<p>The Genome in a Bottle Consortium (GIAB), hosted by the National Institute of Standards and Technology (NIST), is developing new matched tumor-normal samples, the first explicitly consented for public dissemination of genomic data and cell lines. Here, we describe a comprehensive genomic dataset from the first individual, HG008, including DNA from an adherent, epithelial-like pancreatic ductal adenocarcinoma (PDAC) tumor cell line and matched normal cells from duodenal and pancreatic tissues. Data for the tumor-normal matched samples comes from seventeen distinct state-of-the-art whole genome measurement technologies, including high depth short and long-read bulk whole genome sequencing (WGS), single cell WGS, Hi-C, and karyotyping. These data will be used by the GIAB Consortium to develop matched tumor-normal benchmarks for somatic variant detection. We expect these data to facilitate innovation for whole genome measurement technologies, <i>de novo</i> assembly of tumor and normal genomes, and bioinformatic tools to identify small and structural somatic variants. This first-of-its-kind broadly consented open-access resource will facilitate further understanding of sequencing methods used for cancer biology.</p>

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Development and extensive sequencing of a broadly-consented Genome in a Bottle matched tumor-normal pair

  • Jennifer H. McDaniel,
  • Vaidehi Patel,
  • Nathan D. Olson,
  • Hua-Jun He,
  • Zhiyong He,
  • Kenneth D. Cole,
  • Alexander A. Gooden,
  • Anthony Schmitt,
  • Kristin Sikkink,
  • Fritz J. Sedlazeck,
  • Harsha Doddapaneni,
  • Shalini N. Jhangiani,
  • Donna M. Muzny,
  • Marie-Claude Gingras,
  • Heer Mehta,
  • Sairam Behera,
  • Luis F. Paulin,
  • Alex R. Hastie,
  • Hung-Chun Yu,
  • Victor Weigman,
  • Alison Rojas,
  • Katie Kennedy,
  • Jamie Remington,
  • Isai Salas-González,
  • Mitch Sudkamp,
  • Kelly Wiseman,
  • Bryan R. Lajoie,
  • Shawn Levy,
  • Miten Jain,
  • Stuart Akeson,
  • Giuseppe Narzisi,
  • Zoe Steinsnyder,
  • Catherine Reeves,
  • Jennifer Shelton,
  • Sarah B. Kingan,
  • Christine Lambert,
  • Primo Baybayan,
  • Aaron M. Wenger,
  • Ian J. McLaughlin,
  • Aaron Adamson,
  • Christopher Kingsley,
  • Melanie Wescott,
  • Young Kim,
  • Benedict Paten,
  • Jimin Park,
  • Ivo Violich,
  • Karen H. Miga,
  • Joshua Gardner,
  • Brandy McNulty,
  • Gail L. Rosen,
  • Rajiv McCoy,
  • Francesco Brundu,
  • Erfan Sayyari,
  • Konrad Scheffler,
  • Sean Truong,
  • Severine Catreux,
  • Lesley Chapman Hannah,
  • Doron Lipson,
  • Hila Benjamin,
  • Nika Iremadze,
  • Ilya Soifer,
  • Gat Krieger,
  • Stephen Eacker,
  • Mary Wood,
  • Erin Cross,
  • Greg Husar,
  • Stephen Gross,
  • Michael Vernich,
  • Mikhail Kolmogorov,
  • Tanveer Ahmad,
  • Ayse G. Keskus,
  • Asher Bryant,
  • Francoise Thibaud-Nissen,
  • Jonathan Trow,
  • Jacqueline Proszynski,
  • Jeremy Wain Hirschberg,
  • Krista Ryon,
  • Christopher E. Mason,
  • Mital S. Bhakta,
  • J. Zachary Sanborn,
  • Elizabeth M. Munding,
  • Justin Wagner,
  • Chunlin Xiao,
  • Andrew S. Liss,
  • Justin M. Zook

摘要

The Genome in a Bottle Consortium (GIAB), hosted by the National Institute of Standards and Technology (NIST), is developing new matched tumor-normal samples, the first explicitly consented for public dissemination of genomic data and cell lines. Here, we describe a comprehensive genomic dataset from the first individual, HG008, including DNA from an adherent, epithelial-like pancreatic ductal adenocarcinoma (PDAC) tumor cell line and matched normal cells from duodenal and pancreatic tissues. Data for the tumor-normal matched samples comes from seventeen distinct state-of-the-art whole genome measurement technologies, including high depth short and long-read bulk whole genome sequencing (WGS), single cell WGS, Hi-C, and karyotyping. These data will be used by the GIAB Consortium to develop matched tumor-normal benchmarks for somatic variant detection. We expect these data to facilitate innovation for whole genome measurement technologies, de novo assembly of tumor and normal genomes, and bioinformatic tools to identify small and structural somatic variants. This first-of-its-kind broadly consented open-access resource will facilitate further understanding of sequencing methods used for cancer biology.