## 4 Discussion
### Database Choice and Abundance Estimation  
We applied the metagenomic approach to samples from 20 different reactors in order to estimate the classification performance for different AD types. In general, the phylum-level estimates obtained in this work are in line with findings in 16S rRNA studies and MAG (metagenome-assembled genomes) studies pointing to Firmicutes and Bacteroidetes as the main phyla for most AD processes [5; 18; 25]. They are also in line with [PERSON] et al., who identified 790 Firmicutes MAGs among 1600 public available MAGs from ADs [3]. [PERSON] et al. identified mainly Bacteroidetes (17-70%), followed by Firmicutes (5-10%), in five samples from WWTP [26]. The main drawback of 16S rRNA studies is the lack of information on how much of the population might be missing after the targeted PCR amplification, as primers are biased against certain taxons. Therefore, it is difficult to compare the performance of 16S rRNA with that of a metagenomic approach outside standard microbial community samples in a meaningful way.  
While we observed varying performance during sequencing, we were able to use all runs to classify from 25% to 66% of all reads using the GTDB database from [PERSON] et al. [22]. The RefSeq index, on the other hand, did not yield a sufficient amount of taxonomic classification for most samples and is therefore not suitable for abundance tracking in AD. This is mainly attributed to the fact that RefSeq is a smaller database in general and does not include meta-assembled genomes. Still, most organisms in ADs are difficult to cultivate outside of a microbial community for characterisation via whole-genome sequencing. The additional species clusters of GTDB improved the overall resolution, particularly for Firmicutes. In general, indices based on a large number of phylogenetically coherent taxonomic species definitions significantly increase the number of classified reads [22]. Publicly available indices are becoming increasingly important for metagenomic research. De-replication of such an enormous amount of genomes is time-consuming and challenging to compute without access to computation clusters or cloud computing.  
Eukaryotic cells from, e.g., fungi and plants are not included in GTDB and possibly represent part of the unclassified number of reads. Inclusion of eukaryotic cells, including anaerobic fungi [27], in taxonomic read classification remains challenging, mainly due to the large genome size and the resulting index size. DNA remains of cells (e.g., plants) could be expected within AD reactor samples due to the organic substrate used. Moreover, phage DNA or plasmid DNA would not be classified in most cases [28]. We suspect that there are also other currently unknown bacteria or archaea hidden within the unclassified reads.  
Some limitations of the study must be noted. We observed false-positive hits with less than 0.1% abundance for closely related species while validating the centrifuge parameters for taxonomic read classification on datasets of Zymoclean mock communities (GridION data from [[https://lomanlab.github.io/mockcommunity/](https://lomanlab.github.io/mockcommunity/)]([https://lomanlab.github.io/mockcommunity/](https://lomanlab.github.io/mockcommunity/))). The higher read error rate probably contributed to false-positive hits. A plausible explanation is that higher error rates could introduce erroneous kmers that are more similar to another species of the same genus. In any case, we included a taxon only if the relative abundance for a given taxon amounted to more than 0.1% of all classified reads in order to mitigate false-positive results. Moreover, centrifuge was not explicitly developed for long reads. It may work better with Illumina reads than with nanopore reads due to the higher read error rate which, however, has been decreasing steadily.