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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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When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
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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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Gene Duplication and Divergence02:37

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The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
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Related Experiment Video

Updated: Apr 3, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
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Discovering Recurrent Copy Number Aberrations in Complex Patterns via Non-Negative Sparse Singular Value

Jianing Xi, Ao Li

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 16, 2015
    PubMed
    Summary
    This summary is machine-generated.

    A new computational method, non-negative sparse singular value decomposition (NN-SSVD), enhances the discovery of recurrent copy number aberrations (RCNAs) in cancer. This approach improves detection accuracy in complex genomic patterns, aiding cancer research and treatment.

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    Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants
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    Area of Science:

    • Genomics
    • Computational Biology
    • Cancer Research

    Background:

    • Recurrent copy number aberrations (RCNAs) are crucial in tumorigenesis and cancer treatment.
    • Existing RCNA discovery methods struggle with complex patterns like partial aberrations or mixed gains/losses.

    Purpose of the Study:

    • To introduce a novel computational method, non-negative sparse singular value decomposition (NN-SSVD), for discovering RCNAs.
    • To address limitations of current methods in detecting RCNAs within complex genomic profiles.

    Main Methods:

    • Developed NN-SSVD, a computational approach for RCNA discovery.
    • NN-SSVD measures aberration frequency across a subset of samples to handle complex patterns.
    • Evaluated NN-SSVD using synthetic datasets and a breast cancer dataset.

    Main Results:

    • NN-SSVD demonstrated superior performance in RCNA discovery compared to existing methods on synthetic data.
    • The method showed increased robustness against complicating factors in genomic data.
    • Application to a breast cancer dataset identified significant genomic regions and known cancer-associated genes.

    Conclusions:

    • NN-SSVD is an effective computational tool for identifying RCNAs, particularly in complex cancer genomes.
    • The method offers improved accuracy and robustness, advancing cancer research and potential therapeutic strategies.