美国加州大学圣克鲁斯分校Benedict Paten等研究人员合作绘制出个性化泛基因组参考序列。2024年9月11日，《自然—方法学》杂志在线发表了这项成果。

研究人员提出了一种新方法，通过根据读段中的k-mer计数对局部单倍型进行采样，推断个性化的泛基因组子图。

研究人员在Giraffe短读段比对工具中实现了该方法（https://github.com/vgteam/vg），并使用来自人类泛基因组参考联盟的泛基因组图，将其准确性与最先进的方法进行比较。

与Genome Analysis Toolkit相比，该方法将小变异的基因分型错误率降低了四倍，并使已知变异的短读段结构变异分型在准确性上，可与长读段变异发现方法竞争。

据悉，泛基因组通过更好地代表遗传多样性，减少了参考偏差。然而，当将样本与泛基因组进行比较时，泛基因组中不属于该样本的变异可能会产生误导，例如导致错误的读段比对。这些不相关的变异通常在等位基因频率上较为稀有，过去通常通过过滤稀有变异来处理。然而，这种简单的启发式方法既未能去除所有不相关变异，也误删了许多相关变异。

附：英文原文

Title: Personalized pangenome references

Author: Sirn, Jouni, Eskandar, Parsa, Ungaro, Matteo Tommaso, Hickey, Glenn, Eizenga, Jordan M., Novak, Adam M., Chang, Xian, Chang, Pi-Chuan, Kolmogorov, Mikhail, Carroll, Andrew, Monlong, Jean, Paten, Benedict

Issue&Volume: 2024-09-11

Abstract: Pangenomes reduce reference bias by representing genetic diversity better than a single reference sequence. Yet when comparing a sample to a pangenome, variants in the pangenome that are not part of the sample can be misleading, for example, causing false read mappings. These irrelevant variants are generally rarer in terms of allele frequency, and have previously been dealt with by filtering rare variants. However, this blunt heuristic both fails to remove some irrelevant variants and removes many relevant variants. We propose a new approach that imputes a personalized pangenome subgraph by sampling local haplotypes according to k-mer counts in the reads. We implement the approach in the vg toolkit (https://github.com/vgteam/vg) for the Giraffe short-read aligner and compare its accuracy to state-of-the-art methods using human pangenome graphs from the Human Pangenome Reference Consortium. This reduces small variant genotyping errors by four times relative to the Genome Analysis Toolkit and makes short-read structural variant genotyping of known variants competitive with long-read variant discovery methods.

DOI: 10.1038/s41592-024-02407-2

Source: https://www.nature.com/articles/s41592-024-02407-2