## 5 Conclusions  
Nanopore sequencing using the pocket-sized MinION device provides relatively cheap and readily available access to sequencing. Typical issues such as raw-read error rates have been improved in recent years. Our results demonstrated that \"shallow sequenced\" metagenomics is possible by maximising the extraction of taxonomic information via more suitable indices that allow abundance estimations.  
Sequencing at lower depth thus enables much cheaper comparison of multiple metagenomic samples. Metagenomes with many uncultivable microorganisms are especially appropriate for long-read sequencing technologies because these reads can achieve better completeness of a given genome. This facilitates recovery of complete microbial genomes, which is of high interest if the metagenome remains mainly unclassified when using indices such as GTDB. Applying other indices or methods (developed in the future) to identify eukaryotes would enable abundance tracking closer to the reality of any given microbial community. Further improvements in raw read quality in nanopore sequencing would provide more reliable species identification at lower abundances and could facility reactor monitoring with even fewer data.  
The method requires a minimum of technical equipment and technician training and enables community monitoring across different time frames or process parameter changes. It may also reveal whether the number of unclassified reads increases or decreases under certain conditions, indicating whether the currently classified population size is increasing or decreasing. In the case of a decreasing classified population, another currently unclassified, but important, organism might be present.  
The main differences in read classification between Illumina and nanopore sequencing were observed in shallow abundant organisms. In these cases, a greater depth of sequencing makes sense for a precise examination of these organisms in both cases. However, the chosen taxonomic index remains the most crucial influencing factor.  
Nanopore sequencing provides a promising approach to establish a potential real-time correlation between the microbial community and the reactor stability/process efficiency. At the same time, it is possible to assess the functional potential of the samples. The use of automated and biogas reactor-specific monitoring tools, e.g., via machine learning, could help to predict upcoming inefficiency and disturbances.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/21/7518/s1](http://www.mdpi.com/2076-3417/10/21/7518/s1)]([http://www.mdpi.com/2076-3417/10/21/7518/s1](http://www.mdpi.com/2076-3417/10/21/7518/s1)), Figure S1: Summary of abundance for samples from all 20 reactors, calculated on the basis of all classified reads for nanopore and Illumina sequencing (genus taxon).  
Conceptualization, C.B.; methodology, C.B.; software, C.B. and E.B.-R.; validation, C.B. and B.M.; formal analysis, C.B.; investigation, C.B.; resources, C.B., E.B.-R., and B.M.; data curation, C.B.; writing--original draft preparation, C.B.; writing--review and editing, C.B., E.B.-R., and B.M.; visualization, C.B.; supervision, C.B.; project administration, B.M.; funding acquisition, C.B. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) BR 5692/1-1 and BR 5692/1-2. The material is based upon work supported by Google Cloud. B.M. was supported by FORMAS, grant number 942-2015-1008. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  
We are grateful to [PERSON], [PERSON], and [PERSON] for providing us with the reactor samples for this work.  
The authors declare no conflict of interest.