Personalized Pangenome References.
Personalized Pangenome References.
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个性化泛基因组参考。
DOI：
10.1101/2023.12.13.571553
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Paten,Benedict
中科院分区：
文献类型：
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作者：
Sirén,Jouni;Eskandar,Parsa;Ungaro,MatteoTommaso;Hickey,Glenn;Eizenga,JordanM;Novak,AdamM;Chang,Xian;Chang,Pi-Chuan;Kolmogorov,Mikhail;Carroll,Andrew;Monlong,Jean;Paten,Benedict
泛基因组通过比单一参考序列更好地代表遗传多样性来减少参考偏倚。然而，当将样品与泛基因组进行比较时，泛基因组中不属于样品的变体可能会产生误导，例如，导致错误的读取映射。这些不相关的变体在等位基因频率方面通常更罕见，并且先前已经通过过滤罕见变体来处理。然而，这种生硬的启发式既不能去除一些不相关的变体，又去除了许多相关的变体。我们提出了一种新的方法，通过根据读段中的tok-mer计数对局部单倍型进行采样来估算个性化的泛基因组子图。我们在vg工具包（https：//github.com/vgteam/vg）中为Giraffe短读比对器实现了该方法，并将其准确性与使用来自人类泛基因组参考联盟的人类泛基因组图的最先进方法进行了比较。相对于基因组分析工具包，这将小变异基因分型错误减少了四倍，并使已知变异的短读段结构变异基因分型与长读段变异发现方法竞争。
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 tok-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.