Metabarcoding studies in microbial ecology generate taxonomic abundance profiles, with each taxon usually represented by an ASV (Amplicon Sequence Variant). Some taxa may be associated with several, sometimes conflicting, taxonomic affiliations. These ambiguities (or multi-affiliations) [1], may be frequent when analysing short sequences and/or when reference databases are incomplete. This complicates ecological interpretation and decreases the reproducibility of results. According to the origin of the samples (e.g. food, fecal, biowaste, etc.) or the conflict source (spelling typo in the reference databases) , manual curation can sometimes resolve these ambiguities. Likewise, some inconsistencies can be easily corrected programmatically if a manual curation was performed on a similar dataset and needs to be reapplied. The {affiliationExplorer} R-package provides a user interface dedicated to exploring and resolving these conflicts, and can easily be inserting in BIOM (Biological Observation Matrix) data analysis workflows.

Developed in Shiny with the {golem} framework, this package offers a user-friendly interface for ① loading a BIOM file, a multi-affiliation file, and, optionally, one or more expert-verified sequence–taxonomy dictionaries (in FASTA format), ② visualising and ordering multi-affiliated taxa by abundance, ③ automatically resolving conflicts using the provided dictionaries for curated ASV references, ④ performing expert manual curation by selecting the most relevant affiliation among those proposed or specifying it manually, and ⑤ exporting a corrected BIOM file and a sequence–taxonomy dictionary to log the performed curations and for potential reuse in subsequent curation sessions. The produced BIOM file follows the standards of biom-format.org and is fully compatible with standard metabarcoding workflows. The exported FASTA files can be reused as is in the interface.

Interface of the {affiliationExplorer} webapp

Keywords (10) : Biostatistics - R Package - Shiny - Taxonomy - Metabarcoding

1. Bernard M, Rué O, Mariadassou M, Pascal G. FROGS: a powerful tool to analyse the diversity of fungi with special management of internal transcribed spacers. Briefings in Bioinformatics. 2021;22. doi:10.1093/bib/bbab318.