VTAM: A robust pipeline for validating metabarcoding data using optimized parameters based on internal controls

Metabarcoding has become a powerful approach to study biodiversity from environmental samples but it is still prone some pitfalls. Several papers have called for good practice in design, data production and analyses ensure repeatability comparability between studies. Notably, the importance of mock community samples, negative controls, replicates frequently highlighted (Alberdi et al. 2018, O'Rourke 2020). However, their use bioinformatics pipelines often limited post hoc verification expectations by user. Indeed, one biggest challenges metabarcoding take into account trade-off false positive (FP) (FN) occurrences. We thus developed VTAM (Validation Taxonomic Assignation data) pipeline, which first tool explicitly control find optimal parameters minimize In addition, addresses all known technical error types including tag-jumps, among replicates, also able integrate more than overlapping markers further order evaluate VTAM, we compared with two other pipelines: pipeline based on DADA2 (Callahan 2016) LULU (Frøslev 2017), OBITools3 (Boyer metabaR (Zinger Two datasets fish bat diet studies were analysed three different pipelines. Based demonstrate that showed best precision both datasets, while specificity controls comparable (Fig. 1). therefore constitutes complete filter validate data, raw FASTQ Amplicon Sequence Variant tables taxonomic assignments. Our aggregates series features rarely grouped single performs non-arbitrary parameter optimization internal generate conservative informative datasets. believe provides very valuable validation essential conducting robust biodiversity.