# Contributions of DNA Sequencing Technologies to the Integrative Monitoring of Karstic Caves
## 5 Taxonomic and Functional Assessment of Cave Microorganisms by Metagenomics
### Cave Metagenomics Tells Us Who Is There  
The analysis of taxonomic diversity by shotgun metagenomic sequencing is typically carried out by (i) considering taxonomically informative marker genes simultaneously for all microorganisms present in a community (providing an estimate of their contribution to the microbial assemblage), (ii) assigning every metagenomic sequence to a taxonomic group,or (iii) reconstructing Metagenome-Assembled Genomes (MAGs), which is a powerful tool to build synthetic representatives for uncultured microorganisms. Marker gene analysis is an effective way to quantify taxonomic diversity within metagenomes. This method involves comparing reads to a database of taxonomically informative gene markers (i.e., SILVA database for 16S/18S rRNA genes, UNITE database for ITS regions, etc.). It needs to be noted that a high proportion of reads could not be classified using this method. Two studies conducted on Kartchner Cave speleothems (Arizona, USA) [101] and Manao-Pee Cave sediments (Thailand) [103] assessed rRNA gene diversity using BLAST analysis against the NCBI Non-Redundant (NR) nucleotide database and SILVA database, respectively (Supplementary Table S1). On the speleothem surfaces of Kartchner Cave, 85% of the assigned rRNA genes (among those classified) corresponded to _Bacteria_, 10% to _Archaea_, and 5% to _Eukaryota_. In the sediments of Manao-Pee Cave, 96.6% of the assigned rRNA genes pointed to _Bacteria_, 2.6% to _Archaea_, and 0.8% to _Eukaryota_. In both ecosystems, the dominant bacterial phylum was _Proteobacteria_ (52.0% and 32.9% in Kartchner and Manao-Pee caves, respectively), followed by _Actinobacteria_ (13.0% and 51.2%, respectively), whereas _Thuamarchaeota_ represented the major archaeal phylum with 76.0% and 90.7% of the archaeal sequences in Kartchner and Manao-Pee caves, respectively. The dominance of these three phyla in both caves suggests their ecological importance and potential role in C and N biogeochemical cycles [9; 103; 110]. Those results contrast with recent NovaSeq sequencing work conducted in Congo Cave (Africa), which reported an overwhelming dominance of _Bacteria_ (99.66% of sequences) with only 0.05% and 1.20% of archaeal and eukaryotic sequences, respectively [111]. Among _Bacteria_, the _Proteobacteria_, _Bacteroides_, and _Actinobacteria_ were the most abundant phyla, representing 81.74, 10.57, and 4.16% of sequences, respectively [111]. An alternative strategy, known as binning, attempts to group together the metagenomic sequences originating from the same taxonomic population [112]. To this end, each sequence is either (i) classified into a taxonomic group (e.g., genus, family, etc.) through comparison to some referential data, or (ii) clustered into groups of sequences that correspond to taxonomic groups based on shared characteristics (e.g., GC content). This method may provide insight into the genomic composition of uncultured organisms found in communities, and finally gives a way to reduce the complexity of the data, such that post-binning analysis (e.g., assembly) can be performed independently on each set of binned reads rather than on the entire set of data [112; 113; 114]. Using this method, [PERSON] et al. (2018) evidenced a high proportion of _Proteobacteria_ in microbial mats and sediments from Movile Cave (Romania). _Gallionella_ease was the dominant family within sediment microbial communities of caves, suggesting a major role for these microaerophilic iron-oxidizing bacteria in nutrient cycling [115; 116]. However, there is usually a trade-off between the number of reads that are binned and the taxonomic specificity of the annotations assigned to each bin. Additionally, there may be a large variance in the accuracy and specificity of the annotations across reads. Also, genes that have undergone horizontal transfer may not be represented adequately in the training dataset, and experimental data are needed to validate the algorithms predictions made for novel organisms.