A Practical Guide for Structural Variation Detection in the Human Genome

Abstract

Profiling genetic variants—including single nucleotide variants, small insertions and deletions, copy number variations, and structural variations (SVs)—from both healthy individuals and individuals with disease is a key component of genetic and biomedical research. SVs are large‐scale changes in the genome and involve breakage and rejoining of DNA fragments. They may affect thousands to millions of nucleotides and can lead to loss, gain, and reshuffling of genes and regulatory elements. SVs are known to impact gene expression and potentially result in altered phenotypes and diseases. Therefore, identifying SVs from the human genomes is particularly important. In this review, I describe advantages and disadvantages of the available high‐throughput assays for the discovery of SVs, which are the most challenging genetic alterations to detect. A practical guide is offered to suggest the most suitable strategies for discovering different types of SVs including common germline, rare, somatic, and complex variants. I also discuss factors to be considered, such as cost and performance, for different strategies when designing experiments. Last, I present several approaches to identify potential SV artifacts caused by samples, experimental procedures, and computational analysis. © 2020 Wiley Periodicals LLC.

Assembly of a pangenome for global cattle reveals missing sequences and novel structural variations, providing new insights into their diversity and evolutionary history

“…It is noted that these public WGS data have various coverage, ranging from 5.08× to 54.42×, with an average coverage of 16×. A simulation in humans showed that at 30× coverage, almost all deletions were detectable, whereas at 15×, 10×, and 8× coverage, 90%, 75%, and 70% of the deletions remained detectable ( Yang 2020 ). Therefore, we estimated that increasing the coverage from 16× to 30× would lead to 10% more deletions detected per sample.…”

A survey of current methods to detect and genotype inversions

“…Such studies are now relatively common, but some rely solely on long reads whereas others include additional technologies such as Strand‐seq and whole‐genome mapping to phase, group, order, and orient contigs (Audano et al, 2019; Chaisson et al, 2019; Ebert et al, 2021; Goel et al, 2019; Huddleston et al, 2017; Porubsky et al, 2022; Seo et al, 2016)—including the recent gapless telomere‐to‐telomere assembly (Vollger et al, 2022). At present, such studies are rarely high‐throughput and they are often expensive and complex: the 20× or greater high‐fidelity long read data used in Ebert et al (2021) would cost thousands of dollars (Yang, 2020), with additional costs for Strand‐seq and computing. For the more ambitious studies, genome assembly is a nontrivial task that is often performed by teams of specialists, although with the correct input data it may be feasible for nonexperts to run existing assembly pipelines (e.g., https://github.com/ptrebert/project-diploid-assembly; Porubsky et al, 2020).…”

Global mapping of cancers: The Cancer Genome Atlas and beyond

“…Mutations that have been described in the context of cancer signatures, ranging from point mutations to small InDels, are unlikely to affect the genome structurally [ 94 , 95 , 96 ]. Anyway, it is well established that large SVs that endanger the whole structure of chromosomes are at the root of some of the hallmarks of cancers.…”