Background

Production of biogas by anaerobic digestion of biomass is becoming increasingly important, as biogas is regarded a clean, renewable and environmentally compatible energy source [1]. Moreover, generation of energy from biogas relies on a balanced carbon dioxide cycle.

Biogas production takes place under anaerobic conditions and involves microbial decomposition of organic matter, yielding methane as the main final product of the fermentation process. Complex consortia of microorganisms are responsible for biomass decomposition and biogas production. The majority of the participating microbes are still unknown, as is their influence on reactor performance. Because most of the organisms in biogas communities are non-cultivable by today’s conventional microbiological techniques, sequencing of metagenomic total community DNA currently is the best way to obtain unbiased insights into community composition and the metabolic potential of key community members.

Here, we describe the deeply sequenced metagenome and metatranscriptome of an agricultural production-scale biogas plant on the Illumina platform [2]. We sequenced the metagenome 27X and 19X deeper, respectively, than previous studies applying 454 or SOLiD sequencing [3, 4], which focused primarily on community composition.

Metatranscriptomic sequencing of total community RNA, 230X deeper than previously reported [5], complements the metagenome. Combined, these data will enable a deeper exploration of the biogas-producing microbial community, with the objective of developing rational strategies for process optimization.

Digester management and process characterization

The biogas plant, located in North Rhine Westphalia, Germany, features a mesophilic continuous wet fermentation technology characterized recently [6]. It was designed for a capacity of 537 k W _e combined heat and power (CHP) generation. The process comprises three digesters: a primary and secondary digester, where the main proportion of biogas is produced, and a storage tank, where the digestate is fermented thereafter.

Sampling and library construction

Samples from the primary digester of the biogas plant were taken in November 2010. Before the sampling process, approximately 15 l of the fermenter substrate were discarded before aliquots of 1 l were transferred into clean, gastight sampling vessels and transported directly to the laboratory.

For the metagenome, aliquots of 20 g of the fermentation sample were used for total community DNA preparation as described previously [7].

For the metatranscriptome, a random-primed cDNA library was prepared by an external vendor (Vertis Biotechnologie AG). Briefly, total RNA was first treated with 5^′-P dependent Terminator exonuclease (Epicentre) to enrich for full-length mRNA carrying 5^′ cap or triphosphate structures. Then, first-strand cDNA was synthesized using a N6 random primer and M-MLV-RNase H reverse transcriptase, and second-strand cDNA synthesis was performed according to the Gubler-Hoffman protocol [8].

Metagenomic and metatranscriptomic sequencing

Metagenome assembly

We assembled the metagenome with Ray Meta [10] version 2.3.1, trying a range of k-mer sizes from 21 to 61 in steps of 10. To estimate the inclusivity of the set of assemblies, we aligned the post-quality-control sequencing reads to the assembled contigs with bowtie2 [11] version 2.2.4. We then used samtools [12] version 1.1 to convert SAM to BAM, sort the alignment file and calculate the mapping statistics. Given the total assembly size and contiguity and the percentage of mapped back metagenomic reads, we selected the assembly produced with a k-mer size of 31. Here, we assembled approximately 228 M b p in 54,489 contigs greater than 1,000 b p, with an N50 value of 9,796 b p. 77 % (79 %) of metagenomic (metatranscriptomic) reads mapped back to this assembly.

Gene prediction and annotation

Relating the metagenome and the metatranscriptome

To illustrate potential use cases, we first counted the number of reads within genes using BEDTools [16] version 2.22.0. The metagenomic and metatranscriptomic coverage of the methane metabolism pathway is shown in Fig. 1. This shows that we have assembled the majority of genes involved in the methane metabolism from our metagenomic data, with accompanying metatranscriptomic data suggesting active gene expression for many.

For a second example, we calculated the reads per kilobase per million mapped reads (RPKM) for each gene as a crude measure for abundance (metagenome) or expression (metatranscriptome). Figure 2 relates the two and highlights all genes assigned to any of the three known types of methanogenic pathways. Hydrogenotrophic methanogenesis, that is, the reduction of CO₂ with hydrogen, appears to be highly expressed in the reactor analyzed, which is in agreement with results obtained by 454 amplicon and metatranscriptome sequencing [5].

Data accession

Raw sequencing data are available in the European Nucleotide Archive (ENA) under study accession PRJEB8813 (http://www.ebi.ac.uk/ena/data/view/PRJEB8813). The datasets supporting the results of this article are available in GigaScience’s GigaDB [2].

Reproducibility

The complete workflow is organized in a single GNU Makefile and available on GitHub [18]. All data and results can be reproduced by a simple invocation of make. To further support reproducibility, we bundled all tools and dependencies into one Docker container available on DockerHub [19]. docker run executes the aforementioned Makefile inside the container. Reproduction requires roughly 89 G i B memory and 83 G i B storage, and takes less than 24 hours on 32 CPU cores.

Excluding the KEGG analysis, which relies on a commercial license of the KEGG database, all steps are performed using free and open-source software.