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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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Next-generation Sequencing03:00

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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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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RNA-seq03:21

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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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Multi-species Conserved Sequences02:51

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Related Experiment Video

Updated: May 2, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
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Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and

Min Zhao, Qingguo Wang, Quan Wang

    BMC Bioinformatics
    |February 26, 2014
    PubMed
    Summary

    Copy number variation (CNV) detection has shifted to next-generation sequencing (NGS). This review covers recent computational methods for identifying CNVs using NGS data, discussing their pros, cons, and future directions.

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    Area of Science:

    • Genetics
    • Bioinformatics
    • Genomic Medicine

    Background:

    • Copy number variation (CNV) involves abnormal genomic region copy numbers, impacting health.
    • Historically, arrayCGH and genotyping arrays detected large CNVs.
    • Next-generation sequencing (NGS) now enables high-resolution CNV analysis.

    Purpose of the Study:

    • To review computational methods for CNV detection using NGS data.
    • To discuss the strengths and weaknesses of current NGS-based CNV detection tools.
    • To suggest future research directions in computational CNV analysis.

    Main Methods:

    • Review of recent computational methods for CNV detection.
    • Analysis of whole genome sequencing (WGS) data.
    • Analysis of whole exome sequencing (WES) data.

    Main Results:

    • NGS technologies have significantly advanced CNV detection capabilities.
    • Numerous computational tools have been developed for NGS-based CNV analysis.
    • Existing methods vary in sensitivity, specificity, and performance across different genomic regions.

    Conclusions:

    • Computational methods are crucial for interpreting NGS data for CNV detection.
    • Further development is needed to improve accuracy and efficiency of CNV callers.
    • Standardization of methods and validation strategies are essential for clinical applications.