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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

11.5K
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%...
11.5K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Reference-free SNP detection: dealing with the data deluge.

Richard M Leggett, Dan MacLean

    BMC Genomics
    |July 25, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Reference-free SNP detection identifies genetic variations without a reference genome, enabling broader applications in non-model organisms and metagenomics. This technology reduces computational demands, allowing more extensive genetic studies.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Traditional SNP detection relies on reference genomes, limiting applications in non-model organisms and metagenomics.
    • Emerging reference-free SNP detection methods offer a disruptive alternative for variant identification.
    • Advancements in data structures enhance efficiency for large-scale genetic analyses.

    Purpose of the Study:

    • To discuss technologies and tools for reference-free SNP detection.
    • To explore the potential impact of this technology on genetic variation studies.
    • To highlight applications in model/non-model organisms, metagenomics, and personal genomics.

    Main Methods:

    • Review of current reference-free SNP detection technologies and tools.
    • Analysis of computational efficiency and data structure advancements.
    • Discussion of implications for diverse biological research areas.

    Main Results:

    • Reference-free SNP detection provides a powerful approach for variant identification across various biological contexts.
    • Efficient data structures enable scalable analysis with reduced computational overhead.
    • New applications are emerging in non-model organisms, metagenomics, and personal genomics.

    Conclusions:

    • Reference-free SNP detection is a transformative technology in genetic variation analysis.
    • It expands the scope of genomic studies, particularly for organisms lacking reference genomes.
    • The technology promises significant advancements in personalized medicine and ecological genomics.