Biology subjects

Carrere, S.

Publications and source records attributed to Carrere, S..

3 recordsLinked to original sources

bioRxiv2018

Family-Companion: analyse, visualise, browse, query and share your homology clusters

Identifying homology groups in predicted proteomes from different biological sources allows biologists to address questions as diverse as inferring species-specific proteins or retracing the phylogeny of gene families. Nowadays, command-line software exists to infer homology clusters. However, computing and interpreting homology groups with this software remains challenging for biologists and requires computational skills.\n\nWe propose Family-Companion, a web server dedicated to the computation, the analysis and the exploration of homology clusters. Family-Companion aims to fill the gap between analytic software and databases presenting orthologous groups based on a set of public data. It offers a user-friendly interface to launch or upload precomputed homology cluster analysis, to explore and share the results with other users. The exploration of the results is highly facilitated by interactive solutions to visualize proteome intersections via Venn diagrams, phylogenetic trees, multiple alignments, and also by querying the results by blast or by keywords.\n\nFamily-Companion is available at http://family-companion.toulouse.inra.fr with a demo dataset and a set of video tutorials. Source code and installation protocol can be found at https://framagit.org/BBRIC/family-companion/. A container-based package simplifies the installation of the web-suite.

bioinformatics↗

bioRxiv2018

In situ relationships between microbiota and potential pathobiota in Arabidopsis thaliana

A current challenge in microbial pathogenesis is to identify biological control agents that may prevent and/or limit host invasion by microbial pathogens. In natura, hosts are often infected by multiple pathogens. However, most of the current studies have been performed under laboratory controlled conditions and by taking into account the interaction between a single commensal species and a single pathogenic species. The next step is therefore to explore the relationships between host-microbial communities (microbiota) and microbial members with potential pathogenic behavior (pathobiota) in a realistic ecological context. In the present study, we investigated such relationships within root and leaf associated bacterial communities of 163 ecologically contrasted Arabidopsis thaliana populations sampled across two seasons in South-West of France. In agreement with the theory of the invasion paradox, we observed a significant humped-back relationship between microbiota and pathobiota -diversity that was robust between both seasons and plant organs. In most populations, we also observed a strong dynamics of microbiota composition between seasons. Accordingly, the potential pathobiota composition was explained by combinations of season-specific microbiota OTUs. This result suggests that the potential biomarkers controlling pathogens invasion are highly dynamic.

microbiology↗

bioRxiv2017

Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids

Genome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

genetics↗