Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-08-12

AUTHORS

Aaron M. Wenger, Paul Peluso, William J. Rowell, Pi-Chuan Chang, Richard J. Hall, Gregory T. Concepcion, Jana Ebler, Arkarachai Fungtammasan, Alexey Kolesnikov, Nathan D. Olson, Armin Töpfer, Michael Alonge, Medhat Mahmoud, Yufeng Qian, Chen-Shan Chin, Adam M. Phillippy, Michael C. Schatz, Gene Myers, Mark A. DePristo, Jue Ruan, Tobias Marschall, Fritz J. Sedlazeck, Justin M. Zook, Heng Li, Sergey Koren, Andrew Carroll, David R. Rank, Michael W. Hunkapiller

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. More... »

PAGES

1155-1162

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41587-019-0217-9

    DOI

    http://dx.doi.org/10.1038/s41587-019-0217-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1120285368

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/31406327


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