Species-Level Microbial Community Profiling for Full-Length Nanopore 16S Reads

Abstract

16S rRNA based analysis is the established standard for elucidating microbial community composition. However, with short-read data delivering only a portion of the 16S gene, this analysis is limited to genus-level results at best. Obtaining species-level accuracy is imperative since two bacterial species within the same genus have proven to express drastically different behaviors on their community and human health. Full-length 16S sequences have the potential to provide species-level resolution. Yet, taxonomic identification algorithms designed for previous generation sequencers are not optimized for the increased read length and error rate of Oxford Nanopore Technologies (ONT). Here, we present Emu, a novel approach that employs an Expectation-Maximization (EM) algorithm, to generate a taxonomic abundance profile from full length 16S rRNA reads. We demonstrate accurate sample composition estimates by our new software through analysis on two mock communities and one simulated data set. We also show Emu to elicit fewer false positives and false negatives than previous methods on both short and long read data. Finally, we illustrate a real-world application of Emu by processing vaginal microbiome samples from women with and without vaginosis, where we observe distinct species-level differences in the microbial composition between the two groups that are fully concordant with prior research in this important area. In summary, full-length 16S ONT sequences, paired with Emu, opens a new realm of microbiome research possibilities. Emu proves, with the appropriate method, increased accuracy can be obtained with nanopore long reads despite the increased error rate. Our novel software tool Emu allows researchers to further leverage portable, real-time sequencing provided by ONT for accurate, efficient, and low-cost characterization of microbial communities.

Date
Oct 27, 2021 10:00 AM — 10:15 AM
Location
Rice University
6100 Main St, Houston, TX 77005
Check out the Emu preprint here
Kristen Curry
Kristen Curry
PhD student

Kristen (2nd year PhD student) obtained a bachelor’s in computer science from the University of California, Berkeley. Prior to enrolling at Rice, she worked for a private biotech company focused on generating personalized health information from blood protein levels. Her primary research interests are microbial interactions and their impact on host health. Outside of the lab, she enjoys backpacking, running and practicing yoga.

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