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Genetic Variation01:25

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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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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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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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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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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Genetic Variations and Precision Medicine.

Amal Adel Alzu'bi1, Leming Zhou2, Valerie J M Watzlaf2

  • 1The Department of Computer Information Systems at Jordan University of Science and Technology in Irbid, Jordan.

Perspectives in Health Information Management
|April 26, 2019
PubMed
Summary
This summary is machine-generated.

Health information management (HIM) professionals can now analyze patient genomic data for personalized medicine. This review covers genetic variations, databases, and analysis methods to support this growing field.

Keywords:
databasegenetic variationprecision medicine

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Area of Science:

  • Genomics
  • Health Informatics
  • Bioinformatics

Background:

  • Decreasing costs of human genome sequencing enable wider adoption of genomics-based personalized healthcare.
  • Genomics-based precision medicine requires effective management and analysis of patient genomic data by Health Information Management (HIM) professionals.
  • Patient genetic variations are crucial for identifying disease risks and informing pharmacogenomic drug responses.

Purpose of the Study:

  • To introduce genetic variations, including their data types, relevant databases, and analysis methods.
  • To provide Health Information Management (HIM) professionals with an overview of tools for managing and analyzing patient genomic data.
  • To support the integration of genomic data into clinical practice for precision medicine.

Main Methods:

  • Review of current literature on genetic variations and their analysis.
  • Identification and categorization of relevant genomic databases.
  • Overview of existing computational methods and systems for genomic data analysis.

Main Results:

  • Genetic variations are characterized by specific data types and are cataloged in various public databases.
  • Several analysis methods and systems are available for interpreting genomic data.
  • HIM professionals can leverage these resources for disease risk assessment and pharmacogenomic profiling.

Conclusions:

  • Effective management and analysis of genomic data are essential for advancing precision medicine.
  • Health information management (HIM) professionals play a key role in utilizing genomic information for patient care.
  • The presented resources can aid HIM professionals in navigating the complexities of patient genomic data.