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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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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Related Experiment Video

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Detection of Copy Number Alterations Using Single Cell Sequencing
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CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression.

Quanhua Mu, Jiguang Wang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |October 4, 2019
    PubMed
    Summary

    This study introduces CNAPE, a new computational tool that infers DNA copy number alterations from gene expression data. CNAPE offers a cost-effective alternative for cancer research, particularly for gliomas, achieving high prediction accuracy.

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    Area of Science:

    • Genomics
    • Computational Biology
    • Cancer Research

    Background:

    • Detecting DNA copy number alterations (CNAs) is crucial for understanding cancer.
    • Current methods like DNA arrays and sequencing are expensive and require large DNA amounts, limiting their use in clinical biopsies or single-cell studies.

    Purpose of the Study:

    • To develop an accessible and cost-effective computational method for inferring CNAs from gene expression data.
    • To apply this method to study gliomas, a common and aggressive brain cancer.

    Main Methods:

    • Developed CNAPE, a prior knowledge-aided machine learning model.
    • Trained and tested CNAPE on 9,740 cancer samples from The Cancer Genome Atlas.
    • Applied CNAPE to RNA sequencing data from glioma samples.

    Main Results:

    • CNAPE accurately predicted DNA copy number alterations at chromosomal, chromosomal arm, and specific gene levels in gliomas.
    • Achieved over 80% accuracy in broad and some focal genomic regions.
    • Demonstrated CNAPE's utility as an easy-to-use tool.

    Conclusions:

    • CNAPE provides a viable alternative for detecting DNA copy number alterations using gene expression data.
    • The tool is particularly useful for analyzing gliomas and situations with limited DNA availability.
    • CNAPE is available as an open-source tool for the research community.