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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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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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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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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Genetic Variation01:25

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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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.
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Updated: Jul 11, 2025

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EMVC-2: an efficient single-nucleotide variant caller based on expectation maximization.

Guillermo Dufort Y Álvarez1, Martí Xargay-Ferrer2, Alba Pagès-Zamora2

  • 1INCO, Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay.

Bioinformatics (Oxford, England)
|November 14, 2023
PubMed
Summary
This summary is machine-generated.

EMVC-2 is a new method for detecting single-nucleotide variants (SNVs) from next-generation sequencing (NGS) data. This tool offers improved accuracy and speed compared to existing variant callers, aiding genomics and personalized medicine.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-nucleotide variants (SNVs) are common genetic variations crucial for genomics and personalized medicine.
  • Current SNV detection methods from next-generation sequencing (NGS) data face challenges with computational complexity and accuracy.
  • There is a need for advanced methods to achieve fast and reliable SNV calling.

Purpose of the Study:

  • To introduce EMVC-2, a novel and efficient method for SNV calling from NGS data.
  • To address the limitations of existing SNV detection tools in terms of speed and accuracy.

Main Methods:

  • EMVC-2 employs a multi-class ensemble classification strategy.
  • It utilizes the expectation-maximization algorithm to determine the most probable genotype at each locus.
  • A decision tree is incorporated for validating inferred variants and filtering out false positives.

Main Results:

  • EMVC-2 was evaluated on multiple human NGS datasets with known SNV sets.
  • The method demonstrated superior performance over state-of-the-art variant callers.
  • EMVC-2 achieved higher accuracy and faster processing speeds on average.

Conclusions:

  • EMVC-2 represents a significant advancement in SNV detection from NGS data.
  • The method offers a more accurate and computationally efficient solution for genomic analyses.
  • EMVC-2 is freely available, facilitating its adoption in research and clinical settings.