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Related Concept Videos

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,...
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%...
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...
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...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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Related Experiment Video

Updated: May 22, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

Prediction and functional analysis of single nucleotide polymorphisms.

Li Li1, Qi Chen, Dong-Qing Wei

  • 1State Key Laboratory of Microbial Metabolism, College of Life Science and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai, China.

Current Drug Metabolism
|May 18, 2012
PubMed
Summary

Discovering single nucleotide polymorphisms (SNPs), common genetic variations, is crucial for understanding human genome function and disease. Bioinformatics tools aid in SNP analysis, prediction, and understanding their functional impact.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Human Genetics

Background:

  • Genome sequencing advances generate vast genetic variation data.
  • Single nucleotide polymorphisms (SNPs) are key to understanding genome structure, function, and disease.
  • Bioinformatics is vital for analyzing genetic variations and their effects.

Purpose of the Study:

  • To review resources and methods for SNP discovery and analysis.
  • To demonstrate computational SNP prediction using human cytochrome P450 as an example.
  • To provide a comprehensive overview of tools for SNP annotation and functional prediction.

Main Methods:

  • Systematic review of existing literature and online resources.
  • Application of DNA sequence-based prediction for SNPs.
  • Analysis of computational tools for SNP discovery and functional annotation.

Main Results:

  • Identification of key bioinformatics resources and methods for SNP analysis.
  • Successful prediction of SNPs in human cytochrome P450 using computational approaches.
  • Compilation of a comprehensive list of tools and online resources for SNP research.

Conclusions:

  • Bioinformatics plays an increasingly important role in SNP selection, prediction, and functional analysis.
  • Computational methods offer powerful approaches for discovering and understanding genetic variations.
  • The reviewed resources and tools support further research into the genetic basis of disease.