Deeply sequenced metagenome and metatranscriptome of a biogas-producing microbial community from an agricultural production-scale biogas plant. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Bremges, Andreas; Maus, Irena; Belmann, Peter; Eikmeyer, Felix; Winkler, Anika; Albersmeier, Andreas; Pühler, Alfred; Schlüter, Andreas; Sczyrba, Alexander
• Is Part Of: GigaScience. Volume 4:Issue 1(2015)
• Page Start: 1
• Page End: 6
• Publication Date: 2015-12
• Subjects: Biogas -- Anaerobic digestion -- Wet fermentation -- Methanogenesis -- Metagenomics -- Metatranscriptomics -- Sequencing -- Assembly; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1186/s13742-015-0073-6
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract Background The production of biogas takes place under anaerobic conditions and involves microbial decomposition of organic matter. Most of the participating microbes are still unknown and non-cultivable. Accordingly, shotgun metagenome sequencing currently is the method of choice to obtain insights into community composition and the genetic repertoire. Findings Here, we report on the deeply sequenced metagenome and metatranscriptome of a complex biogas-producing microbial community from an agricultural production-scale biogas plant. We assembled the metagenome and, as an example application, show that we reconstructed most genes involved in the methane metabolism, a key pathway involving methanogenesis performed by methanogenicArchaea . This result indicates that there is sufficient sequencing coverage for most downstream analyses. Conclusions Sequenced at least one order of magnitude deeper than previous studies, our metagenome data will enable new insights into community composition and the genetic potential of important community members. Moreover, mapping of transcripts to reconstructed genome sequences will enable the identification of active metabolic pathways in target organisms.