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Updated: Feb 1, 2026
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Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
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CONDEL: Detecting Copy Number Variation and Genotyping Deletion Zygosity from Single Tumor Samples Using Sequence
IEEE/ACM Transactions on Computational Biology and Bioinformatics
|November 30, 2018
Summary
CONDEL is a new method for detecting copy number variations (CNVs) in cancer genomes. It improves accuracy and identifies cancer-associated CNVs, even with low-coverage sequencing data.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Copy number variations (CNVs) are crucial for identifying cancer driver genes.
- Existing CNV detection algorithms have limitations due to incomplete feature utilization.
Purpose of the Study:
- To introduce CONDEL, a novel method for detecting CNVs from single tumor samples using high-throughput sequencing data.
- To improve the performance of CNV detection compared to existing methods.
Main Methods:
- CONDEL employs a novel statistic and peel-off scheme to assess genome bin significance.
- A Bayesian approach with statistical mixture models is used for inferring copy number alterations and deletion zygosity.
- The method is validated on extensive simulation datasets and real-world data from the 1000 Genomes Project and EGA archive.
Main Results:
- CONDEL demonstrates superior performance in true positive and false positive rates compared to six peer methods.
- It shows high consistency with other single-sample CNV detection methods.
- CONDEL uniquely identifies previously known cancer-associated CNVs.
Conclusions:
- CONDEL is an effective tool for CNV detection in single tumor samples, including low-coverage data.
- The method enhances the ability to discover cancer-related genetic alterations.
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