Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Nat Methods. 2024 Jun;21(6):967-970. doi: 10.1038/s41592-024-02269-8. Epub 2024 May 10.
Despite advances in long-read sequencing technologies, constructing a near telomere-to-telomere assembly is still computationally demanding. Here we present hifiasm (UL), an efficient de novo assembly algorithm combining multiple sequencing technologies to scale up population-wide near telomere-to-telomere assemblies. Applied to 22 human and two plant genomes, our algorithm produces better diploid assemblies at a cost of an order of magnitude lower than existing methods, and it also works with polyploid genomes.
尽管长读测序技术取得了进展,但构建近乎端粒到端粒的组装仍然需要大量的计算资源。在这里,我们提出了 hifiasm (UL),这是一种有效的从头组装算法,它结合了多种测序技术,以扩大全人群近端粒到端粒的组装规模。将我们的算法应用于 22 个人类和两个植物基因组,与现有方法相比,它以低一个数量级的成本生成了更好的二倍体组装,并且它也适用于多倍体基因组。