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Inference of Chromosome-Length Haplotypes Using Genomic Data of Three or a Few More Single Gametes

机译:使用三个或几个以上单配子的基因组数据推断染色体长度单倍型

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摘要

Compared with genomic data of individual markers, haplotype data provide higher resolution for DNA variants, advancing our knowledge in genetics and evolution. Although many computational and experimental phasing methods have been developed for analyzing diploid genomes, it remains challenging to reconstruct chromosome-scale haplotypes at low cost, which constrains the utility of this valuable genetic resource. Gamete cells, the natural packaging of haploid complements, are ideal materials for phasing entire chromosomes because the majority of the haplotypic allele combinations has been preserved. Therefore, compared with the current diploid-based phasing methods, using haploid genomic data of single gametes may substantially reduce the complexity in inferring the donor's chromosomal haplotypes. In this study, we developed the first easy-to-use R package, Hapi, for inferring chromosome-length haplotypes of individual diploid genomes with only a few gametes. Hapi outperformed other phasing methods when analyzing both simulated and real single gamete cell sequencing data sets. The results also suggested that chromosome-scale haplotypes may be inferred by using as few as three gametes, which has pushed the boundary to its possible limit. The single gamete cell sequencing technology allied with the cost-effective Hapi method will make large-scale haplotype-based genetic studies feasible and affordable, promoting the use of haplotype data in a wide range of research.
机译:与单个标记的基因组数据相比,单倍型数据为DNA变异提供了更高的分辨率,从而提高了我们在遗传学和进化方面的知识。尽管已经开发了许多用于分析二倍体基因组的计算和实验阶段方法,但以低成本重建染色体规模的单倍型仍然具有挑战性,这限制了这种宝贵遗传资源的效用。配子细胞是单倍体补体的天然包装,是分期整个染色体的理想材料,因为大多数单倍型等位基因组合已被保留。因此,与目前基于二倍体的定相方法相比,使用单配子的单倍体基因组数据可以大大降低推断供体染色体单倍型的复杂性。在这项研究中,我们开发了第一个易于使用的R包happy,用于推断单个二倍体基因组的染色体长度单倍型,只有几个配子。在分析模拟和真实的单配子细胞测序数据集时,Happy的表现优于其他定相方法。研究结果还表明,染色体规模的单倍型可以通过使用少至三个配子来推断,这已经将边界推向了可能的极限。单配子细胞测序技术与具有成本效益的Happy方法相结合,将使基于单倍型的大规模遗传研究变得可行且经济实惠,从而促进单倍型数据在广泛研究中的应用。

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