Sebastian Jünemann

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 Roches 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.

Updating benchtop sequencing performance comparison

In April 2012, your journal published a study by Loman et al.1 that systematically compared desktop next-generation sequencers (NGS) from three instrument providers. Using the custom scripts supplied by the authors, the same software and the same draft genome (with 153 remaining gaps within several scaffolds) as the reference, we reproduced their results with their data of the enterohemorrhagic Escherichia coli (EHEC) strain found in the 2011 outbreak in Germany. However, we wish to bring readers’ attention to some shortcomings in the report from Loman et al.1, focusing particularly on its discussion of read-level error analysis. NGS is a rapidly changing market, which clearly complicates the comparisons such as that made by Loman et al. Since the original study1, Illumina (San Diego) has launched the MiSeq sequencer officially and has released Nextera library construction kits and 2 × 250–base-pair (250-bp) paired-end (PE) sequencing chemistry. Furthermore, Life Technologies (Carlsbad, California), has made 200-bp and 300-bp kits available for the Ion Torrent Personal Genome Machine (PGM). Roche (Basel, Switzerland) has updated the Sequencing System software for its 454 GS Junior (GSJ) from version 2.6 to 2.7. In this report, we provide an up-to-date snapshot of how benchtop platforms have evolved since the previous study1.

Profiling of the metabolically active community from a production-scale biogas plant by means of high-throughput metatranscriptome sequencing.

Structural composition and gene content of a biogas-producing microbial community from a production-scale biogas plant fed with renewable primary products was recently analyzed by means of a metagenome sequencing approach. To determine the transcriptionally active part of the same biogas community and to identify key transcripts for the biogas production process, the metatranscriptome of the microorganisms was sequenced for the first time. The metatranscriptome sequence dataset generated on the Genome Sequencer FLX platform is represented by 484,920 sequence reads. Taxonomic profiling of the active part of the community by classification of 16S ribosomal sequence tags revealed that members of the Euryarchaeota and Firmicutes account for the dominant phyla. Only smaller fractions of the 16S ribosomal sequence tags were assigned to the phyla Bacteroidetes, Actinobacteria and Synergistetes. Among the mRNA-derived sequence tags from the metatranscriptome dataset, transcripts encoding enzymes involved in substrate hydrolysis, acidogenesis, acetate formation and methanogenesis could be identified. Transcripts for enzymes functioning in methanogenesis are among the most abundant mRNA tags indicating that the corresponding pathway is very active in the methanogenic sub-community. As a frame of reference for evaluation of metatranscriptome sequence data, the 16S rDNA-based taxonomic profile of the community was analyzed by means of high-throughput 16S rDNA amplicon sequencing. Processing of the obtained amplicon reads resulted in 18,598 high-quality 16S rDNA sequences covering the V3-V4 hypervariable region of the 16S rRNA gene. Comparison of the taxonomic profiles deduced from 16S rDNA amplicon sequences and the metatranscriptome dataset indicates a high transcriptional activity of archaeal species. Overall, it was shown that the most abundant species dominating the community also contributed the majority of the transcripts. In the future, key transcripts for the biogas production process will provide valuable markers for evaluation of the performance of biogas-producing microbial communities with the objective to optimize the biotechnology of this process.

Bacterial Community Shift in Treated Periodontitis Patients Revealed by Ion Torrent 16S rRNA Gene Amplicon Sequencing

Periodontitis, one of the most common diseases in the world, is caused by a mixture of pathogenic bacteria and inflammatory host responses and often treated by antimicrobials as an adjunct to scaling and root planing (SRP). Our study aims to elucidate explorative and descriptive temporal shifts in bacterial communities between patients treated by SRP alone versus SRP plus antibiotics. This is the first metagenomic study using an Ion Torrent Personal Genome Machine (PGM). Eight subgingival plaque samples from four patients with chronic periodontitis, taken before and two months after intervention were analyzed. Amplicons from the V6 hypervariable region of the 16S rRNA gene were generated and sequenced each on a 314 chip. Sequencing reads were clustered into operational taxonomic units (OTUs, 3% distance), described by community metrics, and taxonomically classified. Reads ranging from 599,933 to 650,416 per sample were clustered into 1,648 to 2,659 non-singleton OTUs, respectively. Increased diversity (Shannon and Simpson) in all samples after therapy was observed regardless of the treatment type whereas richness (ACE) showed no correlation. Taxonomic analysis revealed different microbial shifts between both therapy approaches at all taxonomic levels. Most remarkably, the genera Porphyromonas, Tannerella, Treponema, and Filifactor all harboring periodontal pathogenic species were removed almost only in the group treated with SPR and antibiotics. For the species T. forsythia and P. gingivalis results were corroborated by real-time PCR analysis. In the future, hypothesis free metagenomic analysis could be the key in understanding polymicrobial diseases and be used for therapy monitoring. Therefore, as read length continues to increase and cost to decrease, rapid benchtop sequencers like the PGM might finally be used in routine diagnostic.

WebCARMA: a web application for the functional and taxonomic classification of unassembled metagenomic reads

BackgroundMetagenomics is a new field of research on natural microbial communities. High-throughput sequencing techniques like 454 or Solexa-Illumina promise new possibilities as they are able to produce huge amounts of data in much shorter time and with less efforts and costs than the traditional Sanger technique. But the data produced comes in even shorter reads (35-100 basepairs with Illumina, 100-500 basepairs with 454-sequencing). CARMA is a new software pipeline for the characterisation of species composition and the genetic potential of microbial samples using short, unassembled reads.ResultsIn this paper, we introduce WebCARMA, a refined version of CARMA available as a web application for the taxonomic and functional classification of unassembled (ultra-)short reads from metagenomic communities. In addition, we have analysed the applicability of ultra-short reads in metagenomics.ConclusionsWe show that unassembled reads as short as 35 bp can be used for the taxonomic classification of a metagenome. The web application is freely available at http://webcarma.cebitec.uni-bielefeld.de.

EMMA 2 – A MAGE-compliant system for the collaborative analysis and integration of microarray data

BackgroundUnderstanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems.ResultsThe EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services.ConclusionOur model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.

Ion Torrent Personal Genome Machine Sequencing for Genomic Typing of Neisseria meningitidis for Rapid Determination of Multiple Layers of Typing Information

ABSTRACT Neisseria meningitidis causes invasive meningococcal disease in infants, toddlers, and adolescents worldwide. DNA sequence-based typing, including multilocus sequence typing, analysis of genetic determinants of antibiotic resistance, and sequence typing of vaccine antigens, has become the standard for molecular epidemiology of the organism. However, PCR of multiple targets and consecutive Sanger sequencing provide logistic constraints to reference laboratories. Taking advantage of the recent development of benchtop next-generation sequencers (NGSs) and of BIGSdb, a database accommodating and analyzing genome sequence data, we therefore explored the feasibility and accuracy of Ion Torrent Personal Genome Machine (PGM) sequencing for genomic typing of meningococci. Three strains from a previous meningococcus serogroup B community outbreak were selected to compare conventional typing results with data generated by semiconductor chip-based sequencing. In addition, sequencing of the meningococcal type strain MC58 provided information about the general performance of the technology. The PGM technology generated sequence information for all target genes addressed. The results were 100% concordant with conventional typing results, with no further editing being necessary. In addition, the amount of typing information, i.e., nucleotides and target genes analyzed, could be substantially increased by the combined use of genome sequencing and BIGSdb compared to conventional methods. In the near future, affordable and fast benchtop NGS machines like the PGM might enable reference laboratories to switch to genomic typing on a routine basis. This will reduce workloads and rapidly provide information for laboratory surveillance, outbreak investigation, assessment of vaccine preventability, and antibiotic resistance gene monitoring.

Importance of rare taxa for bacterial diversity in the rhizosphere of Bt - and conventional maize varieties

Ribosomal 16S rRNA gene pyrosequencing was used to explore whether the genetically modified (GM) Bt-maize hybrid MON 89034 × MON 88017, expressing three insecticidal recombinant Cry proteins of Bacillus thuringiensis, would alter the rhizosphere bacterial community. Fine roots of field cultivated Bt-maize and three conventional maize varieties were analyzed together with coarse roots of the Bt-maize. A total of 547 000 sequences were obtained. Library coverage was 100% at the phylum and 99.8% at the genus rank. Although cluster analyses based on relative abundances indicated no differences at higher taxonomic ranks, genera abundances pointed to variety specific differences. Genera-based clustering depended solely on the 49 most dominant genera while the remaining 461 rare genera followed a different selection. A total of 91 genera responded significantly to the different root environments. As a benefit of pyrosequencing, 79 responsive genera were identified that might have been overlooked with conventional cloning sequencing approaches owing to their rareness. There was no indication of bacterial alterations in the rhizosphere of the Bt-maize beyond differences found between conventional varieties. B. thuringiensis-like phylotypes were present at low abundance (0.1% of Bacteria) suggesting possible occurrence of natural Cry proteins in the rhizospheres. Although some genera indicated potential phytopathogenic bacteria in the rhizosphere, their abundances were not significantly different between conventional varieties and Bt-maize. With an unprecedented sensitivity this study indicates that the rhizosphere bacterial community of a GM maize did not respond abnormally to the presence of three insecticidal proteins in the root tissue.

Metagenome analyses reveal the influence of the inoculant Lactobacillus buchneri CD034 on the microbial community involved in grass ensiling

Silage is green fodder conserved by lactic acid fermentation performed by epiphytic lactic acid bacteria under anaerobic conditions. To improve the ensiling process and the quality of the resulting silage, starter cultures are added to the fresh forage. A detailed analysis of the microbial community playing a role in grass ensiling has been carried out by high throughput sequencing technologies. Moreover, the influence of the inoculant Lactobacillus buchneri CD034 on the microbial community composition was studied. For this purpose, grass was ensiled untreated or inoculated with L. buchneri CD034. The fresh forage as well as silages after 14 and 58 days of fermentation were characterized physico-chemically. Characteristic silage conditions such as increased titers of lactic acid bacteria and higher concentrations of acetic acid were observed in the inoculated silage in comparison to the untreated samples. Taxonomic community profiles deduced from 16S rDNA amplicon sequences indicated that the relative abundance of Lactococci diminished in the course of fermentations and that the proportion of bacteria belonging to the phyla Proteobacteria and Bacteroidetes increased during the fermentation of untreated silage. In the inoculated silage, members of these phyla were repressed due to an increased abundance of Lactobacilli. In addition, metagenome analyses of silage samples confirmed taxonomic profiles based on 16S rDNA amplicons. Moreover, Lactobacillus plantarum, Lactobacillus brevis and Lactococcus lactis were found to be dominant species within silages as analyzed by means of fragment recruitments of metagenomic sequence reads on complete reference genome sequences. Fragment recruitments also provided clear evidence for the competitiveness of the inoculant strain L. buchneri CD034 during the fermentation of the inoculated silage. The inoculation strain was able to outcompete other community members and also affected physico-chemical characteristics of the silage.

GABenchToB: A Genome Assembly Benchmark Tuned on Bacteria and Benchtop Sequencers

De novo genome assembly is the process of reconstructing a complete genomic sequence from countless small sequencing reads. Due to the complexity of this task, numerous genome assemblers have been developed to cope with different requirements and the different kinds of data provided by sequencers within the fast evolving field of next-generation sequencing technologies. In particular, the recently introduced generation of benchtop sequencers, like Illuminas MiSeq and Ion Torrents Personal Genome Machine (PGM), popularized the easy, fast, and cheap sequencing of bacterial organisms to a broad range of academic and clinical institutions. With a strong pragmatic focus, here, we give a novel insight into the line of assembly evaluation surveys as we benchmark popular de novo genome assemblers based on bacterial data generated by benchtop sequencers. Therefore, single-library assemblies were generated, assembled, and compared to each other by metrics describing assembly contiguity and accuracy, and also by practice-oriented criteria as for instance computing time. In addition, we extensively analyzed the effect of the depth of coverage on the genome assemblies within reasonable ranges and the k-mer optimization problem of de Bruijn Graph assemblers. Our results show that, although both MiSeq and PGM allow for good genome assemblies, they require different approaches. They not only pair with different assembler types, but also affect assemblies differently regarding the depth of coverage where oversampling can become problematic. Assemblies vary greatly with respect to contiguity and accuracy but also by the requirement on the computing power. Consequently, no assembler can be rated best for all preconditions. Instead, the given kind of data, the demands on assembly quality, and the available computing infrastructure determines which assembler suits best. The data sets, scripts and all additional information needed to replicate our results are freely available at ftp://ftp.cebitec.uni-bielefeld.de/pub/GABenchToB.