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Updated: May 14, 2026
09:45
Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
CNVeM: copy number variation detection using uncertainty of read mapping
Zhanyong Wang1, Farhad Hormozdiari, Wen-Yun Yang
1Computer Science Department, University of California Los Angeles, Los Angeles, CA 90095-1596, USA.
Summary
This study introduces CNVeM, a novel probabilistic model for identifying copy number variations (CNVs) by leveraging read mapping uncertainty. CNVeM enhances accuracy in detecting CNVs, especially in complex genomic regions, offering nucleotide-level resolution.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Copy number variations (CNVs) significantly influence diseases and traits.
- High-throughput sequencing (HTS) enables CNV identification in mammalian genomes.
- Existing methods struggle with multi-mapping reads, limiting CNV detection in repetitive regions.
Purpose of the Study:
- To develop a probabilistic model, CNVeM, that utilizes read mapping uncertainty for improved CNV detection.
- To achieve high-resolution prediction of CNV locations and copy numbers.
- To overcome limitations of existing methods in repetitive genomic sequences.
Main Methods:
- Developed a probabilistic model, CNVeM, incorporating an expectation-maximization (EM) algorithm.
- Utilized maximum likelihood estimation for copy number and location determination.
- Applied the model to both simulated and real genomic datasets.
Main Results:
- CNVeM achieved higher accuracy than CNVnator on simulated data.
- The model successfully detected known CNVs in real genomic data.
- Demonstrated the ability to distinguish between genomic regions with subtle differences (0.1%).
Conclusions:
- CNVeM effectively utilizes read mapping uncertainty for robust CNV detection.
- The method provides high-resolution CNV boundary prediction at the nucleotide level.
- This represents a novel approach to CNV analysis, particularly in complex genomic regions.
Related Concept Videos
02:26Comparing 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%...
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%...
02:46Genome Copying Errors
DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their survival. Therefore, the copying errors are checked and repaired at three levels.
01:05Single Nucleotide Polymorphisms-SNPs
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...