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Structural variation in the sequencing era

Nature Reviews Genetics volume 21, pages 171–189 (2020)Cite this article

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Abstract

Identifying structural variation (SV) is essential for genome interpretation but has been historically difficult due to limitations inherent to available genome technologies. Detection methods that use ensemble algorithms and emerging sequencing technologies have enabled the discovery of thousands of SVs, uncovering information about their ubiquity, relationship to disease and possible effects on biological mechanisms. Given the variability in SV type and size, along with unique detection biases of emerging genomic platforms, multiplatform discovery is necessary to resolve the full spectrum of variation. Here, we review modern approaches for investigating SVs and proffer that, moving forwards, studies integrating biological information with detection will be necessary to comprehensively understand the impact of SV in the human genome.

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Fig. 1: Overview of ensemble algorithms.
Fig. 2: Structural variation signatures in single-molecule and connected-molecule strategies.
Fig. 3: Resolving the molecular context behind structural variants by integrating multimodal information.

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Acknowledgements

The authors thank Y. Wang, W. Zhou, A. Weber and B. Zhou for their valuable comments and help with proofreading the manuscript. S.S.H. was supported through the Michigan Predoctoral Training in Genetics grant (T32 GM007544). A.E.U. acknowledges funding by the National Institutes of Health (NIH) and the Simons Foundation, and is a Tashia and John Morgridge Faculty Scholar of the Stanford Child Health Research Institute.

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Authors and Affiliations

  1. Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA

    Steve S. Ho & Ryan E. Mills

  2. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA

    Alexander E. Urban

  3. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Alexander E. Urban

  4. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Ryan E. Mills

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S.S.H. and R.E.M researched the literature and wrote the article. All authors provided substantial contributions to discussions of the content, and reviewed and/or edited the manuscript before submission.

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Supplementary information

Glossary

Structural variations

(SVs). Operationally defined as sequence variants >50 bp in size. The most recognized forms of SV include deletions, duplications, inversions, insertions and translocations.

Complex rearrangements

A structural variant that consists of multiple combinations of structural variant types nested or clustered with one another.

Read signatures

Specific marks that result from reads that map discordantly to the reference genome.

Short-read HTS

(Short-read high-throughput sequencing). Standard sequencing where libraries are fragmented to ~600–800 bp in length. Two ends are sequenced ~100–250 bp with an unsequenced insert size of ~100–600 bp.

Flow cells

Glass slides containing fluidic channels for sequencing reactions to occur.

Microfluidics

Devices that precisely manipulate and control small amounts of fluids.

SV callers

An algorithm designed to detect structural variations (SVs). Each putative SV detected by a caller is an individual ‘call’. ‘Call’ derives from computer science, meaning to invoke a particular task; detected SVs are the result of each performed ‘task’.

Sensitivity

The ability to detect known variants correctly. Low sensitivity implies low ability to detect bona fide variants.

Reference data sets

High-resolution structural variation data sets typically derived from de novo genome assemblies, population-scale sequencing or projects employing multiple orthogonal detection methods. Reference sets are used to benchmark detection algorithms and determine the novelty and rarity of structural variation calls.

Ensemble algorithm

A detection method that combines the resulting call sets from multiple independent algorithms.

False-discovery rate

The expected number of calls that should be false but are marked as true within the final call set.

Coordinate overlap

The number of base pairs that are identical between two different variant calls.

Purifying selection

A process of natural selection where strongly deleterious alleles are selectively removed from a population.

Phased SVs

(Phased structural variations). Variants that are assigned to a paternal haplotype, often computed using family trio or heterozygous single-nucleotide variant data.

Receiver operating characteristic curves

Plots of the true positive rate against the false positive rate showing the relationship between sensitivity and specificity.

Connected-molecule strategies

Genomic methods that connect shorter reads of a DNA molecule together to provide long-range information.

Sequence coverage

The average number of times a given locus is covered by a sequence read.

Physical coverage

The average number of times a given locus is covered by the cumulative length of the reads, including unsequenced inserts.

Single-molecule strategies

Genomic methods that read the entirety of long strands of DNA.

Specificity

The ability to detect the absence of variants correctly. Low specificity implies many false positives.

Base-calling error

Errors in determining the respective nucleotide from raw signals during sequencing.

Circular consensus sequencing

A single-molecule real-time (SMRT) sequencing method that improves accuracy through multiple passes of the template molecule.

Hybrid assembly

A genome assembly that leverages sequencing data from multiple platforms to reconstruct the original sequence, using the orthogonal data to extend the contig lengths or to branch contigs to one another.

N50

A number that denotes the minimum contig size for which 50% of the nucleotide sequence is contained within. A larger N50 implies a more contiguous assembly.

Topologically associating domain

A spatial partition of the genome where segments within these domains are enriched for interactions with each other when compared with interactions with segments outside the domain.

Allelic bias

Gene expression that is biased towards one allele over the other.

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Ho, S.S., Urban, A.E. & Mills, R.E. Structural variation in the sequencing era. Nat Rev Genet 21, 171–189 (2020). https://doi.org/10.1038/s41576-019-0180-9

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