Genome in a Bottle Workshop 

Thursday, March 21 | 8:30 am - 12:00 pm 


7:30 Workshop Registration

8:30 Chairperson's Opening Remarks

8:40 Reference Material Selection and Design

9:10 Measurements for Reference Material Characterization

9:40 Bioinformatics, Data Integration, and Data Representation

10:10 Networking Coffee Break

10:30 Performance Metrics and Figures of Merit

11:00 NIST plans for Reference Materials

11:20 Integrating multiple data sets to understand bias and form consensus variant calls

11:40 Panel Discussion and Questions

12:00 Close of Workshop


About the Consortium: 

Clinical application of “Next Generation Sequencing” for hereditary genetic diseases, oncology, and other purposes is rapidly growing.  At present, there are no widely accepted genomic standards or quantitative performance metrics for confidence in variant calling. These are needed to achieve the confidence in measurement results expected for sound, reproducible research and regulated applications in the clinic.  On April 13, 2012, the National Institute of Standards and Technology (NIST) convened the workshop “Genome in a Bottle” to initiate a consortium to develop the reference materials, reference methods, and reference data needed to assess confidence in human whole genome variant calls ( A principal motivation for this consortium is to develop widely accepted reference materials and accompanying performance metrics to provide a strong scientific foundation for the development of regulations and professional standards for clinical sequencing.

The consortium has four working groups with the listed responsibilities:

(1) Reference Material (RM) Selection and Design select appropriate cell lines for whole genome RMs and design synthetic DNA constructs that could be spiked-in to samples
(2) Measurements for Reference Material Characterization, design and carry out experiments to characterize the RMs using multiple sequencing methods, other methods, and validation of selected variants using orthogonal technologies
(3) Bioinformatics, Data Integration, and Data Representation develop methods to analyze and integrate the data for each RM, as well as select appropriate formats to represent the data.
(4) Performance Metrics and Figures of Merit develop useful performance metrics and figures of merit that can be obtained through measurement of the RMs.

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