A Flexible Approach for Highly Multiplexed Candidate Gene Targeted Resequencing

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Statistics Papers
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oligonucleotides
sequence alignment
human genomics
sequence motif analysis
sequence analysis
sequence assembly tools
genomic databases
multiple alignment calculation
Biology
Biostatistics
Genetics and Genomics
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Natsoulis, Georges
Bell, John M
Xu, Hua
Buenrostro, Jason D
Ordonez, Heather
Grimes, Susan
Newburger, Daniel
Jensen, Michael
Zahn, Jacob M
Zhang, Nancy R
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Abstract

We have developed an integrated strategy for targeted resequencing and analysis of gene subsets from the human exome for variants. Our capture technology is geared towards resequencing gene subsets substantially larger than can be done efficiently with simplex or multiplex PCR but smaller in scale than exome sequencing. We describe all the steps from the initial capture assay to single nucleotide variant (SNV) discovery. The capture methodology uses in-solution 80-mer oligonucleotides. To provide optimal flexibility in choosing human gene targets, we designed an in silico set of oligonucleotides, the Human OligoExome, that covers the gene exons annotated by the Consensus Coding Sequencing Project (CCDS). This resource is openly available as an Internet accessible database where one can download capture oligonucleotides sequences for any CCDS gene and design custom capture assays. Using this resource, we demonstrated the flexibility of this assay by custom designing capture assays ranging from 10 to over 100 gene targets with total capture sizes from over 100 Kilobases to nearly one Megabase. We established a method to reduce capture variability and incorporated indexing schemes to increase sample throughput. Our approach has multiple applications that include but are not limited to population targeted resequencing studies of specific gene subsets, validation of variants discovered in whole genome sequencing surveys and possible diagnostic analysis of disease gene subsets. We also present a cost analysis demonstrating its cost-effectiveness for large population studies.

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2011-01-01
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PLoS ONE
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At the time of publication, author Nancy Zhang was affiliated with Stanford University. Currently, she is a faculty member at the Statistics Department at the University of Pennsylvania.
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