通过链接读取捕获的远程测序信息,例如10×基因组学(10xG)帮助解决基因组序列重复,并产生准确和连续的草图基因组组装。我们介绍ARKS,一种无需比对的连锁阅读基因组支架方法,该方法使用连锁阅读将基因组组装进一步组织成连续的草稿。我们的方法与其他依赖于读取对齐的链接读取支架不同,包括我们自己的(ARCS),并使用基于kmer的映射方法。kmer映射策略比读取对齐方法有几个优点,包括更好的可用性和更快的处理速度,因为它排除了输入序列格式化和草稿序列汇编索引的需要。对kmers而不是阅读比对配对序列的依赖放松了工作流程要求,并大大减少了运行时间。
这里,我们展示了链接读取,当与脚手架的Hi-C数据结合使用时,将PacBio长读数据的人类基因组组装草图改进五倍(基线与ARKSNG50=4.6vs.23.1Mbp,分别)。我们还演示了该方法如何提供兆碱基级超新星人类基因组组装的进一步改进(NG50=14.74Mbp与ARKS前后25.94Mbp),它本身专门使用链接的读取数据进行汇编,执行速度比竞争性链接读取支架快六到九倍(〜10.5h与75.7h相比,平均而言)。在人类基因组10xG超新星组装(细胞系NA12878)的ARKS支架之后,少于9个支架覆盖每个染色体,除最大外(染色体1,n=13)。
ARKS使用kmer映射策略而不是链接的读段比对来记录和关联排序和定向草稿装配序列所需的条形码信息。简化的工作流程,与我们最初的实施相比,ARCS,显着提高了实验人类基因组数据集上的运行时间性能。此外,ARKS中的新型距离估计器利用来自链接读取的条形码信息来估计间隙大小。它通过对重叠群内区域的已知距离之间的关系进行建模并计算相关的Jaccard指数来实现这一目标。ARKS有可能提供正确的,染色体尺度的基因组组装,迅速。我们希望ARKS在帮助完善基因组草案方面具有广泛的实用性。
The long-range sequencing information captured by linked reads, such as those available from 10× Genomics (10xG), helps resolve genome sequence repeats, and yields accurate and contiguous draft genome assemblies. We introduce ARKS, an alignment-free linked read genome scaffolding methodology that uses linked reads to organize genome assemblies further into contiguous drafts. Our approach departs from other read alignment-dependent linked read scaffolders, including our own (ARCS), and uses a kmer-based mapping approach. The kmer mapping strategy has several advantages over read alignment methods, including better usability and faster processing, as it precludes the need for input sequence formatting and draft sequence assembly indexing. The reliance on kmers instead of read alignments for pairing sequences relaxes the workflow requirements, and drastically reduces the run time.
Here, we show how linked reads, when used in conjunction with Hi-C data for scaffolding, improve a draft human genome assembly of PacBio long-read data five-fold (baseline vs. ARKS NG50 = 4.6 vs. 23.1 Mbp, respectively). We also demonstrate how the method provides further improvements of a megabase-scale Supernova human genome assembly (NG50 = 14.74 Mbp vs. 25.94 Mbp before and after ARKS), which itself exclusively uses linked read data for assembly, with an execution speed six to nine times faster than competitive linked read scaffolders (~ 10.5 h compared to 75.7 h, on average). Following ARKS scaffolding of a human genome 10xG Supernova assembly (of cell line NA12878), fewer than 9 scaffolds cover each chromosome, except the largest (chromosome 1, n = 13).
ARKS uses a kmer mapping strategy instead of linked read alignments to record and associate the barcode information needed to order and orient draft assembly sequences. The simplified workflow, when compared to that of our initial implementation, ARCS, markedly improves run time performances on experimental human genome datasets. Furthermore, the novel distance estimator in ARKS utilizes barcoding information from linked reads to estimate gap sizes. It accomplishes this by modeling the relationship between known distances of a region within contigs and calculating associated Jaccard indices. ARKS has the potential to provide correct, chromosome-scale genome assemblies, promptly. We expect ARKS to have broad utility in helping refine draft genomes.