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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.
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Variation01:19

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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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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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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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The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
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A scalable, aggregated genotypic-phenotypic database for human disease variation.

Ryan Barrett1, Cynthia L Neben1, Anjali D Zimmer1

  • 1Color Genomics, 831 Mitten Road, Suite 100, Burlingame, CA, USA.

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Color Data is a new public database that makes genetic testing data accessible to researchers. This resource aids in exploring genotype-phenotype correlations for hereditary cancer risk and drug discovery.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Genetics

Background:

  • Next-generation sequencing (NGS) multi-gene panels enhance genetic testing for hereditary cancer risk.
  • Increased data volume and complexity from NGS present accessibility challenges for researchers.
  • Genotypic-phenotypic data integration is crucial for advancing cancer research.

Purpose of the Study:

  • To develop a publicly available, cloud-based database for sharing integrated genotypic-phenotypic data.
  • To improve data accessibility and literacy for researchers without advanced analytical skills.
  • To facilitate exploration of genotype-phenotype correlations in hereditary cancer.

Main Methods:

  • Built Color Data, a cloud-based database containing data from 50,000 individuals.
  • Sequenced individuals for 30 genes associated with hereditary cancer risk.
  • Integrated allele frequency, variant classification, and phenotypic information (demographics, family history).
  • Developed a user-friendly interface for data querying, sharing, and downloading.

Main Results:

  • The database provides accessible genotypic-phenotypic data for 50,000 individuals.
  • User-friendly interface enables easy data querying and analysis.
  • Data supports exploration of allele frequencies, variant classifications, and associated phenotypes.
  • Scalable architecture allows for integration of additional genes and hereditary conditions.

Conclusions:

  • Color Data enhances the value and reduces waste of scientific resources by enabling broad data dissemination.
  • The database supports genotype-phenotype correlation studies in hereditary cancer.
  • Facilitates identification of novel variants for functional analysis and data-driven drug discovery.