## 3 Results
### Abundance Estimation and Taxonomic Read Classification  
Classifying as many reads as possible is essential to achieve the best characterisation and abundance estimates for the microbial community. To this end, we used the Centrifuge software for the taxonomic classification because it allows for smaller sub-sequence size matches (kmers) to consider the higher error profile of long reads [21]. We also applied three different taxonomic indices: the default NCBI RefSeq supplied by the centrifuge developer ([[https://ccb.jhu.edu/software/centrifuge/manual.shtml](https://ccb.jhu.edu/software/centrifuge/manual.shtml)]([https://ccb.jhu.edu/software/centrifuge/manual.shtml](https://ccb.jhu.edu/software/centrifuge/manual.shtml)); 3.3 k compressed references), the GTDB index (GTDB version r89, de-replicated to 54 k genomes) from  
Figure 2: Summary of all 20 sequence runs of biogas reactor samples using the MinION device. Different colours indicate properties that influenced quality parameters. [PERSON] et al. [22], and a GTDB index based on 24.7 k bacterial and archaeal representative species from [PERSON] et al. [23]. Figure 3 gives an overview of the proportion of reads classified for each taxonomic index at phylum, genus, and species level. In all cases, we achieved a dramatic increase (range of 1.8- to 13-fold) in the proportion of classified reads when using the GTDB index rather than the RefSeq index. In particular, improvements were obtained for samples 05-GER, 04-SW, 04-GER, and 03-GER (11.5- to 13.2-fold increase in classified reads). We did not observe any noticeable decrease in classified reads when comparing phylum to genus or species level. The average proportion of classified reads was 8.8% of all reads on the genus taxon using the RefSeq database and 42.11% using the \"GTDB-Meric\" index. These results correspond to a median 4.8-fold increase in classified reads across all samples. For the reactors with a 10-fold or higher increase, we are able to have a more meaningful observation of the actual bacterial/archaeal composition. A slightly higher proportion of classified Illumina reads could be observed in most samples compared to nanopore reads. On the basis of these results, we subjected all samples to further abundance analysis based on the \"GTDB-Meric\" index, with abundance calculated on the basis of the proportion of classified taxonomic reads in all classified reads. The results obtained at the phylum taxon are shown in Figure 4 and the results at the genus taxon in Figure S1. In general, we identified a wide range of different phyla via GTDB, especially for Firmicutes, due to more species clusters--which includes meta-assembled genomes--with corrected taxonomic lineages. This increased taxonomic granularity at phylum taxon provided better resolution, especially for Firmicutes, thus improving tracking of changes. As the case for biogas processes, Bacteroidetes (NCBI: Bacteroidetes) and Firmicutes_G and A (NCBI: Firmicutes) were the dominant phyla in most samples [25]. Thermotogota, a phylum frequently found in systems operating at higher temperatures [25], was highly abundant in three of the four thermophilic reactors (02-SW, 09-SW, and 08-GER). Euryarchaeota, including methanogens, represented less than 10% of the total classified reads, as frequently described previously for biogas processes [25]. Both sequencing methods were additionally compared against each other on the basis of their taxonomic genus classifications (see Figure S1). In summary, Illumina identified 999 genus taxons, while nanopore identified 1183 genus taxons across all 20 samples. They both agreed on 945 taxons with an average abundance difference of 0.56% (median 0.15%). Disagreements (only one method identified a particular genus taxon in a sample) had an abundance of 0.49% or less for Illumina and 0.77% or less for nanopore only hits.