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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Next-generation Sequencing03:00

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Related Experiment Video

Updated: Jun 6, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

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SurVIndel2: improving copy number variant calling from next-generation sequencing using hidden split reads.

Ramesh Rajaby1,2,3,4, Wing-Kin Sung5,6,7,8,9

  • 1Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, China.

Nature Communications
|December 2, 2024
PubMed
Summary
This summary is machine-generated.

A new tool, SurVIndel2, identifies copy number variations (CNVs) in repetitive genome regions missed by other methods. This improves variant cataloging and complements existing indel callers for more complete human genome analysis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Structural Variation Analysis

Background:

  • Copy number variations (CNVs), including deletions and tandem duplications, constitute the majority of structural variations in the human genome.
  • Existing short-read sequencing methods struggle to detect CNVs in repetitive genomic regions due to insufficient evidence (split reads, discordant pairs, read depth changes).

Purpose of the Study:

  • To introduce SurVIndel2, a novel short-read-based caller for detecting CNVs, particularly those in challenging repetitive regions.
  • To demonstrate SurVIndel2's superior performance compared to existing callers on human and non-human datasets.
  • To enhance the comprehensiveness of human genome variant catalogs by identifying previously missed CNVs.

Main Methods:

  • Development of SurVIndel2, incorporating novel 'hidden split read' evidence alongside established statistical techniques.
  • Benchmarking SurVIndel2 against popular CNV callers using public datasets.
  • Application of SurVIndel2 to the 1000 Genomes Project dataset to generate an extensive CNV catalog.
  • Integration of SurVIndel2 with Google DeepVariant for combined indel and CNV variant calling.

Main Results:

  • SurVIndel2 significantly outperforms existing callers in detecting CNVs, especially in repetitive regions.
  • A comprehensive CNV catalog for the 1000 Genomes Project was generated, revealing hundreds of thousands of previously undetected variants.
  • Tandem use of SurVIndel2 and DeepVariant produced a remarkably complete catalog of variants for an individual genome.
  • Limitations in current sequencing technologies were identified as a major contributor to missing CNVs.

Conclusions:

  • SurVIndel2 represents a significant advancement in CNV detection using short-read data, particularly in complex genomic regions.
  • The method substantially improves the completeness of structural variant catalogs, aiding in a deeper understanding of human genetic diversity.
  • Addressing sequencing technology limitations is crucial for further improving variant detection accuracy and completeness.