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

View ORCID ProfileKristen D. Curry, View ORCID ProfileQi Wang, View ORCID ProfileMichael G. Nute, Alona Tyshaieva, Elizabeth Reeves, View ORCID ProfileSirena Soriano, Enid Graeber, Patrick Finzer, View ORCID ProfileWerner Mendling, View ORCID ProfileQinglong Wu, View ORCID ProfileTor Savidge, View ORCID ProfileSonia Villapol, View ORCID ProfileAlexander Dilthey, View ORCID ProfileTodd J. Treangen
doi: https://doi.org/10.1101/2021.05.02.442339
Kristen D. Curry
1Rice University, Department of Computer Science, Houston, TX, USA
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  • For correspondence: kristen.d.curry@rice.edu treangen@rice.edu
Qi Wang
1Rice University, Department of Computer Science, Houston, TX, USA
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Michael G. Nute
1Rice University, Department of Computer Science, Houston, TX, USA
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Alona Tyshaieva
2Institute of Medical Microbiology and Hospital Hygiene, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Elizabeth Reeves
1Rice University, Department of Computer Science, Houston, TX, USA
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Sirena Soriano
3Houston Methodist Research Institute, Center for Neuroregeneration, Houston, TX, USA
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Enid Graeber
2Institute of Medical Microbiology and Hospital Hygiene, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Patrick Finzer
2Institute of Medical Microbiology and Hospital Hygiene, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Werner Mendling
4German Center for Infections in Gynaecology and Obstetrics at Helios University Clinic Wuppertal, Wuppertal, Germany
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Qinglong Wu
5Baylor College of Medicine, Department of Pathology and Immunology, Houston, TX, USA
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Tor Savidge
5Baylor College of Medicine, Department of Pathology and Immunology, Houston, TX, USA
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Sonia Villapol
3Houston Methodist Research Institute, Center for Neuroregeneration, Houston, TX, USA
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Alexander Dilthey
2Institute of Medical Microbiology and Hospital Hygiene, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Todd J. Treangen
1Rice University, Department of Computer Science, Houston, TX, USA
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  • For correspondence: kristen.d.curry@rice.edu treangen@rice.edu
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ABSTRACT

16S rRNA based analysis is the established standard for elucidating microbial community composition. While short read 16S analyses are largely confined to genus-level resolution at best since only a portion of the gene is sequenced, full-length 16S 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 of long-read data. Here we present Emu, a novel approach that employs an expectation-maximization (EM) algorithm to generate taxonomic abundance profiles from full-length 16S rRNA reads. Results produced from one simulated data set and two mock communities prove Emu 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 our new software by comparing clinical sample composition estimates generated by an established whole-genome shotgun sequencing workflow to those returned by full-length 16S sequences processed with Emu.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵+ These authors share senior authorship

  • https://gitlab.com/treangenlab/emu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted May 03, 2021.
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Emu: Species-Level Microbial Community Profiling for Full-Length Nanopore 16S Reads
Kristen D. Curry, Qi Wang, Michael G. Nute, Alona Tyshaieva, Elizabeth Reeves, Sirena Soriano, Enid Graeber, Patrick Finzer, Werner Mendling, Qinglong Wu, Tor Savidge, Sonia Villapol, Alexander Dilthey, Todd J. Treangen
bioRxiv 2021.05.02.442339; doi: https://doi.org/10.1101/2021.05.02.442339
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Emu: Species-Level Microbial Community Profiling for Full-Length Nanopore 16S Reads
Kristen D. Curry, Qi Wang, Michael G. Nute, Alona Tyshaieva, Elizabeth Reeves, Sirena Soriano, Enid Graeber, Patrick Finzer, Werner Mendling, Qinglong Wu, Tor Savidge, Sonia Villapol, Alexander Dilthey, Todd J. Treangen
bioRxiv 2021.05.02.442339; doi: https://doi.org/10.1101/2021.05.02.442339

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