Emu: species-level microbial community profiling of full-length 16S rRNA Oxford Nanopore sequencing data

Kristen D. Curry, Qi Wang, Michael G. Nute, Alona Tyshaieva, Elizabeth Reeves, Sirena Soriano, Qinglong Wu, Enid Graeber, Patrick Finzer, Werner Mendling, Tor Savidge, Sonia Villapol, Alexander Dilthey, Todd J. Treangen

Research output: Contribution to journalArticlepeer-review

Abstract

16S ribosomal RNA-based analysis is the established standard for elucidating the composition of microbial communities. While short-read 16S rRNA analyses are largely confined to genus-level resolution at best, given that only a portion of the gene is sequenced, full-length 16S rRNA gene amplicon sequences have the potential to provide species-level accuracy. However, existing taxonomic identification algorithms are not optimized for the increased read length and error rate often observed in long-read data. Here we present Emu, an approach that uses an expectation–maximization algorithm to generate taxonomic abundance profiles from full-length 16S rRNA reads. Results produced from simulated datasets and mock communities show that Emu is capable of accurate microbial community profiling while obtaining fewer false positives and false negatives than alternative methods. Additionally, we illustrate a real-world application of Emu by comparing clinical sample composition estimates generated by an established whole-genome shotgun sequencing workflow with those returned by full-length 16S rRNA gene sequences processed with Emu.

Original languageEnglish (US)
Pages (from-to)845-853
Number of pages9
JournalNature Methods
Volume19
Issue number7
DOIs
StatePublished - Jul 2022

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

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