What Are Structural Variations (SVs)?

Structural Variations (SVs) are the various genetic changes or at times mutations in the chromosomal structure caused by a variety of rearrangements. There are hundreds of thousands of these SVs in each genome and are responsible for genetic diversity, phenotypic traits and diseases susceptibility. Most of the last two decades focused on detecting single nucleotide variations (SNPs) as these point mutations are responsible for causing 85% of Mendelian diseases. Genomics community has been recently focused on developing a better understanding of structural variations and their role in phenotypes and diseases.

Structural Variation Types

Common type of Structural Variation

SV Detection Methods

A number of structural variation detection methods available and use multiple algorithms to detect different type of SVs. Most of the NGS based structural variation detection methods align reads to a reference genome to detect SVs and can be classified into one of the four categories that are:

  • Read-Pair (RP)
  • Split-Read (SP)
  • Read-depth (RD)
  • Assembly


This method depends on the read pair insert size and read mapping pattern on a reference genome. A number of statistical algorithm and noise filtering techniques are being used to circumvent the issues posed by large number of repetitive sequences in an eukariyotic genome that causes incorrect read mapping.


Most structural variation generate split read mapping at the point of break or insertions. These fraction of split reads are used to detect the precise location, size and type of SVs. This method is powerful in detecting small to medium SVs with single base accuracy of detecting breakpoints, however, it is not as accurate when detecting large structural variation or those in repetitive regions due to its local mapping nature.


Sequencing is a random process and ideally should generate an even coverage across all regions of genome baring the effect of GC bias of short reads. Read mapping density is proportional to number of copies of a gene or locus in a given genome. Any changes in read depth caused by increase or loss of  reads is used to detect SVs. Read-dept method is very effective for detection of large copy number variations (>1kb). Again just like other methods above, duplication or deletion in highly repetitive region of genome are difficult to detect by read-depth method.


Most short read methods based on assembly for SV detection use a reference assisted approach. Reads with missing pair or unmapped reads after a reference alignment are collected and a local assembly is performed to generate contig that represents the actual local structural variation. This is in fact method of choice for 1000 Genome project.

Why Choose 1010Genome for SV Analysis

At 1010genome we have developed robust and accurate pipeline for structural variation detection. Our expert bioinformaticians have carefully chosen existing methods and developed in-house tools to combine data across next generation sequencing and optimal mapping platforms to deliver high accuracy structural variation results.

We Offer Following Services for Structural Variation Detection:

  • Short read sequencing on Illumina platform
  • Long read sequencing on PacBio or Oxford Nanopore platforms
  • Microarray (CGH arrays) platform
  • Optical mapping on Bionano platform
  • Structural Variation Detection data analysis for microarray, short reads, long reads and hybrid of short-long-optical mapping.

Unlock the Secrets of Structural Variation with 1010Genome's SV Analysis Services

With 1010Genome, you have the assurance of an experienced, technologically advanced, and collaborative partner dedicated to uncovering the secrets of structural variations in your genomics research. Choose us to dive deep into the genetic architecture of your data, where every structural variation holds the potential to reshape your understanding of genomics and its applications.