Assembly and Automated Annotation of the \u3ci\u3eClostridium scatologenes\u3c/i\u3e Genome

Clostridium scatologenes is an anaerobic bacterium that demonstrates some unusual metabolic traits such as the production of 3-methyl indole. The availability of genome level sequencing has lent itself to the exploration and elucidation of unique metabolic pathways in other organisms such as Clostridium botulinum. The Clostridium scatologenes genome, with an estimated length 4.2 million bp, was sequenced by the Applied Biosystems Solid method and the Roche 454 pyrosequencing method. The resulting DNA sequences were combined and assembled into 8267 contigs with an average length of 1250 bp with the Newbler Assembler program. Comparision of published subunits of csd gene and assembled contigs identified that one contig contained all three subunits. In addition a gene with similarity to clostridium carboxidivorans butyrate kinase was found lined next to csd gene. An alignment of the contig and csdgene sequences identified three deletions in the contig within the 4066 bases of the alignment. This implies that there is about 0.07% error rate in the sequencing itself requiring more finishing. Even without finishing the genome assembly into single contig, contigs were annotated in RAST pipeline predicting 2521 protein encoding genes (PEGs). The PEGs were classified by their metabolic function and compared to classified PEGs found in the closely related clostridium species, Clostridium carboxidivorans and Clostridium. ljungdahlii, which have similarly sized genomes. According to the RAST analysis, Clostridium scatologenes had 35% subsystem coverage of all known metabolic processes with its 2521 PEGs. This compares to 41% for Clostridium carboxidivorans with 4174 PEGs (29) and 42% for Clostridium ljungdahlii with 4184 PEGs (30), indicating that Clostridium scatologenesmay still have more genes to be identified. Comparison of the percent genes found in the metabolic subsystems was similar except in motility and chemotaxis. The contigs, on which the csd gene and tryptophan metabolizing genes lay, were examined to see if additional genes might support these metabolic pathways. Butyrate kinase was associated with the csd genes but no other associations were found for the two tryptophan metabolizing genes. The tryptophan biosynthesis operon genes were all found on one contig (contig 6771) and were syntenic with other bacterial species

MyBASE: a database for genome polymorphism and gene function studies of Mycobacterium

<p>Abstract</p> <p>Background</p> <p>Mycobacterial pathogens are a major threat to humans. With the increasing availability of functional genomic data, research on mycobacterial pathogenesis and subsequent control strategies will be greatly accelerated. It has been suggested that genome polymorphisms, namely large sequence polymorphisms, can influence the pathogenicity of different mycobacterial strains. However, there is currently no database dedicated to mycobacterial genome polymorphisms with functional interpretations.</p> <p>Description</p> <p>We have developed a <b>my</b>cobacterial data<b>base </b>(MyBASE) housing genome polymorphism data and gene functions to provide the mycobacterial research community with a useful information resource and analysis platform. Whole genome comparison data produced by our lab and the novel genome polymorphisms identified were deposited into MyBASE. Extensive literature review of genome polymorphism data, mainly large sequence polymorphisms (LSPs), operon predictions and curated annotations of virulence and essentiality of mycobacterial genes are unique features of MyBASE. Large-scale genomic data integration from public resources makes MyBASE a comprehensive data warehouse useful for current research. All data is cross-linked and can be graphically viewed via a toolbox in MyBASE.</p> <p>Conclusion</p> <p>As an integrated platform focused on the collection of experimental data from our own lab and published literature, MyBASE will facilitate analysis of genome structure and polymorphisms, which will provide insight into genome evolution. Importantly, the database will also facilitate the comparison of virulence factors among various mycobacterial strains. MyBASE is freely accessible via <url>http://mybase.psych.ac.cn</url>.</p

The Genomes On Line Database (GOLD) in 2007: status of genomic and metagenomic projects and their associated metadata

The Genomes On Line Database (GOLD) is a comprehensive resource that provides information on genome and metagenome projects worldwide. Complete and ongoing projects and their associated metadata can be accessed in GOLD through pre-computed lists and a search page. As of September 2007, GOLD contains information on more than 2900 sequencing projects, out of which 639 have been completed and their sequence data deposited in the public databases. GOLD continues to expand with the goal of providing metadata information related to the projects and the organisms/environments towards the Minimum Information about a Genome Sequence’ (MIGS) guideline. GOLD is available at http://www.genomesonline.org and has a mirror site at the Institute of Molecular Biology and Biotechnology, Crete, Greece at http://gold.imbb.forth.gr

GenoList: an integrated environment for comparative analysis of microbial genomes

The multitude of bacterial genome sequences being determined has generated new requirements regarding the development of databases and graphical interfaces: these are needed to organize and retrieve biological information from the comparison of large sets of genomes. GenoList (http://genolist.pasteur.fr/GenoList) is an integrated environment dedicated to querying and analyzing genome data from bacterial species. GenoList inherits from the SubtiList database and web server, the reference data resource for the Bacillus subtilis genome. The data model was extended to hold information about relationships between genomes (e.g. protein families). The web user interface was designed to primarily take into account biologists’ needs and modes of operation. Along with standard query and browsing capabilities, comparative genomics facilities are available, including subtractive proteome analysis. One key feature is the integration of the many tools accessible in the environment. As an example, it is straightforward to identify the genes that are specific to a group of bacteria, export them as a tab-separated list, get their protein sequences and run a multiple alignment on a subset of these sequences

e-Fungi: a data resource for comparative analysis of fungal genomes.

BACKGROUND: The number of sequenced fungal genomes is ever increasing, with about 200 genomes already fully sequenced or in progress. Only a small percentage of those genomes have been comprehensively studied, for example using techniques from functional genomics. Comparative analysis has proven to be a useful strategy for enhancing our understanding of evolutionary biology and of the less well understood genomes. However, the data required for these analyses tends to be distributed in various heterogeneous data sources, making systematic comparative studies a cumbersome task. Furthermore, comparative analyses benefit from close integration of derived data sets that cluster genes or organisms in a way that eases the expression of requests that clarify points of similarity or difference between species. DESCRIPTION: To support systematic comparative analyses of fungal genomes we have developed the e-Fungi database, which integrates a variety of data for more than 30 fungal genomes. Publicly available genome data, functional annotations, and pathway information has been integrated into a single data repository and complemented with results of comparative analyses, such as MCL and OrthoMCL cluster analysis, and predictions of signaling proteins and the sub-cellular localisation of proteins. To access the data, a library of analysis tasks is available through a web interface. The analysis tasks are motivated by recent comparative genomics studies, and aim to support the study of evolutionary biology as well as community efforts for improving the annotation of genomes. Web services for each query are also available, enabling the tasks to be incorporated into workflows. CONCLUSION: The e-Fungi database provides fungal biologists with a resource for comparative studies of a large range of fungal genomes. Its analysis library supports the comparative study of genome data, functional annotation, and results of large scale analyses over all the genomes stored in the database. The database is accessible at http://www.e-fungi.org.uk, as is the WSDL for the web services.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Genome Sequencing Shows that European Isolates of Francisella tularensis Subspecies tularensis Are Almost Identical to US Laboratory Strain Schu S4

BACKGROUND: Francisella tularensis causes tularaemia, a life-threatening zoonosis, and has potential as a biowarfare agent. F. tularensis subsp. tularensis, which causes the most severe form of tularaemia, is usually confined to North America. However, a handful of isolates from this subspecies was obtained in the 1980s from ticks and mites from Slovakia and Austria. Our aim was to uncover the origins of these enigmatic European isolates. METHODOLOGY/PRINCIPAL FINDINGS: We determined the complete genome sequence of FSC198, a European isolate of F. tularensis subsp. tularensis, by whole-genome shotgun sequencing and compared it to that of the North American laboratory strain Schu S4. Apparent differences between the two genomes were resolved by re-sequencing discrepant loci in both strains. We found that the genome of FSC198 is almost identical to that of Schu S4, with only eight SNPs and three VNTR differences between the two sequences. Sequencing of these loci in two other European isolates of F. tularensis subsp. tularensis confirmed that all three European isolates are also closely related to, but distinct from Schu S4. CONCLUSIONS/SIGNIFICANCE: The data presented here suggest that the Schu S4 laboratory strain is the most likely source of the European isolates of F. tularensis subsp. tularensis and indicate that anthropogenic activities, such as movement of strains or animal vectors, account for the presence of these isolates in Europe. Given the highly pathogenic nature of this subspecies, the possibility that it has become established wild in the heartland of Europe carries significant public health implications

EDGAR: A software framework for the comparative analysis of prokaryotic genomes

Blom J, Albaum S, Doppmeier D, et al. EDGAR: a software framework for the comparative analysis of prokaryotic genomes. BMC Bioinformatics. 2009;10(1): 154.Background:The introduction of next generation sequencing approaches has caused a rapid increase in the number of completely sequenced genomes. As one result of this development, it is now feasible to analyze large groups of related genomes in a comparative approach. A main task in comparative genomics is the identification of orthologous genes in different genomes and the classification of genes as core genes or singletons. Results: To support these studies EDGAR – ''Efficient Database framework for comparative Genome Analyses using BLAST score Ratios'' – was developed. EDGAR is designed to automatically perform genome comparisons in a high throughput approach. Comparative analyses for 582 genomes across 75 genus groups taken from the NCBI genomes database were conducted with the software and the results were integrated into an underlying database. To demonstrate a specific application case, we analyzed ten genomes of the bacterial genus Xanthomonas, for which phylogenetic studies were awkward due to divergent taxonomic systems. The resultant phylogeny EDGAR provided was consistent with outcomes from traditional approaches performed recently and moreover, it was possible to root each strain with unprecedented accuracy. Conclusion: EDGAR provides novel analysis features and significantly simplifies the comparative analysis of related genomes. The software supports a quick survey of evolutionary relationships and simplifies the process of obtaining new biological insights into the differential gene content of kindred genomes. Visualization features, like synteny plots or Venn diagrams, are offered to the scientific community through a web-based and therefore platform independent user interface http://edgar.cebitec.uni-bielefeld.de webcite, where the precomputed data sets can be browsed

PATRIC: The VBI PathoSystems Resource Integration Center

The PathoSystems Resource Integration Center (PATRIC) is one of eight Bioinformatics Resource Centers (BRCs) funded by the National Institute of Allergy and Infection Diseases (NIAID) to create a data and analysis resource for selected NIAID priority pathogens, specifically proteobacteria of the genera Brucella, Rickettsia and Coxiella, and corona-, calici- and lyssaviruses and viruses associated with hepatitis A and E. The goal of the project is to provide a comprehensive bioinformatics resource for these pathogens, including consistently annotated genome, proteome and metabolic pathway data to facilitate research into counter-measures, including drugs, vaccines and diagnostics. The project's curation strategy has three prongs: ‘breadth first’ beginning with whole-genome and proteome curation using standardized protocols, a ‘targeted’ approach addressing the specific needs of researchers and an integrative strategy to leverage high-throughput experimental data (e.g. microarrays, proteomics) and literature. The PATRIC infrastructure consists of a relational database, analytical pipelines and a website which supports browsing, querying, data visualization and the ability to download raw and curated data in standard formats. At present, the site warehouses complete sequences for 17 bacterial and 332 viral genomes. The PATRIC website () will continually grow with the addition of data, analysis and functionality over the course of the project

Identifying feasible metabolic routes in Mycobacterium smegmatis and possible alterations under diverse nutrient conditions

Background: Many studies on M. tuberculosis have emerged from using M. smegmatis MC2 155 (Msm), since they share significant similarities and yet Msm is non-pathogenic and faster growing. Although several individual molecules have been studied from Msm, many questions remain open about its metabolism as a whole and its capability to be versatile. Adaptability and versatility are emergent properties of a system, warranting a molecular systems perspective to understand them. Results: We identify feasible metabolic pathways in Msm in reference condition with transcriptome, phenotypic microarray, along with functional annotation of the genome. Together with transcriptome data, specific genes from a set of alternatives have been mapped onto different pathways. About 257 metabolic pathways can be considered to be feasible in Msm. Next, we probe cellular metabolism with an array of alternative carbon and nitrogen sources and identify those that are utilized and favour growth as well as those that do not support growth. In all, about 135 points in the entire metabolic map are probed. Analyzing growth patterns under these conditions, lead us to hypothesize different pathways that can become active in various conditions and possible alternate routes that may be induced, thus explaining the observed physiological adaptations. Conclusions: The study provides the first detailed analysis of feasible pathways towards adaptability. We obtain mechanistic insights that explain observed phenotypic behaviour by studying gene-expression profiles and pathways inferred from the genome sequence. Comparison of transcriptome and phenome analysis of Msm and Mtb provides a rationale for understanding commonalities in metabolic adaptability