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Single Nucleotide Polymorphisms-SNPs

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,...

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Related Experiment Video

Updated: Apr 18, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Estimating the information value of polymorphic sites using pooled sequences.

|January 10, 2015

Summary

This summary is machine-generated.

This study introduces a new method to accurately identify genetic variants from sequencing data. It provides a reliable way to select informative sites, improving the analysis of genetic variation in ecology and population genetics.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Last Updated: Apr 18, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Genomics
Population Genetics
Bioinformatics

Background:

High-throughput sequencing is widely used for genetic variation identification in biology.
Sequencing artifacts and biases complicate accurate variant and allele frequency determination.
Existing methods like FST are suboptimal for selecting diagnostic variants.

Purpose of the Study:

To develop a novel method for quantifying the information gained from variant sites.
To create a robust estimator accounting for sampling bias and sequencing errors.
To compare the new method against existing approaches for identifying diagnostic polymorphisms.

Main Methods:

Direct calculation of expected information content per variant site.
Development of a conservative estimator for uncertainty.
Application and evaluation on simulated and real sequencing data.

Main Results:

The developed method effectively quantifies the information value of variant sites.
The conservative estimator addresses uncertainties from sampling bias and sequencing errors.
Performance comparison with existing methods demonstrates superior accuracy.

Conclusions:

Expected information content offers an interpretable metric for variant site utility.
The method achieves clear discrimination between true variants and noise.
Candidate diagnostic sites can be selected with high confidence.