# 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 What They Are Capable of Doing  
As for other environments, shotgun metagenomic DNA sequencing is an asset for the study of karstic ecosystems. Metagenomes can provide insight into the functional and ecological roles of microorganisms by investigating their genes with annotation tools (e.g., COG, KEGG, Pfam) [118]. Thus, cave metagenomes can reveal how microbes develop under specific cave conditions which are energetically unfavorable and limited in nutrient content [26; 103]. Functional annotation involves two major steps, i.e., gene prediction and gene annotation [47; 102]. Gene prediction determines which metagenomic reads contain coding sequences. Once identified, coding sequences can be functionally annotated. For metagenomes assembled with complete coding sequences, gene prediction can be classic (with default parameters specific to each tool), while some prediction algorithms require specific parameters. In the case where the coding sequences are partial, the prediction is more difficult. There are three ways to predict genes in metagenomes: (i) recruiting gene fragments, (ii) classifying protein families, and (iii) predicting genes de novo. The biggest problem with this prediction is the level of database information, since not all predicted genes will exhibit homology with already identified sequences [119], highlighting the presence of new genes. Once coding sequences in a metagenome are predicted, they can be subjected to functional annotation. Metagenomic proteins are classified, and families of proteins are predicted (protein sequences related to evolution). As proteins in the same family are believed to encode similar biological functions [102], if a metagenomic sequence is determined to be a homologue of this family, then it is deduced that the sequence corresponds to the function of the family. The classification of a metagenomic protein sequence into a family of proteins is usually performed by comparing it to a protein database. Then, the protein is classified into either (i) a single family, (ii) a series of families (all families with a significant classification score), or (iii) no family, suggesting that the protein may be new, very divergent, or false. There are several pitfalls in functional annotation. First, most databases contain families that have no known functional annotation. Second, the database of protein families used to annotate the sequences may be subject to phylogenetic bias, or some communities may be disproportionately annotated. New strategies are needed for functional metagenome annotation and improvements in the way predicted metagenomic proteins are integrated into protein family databases. Few metagenomic studies have investigated functions associated with cave microorganisms. Analysis of the metabolic potential of these microorganisms is performed by mapping the reads into a general database, the Kyoto Encyclopedia of Genes and Genomes (KEGG). For caves, six functional modules are commonly used (metabolism, genetic information processing, environmental information processing, cellular processes, organ systems, and human diseases) [120]. Usually in caves, predictions of the metabolic potential of microorganisms have shown the presence of genes involved in methane metabolism, nitrogen metabolism, sulfur metabolism, carbon fixation, biodegradation, xenobiotic metabolism, and production of secondary metabolites [103; 110; 117; 121]. More recently, shotgun metagenomics has suggested the presence of microbial communities able to change Fe and Mn redox states, especially members of the Comamonadaceae and Hypomicrobiaceae families, in Monte Cristo Cave (Brazil) [122]. In Borra Caves (India), metagenomics suggested the vital role of sulfur reducers and sulfur-disproportionating microorganisms in energy generation in these caves and also the role of various nitrogen modifications for nitrogen pools conservation [123].