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Related Concept Videos

What is Variation?01:14

What is Variation?

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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.
The range, standard deviation, standard error, and variance are the different measures of variation.
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Variation01:19

Variation

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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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Conservative Site-specific Recombination and Phase Variation02:53

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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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Variation of Atmospheric Pressure01:18

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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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Comparing Copy Number Variations and SNPs02:26

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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.
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Variation: Normal Distribution, Range, and Standard Deviation02:32

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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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Related Experiment Video

Updated: Feb 7, 2026

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
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Structural Variation Detection and Analysis Using Bionano Optical Mapping.

Saki Chan1, Ernest Lam1, Michael Saghbini1

  • 1Bionano Genomics, San Diego, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|July 25, 2018
PubMed
Summary
This summary is machine-generated.

Optical mapping provides a de novo assembly approach for comprehensive genome structural variation analysis. This method accurately identifies diverse structural variants from intact, megabase-scale DNA molecules.

Keywords:
DeletionGenome structureGenomicsInsertionInversionLong readsOptical mappingStructural VariationTranslocation

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Accurate identification of complete genome structural variation profiles is crucial.
  • Reference-based methods and comparative genomic hybridization have limitations in detecting all structural variants.
  • De novo assembly methods offer a more comprehensive approach to understanding true genomic structure.

Purpose of the Study:

  • To demonstrate the capability of Bionano Genome Mapping for de novo genome assembly.
  • To showcase the detection of a wide spectrum of structural variants using optical mapping data.
  • To highlight the advantages of optical mapping for analyzing native, intact megabase-scale DNA molecules.

Main Methods:

  • Utilizing Bionano Genome Mapping technology for de novo assembly.
  • Generating assemblies from native, intact megabase-scale DNA molecules.
  • Analyzing assembled data to identify structural variants (SVs).

Main Results:

  • Successful de novo assembly of large DNA molecules.
  • Detection of both balanced and unbalanced structural variants.
  • Identification of de novo genomic events and variations across separate alleles.

Conclusions:

  • Bionano Genome Mapping enables comprehensive de novo genome structural variation analysis.
  • Optical mapping is a powerful tool for discovering diverse SVs, including de novo events.
  • The method facilitates a deeper understanding of true genomic architecture.