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Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads.

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

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.

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