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The sentences: 
After the formation of features, the presence of a long tail of low-abundant units is common. This tail is often discarded, assuming that a large proportion of low-abundant features are artefactual (NAME_1 & NAME_2, 2009; NAME_3 et NAME_4., 2010). However, without applying arbitrary cutoff levels (e.g., removing features with <10 reads per-sample; NAME_5 et NAME_4., 2015), the post-clustering process aids in removing the erroneous features but keeping the rare, potentially real ones. Post-clustering/denoising tools, such as LULU (NAME_6 et NAME_4., 2017) are implemented in AMPTk (Palmer et NAME_4., 2018), eDNAflow (NAME_7 et NAME_4., 2021), APSCALE (NAME_8 et NAME_4., 2022), LotuS2, PipeCraft2, and ReClustOR (Terrat et NAME_4., 2019) in BIOCOM-PIPE. Post-clustering/denosing, however, does not resolve the tag-switching phenomena, where some low-abundant non-artificial features may represent false-positive occurrences across samples. Tag-switching is a well-documented issue (e.g., NAME_9 et NAME_4., 2012; NAME_10-NAME_11 et NAME_4., 2022; NAME_12 et NAME_4., 2015), but is rarely considered in practice because the low proportions of tag-switching errors do not heavily impact the community-level analyses (e.g., Anslan et NAME_4., 2021). Nevertheless, the incorrect sample assignments of features artificially inflate the richness, and artificially decrease differentiation between samples (reduction of beta diversity). For discarding potential tag-switc