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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%...
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.In the early 20th century,...
Genome Copying Errors02:46

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

Updated: Jul 2, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Estimating genome-wide copy number using allele-specific mixture models.

Wenyi Wang1, Benilton Carvalho, Nathaniel D Miller

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

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|August 19, 2008
PubMed
Summary

This study introduces a new mixture model for precise single-point copy number estimation using SNP microarrays. This method improves accuracy and resolution for detecting genomic alterations, aiding human variation studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genomic copy number alterations (CNAs) drive human phenotypic variation.
  • Array comparative genomic hybridization (CGH) offers high-throughput CNA detection but lacks resolution (<30 kb).
  • Single nucleotide polymorphism (SNP) microarrays provide higher resolution but suffer from probe effects and imprecision.

Purpose of the Study:

  • To develop a novel statistical method for precise single-point copy number estimation.
  • To improve the accuracy and resolution of CNA detection using SNP microarrays.
  • To provide a robust method for analyzing genomic variation.

Main Methods:

  • Proposed a mixture model for single-point copy number estimation.
  • Utilized a 314-sample database to fit models for observed intensities given allele-specific copy number.
  • Computed posterior probabilities for prediction rules and confidence measures.

Main Results:

  • The mixture model offers advantages over existing methodologies for CNA detection.
  • Achieved improved accuracy and precision in single-point copy number estimates.
  • Developed a method providing useful prediction rules and confidence measures for each copy number call.

Conclusions:

  • The proposed mixture model enhances the analysis of genomic copy number alterations.
  • This approach improves the resolution and reliability of SNP microarray data interpretation.
  • Software implementation will be available in the Bioconductor oligo package.