Optimizing cancer genome sequencing and analysis
Year: 2015
Type: other
Abstract: Tumors are typically sequenced to depths of 75-100x (exome) or 30-50x (whole genome). We demonstrate that current sequencing paradigms based on this coverage are inadequate for tumors that are impure, aneuploid, and/or clonally heterogeneous. To reassess optimal sequencing strategies, we performed ultra-deep (up to ~312x) whole genome sequencing (WGS) and exome capture (up to ~433x) of a primary acute myeloid leukemia, its subsequent relapse, and a matched normal skin sample. We tested 7 alignment algorithms and 7 single-nucleotide variant callers, and validated ~200,000 putative SNVs by sequencing them to mean depths of ~1,000x. Additional targeted sequencing provided over 10,000x coverage and ddPCR assays provided up to ~250,000x sampling of selected sites. Further we performed extensive filtering and manual review to produce a ‘platinum’ variant list that contained 1,343 high quality validated sites. Using these data, 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. Sequence data is available through dbGaP accession id phs000159.
Authors: Malachi Griffith, Chris Miller, Obi Griffith, Krysiak Kilannin, Zachary Skidmore, Avinash Ramu, Jason Walker, Ha Dang, Trani Lee, David Larson, Ryan Demeter, Michael Wendl, Joshua McMichael, Rachel Austin, Vincent Magrini, McGrath Sean, Amy Ly, Shashikant Kulkarni, Matt Cordes, Catrina Fronick, Robert Fulton, Christopher Maher, Li Ding, Jeffery Klco, Elaine Mardis, Timothy Ley, Richard Wilson
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W2593770935
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