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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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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.
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In most organisms, sex is determined by the ratio of X and Y chromosomes. However, in some organisms, such as Drosophila and C.elegans, sex is determined by the ratio of the number of X chromosomes to the number of sets of autosomes. The Y chromosome in Drosophila is active but does not determine sex. It contains genes responsible for the production of sperms in adult flies.  
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Related Experiment Video

Updated: Dec 2, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
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Efficient detection of copy-number variations using exome data: Batch- and sex-based analyses.

Yuri Uchiyama1,2, Daisuke Yamaguchi3, Kazuhiro Iwama2,4

  • 1Department of Rare Disease Genomics, Yokohama City University Hospital, Yokohama, Japan.

Human Mutation
|November 1, 2020
PubMed
Summary

Optimized exome sequencing analysis efficiently detects rare copy number variations (CNVs) in genetic diseases. This batch-based approach improves pathogenic CNV identification, particularly in epilepsy patients.

Keywords:
XHMMcopy number variationexome sequencingjNordmendelian disorder

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Area of Science:

  • Genomics
  • Bioinformatics
  • Medical Genetics

Background:

  • Numerous algorithms exist for detecting copy number variations (CNVs) from exome sequencing (ES) data, with evaluations focusing on sensitivity, specificity, reproducibility, and precision.
  • However, operational optimization of these algorithms for enhanced performance remains underexplored.

Purpose of the Study:

  • To optimize exome sequencing (ES) data analysis for efficient detection of rare copy number variations (CNVs).
  • To improve the identification of pathogenic CNVs in patients with genetic diseases.

Main Methods:

  • Exome sequencing (ES) data from 1199 samples (763 patients) were analyzed using the eXome Hidden Markov Model (XHMM) and a modified Nord's method.
  • Optimization strategies included batch analysis of samples from the same flow cell to reduce sequencing bias and separate analysis of male and female samples to account for X-linked CNV sex effects.
  • Multiple filtering steps were implemented for efficient CNV selection.

Main Results:

  • The optimized protocol identified an average of fewer than 5 CNVs per sample.
  • Pathogenic/likely pathogenic CNVs were identified in 4.5% (34/763) of patients.
  • In epilepsy patients, the optimized protocol detected clinically relevant CNVs in 13.4% (19/142), an improvement over the previous protocol's 9.9% (14/142).

Conclusions:

  • Batch-based XHMM analysis, combined with targeted CNV analysis, efficiently selects rare pathogenic CNVs in genetic diseases.
  • This optimized approach enhances the diagnostic yield of CNV detection in clinical settings, especially for complex conditions like epilepsy.