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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.
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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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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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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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Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Detection of Copy Number Alterations Using Single Cell Sequencing
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Common copy number variation detection from multiple sequenced samples.

Junbo Duan, Hong-Wen Deng, Yu-Ping Wang

    IEEE Transactions on Bio-Medical Engineering
    |February 22, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for detecting common copy number variations (CNVs) across multiple next-generation sequencing (NGS) samples. The approach improves detection power and breakpoint accuracy for genomic variations.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Copy number variations (CNVs) are crucial genomic alterations.
    • Next-generation sequencing (NGS) enables high-resolution CNV detection.
    • Existing methods often analyze single or paired samples, limiting common CNV discovery.

    Purpose of the Study:

    • To develop a novel method for detecting common CNVs across multiple NGS samples.
    • To leverage complementary information from diverse sequencing data.
    • To address the limitations of current methods in identifying shared genomic variations.

    Main Methods:

    • Utilizing a penalized sparse regression model to analyze multiple read depth profiles.
    • Formulating common CNV identification as a change-point detection problem.
    • Integrating read depth signals from multiple NGS data samples.

    Main Results:

    • The proposed method demonstrates higher detection power for common CNVs.
    • Improved breakpoint estimation accuracy compared to existing methods.
    • Validation on both simulated and real genomic datasets.

    Conclusions:

    • The novel method effectively identifies common CNVs by exploiting concurrency in read depth signals.
    • This approach enhances the ability to detect shared genomic variations across multiple samples.
    • The method offers a significant advancement for CNV analysis in large-scale genomic studies.