Heiko Neuweger

The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1000 Genomes

The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms.

The metagenome of a biogas-producing microbial community of a production-scale biogas plant fermenter analysed by the 454-pyrosequencing technology

Composition and gene content of a biogas-producing microbial community from a production-scale biogas plant fed with renewable primary products was analysed by means of a metagenomic approach applying the ultrafast 454-pyrosequencing technology. Sequencing of isolated total community DNA on a Genome Sequencer FLX System resulted in 616,072 reads with an average read length of 230 bases accounting for 141,664,289 bases sequence information. Assignment of obtained single reads to COG (Clusters of Orthologous Groups of proteins) categories revealed a genetic profile characteristic for an anaerobic microbial consortium conducting fermentative metabolic pathways. Assembly of single reads resulted in the formation of 8752 contigs larger than 500 bases in size. Contigs longer than 10kb mainly encode house-keeping proteins, e.g. DNA polymerase, recombinase, DNA ligase, sigma factor RpoD and genes involved in sugar and amino acid metabolism. A significant portion of contigs was allocated to the genome sequence of the archaeal methanogen Methanoculleus marisnigri JR1. Mapping of single reads to the M. marisnigri JR1 genome revealed that approximately 64% of the reference genome including methanogenesis gene regions are deeply covered. These results suggest that species related to those of the genus Methanoculleus play a dominant role in methanogenesis in the analysed fermentation sample. Moreover, assignment of numerous contig sequences to clostridial genomes including gene regions for cellulolytic functions indicates that clostridia are important for hydrolysis of cellulosic plant biomass in the biogas fermenter under study. Metagenome sequence data from a biogas-producing microbial community residing in a fermenter of a biogas plant provide the basis for a rational approach to improve the biotechnological process of biogas production.

Taxonomic composition and gene content of a methane-producing microbial community isolated from a biogas reactor.

A total community DNA sample from an agricultural biogas reactor continuously fed with maize silage, green rye, and small proportions of chicken manure has recently been sequenced using massively parallel pyrosequencing. In this study, the sample was computationally characterized without a prior assembly step, providing quantitative insights into the taxonomic composition and gene content of the underlying microbial community. Clostridiales from the phylum Firmicutes is the most prevalent phylogenetic order, Methanomicrobiales are dominant among methanogenic archaea. An analysis of Operational Taxonomic Units (OTUs) revealed that the entire microbial community is only partially covered by the sequenced sample, despite that estimates suggest only a moderate overall diversity of the community. Furthermore, the results strongly indicate that archaea related to the genus Methanoculleus, using CO2 as electron acceptor and H2 as electron donor, are the main producers of methane in the analyzed biogas reactor sample. A phylogenetic analysis of glycosyl hydrolase protein families suggests that Clostridia play an important role in the digestion of polysaccharides and oligosaccharides. Finally, the results unveiled that most of the organisms constituting the sample are still unexplored.

MeltDB: a software platform for the analysis and integration of metabolomics experiment data

MOTIVATION The recent advances in metabolomics have created the potential to measure the levels of hundreds of metabolites which are the end products of cellular regulatory processes. The automation of the sample acquisition and subsequent analysis in high-throughput instruments that are capable of measuring metabolites is posing a challenge on the necessary systematic storage and computational processing of the experimental datasets. Whereas a multitude of specialized software systems for individual instruments and preprocessing methods exists, there is clearly a need for a free and platform-independent system that allows the standardized and integrated storage and analysis of data obtained from metabolomics experiments. Currently there exists no such system that on the one hand supports preprocessing of raw datasets but also allows to visualize and integrate the results of higher level statistical analyses within a functional genomics context. RESULTS To facilitate the systematic storage, analysis and integration of metabolomics experiments, we have implemented MeltDB, a web-based software platform for the analysis and annotation of datasets from metabolomics experiments. MeltDB supports open file formats (netCDF, mzXML, mzDATA) and facilitates the integration and evaluation of existing preprocessing methods. The system provides researchers with means to consistently describe and store their experimental datasets. Comprehensive analysis and visualization features of metabolomics datasets are offered to the community through a web-based user interface. The system covers the process from raw data to the visualization of results in a knowledge-based background and is integrated into the context of existing software platforms of genomics and transcriptomics at Bielefeld University. We demonstrate the potential of MeltDB by means of a sample experiment where we dissect the influence of three different carbon sources on the gram-negative bacterium Xanthomonas campestris pv. campestris on the level of measured metabolites. Experimental data are stored, analyzed and annotated within MeltDB and accessible via the public MeltDB web server. AVAILABILITY The system is publicly available at http://meltdb.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.

The complete genome sequence of the dominant Sinorhizobium meliloti field isolate SM11 extends the S. meliloti pan-genome

Isolates of the symbiotic nitrogen-fixing species Sinorhizobium meliloti usually contain a chromosome and two large megaplasmids encoding functions that are absolutely required for the specific interaction of the microsymbiont with corresponding host plants leading to an effective symbiosis. The complete genome sequence, including the megaplasmids pSmeSM11c (related to pSymA) and pSmeSM11d (related to pSymB), was established for the dominant, indigenous S. meliloti strain SM11 that had been isolated during a long-term field release experiment with genetically modified S. meliloti strains. The chromosome, the largest replicon of S. meliloti SM11, is 3,908,022bp in size and codes for 3785 predicted protein coding sequences. The size of megaplasmid pSmeSM11c is 1,633,319bp and it contains 1760 predicted protein coding sequences whereas megaplasmid pSmeSM11d is 1,632,395bp in size and comprises 1548 predicted coding sequences. The gene content of the SM11 chromosome is quite similar to that of the reference strain S. meliloti Rm1021. Comparison of pSmeSM11c to pSymA of the reference strain revealed that many gene regions of these replicons are variable, supporting the assessment that pSymA is a major hot-spot for intra-specific differentiation. Plasmids pSymA and pSmeSM11c both encode unique genes. Large gene regions of pSmeSM11c are closely related to corresponding parts of Sinorhizobium medicae WSM419 plasmids. Moreover, pSmeSM11c encodes further novel gene regions, e.g. additional plasmid survival genes (partition, mobilisation and conjugative transfer genes), acdS encoding 1-aminocyclopropane-1-carboxylate deaminase involved in modulation of the phytohormone ethylene level and genes having predicted functions in degradative capabilities, stress response, amino acid metabolism and associated pathways. In contrast to Rm1021 pSymA and pSmeSM11c, megaplasmid pSymB of strain Rm1021 and pSmeSM11d are highly conserved showing extensive synteny with only few rearrangements. Most remarkably, pSmeSM11b contains a new gene cluster predicted to be involved in polysaccharide biosynthesis. Compilation of the S. meliloti SM11 genome sequence contributes to an extension of the S. meliloti pan-genome.

REGANOR: a gene prediction server for prokaryotic genomes and a database of high quality gene predictions for prokaryotes.

UNLABELLED With >1,000 prokaryotic genome sequencing projects ongoing or already finished, comprehensive comparative analysis of the gene content of these genomes has become viable. To allow for a meaningful comparative analysis, gene prediction of the various genomes should be as accurate as possible. It is clear that improving the state of genome annotation requires automated gene identification methods to cope with the influence of artifacts, such as genomic GC content. There is currently still room for improvement in the state of annotations. We present a web server and a database of high-quality gene predictions. The web server is a resource for gene identification in prokaryote genome sequences. It implements our previously described, accurate gene finding method REGANOR. We also provide novel gene predictions for 241 complete, or almost complete, prokaryotic genomes. We demonstrate how this resource can easily be utilised to identify promising candidates for currently missing genes from genome annotations with several examples. All data sets are available online. AVAILABILITY The gene finding server is accessible via https://www.cebitec.uni-bielefeld.de/groups/brf/software/reganor/cgi-bin/reganor_upload.cgi. The server software is available with the GenDB genome annotation system (version 2.2.1 onwards) under the GNU general public license. The software can be downloaded from https://sourceforge.net/projects/gendb/. More information on installing GenDB and REGANOR and the system requirements can be found on the GenDB project page http://www.cebitec.uni-bielefeld.de/groups/brf/software/wiki/GenDBWiki/AdministratorDocumentation/GenDBInstallation

MeltDB 2.0–advances of the metabolomics software system

Motivation: The research area metabolomics achieved tremendous popularity and development in the last couple of years. Owing to its unique interdisciplinarity, it requires to combine knowledge from various scientific disciplines. Advances in the high-throughput technology and the consequently growing quality and quantity of data put new demands on applied analytical and computational methods. Exploration of finally generated and analyzed datasets furthermore relies on powerful tools for data mining and visualization. Results: To cover and keep up with these requirements, we have created MeltDB 2.0, a next-generation web application addressing storage, sharing, standardization, integration and analysis of metabolomics experiments. New features improve both efficiency and effectivity of the entire processing pipeline of chromatographic raw data from pre-processing to the derivation of new biological knowledge. First, the generation of high-quality metabolic datasets has been vastly simplified. Second, the new statistics tool box allows to investigate these datasets according to a wide spectrum of scientific and explorative questions. Availability: The system is publicly available at https://meltdb.cebitec.uni-bielefeld.de. A login is required but freely available. Contact: nkessler@cebitec.uni-bielefeld.de

Root metabolic response of rice (Oryza sativa L.) genotypes with contrasting tolerance to zinc deficiency and bicarbonate excess

Plants are routinely subjected to multiple environmental stresses that constrain growth. Zinc (Zn) deficiency and high bicarbonate are two examples that co-occur in many soils used for rice production. Here, the utility of metabolomics in diagnosing the effect of each stress alone and in combination on rice root function is demonstrated, with potential stress tolerance indicators identified through the use of contrasting genotypes. Responses to the dual stress of combined Zn deficiency and bicarbonate excess included greater root solute leakage, reduced dry matter production, lower monosaccharide accumulation and increased concentrations of hydrogen peroxide, phenolics, peroxidase and N-rich metabolites in roots. Both hydrogen peroxide concentration and root solute leakage were correlated with higher levels of citrate, allantoin and stigmasterol. Zn stress resulted in lower levels of the tricarboxylic acid (TCA) cycle intermediate succinate and the aromatic amino acid tyrosine. Bicarbonate stress reduced shoot iron (Fe) concentrations, which was reflected by lower Fe-dependent ascorbate peroxidase activity. Bicarbonate stress also favoured the accumulation of the TCA cycle intermediates malate, fumarate and succinate, along with the non-polar amino acid tyrosine. Genotypic differentiation revealed constitutively higher levels of d-gluconate, 2-oxoglutarate and two unidentified compounds in the Zn-efficient line RIL46 than the Zn-inefficient cultivar IR74, suggesting a possible role for these metabolites in overcoming oxidative stress or improving metal re-distribution.

Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets

BackgroundModern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features.ResultsIn this paper we introduce two algorithms for retention time alignment of multiple GC-MS datasets: multiple alignment by bidirectional best hits peak assignment and cluster extension (BIPACE) and center-star multiple alignment by pairwise partitioned dynamic time warping (CeMAPP-DTW). We show how the similarity-based peak group matching method BIPACE may be used for multiple alignment calculation individually and how it can be used as a preprocessing step for the pairwise alignments performed by CeMAPP-DTW. We evaluate the algorithms individually and in combination on a previously published small GC-MS dataset studying the Leishmania parasite and on a larger GC-MS dataset studying grains of wheat (Triticum aestivum).ConclusionsWe have shown that BIPACE achieves very high precision and recall and a very low number of false positive peak assignments on both evaluation datasets. CeMAPP-DTW finds a high number of true positives when executed on its own, but achieves even better results when BIPACE is used to constrain its search space. The source code of both algorithms is included in the OpenSource software framework Maltcms, which is available fromhttp://maltcms.sf.net. The evaluation scripts of the present study are available from the same source.