广泛使用的方法之一。这种方法的主要限制是构建de Bruijn图需要大的计算存储器空间，de Bruijn图以k-mer长度和以碱基对或大致（2k+8）N位表示的基因组中独特k-mer的总多样性（N）为尺度。这种限制对于大规模基因组分析和同时处理多个基因组的测序中心尤其重要。我们提出了一个稀疏的de Bruijn图结构，在此基础上，我们开发了SparseAssembler，大大降低了内存空间的需求。该结构还允许我们引入一种新的方法来去除测序过程中引入的取代错误。稀疏de Bruijn图结构跳过g个中间k聚体，因此将理论内存空间需求降低至~（2k/g+8）N。我们已经发现，g=16的实际值消耗了标准de Bruijn基于图的算法所需的大约10%的内存，但产生了类似的结果。高出错率可能会使SparseAssembler脱轨。因此，我们开发了一种基于稀疏de Bruijn图的去噪算法，该算法可以以\leq 2%的出错率从数据集中去除99%以上的替换错误。鉴于当前一代测序仪的替代出错率低于1%，我们的去噪过程足以有效地保障我们算法的性能。最后，我们还介绍了一种新的Dijkstra样的宽度优先搜索算法的稀疏de Bruijn图结构，以规避残留错误和解决多态性。
英文摘要：
de Bruijn graph-based algorithms are one of the two most widely used approaches for de novo genome assembly. A major limitation of this approach is the large computational memory space requirement to construct the de Bruijn graph, which scales with k-mer length and total diversity (N) of unique k-mers in the genome expressed in base pairs or roughly (2k+8)N bits. This limitation is particularly important with large-scale genome analysis and for sequencing centers that simultaneously process multiple genomes. We present a sparse de Bruijn graph structure, based on which we developed SparseAssembler that greatly reduces memory space requirements. The structure also allows us to introduce a novel method for the removal of substitution errors introduced during sequencing. The sparse de Bruijn graph structure skips g intermediate k-mers, therefore reducing the theoretical memory space requirement to ~(2k/g+8)N. We have found that a practical value of g=16 consumes approximately 10% of the memory required by standard de Bruijn graph-based algorithms but yields comparable results. A high error rate could potentially derail the SparseAssembler. Therefore, we developed a sparse de Bruijn graph-based denoising algorithm that can remove more than 99% of substitution errors from datasets with a \leq 2% error rate. Given that substitution error rates for the current generation of sequencers is lower than 1%, our denoising procedure is sufficiently effective to safeguard the performance of our algorithm. Finally, we also introduce a novel Dijkstra-like breadth-first search algorithm for the sparse de Bruijn graph structure to circumvent residual errors and resolve polymorphisms.
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