EndHiC：使用重叠群末端的Hi-C连接将大重叠群组装到染色体水平支架中
EndHiC: assemble large contigs into chromosomal-level scaffolds using the Hi-C links from contig ends
摘要：
动机：PacBio HiFi和超长ONT reads的应用在重叠群水平组装方面取得了巨大进展，但使用现有的Hi-C scaffolding软件将大重叠群组装成染色体仍然具有挑战性，这些软件都使用来自整个重叠群区域的Hi-C链接来计算重叠群之间的接触值。由于两个相邻叠连群的Hi-C连接仅集中在叠连群的相邻末端，较大的叠连群大小将降低区分相邻（信号）和非相邻（噪声）叠连群连接的能力，导致更高的错误组装率。结果如下：我们提出了一个软件包EndHiC，它适用于组装大的重叠群（> 1-Mb）到染色体水平的支架，使用Hi-C连接仅从重叠群的末端区域，而不是整个重叠群区域。得益于提高的信噪比，EndHiC实现了比现有软件LACHESIS、ALLHiC和3D-DNA更高的支架准确性。此外，EndHiC的参数很少，运行速度比现有软件快10-1000倍，需要微不足道的内存，提供鲁棒性评估，并允许图形查看支架结果。EndHiC的高脚手架精度和用户友好的界面，将用户从劳动密集型的手动检查和修改工作中解放出来。可用性和实现：EndHiC是用Perl编写的，可以在https://github.com/fanagislab/EndHiC上免费获得。联系方式：fanwei@caas.cn和milrazhang@163.com补充信息：补充数据可在生物信息学在线获得。
英文摘要：
Motivation: The application of PacBio HiFi and ultra-long ONT reads have achieved huge progress in the contig-level assembly, but it is still challenging to assemble large contigs into chromosomes with available Hi-C scaffolding software, which all compute the contact value between contigs using the Hi-C links from the whole contig regions. As the Hi-C links of two adjacent contigs concentrate only at the neighbor ends of the contigs, larger contig size will reduce the power to differentiate adjacent (signal) and non-adjacent (noise) contig linkages, leading to a higher rate of mis-assembly. Results: We present a software package EndHiC, which is suitable to assemble large contigs (> 1-Mb) into chromosomal-level scaffolds, using Hi-C links from only the contig end regions instead of the whole contig regions. Benefiting from the increased signal to noise ratio, EndHiC achieves much higher scaffolding accuracy compared to existing software LACHESIS, ALLHiC, and 3D-DNA. Moreover, EndHiC has few parameters, runs 10-1000 times faster than existing software, needs trivial memory, provides robustness evaluation, and allows graphic viewing of the scaffold results. The high scaffolding accuracy and user-friendly interface of EndHiC, liberate the users from labor-intensive manual checks and revision works. Availability and implementation: EndHiC is written in Perl, and is freely available at https://github.com/fanagislab/EndHiC. Contact: fanwei@caas.cn and milrazhang@163.com Supplementary information: Supplementary data are available at Bioinformatics online.
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