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Related Concept Videos

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%...
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...

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Updated: Jun 18, 2026

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

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

R classes and methods for SNP array data.

Robert B Scharpf1, Ingo Ruczinski

  • 1Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Methods in Molecular Biology (Clifton, N.J.)
|December 4, 2009
PubMed
Summary
This summary is machine-generated.

The Bioconductor project provides R-based software for genomic data analysis. Its Biobase package introduces the eSet class, enhancing data analysis through standardized structure and promoting code reuse for high-throughput SNP array data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Bioconductor is an open-source software project for genomic data analysis, primarily using the R programming language.
  • Infrastructure packages like Biobase are crucial for the Bioconductor community, supporting developers and users.
  • The Biobase package defines the eSet class for managing high-dimensional assay data and associated metadata.

Purpose of the Study:

  • To highlight the advantages of adopting and extending Biobase class definitions.
  • To demonstrate the utility of the eSet class for genomic data analysis.
  • To showcase an implementation of classes for high-throughput SNP array analysis.

Main Methods:

  • Utilizing the R programming language and Bioconductor infrastructure.
  • Implementing and extending the eSet class for high-dimensional genomic data.
  • Developing specific class definitions for high-throughput SNP array analysis.

Main Results:

  • The eSet class effectively encapsulates assay data and metadata, ensuring its propagation throughout analyses.
  • Extending the eSet class promotes code reuse, interoperability with other R packages, and reduces errors.
  • A working example demonstrates the successful application of extended eSet classes for SNP array data analysis.

Conclusions:

  • Adopting and extending Biobase class definitions offers significant advantages for genomic data analysis.
  • The eSet class provides a robust framework for managing complex biological data.
  • This approach enhances the efficiency and reliability of analyzing high-throughput genomic data, such as SNP arrays.