However, in contrast to these advances in sampling and molecular processing, there has been limited effort to review and evaluate how bioinformatic processing has been adapted to metazoan wocDNA samples and the COI barcode, nor to examine consistency in bioinformatic approaches across the field. Broadly, bioinformatic tasks involve the computational cleaning, filtering and analysis of raw sequence data to produce biodiversity data comprising taxonomic units and their incidence across samples, implemented in a particular order (a ‘pipeline’). There are a wide array of software tools available for performing different bioinformatic tasks, from standalone tools to catch-all software packages (e.g. OBItools Boyer et al., 2016; QIIME Caporaso et al., 2010; USEARCH/UPARSE Edgar, 2013; and its open-source derivative VSEARCH Rognes et al., 2016). This software has been largely developed for metabarcode loci other than the COI region, with very few tools explicitly developed for protein coding metabarcodes (although see Andújar et al., 2021; Nugent et al., 2020; Ramirez-Gonzalez et al., 2013). To fully capitalise on the COI barcode for metabarcoding, bioinformatics should be specifically tailored to its evolutionary properties, such as the ability to interrogate the amino acid translation, and accounting for established patterns of sequence variation in protein coding genes for strict filtering. Additionally, metabarcoding employs a  number of key bioinformatic tasks for which multiple alternative algorithms have been developed (e.g. denoising algorithms), with considerable variation in outcomes depending on parameters and thresholds applied.