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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%...

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Methods to Increase the Sensitivity of High Resolution Melting Single Nucleotide Polymorphism Genotyping in Malaria
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Published on: November 10, 2015

Single-nucleotide polymorphism masking.

Nicole A R Walter1, Shannon K McWeeney, Sandra T Peters

  • 1Department of Behavioral Science, Oregon Health & Science University, Portland, Oregon.

Alcohol Research & Health : the Journal of the National Institute on Alcohol Abuse and Alcoholism
|April 16, 2013
PubMed
Summary
This summary is machine-generated.

Single nucleotide polymorphisms (SNPs) can impact genome-wide gene expression analysis. This study introduces SNP masking to remove SNP-affected probes, improving data accuracy for genetically diverse samples.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Microarrays are essential for genome-scale gene expression evaluation.
  • Analysis at the individual probe level is often neglected, especially in studies involving genetic variations.
  • Naturally occurring single nucleotide polymorphisms (SNPs) differentiate individuals and can interfere with gene expression data.

Purpose of the Study:

  • To highlight the impact of SNPs on gene expression analysis using microarrays.
  • To introduce and validate a method called SNP masking for improving data accuracy.
  • To address challenges in comparing gene expression across genetically diverse populations.

Main Methods:

  • Describing the potential effects of SNPs on microarray probe hybridization.
  • Implementing a computational approach termed SNP masking.
  • Removing probes affected by single nucleotide polymorphisms (SNPs) from the dataset.

Main Results:

  • SNP masking effectively identifies and removes probes influenced by genetic variations.
  • This method ameliorates hybridization issues caused by SNPs.
  • Improved accuracy in detecting gene expression differences among genetically unique or modified animals.

Conclusions:

  • SNP masking is a valuable and feasible strategy for enhancing the reliability of microarray gene expression data.
  • Addressing probe-level variations, particularly SNPs, is crucial for accurate genomic comparisons.
  • This approach facilitates more precise gene expression studies in diverse animal models.