High-throughput data generated by new biotechnologies require specific and adapted statistical treatment in order to be efficiently used in biological studies. In this article, we propose a powerful framework to manage and analyse multi-omics heterogeneous data to carry out an integrative analysis. We have illustrated this using the mixOmics package for R software as it specifically addresses data integration issues. Our work also aims at applying the most recent functionalities of mixOmics to real datasets. Although multi-block integrative methodologies exist, we hope to encourage a more widespread use of such approaches in an operational framework by biologists. We have used natural populations of the model plant Arabidopsis thaliana in this work, but the framework proposed is not limited to this plant and can be deployed whatever the organisms of interest and the biological question may be. Four omics datasets (phenomics, metabolomics, cell wall proteomics and transcriptomics) were collected, analysed and integrated to study the cell wall plasticity of plants exposed to sub-optimal temperature growth conditions. The methodologies presented here start from basic univariate statistics leading to multi-block integration analysis. We have also highlighted the fact that each method, either unsupervised or supervised, is associated with one biological issue. Using this powerful framework enabled us to arrive at novel conclusions on the biological system, which would not have been possible using standard statistical approaches.

Abstract Summary: GECA is a fast, user-friendly and freely-available tool for representing gene exon/intron organization and highlighting changes in gene structure among members of a gene family. It relies on protein alignment, completed with the identification of common introns in the corresponding genes using CIWOG. GECA produces a main graphical representation showing the resulting aligned set of gene structures, where exons are to scale. The important and original feature of GECA is that it combines these gene structures with a symbolic display highlighting sequence similarity between subsequent genes. It is worth noting that this combination of gene structure with the indications of similarities between related genes allows rapid identification of possible events of gain or loss of introns, or points to erroneous structural annotations. The output image is generated in a portable network graphics format which can be used for scientific publications. Availability and implementation: Web-implemented version and source code are freely available at https://peroxibase.toulouse.inra.fr/geca_input_demo.php and a detailed example can be found at https://peroxibase.toulouse.inra.fr/geca_instructions.php Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.