title: Targeted single molecule mutation detection with massively parallel sequencing.
abstract: Next-generation sequencing (NGS) technologies have transformed genomic research and have the potential to revolutionize clinical medicine. However, the background error rates of sequencing instruments and limitations in targeted read coverage have precluded the detection of rare DNA sequence variants by NGS. Here we describe a method, termed CypherSeq, which combines double-stranded barcoding error correction and rolling circle amplification (RCA)-based target enrichment to vastly improve NGS-based rare variant detection. The CypherSeq methodology involves the ligation of sample DNA into circular vectors, which contain double-stranded barcodes for computational error correction and adapters for library preparation and sequencing. CypherSeq is capable of detecting rare mutations genome-wide as well as those within specific target genes via RCA-based enrichment. We demonstrate that CypherSeq is capable of correcting errors incurred during library preparation and sequencing to reproducibly detect mutations down to a frequency of 2.4 × 10(-7) per base pair, and report the frequency and spectra of spontaneous and ethyl methanesulfonate-induced mutations across the Saccharomyces cerevisiae genome.
authors.affiliation: Translational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
authors.forename: Mark T
authors.initials: MT
authors.lastname: Gregory
authors.affiliation: Translational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
authors.forename: Jessica A
authors.initials: JA
authors.lastname: Bertout
authors.affiliation: Translational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
authors.forename: Nolan G
authors.initials: NG
authors.lastname: Ericson
authors.affiliation: Translational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
authors.forename: Sean D
authors.initials: SD
authors.lastname: Taylor
authors.affiliation: Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
authors.forename: Rithun
authors.initials: R
authors.lastname: Mukherjee
authors.affiliation: Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
authors.forename: Harlan S
authors.initials: HS
authors.lastname: Robins
authors.affiliation: Translational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
authors.forename: Charles W
authors.initials: CW
authors.lastname: Drescher
authors.affiliation: Translational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA Department of Pathology, University of Washington, Seattle, WA 98195, USA jbielas@fredhutch.org.
authors.forename: Jason H
authors.initials: JH
authors.lastname: Bielas
mesh_terms: D002460:Cell Line; D004247:DNA / Q000235:genetics*; D016158:Genes, p53; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D009154:Mutation; D016133:Polymerase Chain Reaction / Q000379:methods; D012441:Saccharomyces cerevisiae / Q000235:genetics
chemical_list: D004247:DNA