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使用 adVNTR 进行可变数目串联重复序列的靶向基因分型。

Targeted genotyping of variable number tandem repeats with adVNTR.

机构信息

Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California 92093, USA.

Department of Medicine, University of California, San Diego, La Jolla, California 92093, USA.

出版信息

Genome Res. 2018 Nov;28(11):1709-1719. doi: 10.1101/gr.235119.118. Epub 2018 Oct 23.

Abstract

Whole-genome sequencing is increasingly used to identify Mendelian variants in clinical pipelines. These pipelines focus on single-nucleotide variants (SNVs) and also structural variants, while ignoring more complex repeat sequence variants. Here, we consider the problem of genotyping (VNTRs), composed of inexact tandem duplications of short (6-100 bp) repeating units. VNTRs span 3% of the human genome, are frequently present in coding regions, and have been implicated in multiple Mendelian disorders. Although existing tools recognize VNTR carrying sequence, genotyping VNTRs (determining repeat unit count and sequence variation) from whole-genome sequencing reads remains challenging. We describe a method, adVNTR, that uses hidden Markov models to model each VNTR, count repeat units, and detect sequence variation. adVNTR models can be developed for short-read (Illumina) and single-molecule (Pacific Biosciences [PacBio]) whole-genome and whole-exome sequencing, and show good results on multiple simulated and real data sets.

摘要

全基因组测序越来越多地用于在临床管道中识别孟德尔变体。这些管道专注于单核苷酸变体 (SNVs),也包括结构变体,而忽略了更复杂的重复序列变体。在这里,我们考虑基因分型(VNTRs)的问题,其由短(6-100bp)重复单元的不精确串联重复组成。VNTRs 占人类基因组的 3%,经常存在于编码区域,并与多种孟德尔疾病有关。尽管现有工具可以识别携带 VNTR 的序列,但从全基因组测序读取中对 VNTR 进行基因分型(确定重复单元计数和序列变异)仍然具有挑战性。我们描述了一种方法,adVNTR,它使用隐马尔可夫模型来对每个 VNTR 进行建模,计算重复单元的数量,并检测序列变异。adVNTR 模型可以针对短读(Illumina)和单分子(Pacific Biosciences [PacBio])全基因组和全外显子组测序进行开发,并在多个模拟和真实数据集上取得了良好的效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e887/6211647/90ec8c5b514b/1709f01.jpg

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