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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,...
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...
Genetic Variation01:25

Genetic Variation

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.
Genes exist in different versions called alleles, which...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
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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...

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Updated: May 23, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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optiCall: a robust genotype-calling algorithm for rare, low-frequency and common variants.

T S Shah1, J Z Liu, J A B Floyd

  • 1Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK.

Bioinformatics (Oxford, England)
|April 14, 2012
PubMed
Summary
This summary is machine-generated.

optiCall, a novel genotype calling algorithm, improves accuracy for rare variants on the Illumina platform. It significantly reduces data loss compared to existing methods, enhancing downstream genetic analysis.

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Area of Science:

  • Genetics and Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Current genotype calling algorithms for microarrays utilize either SNP-wise or sample-wise approaches.
  • These methods face challenges in accurately calling genotypes, particularly for rare and low-frequency variants.
  • Existing algorithms can lead to data loss due to false quality control failures and misclassification of rare variants.

Purpose of the Study:

  • To develop and evaluate optiCall, a novel genotype calling algorithm for the Illumina platform.
  • To improve the accuracy of genotype calling for rare, low-frequency, and common variants.
  • To reduce the proportion of SNPs lost to downstream analysis compared to existing algorithms.

Main Methods:

  • Developed optiCall, a genotype calling algorithm integrating both SNP-wise and sample-wise approaches.
  • Implemented optiCall in C++ for UNIX operating systems.
  • Evaluated optiCall using genotype data from 4537 individuals from the 1958 British Birth Cohort on the Immunochip.

Main Results:

  • optiCall achieved a data loss of only 1.38% due to false quality control failures and misclassification of rare variants.
  • This is a significant improvement compared to existing algorithms: Illuminus (3.87%), GenoSNP (7.85%), and GenCall (4.09%).
  • optiCall accurately captures rare variants and correctly accounts for SNPs with shifted probe intensity clouds.

Conclusions:

  • optiCall offers a more accurate and efficient solution for genotype calling on the Illumina platform.
  • The algorithm effectively minimizes data loss, particularly for rare variants, enhancing the reliability of downstream genetic analyses.
  • optiCall is available for download, facilitating its adoption in genetic research.