Title |
Optimizing Cancer Genome Sequencing and Analysis
|
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Published in |
Cell Systems, September 2015
|
DOI | 10.1016/j.cels.2015.08.015 |
Pubmed ID | |
Authors |
Malachi Griffith, Christopher A. Miller, Obi L. Griffith, Kilannin Krysiak, Zachary L. Skidmore, Avinash Ramu, Jason R. Walker, Ha X. Dang, Lee Trani, David E. Larson, Ryan T. Demeter, Michael C. Wendl, Joshua F. McMichael, Rachel E. Austin, Vincent Magrini, Sean D. McGrath, Amy Ly, Shashikant Kulkarni, Matthew G. Cordes, Catrina C. Fronick, Robert S. Fulton, Christopher A. Maher, Li Ding, Jeffery M. Klco, Elaine R. Mardis, Timothy J. Ley, Richard K. Wilson |
Abstract |
Tumors are typically sequenced to depths of 75-100× (exome) or 30-50× (whole genome). We demonstrate that current sequencing paradigms are inadequate for tumors that are impure, aneuploid or clonally heterogeneous. To reassess optimal sequencing strategies, we performed ultra-deep (up to ~312×) whole genome sequencing (WGS) and exome capture (up to ~433×) of a primary acute myeloid leukemia, its subsequent relapse, and a matched normal skin sample. We tested multiple alignment and variant calling algorithms and validated ~200,000 putative SNVs by sequencing them to depths of ~1,000×. Additional targeted sequencing provided over 10,000× coverage and ddPCR assays provided up to ~250,000× sampling of selected sites. We evaluated the effects of different library generation approaches, depth of sequencing, and analysis strategies on the ability to effectively characterize a complex tumor. This dataset, representing the most comprehensively sequenced tumor described to date, will serve as an invaluable community resource (dbGaP accession id phs000159). |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 52 | 43% |
United Kingdom | 7 | 6% |
Canada | 5 | 4% |
Germany | 3 | 2% |
Norway | 2 | 2% |
India | 2 | 2% |
Switzerland | 2 | 2% |
Australia | 1 | <1% |
Singapore | 1 | <1% |
Other | 7 | 6% |
Unknown | 39 | 32% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 68 | 56% |
Members of the public | 48 | 40% |
Practitioners (doctors, other healthcare professionals) | 3 | 2% |
Science communicators (journalists, bloggers, editors) | 2 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 1% |
United Kingdom | 3 | <1% |
Netherlands | 2 | <1% |
Italy | 1 | <1% |
Australia | 1 | <1% |
Ireland | 1 | <1% |
India | 1 | <1% |
Canada | 1 | <1% |
Brazil | 1 | <1% |
Other | 4 | <1% |
Unknown | 408 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 115 | 27% |
Student > Ph. D. Student | 81 | 19% |
Student > Master | 55 | 13% |
Student > Bachelor | 27 | 6% |
Other | 22 | 5% |
Other | 57 | 13% |
Unknown | 71 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 128 | 30% |
Biochemistry, Genetics and Molecular Biology | 123 | 29% |
Medicine and Dentistry | 36 | 8% |
Computer Science | 27 | 6% |
Neuroscience | 9 | 2% |
Other | 29 | 7% |
Unknown | 76 | 18% |