Suppr超能文献

基于低深度子代样本的遗传多倍体定相分析

Genetic polyploid phasing from low-depth progeny samples.

作者信息

Schrinner Sven, Serra Mari Rebecca, Finkers Richard, Arens Paul, Usadel Björn, Marschall Tobias, Klau Gunnar W

机构信息

Algorithmic Bioinformatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.

出版信息

iScience. 2022 May 25;25(6):104461. doi: 10.1016/j.isci.2022.104461. eCollection 2022 Jun 17.

Abstract

An important challenge in genome assembly is haplotype phasing, that is, to reconstruct the different haplotype sequences of an individual genome. Phasing becomes considerably more difficult with increasing ploidy, which makes polyploid phasing a notoriously hard computational problem. We present a novel genetic phasing method for plant breeding with the aim to phase two deep-sequenced parental samples with the help of a large number of progeny samples sequenced at low depth. The key ideas underlying our approach are to (i) integrate the individually weak Mendelian progeny signals with a Bayesian log-likelihood model, (ii) cluster alleles according to their likelihood of co-occurrence, and (iii) assign them to haplotypes via an interval scheduling approach. We show on two deep-sequenced parental and 193 low-depth progeny potato samples that our approach computes high-quality sparse phasings and that it scales to whole genomes.

摘要

基因组组装中的一个重要挑战是单倍型定相,即重建个体基因组的不同单倍型序列。随着倍性增加,定相变得更加困难,这使得多倍体定相成为一个极其困难的计算问题。我们提出了一种用于植物育种的新型遗传定相方法,目的是借助大量低深度测序的子代样本对两个深度测序的亲本样本进行定相。我们方法的关键思想是:(i)利用贝叶斯对数似然模型整合个体较弱的孟德尔子代信号;(ii)根据等位基因共现的可能性对等位基因进行聚类;(iii)通过区间调度方法将它们分配到单倍型中。我们在两个深度测序的亲本和193个低深度子代马铃薯样本上表明,我们的方法能够计算出高质量的稀疏定相结果,并且可以扩展到整个基因组。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0418/9184567/45d8f0986e0f/fx1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验