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ARS Home » Pacific West Area » Corvallis, Oregon » Horticultural Crops Disease and Pest Management Research Unit » Research » Publications at this Location » Publication #422673
Research Project: Knowledge Based Tools for Exotic and Emerging Diseases of Small Fruit and Nursery Crops

Location: Horticultural Crops Disease and Pest Management Research Unit

Title: Demulticoder: An R package for the simultaneous analysis of multiplexed metabarcodes

Author
item SUDERMANN, MARTHA - Oregon State University
item Foster, Zachary
item DAWSON, SAMANTHA - Oregon State University
item PHAN, HUNG - Oregon State University
item FIELAND, VALERIE - Oregon State University
item Martin, Frank
item CHANG, JEFF - Oregon State University
item Grunwald, Niklaus

Submitted to: Phytopathology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/22/2025
Publication Date: 5/25/2025
Citation: Sudermann, M.A., Foster, Z.S., Dawson, S., Phan, H., Fieland, V., Martin, F.N., Chang, J., Grunwald, N.J. 2025. Demulticoder: An R package for the simultaneous analysis of multiplexed metabarcodes. Phytopathology. https://doi.org/10.1094/PHYTO-02-25-0043-FI.
DOI: https://doi.org/10.1094/PHYTO-02-25-0043-FI

Interpretive Summary: Amplifying and sequencing short DNA sequences, known as metabarcoding, is a widely used approach to characterize microbial diversity from environmental samples. However, analyses are currently cumbersome. To address this, we introduce a software package that reduces the number of computational steps and enables the simultaneous processing of multiple DNA regions. We also update the metabarcode library specific for the oomycetes, a group of plant pathogens often referred to as water molds. The software and library updates will increase our ability to rapidly characterize microbial communities and facilitate identification of pathogens from environmental samples.

Technical Abstract: Metabarcoding is a widely used approach that relies on short DNA sequences to identify and quantify members present in a sample. While workflows have been developed for data analyses, they can be cumbersome when studying diverse phyla, such as those in plant- and soil-associated microbial communities, or when analyzing newly developed metabarcodes. To address this, we introduce demulticoder, a modular DADA2 wrapper package. It has novel capabilities that streamline processing by reducing the number of manual input steps and enabling simultaneous processing of multiplexed metabarcode types. Additionally, demulticoder modularizes data processing to allow iterative quality control and reformats data for downstream analyses. As a companion to demulticoder, we expanded the rps10 barcode database by adding more validated sequences to improve the resolution of taxonomic inference for oomycetes. A multiplex sequenced dataset consisting of ITS1 and rps10 metabarcodes from 162 samples served as proof-of-concept and was analyzed to compare demulticoder against a standard analysis workflow. Demulticoder required manual input at only four steps in comparison to 28 steps required for the standard workflow. Data quality and results from downstream exploratory, diversity, and differential abundance analyses were comparable to those from the standard workflow. Demulticoder is versatile and can be used to analyze datasets consisting of single metabarcodes, multiplexed and pooled metabarcode types, and different metabarcode types generated in separate experiments. The demulticoder R package, example datasets, and instructions are available as open access.