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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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Human Variome Project Quality Assessment Criteria for Variation Databases.

Mauno Vihinen1, John M Hancock2, Donna R Maglott3

  • 1Department of Experimental Medical Science, Lund University, BMC B13, SE-22184, Lund, Sweden.

Human Mutation
|February 27, 2016
PubMed
Summary

The Human Variome Project (HVP) developed quality criteria for gene-specific variant databases (LSDBs) to ensure reliable health information. These criteria assess data, technical quality, accessibility, and timeliness for better decision-making.

Keywords:
Human Variome ProjectLSDBcomponents of qualitydatabase qualitygene variant databasesgenetic variationlocus-specific variation databasesquality scheme

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Area of Science:

  • Genomics
  • Bioinformatics
  • Data Science

Background:

  • Numerous databases exist for DNA, RNA, and protein variations, with locus-specific variation databases (LSDBs) being key resources for specific genes or diseases.
  • LSDBs are considered highly reliable but exhibit variable content, infrastructure, and quality, impacting health decisions, research, and clinical practice.
  • Assessing the quality of these variant databases is crucial for ensuring the accuracy and utility of the information they provide.

Purpose of the Study:

  • To develop a straightforward yet comprehensive system for evaluating the quality of variant databases.
  • To establish quality assessment criteria for locus-specific variation databases (LSDBs) within the framework of the Human Variome Project (HVP).
  • To provide a practical scheme for implementing quality assessments and illustrate its application.

Main Methods:

  • The Human Variome Project (HVP) established a Working Group for Variant Database Quality Assessment.
  • Developed quality evaluation criteria divided into four main components: data quality, technical quality, accessibility, and timeliness.
  • Applied the developed quality criteria to assess two distinct databases: BTKbase (an LSDB) and ClinVar (a central variant archive).

Main Results:

  • The HVP quality evaluation criteria provide a structured approach to assessing variant database quality.
  • The report details the specific criteria within data quality, technical quality, accessibility, and timeliness.
  • Examples demonstrate the current quality status of BTKbase and ClinVar according to the HVP criteria.

Conclusions:

  • The developed HVP quality criteria offer a valuable framework for evaluating the reliability of gene-specific variant databases.
  • Implementation of this quality scheme can enhance the trustworthiness and utility of LSDBs for clinical and research purposes.
  • Standardized quality assessment is essential for variant databases that influence health decision-making and clinical practice.