结构变异
计算生物学
移相器
遗传学
顺序装配
基因组
参考基因组
生物
索引
倍性
计算机科学
单核苷酸多态性
基因
工程类
电气工程
基因型
基因表达
转录组
作者
Yunfei Hu,Chao Yang,Lu Zhang,Xin Zhou
出处
期刊:Methods in molecular biology
日期:2022-11-06
卷期号:: 161-182
标识
DOI:10.1007/978-1-0716-2819-5_11
摘要
Phasing is essential for determining the origins of each set of alleles in the whole-genome sequencing data of individuals. As such, it provides essential information for the causes of hereditary diseases and the sources of individual variability. Recent technical breakthroughs in linked-read (referred to as co-barcoding in other chapters of the book) and long-read sequencing and downstream analysis have brought the goal of accurate and complete phasing within reach. Here we review recent progress related to the assembly and phasing of personal genomes based on linked-reads and related applications. Motivated by current limitations in generating high-quality diploid assemblies and detecting variants, a new suite of software tools, Aquila, was developed to fully take advantage of linked-read sequencing technology. The overarching goal of Aquila is to exploit the strengths of linked-read technology including long-range connectivity and inherent phasing of variants for reference-assisted local de novo assembly at the whole-genome scale. The diploid nature of the assemblies facilitates detection and phasing of genetic variation, including single nucleotide variations (SNVs), small insertions and deletions (indels), and structural variants (SVs). An extension of Aquila, Aquila_stLFR, focuses on another newly developed linked-reads sequencing technology, single-tube long-fragment read (stLFR). AquilaSV, a region-based diploid assembly approach, is used to characterize structural variants and can achieve diploid assembly in one target region at a time. Lastly, we introduce HAPDeNovo, a program that exploits phasing information from linked-read sequencing to improve detection of de novo mutations. Use of these tools is expected to harness the advantages of linked-reads technology, improve phasing, and advance variant discovery.
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