# Contributions of DNA Sequencing Technologies to the Integrative Monitoring of Karstic Caves
## 4 NGS Taxonomic Assessment of Cave Microorganisms by Metabarcoding
### Workflow, Pitfalls, and Care for the Metabarcoding Approach  
Metabarcoding allows the specific analyses of targeted genes (or gene regions) on many samples in parallel (i.e., multiplexing), generating thousands of gene sequences [43]. While metabarcoding can be applied either on taxonomic markers and/or on functional genes, most studies targeted taxonomic markers to investigate archaeal, bacterial, and/or microeukaryotic communities. For example, the 165 rRNA gene can be directly amplified from environmental DNA for the exploration of prokaryotic diversity. This technique presents many advantages: (i) it is cost effective, (ii) data analysis can be carried out using established pipelines, and (iii) large amounts of data are archived in reference databases [101]. However, the PCR step induces several issues: (i) variable amplification efficiency, (ii) formation of chimeras (hybrid products between multiple sequences, which can be falsely interpreted as new microorganisms, artificially increasing the richness of a sample), (iii) punctual mutations, and (iv) potential primer specificity [43; 47; 101; 102; 103]. To limit these biases, the selection of appropriate primers (different primers may uncover different microbial diversity ranges [104; 105]), optimization of the number of PCR cycles, and establishment of a physical separation of the pre- and post-PCR steps are required [43; 47], as well as the use of synthetic communities. Moreover, the number of gene copies per genome can considerably vary from one taxa to another, distorting quantitative estimates of diversity. For example, an average of 160 185 rRNA gene copies is present in the genome of the dinoflagellate _Symbiodiinium kawagutii_, as large and repetitive genomes are typical of dinoflagellates [106]. Single-copytargeted genes (e.g., _rpoB_, _recA_, _gyrB_, etc.) have been proposed as alternatives, but the lack of a well-represented database for these genes is an important limiting factor [45]. Previous studies showed that different ribosomal hypervariable regions of the 16S rRNA and/or 18S rRNA genes were not equivalent for taxonomic affiliation of taxa and community diversity estimation, leading to a debate in the choice of the optimal regions to use for precise phylogenetic and taxonomic analyses [107; 108]. However, the recent literature suggests that there is no ideal hypervariable region to target [55; 107]. In the same way, amplicon size remains a crucial issue for a better assignation of taxonomy, as longer amplicons will provide a more reliable taxonomic affiliation. However, this parameter not only depends on the primer set used, but also on the sequencing technical limitation of the available platforms. For example, the V4 region (250 bp) of the 16S rRNA gene allows complete overlap of DNA sequences using the most commonly used platform to date (Illumina MiSeq 2 \(\times\) 250 bp or 2 \(\times\) 300 bp), unlike the hypervariable V3-V4 and V4-V5 regions, which cannot completely overlap [47]. Large overlapping regions allow merging of R1 and R2 pairs fixing a maximal mismatch rate, thus discarding sequencing errors and avoiding an overestimation of the real microbial diversity [43]. A key issue is the trimming of raw sequences, including overlapping but also the curation of sequences presenting ambiguous bases and/or an unexpected size. The current debate is rather on the treatment of cured sequences, which can be (i) grouped into operational taxonomic units (OTUs) using global or local thresholds or (ii) processed with the Amplicon Single Variant (ASV) method [47; 109]. While powerful rapidly evolving bioinformatic pipelines are available, they also display limits and biases in data processing, such as large differences in sensitivity and specificity. On the one hand, QIIME-uclust attributes more than 12% of the sequences to false OTUs against 0.17% on average in the other tested pipelines, i.e., Mothur and Usearch-Uparse [109].