Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-10-31

AUTHORS

Jon G. Sanders, Sergey Nurk, Rodolfo A. Salido, Jeremiah Minich, Zhenjiang Z. Xu, Qiyun Zhu, Cameron Martino, Marcus Fedarko, Timothy D. Arthur, Feng Chen, Brigid S. Boland, Greg C. Humphrey, Caitriona Brennan, Karenina Sanders, James Gaffney, Kristen Jepsen, Mahdieh Khosroheidari, Cliff Green, Marlon Liyanage, Jason W. Dang, Vanessa V. Phelan, Robert A. Quinn, Anton Bankevich, John T. Chang, Tariq M. Rana, Douglas J. Conrad, William J. Sandborn, Larry Smarr, Pieter C. Dorrestein, Pavel A. Pevzner, Rob Knight

ABSTRACT

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing. More... »

PAGES

226

References to SciGraph publications

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  • Journal

    TITLE

    Genome Biology

    ISSUE

    1

    VOLUME

    20

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13059-019-1834-9

    DOI

    http://dx.doi.org/10.1186/s13059-019-1834-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1122238920

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/31672156


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