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

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. DangVanessa 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

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


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.

Original languageEnglish (US)
Article number226
Pages (from-to)1-14
Number of pages14
JournalGenome biology
Issue number1
StatePublished - Oct 31 2019

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology


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