KSNP: a fast de Bruijn graph-based haplotyping tool approaching data-in time cost

文献类型： 外文期刊

第一作者： Zhou, Qian

作者： Zhou, Qian;Liu, Xianming;Ji, Fahu;Liu, Xianming;Lin, Dongxiao;Zhu, Zexuan;Zhu, Zexuan;Ruan, Jue

作者机构：

期刊名称：NATURE COMMUNICATIONS （ 影响因子：16.6； 五年影响因子：17.0 ）

ISSN：

年卷期： 2024 年 15 卷 1 期

页码：

收录情况： SCI

摘要： Long reads that cover more variants per read raise opportunities for accurate haplotype construction, whereas the genotype errors of single nucleotide polymorphisms pose great computational challenges for haplotyping tools. Here we introduce KSNP, an efficient haplotype construction tool based on the de Bruijn graph (DBG). KSNP leverages the ability of DBG in handling high-throughput erroneous reads to tackle the challenges. Compared to other notable tools in this field, KSNP achieves at least 5-fold speedup while producing comparable haplotype results. The time required for assembling human haplotypes is reduced to nearly the data-in time. Haplotyping is the process of distinguishing alleles inherited together on a chromosome, a crucial step in assembling and interpreting genome sequences. Here, the authors present a computationally efficient haplotype assembly tool for long read sequencing data.

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