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Related Concept Videos

DNA Microarrays02:34

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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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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Change in atmospheric pressure with height is particularly interesting. The decrease in atmospheric pressure with increasing altitude is due to the decreasing gravitational force per unit area as we move away from the surface of the earth.
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Related Experiment Video

Updated: Jan 28, 2026

Methylated DNA Immunoprecipitation
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Measuring DNA Copy Number Variation Using High-Density Methylation Microarrays.

Soonweng Cho1, Hyun-Seok Kim2, Martha A Zeiger3

  • 11 Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, Maryland.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|February 22, 2019
PubMed
Summary
This summary is machine-generated.

This study benchmarks computational methods for detecting copy number variation (CNV) from methylation array data. The developed Epicopy tool and existing methods show comparable performance in identifying genetic alterations crucial for cancer development.

Keywords:
CNVTCGAcopy number variationmethylation microarraymicroarray

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DNA Methylation: Bisulphite Modification and Analysis
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Area of Science:

  • Genomics
  • Cancer Research
  • Bioinformatics

Background:

  • Genetic and epigenetic alterations are key drivers of carcinogenesis.
  • Integrated analysis of these changes offers insights into cancer development mechanisms.
  • Computational tools are essential for analyzing complex genomic and epigenomic data.

Purpose of the Study:

  • To optimize and benchmark computational methods for copy number variation (CNV) detection from methylation array data.
  • To evaluate the performance of Epicopy, a novel method, alongside existing tools like ChAMP-CNV and CN450K.
  • To assess the utility of Illumina 450K array data for CNV identification.

Main Methods:

  • Utilized paired single nucleotide polymorphism (SNP) and methylation array data from The Cancer Genome Atlas (TCGA).
  • Optimized calling thresholds for ChAMP-CNV, CN450K, and Epicopy.
  • Benchmarked the performance of these CNV calling methods.

Main Results:

  • Optimized thresholds led to comparable performance across the evaluated CNV calling methods.
  • Epicopy, using Illumina 450K data, achieved a sensitivity of 0.7 and a positive predictive value of 0.75 for CNV identification.
  • Performance was similar to comparisons between SNP microarray platforms.

Conclusions:

  • Computational methods, including Epicopy, effectively identify CNVs from methylation array data.
  • Illumina 450K array data provides a viable resource for CNV detection in cancer research.
  • The developed optimization strategies enhance the reliability of CNV calling from array data.