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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,...
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 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...
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...
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%...
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...

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Related Experiment Video

Updated: Jul 10, 2026

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

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Published on: December 9, 2015

Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines.

Jian Tian1, Ningfeng Wu, Xuexia Guo

  • 1Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China. tianjian3721@163.com

BMC Bioinformatics
|November 17, 2007
PubMed
Summary

Parepro accurately predicts the impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on protein function. This computational tool aids in understanding genetic diseases and identifying disease-causing nsSNPs.

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Last Updated: Jul 10, 2026

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

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Published on: December 9, 2015

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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Published on: August 20, 2019

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Human genetic variations are often caused by single nucleotide polymorphisms (SNPs).
  • Non-synonymous SNPs (nsSNPs) can alter protein function and are linked to inherited diseases.
  • Identifying disease-associated nsSNPs is crucial for medical genetics.

Purpose of the Study:

  • To develop a computational method for predicting the functional effects of nsSNPs.
  • To distinguish between deleterious and neutral nsSNPs.

Main Methods:

  • A novel method, Parepro (Predicting the amino acid replacement probability), was developed.
  • Two independent datasets, HumVar and NewHumVar, were used for training and validation.
  • A 20-fold cross-validation was performed on the HumVar dataset.

Main Results:

  • Parepro achieved a Matthews correlation coefficient (MCC) of 50% and 76% overall accuracy (Q2) on the HumVar dataset.
  • Performance metrics surpassed those of existing methods like PolyPhen, SIFT, and HydridMeth.
  • Similar high performance was observed on the NewHumVar dataset.

Conclusions:

  • Parepro is an effective tool for predicting nsSNP effects on protein function.
  • The method is suitable for large-scale genomic nsSNP data analysis.
  • This aids in understanding genetic disease mechanisms.