## 1 Introduction  
Anaerobic digestion (AD) has been a key technology in Europe and worldwide for many decades and is vital for a full transition from a fossil fuel-based economy to a sustainable bio-economy. During AD, various metabolic and functional bottlenecks that affect process stability and efficiency can occur. Process efficiency depends on the composition and activity of the microbial community. Therefore much effort has been devoted to understanding how the microbial community correlates with process parameters and performance [1, 2]. In AD, many microorganisms form complex consortia to link and combine metabolic activities, and each member has different requirements for nutrients and physical conditions. However, the majority of the microorganisms involved are unknown [3], as isolation and characterisation of these microorganisms are time-consuming, and many are currently not cultivable. Moreover, the metabolism of pure cultures often differs from that of a microbial consortium. Thus, the function and importance of many AD microorganisms to convert organic material have not yet been fully explored. Meta-barcoding via 16S ribosomal RNA (rRNA) gene amplicon sequencing is widely used for the analysis of the composition, structure, and dynamics of microbial assemblages [4; 5]. However, this approach has two main drawbacks: (1) the experimental set-up does not allow prompt monitoring, and (2) inherent PCR amplification bias (e.g., primer mismatches, different gene copy number, sequence- and primer-dependent polymerase efficiency, choice of hypervariable region) [6; 7; 8; 9] reduces the accuracy, as read abundance does not correlate with species abundance [10; 11]. An alternative approach to circumvent the latter is to sequence the entire microbial DNA (metagenomics). Classifying the reads obtained from metagenomic sequencing gives insights into bacterial diversity and abundance. Metagenomics is usually performed via high-throughput sequencing (e.g., Illumina) [12]. However, long-read sequencing (e.g., nanopore sequencing) may provide a more accurate alternative, since the ratio of correctly classified reads to all reads is as high or even higher as using Illumina sequencing [13]. Although long reads are less accurate, they may allow better overall taxonomic classification due to the higher information content per read [14; 15]. This higher information richness per read is particularly useful as multiple genes can be present within one read for functional assessments. Another crucial advantage is that the organism proportion in a sample is preserved during sequencing and reflected in the sequencing yield [16]. However, long-read sequencing of metagenomic samples has two particularly important bottlenecks: (i) it requires less-fragmented, high-quality DNA while preserving bacterial composition, and (ii) choice of index database has a significant influence on read classification performance. Several isolation protocols based on chemical cell lysis have been developed for pure cultures or filtered bacteria (e.g., water samples). For microbiota, as found in biogas reactors, these protocols are usually not applicable because chemical cell lysis is not equally effective for all bacteria due to very diverse cell wall structures. Mechanical approaches such as bead beating allow, in combination with chemical lysis, more uniform cell disruption, especially of Gram-positive bacteria [17], which are very abundant in AD processes [5; 18]. However, the high shearing forces significantly reduce the length of the DNA fragments. Moreover, AD reactor samples may contain various particles/sediments, inhibitory components, cell debris, and partly digested DNA that further reduce the final quality and length of the recovered DNA and can significantly affect the sequencing performance. Here, we present a DNA isolation protocol adjusted to the properties of digestate from AD processes that result in high-quality DNA suitable for nanopore sequencing.