Title |
Characterizing reduced coverage regions through comparison of exome and genome sequencing data across 10 centers
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Published in |
Genetics in Medicine, November 2017
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DOI | 10.1038/gim.2017.192 |
Pubmed ID | |
Authors |
Rashesh V Sanghvi, Christian J Buhay, Bradford C Powell, Ellen A Tsai, Michael O Dorschner, Celine S Hong, Matthew S Lebo, Ariella Sasson, David S Hanna, Sean McGee, Kevin M Bowling, Gregory M Cooper, David E Gray, Robert J Lonigro, Andrew Dunford, Christine A Brennan, Carrie Cibulskis, Kimberly Walker, Mauricio O Carneiro, Joshua Sailsbery, Lucia A Hindorff, Dan R Robinson, Avni Santani, Mahdi Sarmady, Heidi L Rehm, Leslie G Biesecker, Deborah A Nickerson, Carolyn M Hutter, Levi Garraway, Donna M Muzny, Nikhil Wagle, on behalf of the NHGRI Clinical Sequencing Exploratory Research (CSER) Consortium |
Abstract |
PurposeAs massively parallel sequencing is increasingly being used for clinical decision making, it has become critical to understand parameters that affect sequencing quality and to establish methods for measuring and reporting clinical sequencing standards. In this report, we propose a definition for reduced coverage regions and describe a set of standards for variant calling in clinical sequencing applications.MethodsTo enable sequencing centers to assess the regions of poor sequencing quality in their own data, we optimized and used a tool (ExCID) to identify reduced coverage loci within genes or regions of particular interest. We used this framework to examine sequencing data from 500 patients generated in 10 projects at sequencing centers in the National Human Genome Research Institute/National Cancer Institute Clinical Sequencing Exploratory Research Consortium.ResultsThis approach identified reduced coverage regions in clinically relevant genes, including known clinically relevant loci that were uniquely missed at individual centers, in multiple centers, and in all centers.ConclusionThis report provides a process road map for clinical sequencing centers looking to perform similar analyses on their data.Genetics in Medicine advance online publication, 16 November 2017; doi:10.1038/gim.2017.192. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 23 | 43% |
United Kingdom | 3 | 6% |
France | 2 | 4% |
Saudi Arabia | 2 | 4% |
Japan | 1 | 2% |
South Africa | 1 | 2% |
Ireland | 1 | 2% |
Singapore | 1 | 2% |
Netherlands | 1 | 2% |
Other | 4 | 7% |
Unknown | 15 | 28% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 28 | 52% |
Members of the public | 22 | 41% |
Practitioners (doctors, other healthcare professionals) | 3 | 6% |
Science communicators (journalists, bloggers, editors) | 1 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 45 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 14 | 31% |
Student > Ph. D. Student | 6 | 13% |
Other | 4 | 9% |
Student > Bachelor | 3 | 7% |
Student > Master | 3 | 7% |
Other | 4 | 9% |
Unknown | 11 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 16 | 36% |
Medicine and Dentistry | 5 | 11% |
Agricultural and Biological Sciences | 5 | 11% |
Engineering | 2 | 4% |
Psychology | 2 | 4% |
Other | 3 | 7% |
Unknown | 12 | 27% |