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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%...
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,...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Genotype and SNP calling from next-generation sequencing data.

Rasmus Nielsen1, Joshua S Paul, Anders Albrechtsen

  • 1Department of Integrative Biology, University of California, Berkeley, CA 94720, USA. rasmus_nielsen@berkeley.edu

Nature Reviews. Genetics
|May 19, 2011
PubMed
Summary
This summary is machine-generated.

Accurate single nucleotide polymorphism (SNP) and genotype calling is vital for analyzing next-generation sequencing (NGS) data. New statistical methods improve accuracy and quantify uncertainty, particularly for low-coverage studies.

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Targeted DNA Methylation Analysis by Next-generation Sequencing

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Last Updated: Jun 1, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

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Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Area of Science:

  • Genetics and Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Next-generation sequencing (NGS) generates vast amounts of genetic data.
  • Accurate single nucleotide polymorphism (SNP) and genotype calling are essential for interpreting this data.
  • Existing methods face challenges with low- to medium-coverage sequencing data.

Purpose of the Study:

  • To review recently developed statistical methods for genotype calling in NGS data.
  • To provide guidance on applying these methods to genetic and genomics studies.
  • To address the challenges of genotype calling with limited sequencing coverage.

Main Methods:

  • Review of advanced statistical approaches for genotype calling.
  • Evaluation of methods that quantify uncertainty in genotype calls.
  • Focus on techniques applicable to low- and medium-coverage NGS data.

Main Results:

  • Newly developed statistical methods enhance the accuracy of SNP and genotype calling.
  • These methods effectively quantify the uncertainty inherent in genotype calls.
  • Significant improvements are observed when applying these methods to low- to medium-coverage data.

Conclusions:

  • Accurate genotype calling is critical for meaningful NGS data analysis.
  • Advanced statistical methods offer improved accuracy and uncertainty quantification.
  • These methods are particularly beneficial for studies with limited sequencing depth.