Genome-in-a-Bottle Consortium  Reference Materials for Clinical Applications of          Human Genome Sequencing      Marc...
Vision        Reference Samples                12889           12890           12891           12892                      ...
NIST in partnership with FDA• FDA calls for NIST RMs for         • FDA funding work at NIST  WGS                          ...
Value of a NIST RM• NIST commitment to:    – Maintain availability of RM    – Maintain data on RM – ongoing      aggregati...
Why a consortium?
NHGRI        ACLA
Genome in a Bottle             Consortium Development• NIST met with sequencing                 • Open, public meeting at ...
Genome in a Bottle Working GroupsReference Material               Meaurements for               Bioninformatics, Dat      ...
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March 2013 Introduction

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March 2013 Introduction

  1. 1. Genome-in-a-Bottle Consortium Reference Materials for Clinical Applications of Human Genome Sequencing Marc Salit, Ph.D. and Justin Zook, Ph.D National Institute of Standards and Technology
  2. 2. Vision Reference Samples 12889 12890 12891 12892 12877 12878 Sample Preparation12879 12880 12881 12882 12883 12884 12885 12886 12887 12888 12893 Sequencing Variant Bioinformatics List, Performanc e Metrics
  3. 3. NIST in partnership with FDA• FDA calls for NIST RMs for • FDA funding work at NIST WGS to develop reference – “An RM from NIST has materials suitable to great potential to facilitate support regulatory FDAs regulatory approach oversight to WGS, and would help provide assurance that – materials to be used as different sequencers at part of evaluation of different locations had a technical performance of particular level of ongoing sequencing instruments as performance.” devices • Elizabeth Mansfield, Director, Personalized Medicine Staff, OIVD/CDRH/FDA
  4. 4. Value of a NIST RM• NIST commitment to: – Maintain availability of RM – Maintain data on RM – ongoing aggregation of sequence data to increase accuracy and minimize biases – Be a neutral arbiter in aggregation of data from different platforms• NIST infrastructure to distribute RM• NIST investment in genomic measurement science• NIST imprimatur as an internationally recognized source of “higher order” RMs for regulatory and commercial purposes
  5. 5. Why a consortium?
  6. 6. NHGRI ACLA
  7. 7. Genome in a Bottle Consortium Development• NIST met with sequencing • Open, public meeting at NIST to technology developers to assess formally establish consortium, standards needs present draft work plan – Stanford, June 2011 – formed working groups• Open, exploratory workshop – identified candidate genomes – ASHG, Montreal, Canada – established principles of: – October 2011 • reference material selection • characterization• Small, invitational workshop at • informatics NIST to develop consortium for • performance metrics human genome reference – August 2012 materials • Expect to be sequencing – FDA, NCBI, NHGRI, NCI, CDC, Wash candidate genomes Q4 2012 U, Broad, technology developers, clinical labs, CAP, PGP, Partners, – developing large RM batches to ABRF, others characterize in 2013 – developed draft work plan • Website – April 2012 – www.genomeinabottle.org
  8. 8. Genome in a Bottle Working GroupsReference Material Meaurements for Bioninformatics, Dat Performance MetricsSelection Reference Material a Integration, and & Figures of Merit& Design Characterization Data RepresentationAndrew Grupe, Elliott Margulies & Steve Sherry, NCBI Justin Johnson,Celera Mike Eberle, Illumina EdgeBio •Develop prioritized list •Develop consensus •Develop plan for •User interface to the of whole human plan for experimental integrating Genome-in-a-Bottle genomes for Reference characterization of experimental data and Reference Material Materials Reference Materials forming consensus •“Dashboard” •Identify candidate variant calls and •what an end user will approaches and confidence estimates see and report to materials for artificial •Develop consensus understand and RMs plan for data describe the •Develop prioritized representation performance of their list experiment •variant call accuracy •process performance measures to enable optimization
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