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African rice (Oryza glaberrima Steud.) is a cultivated rice species closely related to Asian rice(Oryza sativa L.). We have conducted GWAS and Haplotype analysis to dissect molecular processes underlying panicle architecture in this species. Here we provide genotypic data that we have used to conduct theseanalysesand that can be used to conduct other related analyses. The files provided are cleaned (MAF > 0.05, missing rate < 0.2, only homozygous)and imputed as described in the manuscript. Phenotypic data and scripts used to conduct GWAS using LFMM and FarnCPU methods can be found athttps://github. com/Africrop/gwas_african_rice.

We present here FrangiPANe, a pipeline developed to build panreference using short reads through a map-then-assemble strategy. Applying it to 248 African rice genomes using an improved CG14 reference genome, we identified an average of 8 Mb of new sequences and 5,290 new contigs per individual. In total, 1.4 G of new sequences, consisting of 1,306,676 contigs, were assembled. We validated 97.7% of the contigs of the TOG5681 cultivar individual assembly from short reads on a newly long reads genome assembly of the same TOG5681 cultivar.FrangiPANe also allowed the anchoring of 31.5% of the new contigs within the CG14 reference genome, with a 92.5% accuracy at 2kb span. We annotated in addition 3,252 new genes absent from the reference.FrangiPANe was developed as a modular and interactive application to simplify the construction of a panreference using the map-then-assemble approach. It is available as a Docker image containing (i) a Jupyter notebook centralizing codes, documentation and interactive visualization of results, (ii) python scripts and (iii) all the software and libraries requested for each step of the analysis.We foreseen our approach will help leverage large-scale illumina dataset for pangenome studies in GWAS or detection of selection.

Additional file 11: Figure S8. Log scale QQ-plots corresponding to association analysis performed independently on each trait and repetition. Three different models (i.e. EMMA, LFMM, MLM) taking into account relatedness and/or structure were used for association and ANOVA was used as a benchmark. For a given trait, the transformed data were used if at least one of the replicates failed to reach normality, otherwise non-transformed data were used.

Additional file 8: Figure S5. Correlations between traits and structure. For each ancestry group (A1 to A4), we made a Spearman’s rank correlation test and plotted it as a correlogram. Colors sign the intensity of the correlation and white stars were added when the correlation was significant given the threshold retained, i.e. either a p-value cutoff of 0.01 (a) or a False Discovery Rate of 5% (b).

We have developed the Rice Genome Hub, an integrative genome information system that allows centralized access to genomics and genetics data, and analytical tools to facilitate translational and applied research in rice. The hub is built using the Content Management System Drupal with the Tripal module that interacts with the Chado database. The Hub interface provides several functionalities (Blast, DotPlots, Gene Search, JBrowse, Primer Blaster, Primer Designer) to make it easy for querying, visualizing and downloading research data. We also plugged in-house tools developed by the South Green bioinformatics platform. Among these tools, Gigwa is a Web-based tool which provides an easy and intuitive way to explore large amounts of genotyping data by filtering the latter based not only on variant features, including functional annotations, but also on genotype patterns. We also developed RedOak, a reference-free and alignment-free software package that allows for the indexing of a large collection of similar genomes. RedOak can be applied to reads from unassembled genomes, and it provides a nucleotide sequence query function. This software is based on a k-mer approach and has been developed to be heavily parallelized and distributed on several nodes of a cluster. Analysis of presence-absence variation (PAV) of genes among different genomes is a classical output of pan-genomic approaches. RedOak has a nucleotide sequence query function, including reverse complements, that can be used to quickly analyze the PAV of a specific gene among a large collection of genomes.

Additional file 5: Figure S2. Genome wide Linkage disequilibrium (LD) decay.

Additional file 9: Figure S6. Linear scale QQ-plots corresponding to association analysis performed independently on each trait and repetition. Three different methods (EMMA, LFMM, MLM) taking into account relatedness and/or structure were used for association and ANOVA was used as a benchmark. For a given trait, the transformed data were used if at least one of the replicates failed to reach normality, otherwise non-transformed data were used.

We have developed the Rice Genome Hub (https://rice-genome-hub.southgreen.fr), an integrative genome information system that allows centralized access to genomics and genetics data, and analytical tools to facilitate translational and applied research in rice.Rice is the world's most consumed staple food, especially in Asia. It is also one of the most cultivated crop with maize and wheat. The Hub currently contains seven annotated genome sequences ofOryza sativa(Asian rice), and one ofOryza glaberrima(African rice).The hub is built using the the Content Management System Drupal with the Tripal module that interacts with the Chado database. We opted for GMOD components that are open source, modular, portable and benefiting from a large community support in which we have been involved. The Hub interface provides several functionalities (Blast, DotPlots, Gene Search, JBrowse, Primer Blaster, Primer Designer) to make it easy for querying, visualizing and downloading research data.We also plugged in-house tools developed by the South Green bioinformatics platform such as SNiPlay (detection and analyses of SNPs),Gigwa (filtering on genomic variations), daTALbase (exploration of data related to Xanthomonas TAL effectors), and DiffExDB (differential expression analysis).

Constant progress in sequencing technologies creates a huge data overload. These big data combine Volume, Velocity and Variability constraints. Therefore, it is crucial to be able to integrate large amounts of heterogeneous data, with different formats and semantics, and manipulate them through complex workflows. This requires new, automated methods and tools for data integration and workflow management, to enable users with different backgrounds and interests to easily integrate and manipulate various datasets. We have developed the Rice Genome Hub, an integrative genome information system that allows centralized access to genomics and genetics data, and analytical tools to facilitate translational and applied research in rice. The hub is built using the Content Management System Drupal with the Tripal module that interacts with the Chado database. The Hub interface provides several functionalities (Blast, DotPlots, Gene Search, JBrowse, Primer Blaster, Primer Designer) to make it easy for querying, visualizing and downloading research data. We also plugged in-house tools developed by the South Green bioinformatics platform such as SNiPlay (detection and analyses of SNPs), Gigwa (filtering on genomic variations), daTALbase (exploration of data related to Xanthomonas TAL effectors), and DiffExDB (differential expression analysis).We also developed RedOak, a reference-free and alignment-free software package that allows for the indexing of a large collection of similar genomes. RedOak can be applied to reads from unassembled genomes, and it provides a nucleotide sequence query function. This software is based on a k-mer approach and has been developed to be heavily parallelized and distributed on several nodes of a cluster. Analysis of presence-absence variation (PAV) of genes among different genomes is a classical output of pan-genomic approaches. RedOak has a nucleotide sequence query function, including reverse complements, that can be used to quickly analyze the PAV of a specific gene among a large collection of genomes.

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.

Dear biologist, have you ever dreamed of using the whole power of those numerous NGS tools that your bioinformatician colleagues uses through this awful list of command lines ? Dear bioinformatician, have you ever wished for a really quick way of designing a new NGS pipeline without having to retype again dozens of code lines to readapt your scripts or starting from scratch ? So, be happy ! TOGGLE is for you ! With TOGGLE (TOolbox for Generic nGs anaLysEs), you can create your own pipeline through an easy and user-friendly approach. Indeed, TOGGLE integrate a large set of NGS softwares and utilities to easily design pipelines able to handle hundreds of samples. The pipelines can start from Fastq (plain or compressed), SAM, BAM or VCF (plain or compressed) files, with parallel (by sample) and global analyses (by multi samples). Moreover, TOGGLE offers an easy way to configure your pipeline with a single configuration file: – Organizing the different steps of workflow, – Setting the parameters for the different softwares, – Managing storage space through compressing/deleting intermediate data, – Determining the way the jobs are managed (serial or parallel jobs through scheduler SGE, SLURM and LSF). TOGGLE can work on your laptop, on a single machine server as well as on a HPC system, as a local instance or in a Docker machine. The only limit will be your available space on the storage system, not the amount of samples to be treated or the number of steps. TOGGLE was used on different organisms, from a single sample to more than one hundred at a time, in RNAseq, DNAreseq/SNP discovery and GBS analyses. List of bioinformatics tools included: – Cleaning and Quality checking : FastQC, CutAdapt, fastxTrimmer – Assembly : TransAbyss, Trinity, TGI-CL – Mapping : BWA (aln/sampe and mem), TopHat – SAM/BAM management : picardTools, SAMtools, GATK – SNP calling/cleaning/annotation : SAMtools, GATK, VarScan, snpEff – ReadCount : HTseq-count – Structural Variations : BreakDancer, Pindel Web site: https://github.com/SouthGreenPlatform/TOGGLE (Texte integral)