Please use this identifier to cite or link to this item: doi:10.22028/D291-28704
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Title: Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome
Author(s): Wenger, Aaron M.
Peluso, Paul
Rowell, William J.
Chang, Pi-Chuan
Hall, Richard J.
Concepcion, Gregory T.
Ebler, Jana
Fungtammasan, Arkarachai
Kolesnikov, Alexey
Olson, Nathan D.
Töpfer, Armin
Alonge, Michael
Mahmoud, Medhat
Qian, Yufeng
Chin, Chen-Shan
Phillippy, Adam M.
Schatz, Michael C.
Myers, Gene
DePristo, Mark A.
Ruan, Jue
Marschall, Tobias
Sedlazeck, Fritz J.
Zook, Justin M.
Li, Heng
Koren, Sergey
Carroll, Andrew
Rank, David R.
Hunkapiller, Michael W.
Language: English
Title: Nature biotechnology
Publisher/Platform: Nature Publishing Group
Year of Publication: 2019
Publikation type: Journal Article
Abstract: The DNA sequencing technologies in use today produce either highly accurate short reads or less-accurate long reads. We report the optimization of circular consensus sequencing (CCS) to improve the accuracy of single-molecule real-time (SMRT) sequencing (PacBio) and generate highly accurate (99.8%) long high-fidelity (HiFi) reads with an average length of 13.5 kilobases (kb). We applied our approach to sequence the well-characterized human HG002/NA24385 genome and obtained precision and recall rates of at least 99.91% for single-nucleotide variants (SNVs), 95.98% for insertions and deletions <50 bp (indels) and 95.99% for structural variants. Our CCS method matches or exceeds the ability of short-read sequencing to detect small variants and structural variants. We estimate that 2,434 discordances are correctable mistakes in the 'genome in a bottle' (GIAB) benchmark set. Nearly all (99.64%) variants can be phased into haplotypes, further improving variant detection. De novo genome assembly using CCS reads alone produced a contiguous and accurate genome with a contig N50 of >15 megabases (Mb) and concordance of 99.997%, substantially outperforming assembly with less-accurate long reads.
DOI of the first publication: 10.1038/s41587-019-0217-9
Link to this record: hdl:20.500.11880/27707
ISSN: 1087-0156
Date of registration: 7-Sep-2019
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Prof. Dr. Tobias Marschall
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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