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Uniform genomic data analysis in the NCI Genomic Data Commons

Author

Listed:
  • Zhenyu Zhang

    (Center for Translational Data Science, University of Chicago)

  • Kyle Hernandez

    (Center for Translational Data Science, University of Chicago)

  • Jeremiah Savage

    (Center for Translational Data Science, University of Chicago
    AbbVie Inc.)

  • Shenglai Li

    (Center for Translational Data Science, University of Chicago)

  • Dan Miller

    (Center for Translational Data Science, University of Chicago
    Children’s Hospital of Philadelphia)

  • Stuti Agrawal

    (Center for Translational Data Science, University of Chicago
    Merck Healthcare KGaA)

  • Francisco Ortuno

    (Center for Translational Data Science, University of Chicago
    Clinical Bioinformatics Area, Fundacion Progreso y Salud (FPS))

  • Louis M. Staudt

    (National Cancer Institute)

  • Allison Heath

    (Children’s Hospital of Philadelphia)

  • Robert L. Grossman

    (Center for Translational Data Science, University of Chicago)

Abstract

The goal of the National Cancer Institute’s (NCI’s) Genomic Data Commons (GDC) is to provide the cancer research community with a data repository of uniformly processed genomic and associated clinical data that enables data sharing and collaborative analysis in the support of precision medicine. The initial GDC dataset include genomic, epigenomic, proteomic, clinical and other data from the NCI TCGA and TARGET programs. Data production for the GDC started in June, 2015 using an OpenStack-based private cloud. By June of 2016, the GDC had analyzed more than 50,000 raw sequencing data inputs, as well as multiple other data types. Using the latest human genome reference build GRCh38, the GDC generated a variety of data types from aligned reads to somatic mutations, gene expression, miRNA expression, DNA methylation status, and copy number variation. In this paper, we describe the pipelines and workflows used to process and harmonize the data in the GDC. The generated data, as well as the original input files from TCGA and TARGET, are available for download and exploratory analysis at the GDC Data Portal and Legacy Archive ( https://gdc.cancer.gov/ ).

Suggested Citation

  • Zhenyu Zhang & Kyle Hernandez & Jeremiah Savage & Shenglai Li & Dan Miller & Stuti Agrawal & Francisco Ortuno & Louis M. Staudt & Allison Heath & Robert L. Grossman, 2021. "Uniform genomic data analysis in the NCI Genomic Data Commons," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21254-9
    DOI: 10.1038/s41467-021-21254-9
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    Cited by:

    1. Michael J. P. Crowley & Bhavneet Bhinder & Geoffrey J. Markowitz & Mitchell Martin & Akanksha Verma & Tito A. Sandoval & Chang-Suk Chae & Shira Yomtoubian & Yang Hu & Sahil Chopra & Diamile A. Tavarez, 2023. "Tumor-intrinsic IRE1α signaling controls protective immunity in lung cancer," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Kelsy C. Cotto & Yang-Yang Feng & Avinash Ramu & Megan Richters & Sharon L. Freshour & Zachary L. Skidmore & Huiming Xia & Joshua F. McMichael & Jason Kunisaki & Katie M. Campbell & Timothy Hung-Po Ch, 2023. "Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    3. Joshua Borycz & Robert Olendorf & Alison Specht & Bruce Grant & Kevin Crowston & Carol Tenopir & Suzie Allard & Natalie M. Rice & Rachael Hu & Robert J. Sandusky, 2023. "Perceived benefits of open data are improving but scientists still lack resources, skills, and rewards," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.

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