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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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%...
Next-generation Sequencing03:00

Next-generation Sequencing

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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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
Sanger Sequencing01:57

Sanger Sequencing

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...
RNA-seq03:21

RNA-seq

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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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

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Related Experiment Video

Updated: May 19, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

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Published on: October 18, 2013

SNP calling, genotype calling, and sample allele frequency estimation from New-Generation Sequencing data.

Rasmus Nielsen1, Thorfinn Korneliussen, Anders Albrechtsen

  • 1BGI-Shenzhen, Shenzhen, China. rasmus_nielsen@berkeley.edu

Plos One
|August 23, 2012
PubMed
Summary

We developed a statistical framework to accurately estimate allele frequency spectra from next-generation sequencing (NGS) data. This method improves genotype and SNP calling, even with deviations from Hardy-Weinberg equilibrium.

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Last Updated: May 19, 2026

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

Area of Science:

  • Population Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Accurate estimation of allele frequency spectra is crucial for understanding genetic variation.
  • Existing methods for analyzing next-generation sequencing (NGS) data have limitations.

Purpose of the Study:

  • To present a novel statistical framework for estimating and applying sample allele frequency spectra from NGS data.
  • To improve genotype calling and single nucleotide polymorphism (SNP) calling.

Main Methods:

  • Maximum likelihood estimation of allele frequency spectra.
  • Dynamic programming algorithm for likelihood function calculation.
  • Bayesian inference for estimating sample allele frequency at single sites.

Main Results:

  • The framework accurately estimates allele frequency spectra.
  • The method enhances genotype and SNP calling.
  • The framework accommodates deviations from Hardy-Weinberg equilibrium.

Conclusions:

  • The proposed statistical framework offers a robust approach for analyzing NGS data.
  • This method has broad applications in population genetics and genomic studies.