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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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Updated: May 10, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Published on: August 3, 2018

Quantifying single nucleotide variant detection sensitivity in exome sequencing.

Alison M Meynert1, Louise S Bicknell, Matthew E Hurles

  • 1MRC Human Genetics Unit, MRC Institute for Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK. alison.meynert@igmm.ed.ac.uk

BMC Bioinformatics
|June 19, 2013
PubMed
Summary
This summary is machine-generated.

Heterozygous single nucleotide variants (SNVs) are frequently missed in genetic studies due to variable sequencing coverage. This study quantifies the probability of missing SNVs, crucial for interpreting genetic data and understanding disease associations.

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Area of Science:

  • Genomics
  • Bioinformatics
  • Genetic Epidemiology

Background:

  • Targeted capture and sequencing are vital for genomic studies.
  • Coverage heterogeneity in sequencing can impact the detection of genuine polymorphisms.
  • Understanding missed polymorphisms is essential for accurate genetic study interpretation.

Purpose of the Study:

  • To develop an empirical model for predicting single nucleotide variant (SNV) detection sensitivity based on read depth.
  • To quantify the probability of missing both heterozygous and homozygous SNVs at various coverage levels.
  • To provide accurate sensitivity estimates for genomic regions to improve genetic study power.

Main Methods:

  • Down-sampling of 30 deeply sequenced exomes.
  • Utilizing gold-standard single nucleotide variant (SNV) genotype calls.
  • Developing an empirical model correlating read depth with genotype calling accuracy.

Main Results:

  • Measured SNV detection sensitivity is lower than predicted by simple binomial sampling.
  • A local read depth of 13X is needed for 95% detection of heterozygous SNVs, and 3X for homozygous SNVs.
  • At 20X mean coverage, 5-15% of heterozygous and 1-4% of homozygous SNVs may be missed.

Conclusions:

  • Common read coverage thresholds often miss non-reference alleles in heterozygotes.
  • Missed heterozygous alleles can be functionally important in rare disease, cancer, and quantitative trait studies.
  • Accurate SNV sensitivity metrics are crucial for robust genetic association studies.