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Background and AimsBananas (Musa spp.) are a major staple food for hundreds of millions of people in developing countries. The cultivated varieties are seedless and parthenocarpic clones of which the ancestral origin remains to be clarified. The most important cultivars are triploids with an AAA, AAB, or ABB genome constitution, with A and B genomes provided by M. acuminata and M. balbisiana, respectively. Previous studies suggested that inter-genome recombinations were relatively common in banana cultivars and that triploids were more likely to have passed through an intermediate hybrid. In this study, we investigated the chromosome structure within the ABB group, composed of starchy cooking bananas that play an important role in food security.MethodsUsing SNP markers called from RAD-Seq data, we studied the chromosome structure of 36 ABB genotypes spanning defined taxonomic subgroups. To complement our understanding, we search for similar events within nine AB hybrid genotypes.Key ResultsRecurrent Homoeologous Exchanges (HEs), i.e. chromatin exchanges between A and B subgenomes were unraveled with at least 9 founding events at the origin of the ABB bananas prior to the clonal diversification. The discovery of this nine founding events allows discussing the possible routes that led to the creation of the different subgroups and formulate new hypotheses. Based on our observations, we suggest different routes that gave rise to the current diversity in the ABB cultivars. Routes involving primary AB hybrids, routes leading to shared HEs and routes leading to a B excess ratio. Genetic fluxes took place between M. acuminata and M. balbisiana, particularly in India, where these unbalanced AB hybrids and ABB allotriploid originated and where cultivated M. balbisiana are abundant.ConclusionsThe result of this study clarifies the classification of ABB cultivars and leading possibly to the revision of the classification of this subgroup. This is an important step to unravel the origin of polyploid bananas, and contributes to possible scenarios on the origin. ABB bananas are hypothesized to be more drought tolerant. Knowing the origin of our current cultivars and so their potential parents will help breeders to make the right choices for future crosses. The M. balbisiana genome is a good source to create new cultivars able to answer the numerous challenges of banana breeding.

International audience; Background: The study of genetic variations is the basis of many research domains in biology. From genome structure to population dynamics, many applications involve the use of genetic variants. The advent of next-generation sequencing technologies led to such a flood of data that the daily work of scientists is often more focused on data management than data analysis. This mass of genotyping data poses several computational challenges in terms of storage, search, sharing, analysis, and visualization. While existing tools try to solve these challenges, few of them offer a comprehensive and scalable solution. Results: Gigwa v2 is an easy-to-use, species-agnostic web application for managing and exploring high-density genotyping data. It can handle multiple databases and may be installed on a local computer or deployed as an online data portal. It supports various standard import and export formats, provides advanced filtering options, and offers means to visualize density charts or push selected data into various stand-alone or online tools. It implements 2 standard RESTful application programming interfaces, GA4GH, which is health-oriented, and BrAPI, which is breeding-oriented, thus offering wide possibilities of interaction with third-party applications. The project home page provides a list of live instances allowing users to test the system on public data (or reasonably sized user-provided data). Conclusions: This new version of Gigwa provides a more intuitive and more powerful way to explore large amounts of genotyping data by offering a scalable solution to search for genotype patterns, functional annotations, or more complex filtering. Furthermore, its user-friendliness and interoperability make it widely accessible to the life science community.

With the decreasing cost of genome sequencing, many laboratories are increasingly adopting genotyping technologies as a routine component of their analytical workflows, generating large datasets (e.g. VCF files) of genotyping information. Nevertheless, manipulating such large datasets remains a challenge for many scientists. In this context, we developed Gigwa (Genotype Investigator for Genome-Wide Analyses) with the aim of providing a user-friendly system to meet the requirements of scientists who need to filter large datasets and export them into various formats for subsequent analyses. Gigwa is species-agnostic, cross-platform, scalable and easy to deploy. It can be configured to run on a local computer or setup across servers to act as a data portal. It may be used to share data with collaborators while providing means to seek variants of interest based on location, functional annotations or genotype patterns. Based on NoSQL technology, it supports very large datasets (up to tens of millions of genotypes) when configured on suitable hardware. Its most attractive features are: ergonomic interface including user management, numerous import and export formats, powerful filtering engine, interoperability via REST APIs and connection to online or standalone tools. Gigwa now in version 2 (http://gigwa.southgreen.fr), is developed within the scope of South Green bioinformatics, a cross-institute platform and community dedicated to genetics and genomics of tropical and Mediterranean plants, based in Montpellier, France.

Sample of genes differentially expressed in ‘Cachaco’ (ABB) compared to AAA cultivars (‘Mbwazirume’ and ‘Grande Naine’). (DOCX 18 kb)

Chromosome intervals (defined on genes coordinates) showing B:A ratio deviating from the expected genomic constitution. CDM indicates the three cultivars sharing the same pattern of deviating regions (‘Cachaco’, ‘Dole’ and ‘Monthan’). (DOCX 16 kb)

Histograms representing the paralog included expression (normalized read count, Y axis) for 58 genes having significant higher expression in ‘Cachaco’ (Ca) than in ‘Grande Naine’ (GN) and ‘Mbwazirume’ (Mb). Blue color represents expression level of DEG, other colors expression level of respective paralogs. (PPTX 146 kb)

Analysis and visualization of massive genomics datasets are an ongoing trend in plant sciences. The South Green Bioinformatics platform provides an ecosystem of tools that were originally developed as independent entities to fulfill the need for specific projects or crops, but have evolved over time to generic tools to comprehensively study crop genomics. We have built a large panel of public information systems dedicated to specialized datasets (markers, genes, gene families, transcriptomes, genotypes, phenotypes, etc.) and crop-specific resources called Genome Hubs. Target users of bioinformatic analytical workflows are usually divided between people who use command-line and those who do not. We addressed both categories by offering complementary solutions, like Galaxy-based and command-line applications. Various groups used the South Green infrastructure to obtain their data and results, and were able to publish high-quality biological information, on Coffee genome, Banana, Cocoa, African rice or large transcriptome resources. Tools developed for these studies are adaptable to a wide range of other organisms.

Banana is a fruit crop with a complex diversity pattern resulting from a complex domestication scheme. Due to the availability of plant genome sequences and to the accessibility of next-generation genotyping technologies, Genome-Wide Association Studies (GWAS) have been increasingly performed in crop plants as a start to resolve genetic architecture of traits. The GWAS method was developed to perform association studies between phenotypes such as diseases and genotypes in Humans and is now successfully applied to many plants and animals to support the breeding process. However, there are prerequisites for such methods as it has been designed for populations of diploids organisms which follow the mendelian genetics model (i.e. “infinite” population and panmixia). In banana, the most popular cultivars are triploids, often hybrids between different species, and due to the absence of seeds in the fruit, a wide amount of the diversity observed ensues from the clonal diversification of a few initial genotypes. Therefore, the application of such approach to the crop is challenging and innovative. We selected a set of 106 diploid accessions with pure M. acuminata background avoiding clone duplicates and generated the appropriate molecular markers to support GWAS for any given trait. Finally, we validated the approach on the major domestication syndrome in banana, the seedless phenotype, and identified six candidate regions in which are located two strong candidate genes for female sterility. (Texte intégral)

International audience; In 2015, the South Green Bioinformatics Platform http://www.southgreen.fr/ is a network of 35 bioinformaticians from five biology research institutes working with two High - Performance Computing Data Centres to develop and use new tools for NGS/ Omic analytics of tropical and Mediterranean crops under projects studying relationsh ip between genetic diversity, agronomic performance and response to selection. South Green is affiliated to the South regional centre of the French Institute of Bioinformatics (the French node of the European research infrastructure, ELIXIR). This communit y and the HPC data centres are all located in Montpellier, which facilitates close collaboration and significant pooling to best meet the biologists' demands of our research units. Since 2004, we developed web - based applications with both generic and in - ho use components, for databases, analysis workflows and web interfaces, in order to: manage genetic and phenotypic information ( e.g. TropGeneDB), analyse molecular markers and genetic diversity ( e.g. SNiPlay), assemble transcriptomes ( e.g. ESTtik) map RNA - Se q ( e.g. ARCAD), annotate and compare genomes ( e.g. GNPAnnot), reconstruct evolutionary history of gene families by phylogenomics ( e.g. GreenPhyl). We also participate to the analysis of numerous crop species, that requires computing and storage facilities as well as interoperable information systems, such as rice ( e.g. OryGenesDB), wheat, sorghum, sugarcane, banana (Banana Genome Hub), palms, yam, coffee (CGH), rubber, cacao (CocoaGenDB), cotton, apple, grapevine, olive, eucalyptus, cassava. To face the dat a deluge, we must increase our analytics capabilities. We document our operation at both, administrator/ developer and user/ scientist level, to provide high quality services and reproducible research. We pool into working groups on key themes such as GBS, at both, developer (extreme pair programming) and user (interdisciplinary knowledge exchange) level. We provide training sessions each year. Finally, we implemented several instances of the Galaxy workflow manager and encapsulated our tools. These instanc es serve as a catalyst for massive NGS analyses but it remains to increase storage capacity and improve data management plans. (Résumé d'auteur)

Unraveling the genetic diversity held in genebanks on a large scale is underway, given the advances in NGS-based technologies that produce high-density genetic markers for a large number of samples at low cost. As SNP markers are being mapped on the reference genomes, it is important to develop interoperable system for managing both genomic data and genetic resources. The latter are often managed through bespoke information systems. The Musa Germplasm Information System (MGIS), the main banana genetic resource website, has recently been developed to address those needs in a convenient and flexible way. First of all, we decided to head for a generic database schema, such as CHADO, and to use a robust content management system such as Drupal as the web interface. The Tripal module provides the "glue" between these two parts and drastically reduces development time. Using this trio, we were able to quickly recreate in few months a complete and scalable site and expand it with new features. We now have a germplasm management system allowing collection browsing, and accession browsing, searching and ordering. Accession passport data have been enriched by geographical data, phenotyping characterization data, and information from diversity studies based on molecular markers and SNPs. As a first-use case, such genotyping studies allow linking with the Banana Genome Hub (BGH) website to explore these data in the genome context. (Texte intégral)