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In silico chromosome painting is a technique by which contributions of distinct genetic groups are represented along chromosomes of hybrid individuals. This type of analysis is used to study the mechanisms by which these individuals were formed. Such techniques are well adapted to identify genetic groups contributing to these individuals as well as hybridization events. It can also be used to follow chromosomal recombinations that occurred naturally or were generated by selective breeding. Here, we present GeMo, a novel interactive web-based and user-oriented interface to visualize in a linear-based fashion results of in silico chromosome painting. To facilitate data input generation, a script to execute analytical commands is provided and an interactive data curation mode is supported to ensure consistency of the automated procedure. GeMo contains preloaded datasets from published studies on crop domestication but can be applied to other purposes, such as breeding programs Although only applied so far on plants, GeMo can handle data from animals as well.

Motivation: As pan-genome approaches are largely employed for bacterial comparative genomics and evolution analyses, but still difficult to be carried out by non-bioinformatician biologists, there is a need for an innovative tool facilitating the exploration of bacterial pan-genomes. Results: PanExplorer is a web application providing various genomic analyses and reports, giving intuitive views that enable a better understanding of bacterial pan-genomes. As an example, we produced the pan-genome for 121 Anaplasmataceae strains (including 30 Ehrlichia, 15 Anaplasma, 68 Wolbachia.) Availability and implementation: PanExplorer is written in Perl CGI and relies on several JavaScript libraries for visualization (hotmap.js, MauveViewer, CircosJS). It is freely available at http://panexplorer.southgreen.fr. The source code has been released in a GitHub repository https://github.com/SouthGreenPlatform/PanExplorer. A documentation section is available on PanExplorer website.

The Banana Genome Hub provides centralized access for genome assemblies, annotations, and the extensive related omics resources available for bananas and banana relatives. A series of tools and unique interfaces are implemented to harness the potential of genomics in bananas, leveraging the power of comparative analysis, while recognizing the differences between datasets. Besides effective genomic tools like BLAST and the JBrowse genome browser, additional interfaces enable advanced gene search and gene family analyses including multiple alignments and phylogenies. A synteny viewer enables the comparison of genome structures between chromosome-scale assemblies. Interfaces for differential expression analyses, metabolic pathways and GO enrichment were also added. A catalogue of variants spanning the banana diversity is made available for exploration, filtering, and export to a wide variety of software. Furthermore, we implemented new ways to graphically explore gene presence-absence in pangenomes as well as genome ancestry mosaics for cultivated bananas. Besides, to guide the community in future sequencing efforts, we provide recommendations for nomenclature of locus tags and a curated list of public genomic resources (assemblies, resequencing, high density genotyping) and upcoming resources—planned, ongoing or not yet public.

Oryza sativa (rice) plays an essential food security role for more than half of the world's population. Obtaining crops with high levels of disease resistance is a major challenge for breeders, especially today, given the urgent need for agriculture to be more sustainable. Plant resistance genes are mainly encoded by three large leucine-rich repeat (LRR)-containing receptor (LRR-CR) families: the LRR-receptor-like kinase (LRR-RLK), LRR-receptor-like protein (LRR-RLP) and nucleotide-binding LRR receptor (NLR). Using lrrprofiler, a pipeline that we developed to annotate and classify these proteins, we compared three publicly available annotations of the rice Nipponbare reference genome. The extended discrepancies that we observed for LRR-CR gene models led us to perform an in-depth manual curation of their annotations while paying special attention to nonsense mutations. We then transferred this manually curated annotation to Kitaake, a cultivar that is closely related to Nipponbare, using an optimized strategy. Here, we discuss the breakthrough achieved by manual curation when comparing genomes and, in addition to 'functional' and 'structural' annotations, we propose that the community adopts this approach, which we call 'comprehensive' annotation. The resulting data are crucial for further studies on the natural variability and evolution of LRR-CR genes in order to promote their use in breeding future resilient varieties.

To assess the genetic and environmental components of gene-expression variation among trees we used RNA-seq technology and Eucalyptus urophylla x grandis hybrid clones tested in field conditions. Leaf and xylem transcriptomes of three 20 month old clones differing in terms of growth, repeated in two blocks, were investigated. Transcriptomes were very similar between ramets. The number of expressed genes was significantly (P<0.05) higher in leaf (25,665±634) than in xylem (23,637±1,241). A pairwise clone comparisons approach showed that 4.5 to 14 % of the genes were diffe¬rentially expressed (false discovery rate [FDR]<0.05) in leaf and 7.1 to 16 % in xylem. An assessment of among clone variance components revealed significant results in leaf and xylem in 3431 (248) genes (at FDR<0.2) and 160 (3) (at FDR<0.05), respectively. These two complementary approa¬ches displayed correlated results. A focus on the phenylpro¬panoid, cellulose and xylan pathways revealed a large majo¬rity of low expressed genes and a few highly expressed ones, with RPKM values ranging from nearly 0 to 600 in leaf and 10,000 in xylem. Out of the 115 genes of these pathways, 45 showed differential expression for at least one pair of geno¬type, five of which displaying also clone variance compo¬nents. These preliminary results are promising in evaluating whether gene expression can serve as possible 'intermediate phenotypes' that could improve the accuracy of selection of grossly observable traits.

South Green (www.southgreen.fr) est une plateforme de bio-informatique dédiée `a la génétique et la génomique des plantes tropicales et méditerranéennes d'intérêt agronomique et de leurs pathogènes. Elle fédère un réseau de bio informaticiens appartenant à différentes unités et instituts de Montpellier (Bioversity International, CIRAD, INRA et IRD) soit environ une vingtaine de personnes en interaction avec les équipes de recherches, avec une expertise multidisciplinaire allant de l'intégration de données et de connaissance au développement de logiciels en bio-informatique, à l'analyse de données de séquençage (détection de polymorphismes et variants structuraux, pangénomique, métagénomique, analyse différentielle de données RNAseq) et le calcul haute performance. Le plateforme South Green a pour objectifs de : Promouvoir des outils originaux issus de la recherche méthodologique. Promouvoir l'interopérabilité des applications développées au sein du réseau. Centraliser l'ensemble des logiciels et systèmes d'information développées au sein d'un portail Web unique (http://www.southgreen.fr) Promouvoir les échanges et les développements collaboratifs Proposer des formations en bio-informatique, bio analyse de données et `a l'utilisation de clusters de calcul Promouvoir la démarche qualité au sein du réseau. Proposer un support pour le calcul à haute performance La plateforme assure le développement de systèmes d'informations et d'outils innovants, nécessaires aux projets scientifiques, réalises au sein de la plateforme et en lien avec l'analyse des données produites par les technologies de séquençage à haut débit (annotation des génomes et de transcriptomes, phylogénie, génotypage) tels que GreenPhylDB, SNiPlay, Gigwa ou AgroLD. Elle propose également des pipelines d'analyses de données de séquençage au travers de deux gestionnaires de workflows : Galaxy et TOGGLe. Enfin, impliquée dans plusieurs projets de séquençage international, elle possède une forte expertise en d

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.

The South Green Web portal (http://www.southgreen.fr/) provides access to a large panel of public databases, analytical workflows and bioinformatics resources dedicated to the genomics of tropical and Mediterranean crops. The portal contains currently about 20 information systems and tools and targets a broad range of crops such as Banana, Cacao, Cassava, Coconut, Coffee, Grape, Rice and Sugarcane.

Background: The explosion of NGS (Next Generation Sequencing) sequence data requires a huge effort in Bioinformatics methods and analyses. The creation of dedicated, robust and reliable pipelines able to handle dozens of samples from raw FASTQ data to relevant biological data is a time-consuming task in all projects relying on NGS. To address this, we created a generic and modular toolbox for developing such pipelines. Results: TOGGLE (TOolbox for Generic nGs anaLysEs) is a suite of tools able to design pipelines that manage large sets of NGS softwares and utilities. Moreover, TOGGLE offers an easy way to manipulate the various options of the different softwares through the pipelines in using a single basic configuration file, which can be changed for each assay without having to change the code itself. We also describe one implementation of TOGGLE in a complete analysis pipeline designed for SNP discovery for large sets of genomic data, ready to use in different environments (from a single machine to HPC clusters). Conclusion: TOGGLE speeds up the creation of robust pipelines with reliable log tracking and data flow, for a large range of analyses. Moreover, it enables Biologists to concentrate on the biological relevance of results, and change the experimental conditions easily. The whole code and test data are available at https://github.com/SouthGreenPlatform/TOGGLE.

The South Green platform (http://www.southgreen.fr/) is a local network of scientists gathering Bioinformatics skills based on the Agropolis campus that hosts research institutes such as CIRAD, IRD, INRA, SupAgro and Bioversity international. Based on this strong local community in the field of agriculture, food and biodiversity, various bioinformatics applications and resources dedicated to genomics of tropical and Mediterranean plants has been developed and published. The objectives of South Green are to promote these original tools as well as their interoperability. Exchange and collaborative developments are also fostered through regular hands-on sessions on synergistic themes such as Galaxy, genome annotation or next generation genotyping. Finally, we provide access to computing facilities and hands-on training for both users and developers engaged in the network. The South Green web portal contains currently 20 information systems and tools and targets about 30 plants. As a proof of concept for system interoperability, we recently released the Banana Genome Hub powered by major GMOD components (i.e. Chado, Cmap, Gbrowse, Tripal, Galaxy, Pathway tools) and South Green tools (e.g. GnpAnnot, GreenPhylDB, SNiPlay, TropGeneDB, ESTtik, OryGenesDB). This concept of hub can be extended to other crops as currently done for the Coffee genome. (Texte intégral)

The AP2/ERF superfamily encodes transcription factors that play a key role in plant development and responses to abiotic and biotic stress. In Hevea brasiliensis, ERF genes have been identified by RNA sequencing. This study set out to validate the number of HbERF genes, and identify ERF genes involved in the regulation of latex cell metabolism. A comprehensive Hevea transcriptome was improved using additional RNA reads from reproductive tissues. Newly assembled contigs were annotated in the Gene Ontology database and were assigned to 3 main categories. The AP2/ERF superfamily is the third most represented compared with other transcription factor families. A comparison with genomic scaffolds led to an estimation of 114 AP2/ERF genes and 1 soloist in Hevea brasiliensis. Based on a phylogenetic analysis, functions were predicted for 26 HbERF genes. A relative transcript abundance analysis was performed by real-time RT-PCR in various tissues. Transcripts of ERFs from group I and VIII were very abundant in all tissues while those of group VII were highly accumulated in latex cells. Seven of the thirty-five ERF expression marker genes were highly expressed in latex. Subcellular localization and transactivation analyses suggested that HbERF-VII candidate genes encoded functional transcription factors.

In this study of the International Oil Palm Genome Projects (OPGP) Consortium, an Elaeis interspecific pseudo-backcross of first generation (E. oleifera x E. guineensis) x E. guineensis allowed tracing segregating alleles from both Elaeis genomes. Nineteen quantitative trait loci (QTLs) associated to palm oil fatty acid proportions compared to 16 QTLs of same traits in an intra-specific oil palm cross evidenced common or specific QTLs in E. guineensis and E. oleifera. Genes related to palm oil fatty acids were investigated using full-length cDNA libraries and mRNA extracts from the mesocarp of developing fruits in each Elaeis species represented by four genetic pools. Expression of 113 key Elaeis genes or putative regulation factors of de novo fatty acid and triacylglycerol pathways were characterized at species level by in silico transcript profiling and Real Time RT-qPCR analysis. Genes were mapped on the pseudo-backcross using 180 intra-gene SNP markers. Forty-eight genes were differentially expressed between Elaeis species, with 94% over expressed in E. guineensis. The EgAP2-2 (WRI1-like) transcription factor might influence the start of the de novo fatty acid pathway by main action only on the MAT and KAS III genes, as independent in expression from other genes and not differential between species. Between species variations in C16:0 and C18:1 can be deciphered by relative expression levels and enzyme activities of the isoforms in the genome of FATB (2), KAS II (1) and SAD (3). (Texte intégral)

Background: Rubber tree (Hevea brasiliensis) laticifers are the source of natural rubber. Rubber production depends on endogenous and exogenous ethylene (ethephon). AP2/ERF transcription factors, and especially Ethylene-Response Factors, play a crucial role in plant development and response to biotic and abiotic stresses. This study set out to sequence transcript expressed in various tissues using next-generation sequencing and to identify AP2/ERF superfamily in the rubber tree. Results: The 454 sequencing technique was used to produce five tissue-type transcript libraries (leaf, bark, latex, embryogenic tissues and root). Reads from all libraries were pooled and reassembled to improve mRNA lengths and produce a global library. One hundred and seventy-three AP2/ERF contigs were identified by in silico analysis based on the amino acid sequence of the conserved AP2 domain from the global library. The 142 contigs with the full AP2 domain were classified into three main families (20 AP2 members, 115 ERF members divided into 11 groups, and 4 RAV members) and 3 soloist members. Fifty-nine AP2/ERF transcripts were found in latex. Alongside the microRNA172 already described in plants, eleven additional microRNAs were predicted to inhibit Hevea AP2/ERF transcripts. Conclusions: Hevea has a similar number of AP2/ERF genes to that of other dicot species. We adapted the alignment and classification methods to data from next-generation sequencing techniques to provide reliable information. We observed several specific features for the ERF family. Three HbSoloist members form a group in Hevea. Several AP2/ERF genes highly expressed in latex suggest they have a specific function in Hevea. The analysis of AP2/ERF transcripts in Hevea presented here provides the basis for studying the molecular regulation of latex production in response to abiotic stresses and latex cell differentiation.

With NGS development, bioinformatics has become central in plant breeding laboratories, and researchers are in need of some autonomy in its use. The Southgreen platform (CIRAD, IRD, INRA) performs bioinformatics analyses for many plant breeding research teams in Montpellier (France), and offers many systems to users: for example GNPAnnot (automatic genomic sequence annotation), Greenphyl (phylogenetic orthology prediction), ESTtik (EST annotation) or the Bacchus analysis pipeline. Most of these systems have been translated into Galaxy workflows. As for the Bacchus pipeline, it has been created at INRA Montpellier (France) to investigate clonal diversity in grapevine genomes. For this task, many softwares have been wrapped in the Galaxy framework. Bacchus can be decomposed in three steps: i) Genome reconstruction, ii) test of reconstruction results, and iii) diversity analysis. This last step is done using SNP's and structural variations. To detect SNP's, the latest Freebayes version was used, while the IDfixe software was developped for structural variation detection. Some of the softwares developped for this pipeline are now used in the international project Grapereseq. Today, the Galaxy framework is widely used by Southgreen plateform users as an alternative to the command line system. In this context, dozens of users have already been trained in Galaxy-using bioinformatics. During weekly collective pair-programming sessions, platform engineers and interested scientists integrate new tools and functionalities.Thus, Galaxy is now a core component of the plant breeding community around the Southgreen platform, and the main access portal for non- bioinformatics specialists to our computing clusters.