Demulticoder: An R Package for the Simultaneous Analysis of Multiplexed Metabarcodes

Metabarcoding is a widely used approach relying on short DNA sequences to identify organisms present in a community. Although established workflows exist for analysis of single metabarcodes, these are cumbersome when multiple metabarcodes are required to study diverse taxa, such as those in plant- and soil-associated microbial communities, or when analyzing newly developed metabarcodes. To address this, we developed demulticoder, an R package automating the use of DADA2 to analyze data derived from multiple metabarcodes. It has novel capabilities that streamline data analysis by reducing the number of manual input steps and enabling automated processing of multiplexed metabarcodes. Additionally, demulticoder modularizes data processing to allow for iterative quality control and reformats data for downstream analyses. We also updated the oomycete-specific <i>rps10</i> barcode database by revising the taxonomic information of select entries based on updates to the classifications within the NCBI Taxonomy database. A multiplex sequenced dataset consisting of ITS1 and <i>rps10</i> metabarcodes from 162 samples and 12 controls was analyzed to compare demulticoder against a standard analysis workflow. Demulticoder required manual input at only four steps in comparison with 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 publicly accessible and open source.