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Updated: May 16, 2026
09:45
Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
Copy Number Variation detection from 1000 Genomes Project exon capture sequencing data.
Jiantao Wu1, Krzysztof R Grzeda, Chip Stewart
1Boston College, Boston, Chestnut Hill, MA, USA.
BMC Bioinformatics
|November 20, 2012
Summary
This study shows exome sequencing can detect gene copy number variations (CNVs), finding an average of 16 deletions per person. This method is valuable for genetic research and clinical applications.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- High-throughput sequencing enables cost-effective exome sequencing for SNP discovery.
- Copy Number Variation (CNV) detection in exome data is underexplored despite its impact on protein function.
Purpose of the Study:
- To develop and validate a method for detecting genic Copy Number Variations (CNVs) from exome capture sequencing data.
- To assess the utility of exome sequencing datasets for CNV detection.
Main Methods:
- A rigorous Bayesian method was developed to detect CNVs based on read depth within targeted exonic regions.
- Analyzed 697 samples from the 1000 Genomes Project using 454 or Illumina paired-end sequencing.
- Investigated 931 targeted genes.
Main Results:
- Identified 107 heterozygous deletions in the targeted genes.
- Achieved a 12.5% false discovery rate (FDR) on the cleanest dataset.
- Estimated call-set sensitivity at 45%, with improved FDR for adjacent deletion calls.
Conclusions:
- Exome sequencing datasets are a viable resource for detecting genic CNV events, especially deletions.
- Estimated an average of 16 genic heterozygous deletions per individual genome.
- Provided power analysis to guide future sequencing projects on depth and coverage uniformity.
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