Tools for discovering structural variants in WGS data

This repository contains protocols that I am developing for various tools I am using to identify structural variants in WGS data.

Protocols:

• SVP callers
  • VCFTools
  • Varscan
  • Freebayes
• SV callers
  • SVDetect
  • LUMPY
  • Delly
• CNV callers
  • CNV-Seq
  • Control-FREEC

Table of contents

• Background
• Types of structural variation
  • Copy number variation
• Overview of detection strategies
  • Paired-end
  • Split-read
  • Read-depth
  • Assembly
• Identification of structural variation
  • CNVs

Background

Most of the following heavily borrows from Tattini et al (2015)

Structural variants (SVs) are genomic rearrangements generally affecting more then 50 bp. These can include balanced (where a portion of the genome is moved with no net change in bps) and unbalanced (resulting in an increase/decrease in bps) events and include:

• Deletions
• Insertions
• Inversions
• Mobile-element transpositions
• Translocations
• Tandem repeats
• Copy number variants (CNVs)

Types of structural variation

Copy number variation

See Zhao et al 2015(1) CNV refers to a type of intermediate-scale SVs with copy number changes involving a DNA fragment that is typically greater than 1Kb and less than 5Mb. In humans it is estimated that ~ 12% of the genome is subject to copy number change. Generally, CNVs include deletions, insertions, and duplications of genomic regions.

Overview of detection strategies

So far, the NGS-based CNV detection methods can be categorized into five different strategies, including:

• Paired-end mapping (PEM)
• Split read (SR)
• Read depth (RD)
• De novo assembly of a genome (AS)
• Combination of the above approaches (CB)

Paired-end

Paired-end mapping (PEM) strategy detects SVs/CNVs through discordantly mapped reads. A discordant mapping is produced if the distance between two ends of a read pair is significantly different from the average insert size.

Split read

Split read (SR)-based methods use incompletely mapped read from each read pair to identify small SVs/CNVs.

Read depth

Read depth (RD) approach detects CNVs by counting the number of reads mapped to each genomic region. In the figure, reads are mapped to three exome regions.

Assembly

Assembly (AS)-based approach detects CNVs by mapping contigs to the reference genome.

Combined

Combinatorial approach combines RD and PEM information to detect CNVs.

Identification of structural variation

CNVs

1. Zhao M, Wang Q, Wang Q, Jia P, Zhao Z. Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives. BMC Bioinformatics. 2013