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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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Related Experiment Video

Updated: Feb 24, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Extending TCGA queries to automatically identify analogous genomic data from dbGaP.

Erin K Wagner1, Satyajeet Raje2, Liz Amos2

  • 1BioStat Solutions, Frederick, USA.

F1000Research
|August 11, 2017
PubMed
Summary

Researchers can now easily find cancer genomics data in dbGaP by using a new tool that matches metadata from The Cancer Genome Atlas (TCGA). This promotes data sharing and reproducible research.

Keywords:
GDCSRATCGAThe Cancer Genome AtlascancerdatabasedbGaPgenome

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Data sharing is crucial for advancing genomic research and ensuring reproducibility.
  • The Cancer Genome Atlas (TCGA) provides valuable genotype-phenotype cancer data.
  • The Database of Genotypes and Phenotypes (dbGaP) hosts similar datasets, but discovery can be challenging.

Purpose of the Study:

  • To develop a software pipeline for discovering relevant genomic data in dbGaP.
  • To enable researchers to leverage existing data by matching TCGA metadata.
  • To facilitate the connection between TCGA and dbGaP data resources.

Main Methods:

  • A software pipeline was created to process and query genomic datasets.
  • Metadata from The Cancer Genome Atlas (TCGA) was used for matching.
  • The pipeline identifies relevant datasets within the Database of Genotypes and Phenotypes (dbGaP).

Main Results:

  • A functional software pipeline has been successfully developed.
  • The pipeline effectively connects TCGA metadata to dbGaP datasets.
  • Researchers now have an accessible tool for genomic data discovery.

Conclusions:

  • The developed tool simplifies the discovery of cancer genomic data across major repositories.
  • Enhanced data accessibility supports collaborative research and accelerates scientific discovery.
  • This facilitates the reuse of existing data, promoting reproducible cancer genomics research.