A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets
- University of California-Berkeley, United States
- UC Berkeley, United States
Abstract
Environmental DNA (eDNA) is becoming an increasingly important tool in diverse scientific fields from ecological biomonitoring to wastewater surveillance of viruses. The fundamental challenge in eDNA analyses has been the bioinformatical assignment of reads to taxonomic groups. It has long been known that full probabilistic methods for phylogenetic assignment are preferable, but unfortunately, such methods are computationally intensive and are typically inapplicable to modern Next-Generation Sequencing data. We here present a fast approximate likelihood method for phylogenetic assignment of DNA sequences. Applying the new method to several mock communities and simulated datasets, we show that it identifies more reads at both high and low taxonomic levels more accurately than other leading methods. The advantage of the method is particularly apparent in the presence of polymorphisms and/or sequencing errors and when the true species is not represented in the reference database.
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The current manuscript is a computational study, so no data have been generated for this manuscript.
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Funding
National Institute of General Medical Sciences (1R01GM138634-01)
- Lenore Pipes
- Rasmus Nielsen
National Institute of General Medical Sciences (1K99GM144747-01)
- Lenore Pipes
- Rasmus Nielsen
Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (BIO180028)
- Lenore Pipes
- Rasmus Nielsen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2024, Pipes & Nielsen
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets
eLife 13:e85794.
https://doi.org/10.7554/eLife.85794
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