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Winter dormancy is an adaptative mechanism that temperate and boreal trees have developed to protect their meristems against low temperatures. In apple trees (Malus domestica), cold temperatures induce bud dormancy at the end of summer/beginning of the fall. Apple buds stay dormant during winter until they are exposed to a period of cold, after which they can resume growth (budbreak) and initiate flowering in response to warmer temperatures in spring. It is well-known that small RNAs modulate temperature responses in many plant species, but however, how small RNAs are involved in genetic networks of temperature-mediated dormancy control in fruit tree species remains unclear. Here, we have made use of a recently developed ARGONAUTE (AGO)-purification technique to isolate small RNAs from apple buds. A small RNA-seq experiment resulted in the identification of 17 micro RNAs (miRNAs) that change their pattern of expression in apple buds during dormancy. Furthermore, the functional analysis of their predicted target genes suggests a main role of the 17 miRNAs in phenylpropanoid biosynthesis, gene regulation, plant development and growth, and response to stimulus. Finally, we studied the conservation of the Arabidopsis thaliana regulatory miR159-MYB module in apple in the context of the plant hormone abscisic acid homeostasis.

International audience; Dormancy is an adaptive mechanism that enables plants to survive unfavorable climatic conditions and allows budbreak and flowering to occur only when the conditions are permissive. This resting stage is defined by the absence of visible growth. In apple (Malus x domestica Borkh.) and many other Rosaceae species, winter bud dormancy can be divided in at least two phases: endo- and eco-dormancy. During endo-dormancy, growth repression is controlled by internal cues and it is released by long periods of cold (chilling requirement). Then, buds undergo into eco-dormancy phase and become competent to budbreak in response to warm temperatures. In the model plant species Arabidopsis thaliana, changes in ambient temperature regulate flowering time through mechanisms that involved the alternative splicing (AS) of MADS-box genes. We have hypothesized that a similar mechanism could operate to control the temperature dependent transition between endo- and eco-dormancy phases in apple tree. In order to address this question, we have performed a RNA-seq time-course experiment on buds of apple tree (cv. Golden delicious) during the dormancy cycle. RNA from time-points covering the endo- to eco-dormancy transition were sequenced and changes in gene expression and AS quantified. Our bioinformatics analyses showed that pathways related to cell wall modifications, cell cycle, chromatin silencing and hormones, among others, were affected during the transition between endo- to eco-dormancy. Moreover, we implemented a bioinformatics pipeline to identify differential alternatively spliced genes associated to this developmental transition. Here, we will show how these data are instrumental to isolate candidate genes potentially involved in dormancy-cycle control and describe mechanisms of post-transcriptional regulation involved in fruit tree development.

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.

Summary Hybridizations between closely related species commonly occur in the domestication process of many crops. Banana cultivars are derived from such hybridizations between species and subspecies of the Musa genus that have diverged in various tropical Southeast Asian regions and archipelagos. Among the diploid and triploid hybrids generated, those with seedless parthenocarpic fruits were selected by humans and thereafter dispersed through vegetative propagation. Musa acuminata subspecies contribute to most of these cultivars. We analyzed sequence data from 14 M. acuminata wild accessions and 10 M. acuminata‐based cultivars, including diploids and one triploid, to characterize the ancestral origins along their chromosomes. We used multivariate analysis and single nucleotide polymorphism clustering and identified five ancestral groups as contributors to these cultivars. Four of these corresponded to known M. acuminata subspecies. A fifth group, found only in cultivars, was defined based on the ‘Pisang Madu’ cultivar and represented two uncharacterized genetic pools. Diverse ancestral contributions along cultivar chromosomes were found, resulting in mosaics with at least three and up to five ancestries. The commercially important triploid Cavendish banana cultivar had contributions from at least one of the uncharacterized genetic pools and three known M. acuminata subspecies. Our results highlighted that cultivated banana origins are more complex than expected – involving multiple hybridization steps – and also that major wild banana ancestors have yet to be identified. This study revealed the extent to which admixture has framed the evolution and domestication of a crop plant., Significance Statement Hybridizations between subspecies of Musa acuminata are at the origin of a diversity of banana cultivars whose genomes remain to be fully characterized for their ancestry. We developed a bioinformatics approach to decipher the genome ancestry mosaic in a set of banana cultivars including the commercially important Cavendish banana. We revealed that the cultivars resulted from multiple hybridization events involving known M. acuminata subspecies but also unknown contributors still to be discovered. These results bring essential information for breeding strategies in this crop that is particularly threatened by disease.

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.

AGAP : équipes DAAV, GE2pop; International audience; Understanding the genetic determinism of crop yield, grape quality traits and adaptation potential is of paramount importance both for practical viticulture and for breeding new varieties to face future climate change challenges. The rapid development of advanced phenotyping and genotyping technologies will provide the information needed to cope with the efficient analysis of these complex traits by the use of relatively low-cost and robust systems. Here, we report the use of genotyping-by-sequencing (GBS, Keygene patents) to a large half-diallel grapevine population (N ~ 600) from 10 connected segregating progenies. The approach was useful to identify a total of 71,720 genetic polymorphisms, including biallelic SNPs and multiallelic markers. A subset of fully-informative and/or common markers to all (or 9) progenies were selected for the subsequent genetic grouping and mapping within each progeny. This process led to individual consensus maps with similar length than those previously published, but higher marker density. Individual maps were then merged to create a high-density grapevine integrated genetic map, with high informative content and acceptable marker order agreement between progenies. On the other hand, novel image-based phenotyping systems are designed for the automatic and non-destructive estimation of flower number, berry number and berry size, and available high-throughput phenotyping technologies are used to evaluate stress indicators of photosystems in selected individuals from the population. This novel genetic and phenotypic information will be integrated to identify QTLs and candidate genes involved in the genetic determinism of these relevant grapevine traits.

UMR AGAP équipe DAAV; A novel high-density grapevine integrated linkage map using GBS in a multi-parental population: preliminary results. 12. International Conference on Grapevine Breeding and Genetics

Coadsorption is a key initial step in heterogeneous catalysis, which by bringing the reactants together at high coverage on the surface of the catalyst has a clear implication on the catalytic reaction activity. We show that when using density functional theory (DFT) calculations, the choice of the exchange correlation functional can have a qualitative influence on the nature of the obtained most stable coadsorption state. The coadsorption of butadiene and hydrogen, the precursor state for catalytic hydrogenation, is studied on Pt(111) and on the surface alloy Pt2Sn/Pt(111). At typical hydrogenation conditions, the PBE exchange correlation functional gives as most stable situation on both model catalysts a surface fully covered with hydrogen, butadiene remaining in gas phase. This behavior does not agree with available experimental data and results from an incorrect balance between H and butadiene adsorption energy, mainly by a poor description of dispersion energy for butadiene adsorption. The nonlocal o...

International audience; Cultivated bananas are derived from hybridization events among species and subspecies of the genus Musa that differentiated in various regions and archipelagos of tropical South-East Asia. These hybridizations generated diploid and triploid inter(sub)specific hybrids with impaired fertility. Some of them, having seedless parthenocarpic fruits, were selected by humans and further dispersed through vegetative propagation. The genomes of these domesticated bananas are expected to have gone through few/very limited rounds of recombination and to be organized in a mosaic of large blocks of sequences from different ancestral origins. To characterize mosaic genome structures of diploid bananas, we generated a set of SNPs from RNAseq data of 24 selected seedy (wild) and parthenocarpic (cultivated) diploid banana accessions. We applied multivariate (COA) and SNP clustering approaches to assign alleles to ancestral banana groups. Group assigned alleles were then used to locally infer the ancestral origin of genomic regions along the 11 banana chromosomes. The results showed instance of relatively simple hybrid mosaic structures for some accessions derived from two ancestral groups that corresponded to two Musa acuminata subspecies. Other cultivated accessions showed more complex mosaics and involved more than two ancestors. Interestingly, the results also suggested that at least one unknown ancestral group, in addition to the four main groups already identified; contributed to the studied cultivated bananas. Globally, this study showed that some of the domesticated banana cultivars originated from a more complex history of hybridization events than previously thought. Deciphering the mosaic structure of banana genomes will improve our understanding of banana domestication and help breeding programs in their strategies to reconstruct improved hybrids. (Résumé d'auteur)

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)

As the cost of DNA sequencing decreases, the high throughput sequencing technologies become more and more accessible to many laboratories. Consequently, new issues emerge that require new algorithms including tools for indexing and compressing thousands of genomes, as for example the 3000 rice genomes project [1], for which we are particularly interested in. Genomes can be considered as very large texts on a simple alphabet ∑ = {A, C, G, T }, We can refer to indexable dictionary problem which consists in storing a set ⊆ {0, . . . , i, . . . , m− 1} of an universe U = n. B(n) where B[i] = 1 () i ∈ S. The indexable dictionary problem support two additionnal operations ranks(i) and selects(i) for s ∈ {0, 1}. The function ranks(i) returns the number of elements (s) up to i and selects(i) returns the position of the ith occurence of s. The indexation of complete genomes is an important stage in the exploration and understanding of data from living organisms. An efficient index should provide a quick answer to the following questions. -How many times a given pattern does appear in the genome? - What are the positions of a given pattern? -What is the pattern length at the ith position in the genome? The common way to structure index and compress one genome is to use the Burrows-Wheeler Transform –BWT)[2] with the FM-index [3] on BWT sequences for requests. If you want to index several genomes with one reference genome you may use MuGI [4]. To build MuGI index they store the reference in compact form (4 bits to encode single char), a variant database, one bit vector for each variant and an array kMA keeping information about each k-mers. This is a really interesting approach but it needs to have a reference genom. We present a structure which proposes a solution to index and compress very repetitive sequences over small alphabet in texts using k-mers. k-mers are factors of length k in the considered sequences. We built a 4k1 array, where k1 < k, and each entry, namely an array, is indexed by a prefix of size k1 of existing k-mers. In each prefix array we insert a 4k2 bit vector which represents all possible k-mers begining with the considered prefix. We will use libGkArray [5] to query a large read collections and update our structure. We chose libGkArray instead of JellyFish [6] and KMC (any versions) [7] in main memory. To build the index, we cut our genomes into k-mers, for each k-mer we split the k-mer into prefix suffix of respective size k1 and k2. We call the function kmer _ to _int() which takes a k-mer and returns its integer value. We then go into the prefix array PA[kmer to int(k1)] and we add k2 to our suÿx array. We also add a 1 in the succint structure to Gi i ϵ n with n the number of genomes as you can see at Fig.1. Given a n for the number of genomes and N for all k-mers in the genome set, we can estimate the time and space complexity as respectively O(N log(n)) and O(N × 2k2 log(n + N )). Our structure has to be eÿcient in memory space and comuting time.

International audience

AGAP: équipe ID; National audience