LAUSR

Background High-throughput sequencing of phylogenetically informative marker genes is a widely used method to assess the diversity and composition of microbial communities. Taxonomic assignment of sampled marker gene sequences (referred to as amplicon sequence variants, or ASVs) imparts ecological significance to these genetic data. To assign taxonomy to an ASV, a taxonomic assignment algorithm compares the ASV to a collection of reference sequences (a reference database) with known taxonomic affiliations. However, many taxonomic assignment algorithms and reference databases are available, and the optimal algorithm and database for a particular scientific question is often unclear. Here, we present the ensembleTax R package, which provides an efficient framework for integrating taxonomic assignments predicted with any number of taxonomic assignment algorithms and reference databases to determine ensemble taxonomic assignments for ASVs. Methods The ensembleTax R package relies on two core algorithms: taxmapper and assign.ensembleTax. The taxmapper algorithm maps taxonomic assignments derived from one reference database onto the taxonomic nomenclature (a set of taxonomic naming and ranking conventions) of another reference database. The assign.ensembleTax algorithm computes ensemble taxonomic assignments for each ASV in a data set based on any number of taxonomic assignments determined with independent methods. Various parameters allow analysts to prioritize obtaining either more ASVs with more predicted clade names or more robust clade name predictions supported by multiple independent methods in ensemble taxonomic assignments. Results The ensembleTax R package is used to compute two sets of ensemble taxonomic assignments for a collection of protistan ASVs sampled from the coastal ocean. Comparisons of taxonomic assignments predicted by individual methods with those predicted by ensemble methods show that conservative implementations of the ensembleTax package minimize disagreements between taxonomic assignments predicted by individual and ensemble methods, but result in ASVs with fewer ranks assigned taxonomy. Less conservative implementations of the ensembleTax package result in an increased fraction of ASVs classified at all taxonomic ranks, but increase the number of ASVs for which ensemble assignments disagree with those predicted by individual methods. Discussion We discuss how implementation of the ensembleTax R package may be optimized to address specific scientific objectives based on the results of the application of the ensembleTax package to marine protist communities. While further work is required to evaluate the accuracy of ensemble taxonomic assignments relative to taxonomic assignments predicted by individual methods, we also discuss scenarios where ensemble methods are expected to improve the accuracy of taxonomy prediction for ASVs.