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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,...
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...
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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

SNP calling using genotype model selection on high-throughput sequencing data.

Na You1, Gabriel Murillo, Xiaoquan Su

  • 1Department of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, China.

Bioinformatics (Oxford, England)
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

A new method, Genotype Model Selection (GeMS), improves single nucleotide polymorphism (SNP) calling for Illumina high-throughput sequencing data by accounting for genomic sample preparation errors. GeMS demonstrates superior performance in sensitivity and positive predictive value compared to existing SNP callers.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Existing single nucleotide polymorphism (SNP) calling methods for Illumina high-throughput sequencing (HTS) primarily focus on base-calling and mapping qualities.
  • These methods often overlook errors introduced during genomic sample preparation, potentially impacting SNP calling accuracy.

Purpose of the Study:

  • To introduce a novel SNP calling method, Genotype Model Selection (GeMS).
  • To address limitations in current SNP calling procedures by incorporating errors from genomic sample preparation.

Main Methods:

  • Development of the Genotype Model Selection (GeMS) algorithm.
  • Evaluation through simulations and real data analyses.

Main Results:

  • GeMS accounts for errors originating during genomic sample preparation.
  • GeMS exhibits a balanced and superior performance in sensitivity and positive predictive value compared to other tested SNP callers.

Conclusions:

  • GeMS offers an improved approach to SNP calling for HTS data.
  • The method enhances accuracy by considering a broader range of error sources in genomic data analysis.