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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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.
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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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.
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Single Nucleotide Polymorphisms-SNPs01:05

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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,...
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Related Experiment Video

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Detection of Copy Number Alterations Using Single Cell Sequencing
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A Sparse Model Based Detection of Copy Number Variations From Exome Sequencing Data.

Junbo Duan, Mingxi Wan, Hong-Wen Deng

    IEEE Transactions on Bio-Medical Engineering
    |August 11, 2015
    PubMed
    Summary

    A new sparse model effectively detects copy number variations (CNVs) from exome sequencing data, outperforming existing methods. This approach offers high power and precision for genetic variant discovery using whole-exome sequencing.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Whole-exome sequencing (WES) is a cost-effective method for detecting genetic variants like copy number variations (CNVs).
    • Existing CNV detection methods for whole-genome sequencing are often unsuitable for WES data due to the discrete nature of exons.
    • A specialized method is required to accurately identify CNVs from WES data.

    Purpose of the Study:

    • To develop and validate a novel sparse model-based method for discovering CNVs from multiple WES datasets.
    • To address the unique challenges of analyzing WES data for CNV detection.

    Main Methods:

    • A penalized matrix approximation represents WES data.
    • A generalized Gaussian distribution models technical variability and sequencing errors.
    • An iteratively reweighted least squares algorithm is employed for parameter estimation.

    Main Results:

    • The proposed sparse model method demonstrates superior performance compared to established approaches like CoNIFER, XHMM, and cn.MOPS.
    • Validation on both synthetic and real datasets confirms the method's effectiveness.
    • The method achieves high power and precision in CNV detection.

    Conclusions:

    • The developed sparse model is a powerful tool for detecting CNVs from WES data.
    • This method effectively leverages the specific characteristics of exome sequencing data.
    • Freely available software facilitates the application of this approach.