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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%...
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...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Related Experiment Video

Updated: May 21, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Coding SNPs as intrinsic markers for sample tracking in large-scale transcriptome studies.

Weihong Xu1, Hong Gao, Junhee Seok

  • 1Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.

Biotechniques
|June 7, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a method using coding single nucleotide polymorphisms (cSNPs) to detect mislabeled samples in transcriptome profiling. This approach enhances data integrity in clinical studies by identifying sample outliers.

Related Experiment Videos

Last Updated: May 21, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

  • Genomics
  • Bioinformatics
  • Clinical Research

Background:

  • Large-scale transcriptome profiling is crucial for clinical studies monitoring disease and treatment.
  • Human error can lead to mislabeled samples, compromising data accuracy.
  • Accurate sample tracking is essential for reliable clinical study outcomes.

Purpose of the Study:

  • To develop and validate a method for detecting mislabeled samples in transcriptome profiling.
  • To assess the utility of coding single nucleotide polymorphisms (cSNPs) for sample tracking.
  • To improve the reliability of clinical transcriptomic data.

Main Methods:

  • Designed microarrays with specifically incorporated coding single nucleotide polymorphisms (cSNPs).
  • Utilized allele-specific expression scores for sample clustering.
  • Employed Mahalanobis distance-based outlier detection to identify mislabeled samples.

Main Results:

  • Demonstrated efficient detection of mislabeled samples using cSNPs.
  • Clustering of allele-specific expression scores effectively identified outliers.
  • Mahalanobis distance-based method also proved successful in outlier detection.

Conclusions:

  • Coding single nucleotide polymorphisms (cSNPs) are effective intrinsic markers for sample tracking in transcriptomics.
  • Recommends integrating cSNPs into future microarray designs to prevent sample mix-ups.
  • This method enhances the quality control of clinical transcriptome profiling data.