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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,...

Comparing Copy Number Variations and SNPs

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%...

Genome-wide Association Studies-GWAS

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...

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Related Experiment Video

Updated: May 29, 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

SNIT: SNP identification for strain typing.

Ravi Vijaya Satya¹, Nela Zavaljevski, Jaques Reifman

¹Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, U,S, Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA. jaques.reifman@us.army.mil.

Source Code for Biology and Medicine

|September 10, 2011

Summary

This summary is machine-generated.

A new software pipeline called SNP identification for strain typing (SNIT) accurately identifies bacterial genome strains. SNIT efficiently compares genomes to find the closest match, crucial for microbial genomics research.

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Published on: April 1, 2019

Area of Science:

Microbial genomics
Bioinformatics
Computational biology

Background:

The rapid increase in microbial genome sequencing necessitates efficient tools for strain-level identification.
Accurate strain typing is critical for understanding microbial populations, disease outbreaks, and evolutionary relationships.

Purpose of the Study:

To introduce the SNP identification for strain typing (SNIT) pipeline, a novel software for fast and accurate bacterial genome strain identification.
To evaluate the performance of the SNIT pipeline in identifying the closest genomic neighbor for newly sequenced bacterial genomes.

Main Methods:

The SNIT pipeline identifies single nucleotide polymorphisms (SNPs) and small insertions/deletions (indels) by comparing a query genome against a database of related genomes.
It analyzes polymorphic loci to determine the genome with the fewest genetic differences from the query genome.
The pipeline was tested on five different bacterial species.

Main Results:

The SNIT pipeline demonstrated high accuracy, correctly identifying the closest genomic neighbor in 75% to 100% of cases across five bacterial species.
The software provides a fast and accurate method for strain-level identification.

Conclusions:

The SNIT pipeline is an effective tool for rapid and accurate strain-level identification of bacterial genomes.
This software addresses the growing need for efficient genomic analysis in the field of microbial genomics.