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Related Concept Videos

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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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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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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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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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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CoNVaQ: a web tool for copy number variation-based association studies.

Simon Jonas Larsen1, Luisa Matos do Canto2, Silvia Regina Rogatto3

  • 1Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, Odense, DK-5230, DK, Denmark. sjlarsen@imada.sdu.dk.

BMC Genomics
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Summary

CoNVaQ is a new web tool for copy number variation (CNV) association studies. It helps researchers easily find genomic regions linked to diseases using CNV data.

Keywords:
Association studiesCopy number variation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Copy number variations (CNVs) are genomic alterations linked to complex diseases.
  • Genome-Wide Association Studies (GWAS) extended to CNVs identify structural variants associated with human traits.

Purpose of the Study:

  • To present CoNVaQ, a user-friendly web-based tool for CNV association studies.
  • To enable biologists and clinicians to perform CNV data analysis without software installation.

Main Methods:

  • CoNVaQ utilizes two models: Fisher's exact test and a novel query-based model.
  • It computes global q-value statistics through repeated sample permutations.
  • The platform analyzes CNV segments to identify associations with phenotypes.

Main Results:

  • CoNVaQ facilitates the discovery of genomic regions with significant CNV associations.
  • The tool was demonstrated using a dataset of HPV-positive and HPV-negative penile cancer patients.
  • Analysis of results can identify overrepresented Gene Ontology (GO) terms and pathways.

Conclusions:

  • CoNVaQ offers a simplified workflow for CNV association studies via a web platform.
  • It empowers researchers without requiring software installation.
  • The tool is also available as an R package and accessible at https://convaq.compbio.sdu.dk.