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Abstract Background Dystrophin is a large essential protein of skeletal and heart muscle. It is a filamentous scaffolding protein with numerous binding domains. Mutations in the DMD gene, which encodes dystrophin, mostly result in the deletion of one or several exons and cause Duchenne (DMD) and Becker (BMD) muscular dystrophies. The most common DMD mutations are frameshift mutations resulting in an absence of dystrophin from tissues. In-frame DMD mutations are less frequent and result in a protein with partial wild-type dystrophin function. The aim of this study was to highlight structural and functional modifications of dystrophin caused by in-frame mutations. Methods and results We developed a dedicated database for dystrophin, the eDystrophin database. It contains 209 different non frame-shifting mutations found in 945 patients from a French cohort and previous studies. Bioinformatics tools provide models of the three-dimensional structure of the protein at deletion sites, making it possible to determine whether the mutated protein retains the typical filamentous structure of dystrophin. An analysis of the structure of mutated dystrophin molecules showed that hybrid repeats were reconstituted at the deletion site in some cases. These hybrid repeats harbored the typical triple coiled-coil structure of native repeats, which may be correlated with better function in muscle cells. Conclusion This new database focuses on the dystrophin protein and its modification due to in-frame deletions in BMD patients. The observation of hybrid repeat reconstitution in some cases provides insight into phenotype-genotype correlations in dystrophin diseases and possible strategies for gene therapy. The eDystrophin database is freely available: http://edystrophin.genouest.org/.

Abstract Background Superoxide reductases (SOR) catalyse the reduction of superoxide anions to hydrogen peroxide and are involved in the oxidative stress defences of anaerobic and facultative anaerobic organisms. Genes encoding SOR were discovered recently and suffer from annotation problems. These genes, named sor, are short and the transfer of annotations from previously characterized neelaredoxin, desulfoferrodoxin, superoxide reductase and rubredoxin oxidase has been heterogeneous. Consequently, many sor remain anonymous or mis-annotated. Description SORGOdb is an exhaustive database of SOR that proposes a new classification based on domain architecture. SORGOdb supplies a simple user-friendly web-based database for retrieving and exploring relevant information about the proposed SOR families. The database can be queried using an organism name, a locus tag or phylogenetic criteria, and also offers sequence similarity searches using BlastP. Genes encoding SOR have been re-annotated in all available genome sequences (prokaryotic and eukaryotic (complete and in draft) genomes, updated in May 2010). Conclusions SORGOdb contains 325 non-redundant and curated SOR, from 274 organisms. It proposes a new classification of SOR into seven different classes and allows biologists to explore and analyze sor in order to establish correlations between the class of SOR and organism phenotypes. SORGOdb is freely available at http://sorgo.genouest.org/index.php.

Abstract Background The functions of proteins are strongly related to their localization in cell compartments (for example the cytoplasm or membranes) but the experimental determination of the sub-cellular localization of proteomes is laborious and expensive. A fast and low-cost alternative approach is in silico prediction, based on features of the protein primary sequences. However, biologists are confronted with a very large number of computational tools that use different methods that address various localization features with diverse specificities and sensitivities. As a result, exploiting these computer resources to predict protein localization accurately involves querying all tools and comparing every prediction output; this is a painstaking task. Therefore, we developed a comprehensive database, called CoBaltDB, that gathers all prediction outputs concerning complete prokaryotic proteomes. Description The current version of CoBaltDB integrates the results of 43 localization predictors for 784 complete bacterial and archaeal proteomes (2.548.292 proteins in total). CoBaltDB supplies a simple user-friendly interface for retrieving and exploring relevant information about predicted features (such as signal peptide cleavage sites and transmembrane segments). Data are organized into three work-sets ("specialized tools", "meta-tools" and "additional tools"). The database can be queried using the organism name, a locus tag or a list of locus tags and may be browsed using numerous graphical and text displays. Conclusions With its new functionalities, CoBaltDB is a novel powerful platform that provides easy access to the results of multiple localization tools and support for predicting prokaryotic protein localizations with higher confidence than previously possible. CoBaltDB is available at http://www.umr6026.univ-rennes1.fr/english/home/research/basic/software/cobalten.

Abstract Background Oxidative stress is a common stress encountered by living organisms and is due to an imbalance between intracellular reactive oxygen and nitrogen species (ROS, RNS) and cellular antioxidant defence. To defend themselves against ROS/RNS, bacteria possess a subsystem of detoxification enzymes, which are classified with regard to their substrates. To identify such enzymes in prokaryotic genomes, different approaches based on similarity, enzyme profiles or patterns exist. Unfortunately, several problems persist in the annotation, classification and naming of these enzymes due mainly to some erroneous entries in databases, mistake propagation, absence of updating and disparity in function description. Description In order to improve the current annotation of oxidative stress subsystems, an innovative platform named OxyGene has been developed. It integrates an original database called OxyDB, holding thoroughly tested anchor-based signatures associated to subfamilies of oxidative stress enzymes, and a new anchor-driven annotator, for ab initio detection of ROS/RNS response genes. All complete Bacterial and Archaeal genomes have been re-annotated, and the results stored in the OxyGene repository can be interrogated via a Graphical User Interface. Conclusion OxyGene enables the exploration and comparative analysis of enzymes belonging to 37 detoxification subclasses in 664 microbial genomes. It proposes a new classification that improves both the ontology and the annotation of the detoxification subsystems in prokaryotic whole genomes, while discovering new ORFs and attributing precise function to hypothetical annotated proteins. OxyGene is freely available at: http://www.umr6026.univ-rennes1.fr/english/home/research/basic/software