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. 2012;8(12):e1002821.
doi: 10.1371/journal.pcbi.1002821. Epub 2012 Dec 27.

Chapter 6: Structural variation and medical genomics

Benjamin J Raphael  1
Affiliations

Chapter 6: Structural variation and medical genomics

Benjamin J Raphael. PLoS Comput Biol. 2012.

Abstract

Differences between individual human genomes, or between human and cancer genomes, range in scale from single nucleotide variants (SNVs) through intermediate and large-scale duplications, deletions, and rearrangements of genomic segments. The latter class, called structural variants (SVs), have received considerable attention in the past several years as they are a previously under appreciated source of variation in human genomes. Much of this recent attention is the result of the availability of higher-resolution technologies for measuring these variants, including both microarray-based techniques, and more recently, high-throughput DNA sequencing. We describe the genomic technologies and computational techniques currently used to measure SVs, focusing on applications in human and cancer genomics.

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Conflict of interest statement

The author has declared that no competing interests exist.

Figures

Figure 1
Figure 1. An inversion resulting from non-allelic homologous recombination (NAHR) between two nearly identical segmental duplications (blue boxes) with opposite orientations (arrows).
The inversion flips the orientation of the subsequence, or block, formula image in one genome relative to the other genome.
Figure 2
Figure 2. Two major approaches to detect structural variants in an individual genome from next-generation sequencing data are de novo assembly and resequencing.
In de novo assembly, the individual genome sequence is constructed by examining overlaps between reads. In resequencing approaches, reads from the individual genome are aligned to a closely related reference genome. Examination of the resulting alignments reveals differences between the individual genome and the reference genome.
Figure 3
Figure 3. A strobe with 3 subreads.
Figure 4
Figure 4. Identification of a deletion in an individual genome by split read analysis (middle), and by depth of coverage analysis (bottom).
Figure 5
Figure 5. Paired end mapping (PEM).
Fragments from an individual genome are sequenced from both ends and the resulting paired reads are aligned to a reference genome. Most paired reads correspond to concordant pairs, where the distance between the alignment of each read agrees with the distribution of fragment lengths (right). The remaining discordant pairs suggest structural variants (here a deletion) that distinguish the individual and reference genomes.
Figure 6
Figure 6
(Top) A discordant pair (arc) indicates a deletion with unknown breakpoints formula image and formula image located in orange blocks. Positions formula image, formula image and the minimum formula image and maximum formula image length of end-sequenced fragments constrain breakpoints formula image to lie within the indicated orange blocks, and are governed by the indicated linear inequalities. (Bottom) A polygon in 2D genome space expresses the linear dependency between breakpoints formula image and formula image and records the uncertainty in the location of the breakpoints.
Figure 7
Figure 7. Mutation, selection, and clonal expansion in tumor development leads to genomic heterogeneity between cells in a tumor.
Current DNA sequencing approaches sequence DNA from many cells and thus result in a heterogenous mixture of mutations, with varying numbers of both passenger mutations (black) and driver mutations (red).
See this image and copyright information in PMC

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