Genes of the most conserved WOX clade in plants affect root and flower development in Arabidopsis

Background: The Wuschel related homeobox (WOX) family proteins are key regulators implicated in the determination of cell fate in plants by preventing cell differentiation. A recent WOX phylogeny, based on WOX homeodomains, showed that all of the Physcomitrella patens and Selaginella moellendorffii WOX proteins clustered into a single orthologous group. We hypothesized that members of this group might preferentially share a significant part of their function in phylogenetically distant organisms. Hence, we first validated the limits of the WOX13 orthologous group (WOX13 OG) using the occurrence of other clade specific signatures and conserved intron insertion sites. Secondly, a functional analysis using expression data and mutants was undertaken. Results: The WOX13 OG contained the most conserved plant WOX proteins including the only WOX detected in the highly proliferating basal unicellular and photosynthetic organism Ostreococcus tauri. A large expansion of the WOX family was observed after the separation of mosses from other land plants and before monocots and dicots have arisen. In Arabidopsis thaliana, AtWOX13 was dynamically expressed during primary and lateral root initiation and development, in gynoecium and during embryo development. AtWOX13 appeared to affect the floral transition. An intriguing clade, represented by the functional AtWOX14 gene inside the WOX13 OG, was only found in the Brassicaceae. Compared to AtWOX13, the gene expression profile of AtWOX14 was restricted to the early stages of lateral root formation and specific to developing anthers. A mutational insertion upstream of the AtWOX14 homeodomain sequence led to abnormal root development, a delay in the floral transition and premature anther differentiation. Conclusion: Our data provide evidence in favor of the WOX13 OG as the clade containing the most conserved WOX genes and established a functional link to organ initiation and development in Arabidopsis, most likely by preventing premature differentiation. The future use of Ostreococcus tauri and Physcomitrella patens as biological models should allow us to obtain a better insight into the functional importance of WOX13 OG genes

Genomic Exploration of the Hemiascomycetous Yeasts: 1. A set of yeast species for molecular evolution studies11Sequences and annotations are accessible at: Génoscope (http://www.genoscope.cns.fr), FEBS Letters Website (http://www.elsevier.nl/febs/show/), Bordeaux (http://cbi.genopole-bordeaux.fr/Genolevures) and were deposited into the EMBL database (accession number from AL392203 to AL441602).

AbstractThe identification of molecular evolutionary mechanisms in eukaryotes is approached by a comparative genomics study of a homogeneous group of species classified as Hemiascomycetes. This group includes Saccharomyces cerevisiae, the first eukaryotic genome entirely sequenced, back in 1996. A random sequencing analysis has been performed on 13 different species sharing a small genome size and a low frequency of introns. Detailed information is provided in the 20 following papers. Additional tables available on websites describe the ca. 20 000 newly identified genes. This wealth of data, so far unique among eukaryotes, allowed us to examine the conservation of chromosome maps, to identify the ‘yeast-specific’ genes, and to review the distribution of gene families into functional classes. This project conducted by a network of seven French laboratories has been designated ‘Génolevures’

GeneFarm, structural and functional annotation of Arabidopsis gene and protein families by a network of experts

Genomic projects heavily depend on genome annotations and are limited by the current deficiencies in the published predictions of gene structure and function. It follows that, improved annotation will allow better data mining of genomes, and more secure planning and design of experiments. The purpose of the GeneFarm project is to obtain homogeneous, reliable, documented and traceable annotations for Arabidopsis nuclear genes and gene products, and to enter them into an added-value database. This re-annotation project is being performed exhaustively on every member of each gene family. Performing a family-wide annotation makes the task easier and more efficient than a gene-by-gene approach since many features obtained for one gene can be extrapolated to some or all the other genes of a family. A complete annotation procedure based on the most efficient prediction tools available is being used by 16 partner laboratories, each contributing annotated families from its field of expertise. A database, named GeneFarm, and an associated user-friendly interface to query the annotations have been developed. More than 3000 genes distributed over 300 families have been annotated and are available at http://genoplante-info.infobiogen.fr/Genefarm/. Furthermore, collaboration with the Swiss Institute of Bioinformatics is underway to integrate the GeneFarm data into the protein knowledgebase Swiss-Prot

GeneFarm, structural and functional annotation of Arabidopsis gene and protein families by a network of experts

Genomic projects heavily depend on genome annotations and are limited by the current deficiencies in the published predictions of gene structure and function. It follows that, improved annotation will allow better data mining of genomes, and more secure planning and design of experiments. The purpose of the GeneFarm project is to obtain homogeneous, reliable, documented and traceable annotations for Arabidopsis nuclear genes and gene products, and to enter them into an added-value database. This re-annotation project is being performed exhaustively on every member of each gene family. Performing a family-wide annotation makes the task easier and more efficient than a gene-by-gene approach since many features obtained for one gene can be extrapolated to some or all the other genes of a family. A complete annotation procedure based on the most efficient prediction tools available is being used by 16 partner laboratories, each contributing annotated families from its field of expertise. A database, named GeneFarm, and an associated user-friendly interface to query the annotations have been developed. More than 3000 genes distributed over 300 families have been annotated and are available at http://genoplante-info.infobiogen.fr/Genefarm/. Furthermore, collaboration with the Swiss Institute of Bioinformatics is underway to integrate the GeneFarm data into the protein knowledgebase Swiss-Pro

Fine Scale Analysis of Crossover and Non-Crossover and Detection of Recombination Sequence Motifs in the Honeybee (Apis mellifera)

BACKGROUND: Meiotic exchanges are non-uniformly distributed across the genome of most studied organisms. This uneven distribution suggests that recombination is initiated by specific signals and/or regulations. Some of these signals were recently identified in humans and mice. However, it is unclear whether or not sequence signals are also involved in chromosomal recombination of insects. METHODOLOGY: We analyzed recombination frequencies in the honeybee, in which genome sequencing provided a large amount of SNPs spread over the entire set of chromosomes. As the genome sequences were obtained from a pool of haploid males, which were the progeny of a single queen, an oocyte method (study of recombination on haploid males that develop from unfertilized eggs and hence are the direct reflect of female gametes haplotypes) was developed to detect recombined pairs of SNP sites. Sequences were further compared between recombinant and non-recombinant fragments to detect recombination-specific motifs. CONCLUSIONS: Recombination events between adjacent SNP sites were detected at an average distance of 92 bp and revealed the existence of high rates of recombination events. This study also shows the presence of conversion without crossover (i. e. non-crossover) events, the number of which largely outnumbers that of crossover events. Furthermore the comparison of sequences that have undergone recombination with sequences that have not, led to the discovery of sequence motifs (CGCA, GCCGC, CCGCA), which may correspond to recombination signals

Genomic Exploration of the Hemiascomycetous Yeasts: 19. Ascomycetes-specific genes

AbstractComparisons of the 6213 predicted Saccharomyces cerevisiae open reading frame (ORF) products with sequences from organisms of other biological phyla differentiate genes commonly conserved in evolution from ‘maverick’ genes which have no homologue in phyla other than the Ascomycetes. We show that a majority of the ‘maverick’ genes have homologues among other yeast species and thus define a set of 1892 genes that, from sequence comparisons, appear ‘Ascomycetes-specific’. We estimate, retrospectively, that the S. cerevisiae genome contains 5651 actual protein-coding genes, 50 of which were identified for the first time in this work, and that the present public databases contain 612 predicted ORFs that are not real genes. Interestingly, the sequences of the ‘Ascomycetes-specific’ genes tend to diverge more rapidly in evolution than that of other genes. Half of the ‘Ascomycetes-specific’ genes are functionally characterized in S. cerevisiae, and a few functional categories are over-represented in them

FAIR_bioinfo : une formation et un protocole clés en main pour la reproductibilité en bioinformatique

International audienceReproducibility plays an essential part in the success of a bioinformatics project. Indeed, Reproducibility makes it possible to guarantee the validity of scientific results and to simplify the dissemination of projects. To help disseminate Reproducibility principles among bioinformatics students, engineers and scientists, we created the FAIR_Bioinfo course, which presents a set of features we consider necessary to make a complete bioinformatics analysis reproducible. To illustrate the theoretical concepts of reproducibility, we use as an example a classic bioinformatics analysis (differential gene expression analysis from RNA-seq data). In short, we retrieve the data from public databases (ENA/SRA), we perform a reproducible analysis using a workflow management system (snakemake) in a virtual environment (Docker). The entire versioned (git) code is open source (Github https://github.com/thomasdenecker/FAIR_Bioinfo and dockerhub https://hub.docker.com/r/tdenecker/fair_bioinfo). The course book is available in English on GitBook (https://fair-bioinfo.gitbook.io/fair-bioinfo/) and the slides in French on Github. The visualization of the results is dynamic (Shiny app) and the PDF or HTML report (Rmarkdown) provides the results of the analysis and lists all user-selected parameters.La reproductibilité joue un rôle essentiel dans la réussite d'un projet de bioinformatique. En effet, la reproductibilité permet de garantir la validité des résultats scientifiques et de simplifier la diffusion des projets. Pour aider à diffuser les principes de de la reproductibilité auprès des étudiants en bioinformatique, des ingénieurs et des scientifiques, nous avons créé le cours FAIR_Bioinfo, qui présente un ensemble de fonctionnalités que nous considérons comme nécessaires pour rendre reproductible une analyse bioinformatique. Pour illustrer les concepts théoriques de la reproductibilité, nous utilisons comme exemple une analyse bioinformatique classique (analyse de l'expression différentielle des gènes à partir de données de séquences d'ARN). En bref, nous récupérons les données dans des bases de données publiques (ENA/SRA), nous effectuons une analyse reproductible en utilisant un système de gestion de workflows (snakemake) dans un environnement virtuel (Docker). L'ensemble du code versionné (git) est open source (GitHub https://github.com/thomasdenecker/FAIR_Bioinfo et dockerhub https://hub.docker.com/r/tdenecker/fair_bioinfo). Le manuel de cours est disponible en anglais sur GitBook (https://fair-bioinfo.gitbook.io/fair-bioinfo/) et les diapositives en français sur GitHub. La visualisation des résultats est dynamique (Shiny app) et le rapport PDF ou HTML (Rmarkdown) fournit les résultats de l'analyse et liste tous les paramètres sélectionnés par l'utilisateur

FAIR_bioinfo : a software perspective of reproducibility of bioinformatics analyses

International audienc