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Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation

Sergey Koren, Brian P. Walenz, Konstantin Berlin, Jason R. Miller, Adam M. Phillippy
doi: https://doi.org/10.1101/071282
Sergey Koren
1Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
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Brian P. Walenz
1Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
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Konstantin Berlin
2Invincea Labs, Arlington, VA USA
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Jason R. Miller
3J. Craig Venter Institute, Rockville, MD USA
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Adam M. Phillippy
1Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
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  • For correspondence: adam.phillippy@nih.gov
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Abstract

Long-read single-molecule sequencing has revolutionized de novo genome assembly and enabled the automated reconstruction of reference-quality genomes. However, given the relatively high error rates of such technologies, efficient and accurate assembly of large repeats and closely related haplotypes remains challenging. We address these issues with Canu, a complete reworking of Celera Assembler that is specifically designed for noisy single-molecule sequences. Canu introduces support for nanopore sequencing, halves depth-of-coverage requirements, and improves assembly continuity while simultaneously reducing runtime by an order of magnitude on large genomes. These advances result from new overlapping and assembly algorithms, including an adaptive overlapping strategy based on tf-idf weighted MinHash and a sparse assembly graph construction that avoids collapsing diverged repeats and haplotypes. We demonstrate that Canu can reliably assemble complete microbial genomes and near-complete eukaryotic chromosomes using either PacBio or Oxford Nanopore technologies, and achieves a contig NG50 of greater than 21 Mbp on both human and Drosophila melanogaster PacBio datasets. For assembly structures that cannot be linearly represented, Canu provides graph-based assembly outputs for analysis or integration with complementary phasing and scaffolding techniques. Canu source code and pre-compiled binaries are freely available under a GPLv2 license from https://github.com/marbl/canu.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 24, 2016.
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Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation
Sergey Koren, Brian P. Walenz, Konstantin Berlin, Jason R. Miller, Adam M. Phillippy
bioRxiv 071282; doi: https://doi.org/10.1101/071282
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Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation
Sergey Koren, Brian P. Walenz, Konstantin Berlin, Jason R. Miller, Adam M. Phillippy
bioRxiv 071282; doi: https://doi.org/10.1101/071282

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