利用 double graph 实现二倍体和多倍体基因组的可扩展端粒到端粒组装
Scalable telomere-to-telomere assembly for diploid and polyploid genomes with double graph
AI总结：
提出结合多种测序技术与 double graph 的高效从头组装算法，以更低成本实现二倍体及多倍体基因组的端粒到端粒单倍型解析组装。
摘要：
尽管长读长数据在长度和准确性方面近期取得进展，但构建从端粒到端粒的单倍型解析基因组组装仍需大量计算资源。本研究提出一种高效的从头组装算法，结合多种测序技术，以扩展群体范围的端粒到端粒组装。通过利用22个人类和2个植物基因组，我们证明该算法比现有方法成本低约一个数量级，同时产生更好的二倍体和单倍体组装。值得注意的是，该算法是多倍体基因组单倍型解析组装唯一可行的解决方案。
英文摘要：
Despite recent advances in the length and the accuracy of long-read data, building haplotype-resolved genome assemblies from telomere to telomere still requires considerable computational resources. In this study, we present an efficient de novo assembly algorithm that combines multiple sequencing technologies to scale up population-wide telomere-to-telomere assemblies. By utilizing twenty-two human and two plant genomes, we demonstrate that our algorithm is around an order of magnitude cheaper than existing methods, while producing better diploid and haploid assemblies. Notably, our algorithm is the only feasible solution to the haplotype-resolved assembly of polyploid genomes.
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