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Thedemulticoder package is aCutadapt andDADA2wrapper package for metabarcodng analyses. The main commands and outputsare intuitive and comprehensive, which helps to account for the complexand iterative nature of metabarcoding analyses.

Here is a brief schematic of the general workflow:

• It automates the use ofDADA2 to analyze data derived frommultiple metabarcodes.
• It reduces the number of manual input steps
• Handles analysis of two metabarcodes multiplexed into the samesequencing batch
• Analyze different types of metabarcodes simultaneously
• Reproducible workflows for oomycetes
• Supported metabarcodes: 16S rDNA, ITS1,rps10, and up to twoadditional metabarcodes

Dependencies:
First installCutadapt program following the instructions here:https://cutadapt.readthedocs.io/en/stable/installation.html

Let’s locate where theCutadapt executable is. You must do thisfrom aTerminal window:

#If you installed with pip or pipx, or homebrew, run this command from a Terminal windowwhich cutadaptcutadapt --version

If you followed theCutadapt installation instructions to create aconda environment called cutadapt (change to whatever you named yourenvironment), to install it in, open up aTerminal window and typethese commands:

#Run commands from a Terminal windowconda activate cutadaptwhich cutadaptcutadapt --version

Second, make sure the following R packages are installed:

• demulticoder (Available through CRAN)
• DADA2 (Latest version is 3.20)
  • To install, follow these instructions:https://www.bioconductor.org/packages/release/bioc/html/dada2.html
• phyloseq
  • To install:https://www.bioconductor.org/packages/release/bioc/html/phyloseq.html
• metacoder (Available through CRAN)

To installdemulticoder from CRAN:

install.packages("demulticoder")

To instead install the development version for the newest features orbug fixes:

install.packages("devtools")devtools::install_github("grunwaldlab/demulticoder")

Let’s make sure the other packages are also loaded (followinginstallation):

library("dada2")library("phyloseq")library("metacoder")library("demulticoder")

1. Set-up input directory and files

To demonstrate how to use the package, we have a small test data setthat comes loaded with the package. This data set will be used in theworkflow example below.

Already loaded in the test data set directory are the following files:

• PE short read amplification data
  • Files: S1_R1.fastq.gz, S1_R2.fastq.gz, S2_R1.fastq.gz,S2_R1.fastq.gz
  • The files must end in eitherR1.fastq.gz , orR2.fastq.gz andeach sample must have both R1 and R2 files.
• metadata.csv
  • New row for each unique sample
  • Samples entered twice if samples contain two pooled metabolites, asin the test data template
• primerinfo_params.csv
  • New row for each unique barcode and associated primer sequence
  • OptionalCutadapt andDADA2 parameters
• Taxonomy databases
  • UNITE fungal database (abridged version)
  • oomyceteDB (abridged version)

SeeDocumentationfor how to format databases and input files.

For more details on each step, check out theGettingStartedtab on the package website

2. Prepare reads

output<-prepare_reads(data_directory= system.file("extdata",package="demulticoder"),# This allows us to use the test directory located within the packageoutput_directory= tempdir(),# Change to you preferred location on your local computer (Example: "~/demulticoder_test")overwrite_existing=TRUE)

3. Cut and trim reads
User must installCutadapt on their local machine and append thepath to the executable.

cut_trim(output,cutadapt_path="/usr/bin/cutadapt",# Change to the location on your computer. (Example: "/usr/bin/cutadapt")overwrite_existing=TRUE)

4. Make ASV abundance matrix

make_asv_abund_matrix(output,overwrite_existing=TRUE)

5. Assign taxonomy

assign_tax(output,asv_abund_matrix,overwrite_existing=TRUE)

6. Convert ASV matrix to taxmap and phyloseq objects

objs<-convert_asv_matrix_to_objs(output)

For more information on source code, check out the package repository:https://grunwaldlab.github.io/demulticoder/

https://github.com/grunwaldlab/demulticoder/

The package was developed by Martha Sudermann, Zachary Foster, SamanthaDawson, Hung Phan, Jeff Chang, and Niklaus Grünwald

Sudermann MA, Foster ZSL, Dawson SCL, Phan H, Fieland VJ, Martin FN,Chang JH, Grünwald NJ. Demulticoder: An R Package for the SimultaneousAnalysis of Multiplexed Metabarcodes. Phytopathology. 2025.10.1094/PHYTO-02-25-0043-FI.Epub ahead of print.

This project was funded by the 2020 CDFA Specialty Block Grant ProgramProject 45.