metaBEETL: high-throughput analysis of heterogeneous microbial populations from shotgun DNA sequences.

Environmental shotgun sequencing (ESS) has potential to give greater insight into microbial communities than targeted sequencing of 16S regions, but requires much higher sequence coverage. The advent of next-generation sequencing has made it feasible for the Human Microbiome Project and other initiatives to generate ESS data on a large scale, but computationally efficient methods for analysing such data sets are needed.Here we present metaBEETL, a fast taxonomic classifier for environmental shotgun sequences. It uses a Burrows-Wheeler Transform (BWT) index of the sequencing reads and an indexed database of microbial reference sequences. Unlike other BWT-based tools, our method has no upper limit on the number or the total size of the reference sequences in its database. By capturing sequence relationships between strains, our reference index also allows us to classify reads which are not unique to an individual strain but are nevertheless specific to some higher phylogenetic order.Tested on datasets with known taxonomic composition, metaBEETL gave results that are competitive with existing similarity-based tools: due to normalization steps which other classifiers lack, the taxonomic profile computed by metaBEETL closely matched the true environmental profile. At the same time, its moderate running time and low memory footprint allow metaBEETL to scale well to large data sets.Code to construct the BWT indexed database and for the taxonomic classification is part of the BEETL library, available as a github repository at git@github.com:BEETL/BEETL.git.

MetaSAMS--a novel software platform for taxonomic classification, functional annotation and comparative analysis of metagenome datasets.

Metagenomics aims at exploring microbial communities concerning their composition and functioning. Application of high-throughput sequencing technologies for the analysis of environmental DNA-preparations can generate large sets of metagenome sequence data which have to be analyzed by means of bioinformatics tools to unveil the taxonomic composition of the analyzed community as well as the repertoire of genes and gene functions. A bioinformatics software platform is required that allows the automated taxonomic and functional analysis and interpretation of metagenome datasets without manual effort. To address current demands in metagenome data analyses, the novel platform MetaSAMS was developed. MetaSAMS automatically accomplishes the tasks necessary for analyzing the composition and functional repertoire of a given microbial community from metagenome sequence data by implementing two software pipelines: (i) the first pipeline consists of three different classifiers performing the taxonomic profiling of metagenome sequences and (ii) the second functional pipeline accomplishes region predictions on assembled contigs and assigns functional information to predicted coding sequences. Moreover, MetaSAMS provides tools for statistical and comparative analyses based on the taxonomic and functional annotations. The capabilities of MetaSAMS are demonstrated for two metagenome datasets obtained from a biogas-producing microbial community of a production-scale biogas plant. The MetaSAMS web interface is available at https://metasams.cebitec.uni-bielefeld.de.

Comparative and joint analysis of two metagenomic datasets from a biogas fermenter obtained by 454-pyrosequencing.

Biogas production from renewable resources is attracting increased attention as an alternative energy source due to the limited availability of traditional fossil fuels. Many countries are promoting the use of alternative energy sources for sustainable energy production. In this study, a metagenome from a production-scale biogas fermenter was analysed employing Roche's GS FLX Titanium technology and compared to a previous dataset obtained from the same community DNA sample that was sequenced on the GS FLX platform. Taxonomic profiling based on 16S rRNA-specific sequences and an Environmental Gene Tag (EGT) analysis employing CARMA demonstrated that both approaches benefit from the longer read lengths obtained on the Titanium platform. Results confirmed Clostridia as the most prevalent taxonomic class, whereas species of the order Methanomicrobiales are dominant among methanogenic Archaea. However, the analyses also identified additional taxa that were missed by the previous study, including members of the genera Streptococcus, Acetivibrio, Garciella, Tissierella, and Gelria, which might also play a role in the fermentation process leading to the formation of methane. Taking advantage of the CARMA feature to correlate taxonomic information of sequences with their assigned functions, it appeared that Firmicutes, followed by Bacteroidetes and Proteobacteria, dominate within the functional context of polysaccharide degradation whereas Methanomicrobiales represent the most abundant taxonomic group responsible for methane production. Clostridia is the most important class involved in the reductive CoA pathway (Wood-Ljungdahl pathway) that is characteristic for acetogenesis. Based on binning of 16S rRNA-specific sequences allocated to the dominant genus Methanoculleus, it could be shown that this genus is represented by several different species. Phylogenetic analysis of these sequences placed them in close proximity to the hydrogenotrophic methanogen Methanoculleus bourgensis. While rarefaction analyses still indicate incomplete coverage, examination of the GS FLX Titanium dataset resulted in the identification of additional genera and functional elements, providing a far more complete coverage of the community involved in anaerobic fermentative pathways leading to methane formation.