# Comparison of Methods to Identify and Monitor Mold Damages in Buildings
## 4 Discussion
### Pros and Cons of Different Methods: Microscopy vs. DNA Metabarcoding
[44] implemented different methods (microscopy cell counting, flow cytometry, and DNA metabarcoding) to monitor the microbial growth on materials and demonstrated that small changes in relative abundance often resulted from large changes in absolute abundance. As mentioned before, an important weakness of DNA-based methods is that it is impossible to distinguish between viable and non-viable organisms. Due to the longevity of the relic DNA (extracellular or from dead cells), DNA techniques may, after the reparation of water damage, detect the presence of senescent indicator fungi. A recent study showed that relic DNA accounted for about 40% of the ITS sequences recovered from soil samples [45]. This highlights the importance of knowing the building history in order to draw sound conclusions [13]. Interestingly, abundant DNA from the dry-rot fungus (_Serpula lacrymans_) was detected in dust samples from apartment 15 (85-93% of sequences) despite the lack of dry-rot signs in the corresponding mold sample (M36; Table 2). This result agrees with the visual inspection of this apartment, where _S. lacrymans_ was pinpointed in other parts of the apartment (Mycoteam AS' communication) and on surfaces which were not sampled in this study. This finding illustrates the sensitivity of dust DNA metabarcoding to detect and monitor fungal attacks in buildings but also the need for a proper sampling design based on the previous knowledge about the building structure and its history. Like for microscopy, the taxonomic assignment based on DNA sequences is a challenging task due to several well-known constraints [21]. Two of them are the limited accuracy of incomplete reference sequence databases and the insufficient intraspecific sequence variation in barcodes (including ITS1 and ITS2) to resolve the taxonomy at the species level, as discussed above for some common mold genera, such as _Penicillium_, _Cladosporium_, _Aspegillus_, and _Alternaria_. In this study, the percentages of non-identified OTUs were similar to those observed in other DNA metabarcoding studies [22; 23], increasing considerably from the phylum (4.5%) to order (10.8%), genus (28.5%), and species (47.7%) levels. Therefore, the identity and ecology of a significant proportion of the fungi detected by DNA sequencing remain unknown. In order to overcome the less reliable identification at the species level, we decided to focus on the most abundant genera, those with a total RA > 1% of sequences after merging all the OTUs affiliated to the same genus. This conversion allowed us to compare two datasets characterized by very different resolutions (microscopy vs. DNA metabarcoding) but also added an additional bias to our results. Even when focusing on the genus level, the taxonomic annotation was not exempt of errors, as demonstrated by the automatic identification of _Austropaxillus_ among the most abundant genera detected. This ectomycorrhizal-forming genus has exclusively been reported in the temperate Southern Hemisphere, and its phylogenetic position is close to the genus _Serpula_[46]. We double-checked the two OTUs that were initially identified as _Austropaxillus squarrosus_, by an additional manual BLAST search against both the UNITE and INSDC databases, and we were able to correct their affiliation to the species _Serpula lacrymans_ (100% in both sequence similarity and coverage). These problems (errors and low precision) during the taxonomic assignment make it highly recommended to include a further semi-automated or manual curation of results, at least for the major OTUs, which often leads to a refined taxonomic resolution [21].