Comparative genomics applied to Mucor species with different lifestyles.

BACKGROUND: Despite a growing number of investigations on early diverging fungi, the corresponding lineages have not been as extensively characterized as Ascomycota or Basidiomycota ones. The Mucor genus, pertaining to one of these lineages is not an exception. To this date, a restricted number of Mucor annotated genomes is publicly available and mainly correspond to the reference species, Mucor circinelloides, and to medically relevant species. However, the Mucor genus is composed of a large number of ubiquitous species as well as few species that have been reported to specifically occur in certain habitats. The present study aimed to expand the range of Mucor genomes available and identify potential genomic imprints of adaptation to different environments and lifestyles in the Mucor genus. RESULTS: In this study, we report four newly sequenced genomes of Mucor isolates collected from non-clinical environments pertaining to species with contrasted lifestyles, namely Mucor fuscus and Mucor lanceolatus, two species used in cheese production (during ripening), Mucor racemosus, a recurrent cheese spoiler sometimes described as an opportunistic animal and human pathogen, and Mucor endophyticus, a plant endophyte. Comparison of these new genomes with those previously available for six Mucor and two Rhizopus (formerly identified as M. racemosus) isolates allowed global structural and functional description such as their TE content, core and species-specific genes and specialized genes. We proposed gene candidates involved in iron metabolism; some of these genes being known to be involved in pathogenicity; and described patterns such as a reduced number of CAZymes in the species used for cheese ripening as well as in the endophytic isolate that might be related to adaptation to different environments and lifestyles within the Mucor genus. CONCLUSIONS: This study extended the descriptive data set for Mucor genomes, pointed out the complexity of obtaining a robust phylogeny even with multiple genes families and allowed identifying contrasting potentially lifestyle-associated gene repertoires. The obtained data will allow investigating further the link between genetic and its biological data, especially in terms of adaptation to a given habitat.

Re-annotation, improved large-scale assembly and establishment of a catalogue of noncoding loci for the genome of the model brown alga Ectocarpus.

The genome of the filamentous brown alga Ectocarpus was the first to be completely sequenced from within the brown algal group and has served as a key reference genome both for this lineage and for the stramenopiles. We present a complete structural and functional reannotation of the Ectocarpus genome. The large-scale assembly of the Ectocarpus genome was significantly improved and genome-wide gene re-annotation using extensive RNA-seq data improved the structure of 11 108 existing protein-coding genes and added 2030 new loci. A genome-wide analysis of splicing isoforms identified an average of 1.6 transcripts per locus. A large number of previously undescribed noncoding genes were identified and annotated, including 717 loci that produce long noncoding RNAs. Conservation of lncRNAs between Ectocarpus and another brown alga, the kelp Saccharina japonica, suggests that at least a proportion of these loci serve a function. Finally, a large collection of single nucleotide polymorphism-based markers was developed for genetic analyses. These resources are available through an updated and improved genome database. This study significantly improves the utility of the Ectocarpus genome as a high-quality reference for the study of many important aspects of brown algal biology and as a reference for genomic analyses across the stramenopiles.

Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics.

SUMMARY: The complex, rapidly evolving field of computational metabolomics calls for collaborative infrastructures where the large volume of new algorithms for data pre-processing, statistical analysis and annotation can be readily integrated whatever the language, evaluated on reference datasets and chained to build ad hoc workflows for users. We have developed Workflow4Metabolomics (W4M), the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy web-based platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation. AVAILABILITY AND IMPLEMENTATION: http://workflow4metabolomics.org homepage enables users to open a private account and access the infrastructure. W4M is developed and maintained by the French Bioinformatics Institute (IFB) and the French Metabolomics and Fluxomics Infrastructure (MetaboHUB). CONTACT: contact@workflow4metabolomics.org.

The genome of Ectocarpus subulatus - A highly stress-tolerant brown alga.

Brown algae are multicellular photosynthetic stramenopiles that colonize marine rocky shores worldwide. Ectocarpus sp. Ec32 has been established as a genomic model for brown algae. Here we present the genome and metabolic network of the closely related species, Ectocarpus subulatus Kützing, which is characterized by high abiotic stress tolerance. Since their separation, both strains show new traces of viral sequences and the activity of large retrotransposons, which may also be related to the expansion of a family of chlorophyll-binding proteins. Further features suspected to contribute to stress tolerance include an expanded family of heat shock proteins, the reduction of genes involved in the production of halogenated defence compounds, and the presence of fewer cell wall polysaccharide-modifying enzymes. Overall, E. subulatus has mainly lost members of gene families down-regulated in low salinities, and conserved those that were up-regulated in the same condition. However, 96% of genes that differed between the two examined Ectocarpus species, as well as all genes under positive selection, were found to encode proteins of unknown function. This underlines the uniqueness of brown algal stress tolerance mechanisms as well as the significance of establishing E. subulatus as a comparative model for future functional studies.

Create, run, share, publish, and reference your LC-MS, FIA-MS, GC-MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics.

Metabolomics is a key approach in modern functional genomics and systems biology. Due to the complexity of metabolomics data, the variety of experimental designs, and the multiplicity of bioinformatics tools, providing experimenters with a simple and efficient resource to conduct comprehensive and rigorous analysis of their data is of utmost importance. In 2014, we launched the Workflow4Metabolomics (W4M; http://workflow4metabolomics.org) online infrastructure for metabolomics built on the Galaxy environment, which offers user-friendly features to build and run data analysis workflows including preprocessing, statistical analysis, and annotation steps. Here we present the new W4M 3.0 release, which contains twice as many tools as the first version, and provides two features which are, to our knowledge, unique among online resources. First, data from the four major metabolomics technologies (i.e., LC-MS, FIA-MS, GC-MS, and NMR) can be analyzed on a single platform. By using three studies in human physiology, alga evolution, and animal toxicology, we demonstrate how the 40 available tools can be easily combined to address biological issues. Second, the full analysis (including the workflow, the parameter values, the input data and output results) can be referenced with a permanent digital object identifier (DOI). Publication of data analyses is of major importance for robust and reproducible science. Furthermore, the publicly shared workflows are of high-value for e-learning and training. The Workflow4Metabolomics 3.0 e-infrastructure thus not only offers a unique online environment for analysis of data from the main metabolomics technologies, but it is also the first reference repository for metabolomics workflows.