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Boundary domains delimit and organize organ growth throughout plant development almost relentlessly, building plant architecture and morphogenesis. Boundary domains display reduced growth and orchestrate development of adjacent tissues in a non-cell-autonomous manner. How these two functions are achieved remains elusive despite the identification of several boundary-specific genes. Here, we show using morphometrics at the organ and cellular levels that leaf boundary domain development requires SPINDLY (SPY), an O-fucosyltransferase, to act as cell growth repressor. Furthermore, we show that SPY acts redundantly with the CUP-SHAPED COTYLEDON transcription factors (CUC2 and CUC3), which are major determinants of boundaries development. Accordingly, at the molecular level CUC2 and SPY repress a common set of genes involved in cell wall loosening, providing a molecular framework for the growth repression associated with boundary domains. Atomic force microscopy confirmed that young leaf boundary domain cells have stiffer cell walls than marginal outgrowth. This differential cell wall stiffness was reduced in spy mutant plants. Taken together, our data reveal a concealed CUC2 cell wall-associated gene network linking tissue patterning with cell growth and mechanics.

Drought stress can considerably affect maize yields, which is a source of concern for maintaining global food security in the coming years. Identification of quantitative traits loci (QTLs) involved in the genotype x environment interaction (GxE) is a way to increase maize resilience to drought through marker-assisted breeding or candidate gene identification. However, these QTLs generally have low effects, which make them difficult to detect. We hypothesized that computing ratios from phenotypic values measured in two contrasted watering conditions would help in identifying QTLs involved in GxE interaction that would not be detected when considering each condition separately. To test this hypothesis, we used previously published data containing phenotypic measurements for six drought-responsive ecophysiological traits acquired on 254 maize hybrids grown under two watering conditions, well-watered (WW) and water deficit (WD), in four greenhouse experiments (Prado et al., 2018). We conducted a genome wide association study on the trait values obtained in each watering condition as well as on the WD/WW ratios, taking into account an effect of the experiment. A total of 140 QTLs were detected, 102 (73%) were detected in one of the two watering conditions and 38 (27%) were detected only on the ratios. We quantified the contribution of the detected QTLs to the variations of the traits by fitting statistical mixed models including fixed effects of the QTLs and random effects of the genotype, genotype x condition interaction and genotype x experiment interaction. This showed that the QTLs detected in each watering condition mostly contributed the genotype variation (39%-65% of the variance captured depending on the trait) while the QTLs detected only for the ratios mainly contributed to the genotype x condition interaction (69%-100% of the variance captured). Altogether, these results show that ratio-specific QTLs are complementary to the QTLs detected in each condition separately and are worth being considered to explain the GxE interaction.

Control of organ morphology is a fundamental feature of living organisms. There is, however, observable variation in organ size and shape within a given genotype. Taking the sepal of Arabidopsis as a model, we investigated whether we can use variability of gene expression alongside variability of organ morphology to identify gene regulatory networks potentially involved in organ size and shape determination. To address this question, we produced a dataset composed of both transcriptomic and morphological information obtained from 27 individual sepals from wild-type plants. Although nearly identical in their genetic background, environment, and developmental stage, these sepals exhibited appreciable variability in both morphology and transcriptome. We found the expression of genes involved in response to stimulus and cell-wall to be highly variable among our samples. We additionally identified five modules of co-expressed genes which correlated significantly with morphology, revealing biologically relevant gene regulatory networks. Interestingly, cell-wall related genes were overrepresented in two of the top three modules. Overall, our work highlights the benefit of using coupled variation in gene expression and phenotype in wild-type plants to shed light on the mechanisms underlying organ size and shape determination. Although causality between gene expression and sepal morphology has not been established, our approach opens the way to informed analysis for mutant characterization and functional studies.

SummaryDespite the essential role of Specialized Metabolites (SMs) in the interaction of plants with the environment, studying the ability of crop seeds to produce these protective compounds has been neglected. Furthermore, seeds produce a myriad of SMs providing an interesting model to investigate their diversity and plasticity. Camelina sativa gained a lot of interest in the past few years as rustic oil seed crop. A characterization of seed SM landscapes in six camelina genotypes grown in the field and harvested during five growing seasons has been undertaken in this work. This allowed a comprehensive annotation of seed SMs combining analyses that cluster SMs based on their chemical structures and co-accumulation patterns. These data showed broad effects of the environment on the stimulation of the seed-specialized metabolome. Among well annotated compounds, flavonols were identified as the metabolic class characterised by high plasticity, revealing significant variable accumulation according to the year and/or the genotype. Lastly, a deeper characterisation of primary metabolites and lipids in two selected genotypes has been performed. We showed that, in addition to flavonols, alkaloids and glucosinolates displayed a higher phenotypic plasticity with respect to most of the primary metabolites, including some sugars and major storage compounds such as fatty acids, proteins and most lipid classes (e.g. DAG, TAG), but similar plasticity compared to free aminoacids and carboxylic acids. This work highlighted major and unexplored effects of the environment on the seed specialized metabolome demonstrating that seeds exhibit a dynamic and plastic metabolism, with an impact on seed quality.Significance statementSeeds produce a myriad of Specialized Metabolites (SMs) with an essential role in the interaction of plants with the environment. We characterized SM landscapes, primary metabolites and lipid composition in the seeds of camelina genotypes grown in the open field in five consecutive growing seasons. Our results showed the predominant effect of the environment on the regulation of the seed - specialized metabolome, with a potential impact on seed quality of camelina that may also occur in other oilseed crops.

Boundary domains delimit and organize organ growth throughout plant development almost relentlessly building plant architecture and morphogenesis. Boundary domains display reduced growth and orchestrate development of adjacent tissues in a non-cell autonomous manner. How these two functions are achieved remains elusive despite the identification of several boundary-specific genes. Here, we show using morphometrics at the organ and cellular levels that leaf boundary domain development requires SPINDLY (SPY), an O-fucosyltransferase, to act as cell growth repressor. Further we show that SPY acts redundantly with the CUP-SHAPED COTYLEDON transcription factors (CUC2 and CUC3), which are major determinants of boundaries development. Accordingly at the molecular level, CUC2 and SPY repress a common set of genes involved in cell wall loosening providing a molecular framework for the growth repression associated with boundary domains. Atomic force microscopy (AFM) confirmed that young leaf boundary domain cells have stiffer cell walls than marginal outgrowth. This differential cell wall stiffness was reduced in spy mutant. Taken together our data reveal a concealed CUC2 cell wall associated gene network linking tissue patterning with cell growth and mechanics.Summary statementDecreased cell-wall loosening gene expression contributes to the coordination of cell growth and mechanics with tissue patterning thus driving boundary development.

In legumes interacting with rhizobia the formation of symbiotic organs responsible for the acquisition of atmospheric nitrogen is depending of the plant nitrogen (N) demand. We discriminated between local and systemic impact of nitrogen on nodule formation using Medicago truncatula plants cultivated in split-root systems. We obtained evidence of the control of nodule formation by whole plant systemic N-satisfaction signaling but obtained little evidence of a local control by mineral nitrogen. We characterized the impact of systemic N signaling on the root transcriptome reprogramming associated to nodule formation. We identified, large genes clusters displaying common expression profiles in response to systemic N signaling enriched in particular fonctions required during these biological processes. We found evidence of a strong effect of SUNN in the control by systemic N signaling of many genes involved in the early interaction with rhizobium as well as organogenesis supporting a role of autoregulation pathway in systemic N signaling. However, we also found evidence that major SUNN independent systemic N signaling controls were maintained in the mutant. This study shed light on the unexpected high complexity of the control of nodule formation by systemic N signaling, that probably involves multiple pathways.

International audience; Uncertainty is a crucial issue in statistics which can be considered from different points of view. One type of uncertainty, typically referred to as sampling uncertainty, arises through the variability of results obtained when the same analysis strategy is applied to different samples. Another type of uncertainty arises through the variability of results obtained when using the same sample but different analysis strategies addressing the same research question. We denote this latter type of uncertainty as method uncertainty. It results from all the choices to be made for an analysis, for example, decisions related to data preparation, method choice, or model selection. In medical sciences, a large part of omics research is focused on the identification of molecular biomarkers, which can either be performed through ranking or by selection from among a large number of candidates. In this paper, we introduce a general resampling-based framework to quantify and compare sampling and method uncertainty. For illustration, we apply this framework to different scenarios related to the selection and ranking of omics biomarkers in the context of acute myeloid leukemia: variable selection in multivariable regression using different types of omics markers, the ranking of biomarkers according to their predictive performance, and the identification of differentially expressed genes from RNA-seq data. For all three scenarios, our findings suggest highly unstable results when the same analysis strategy is applied to two independent samples, indicating high sampling uncertainty and a comparatively smaller, but non-negligible method uncertainty, which strongly depends on the methods being compared.

International audience; Mycoheterotrophic plants have lost photosynthesis and obtain carbon through mycorrhizal fungi colonizing their roots. They are likely to have evolved from mixotrophic ancestors, which rely on both photosynthesis and fungal carbon for their development. Whereas our understanding of the ecological and genomic changes associated with the evolutionary shift to mycoheterotrophy is deepening, little information is known about the specific metabolic and physiological features driving this evolution. We investigated this issue in naturally occurring achlorophyllous variants of temperate mixotrophic orchids. We carried out an integrated transcriptomic and metabolomic analysis of the response to achlorophylly in the leaves of three mixotrophic species sampled in natura. Achlorophyllous leaves showed major impairment of their photosynthetic and mineral nutrition functions, strong accumulation of free amino acids, overexpression of enzymes and transporters related to sugars, amino acids and fatty acid catabolism, as well as induction of some autophagy-related and biotic stress genes. Such changes were reminiscent of these reported for variegated leaves and appeared to be symptomatic of a carbon starvation response. Rather than decisive metabolic innovations, we suggest that the evolution towards mycoheterotrophy in orchids is more likely to be reliant on the versatility of plant metabolism and an ability to exploit fungal organic resources, especially amino acids, to replace missing photosynthates.

In legumes interacting with rhizobia, the formation of symbiotic organs involved in the acquisition of atmospheric nitrogen gas (N2) is dependent on the plant nitrogen (N) demand. We used Medicago truncatula plants cultivated in split-root systems to discriminate between responses to local and systemic N signaling. We evidenced a strong control of nodule formation by systemic N signaling but obtained no clear evidence of a local control by mineral nitrogen. Systemic signaling of the plant N demand controls numerous transcripts involved in root transcriptome reprogramming associated with early rhizobia interaction and nodule formation. SUPER NUMERIC NODULES (SUNN) has an important role in this control, but we found that major systemic N signaling responses remained active in the sunn mutant. Genes involved in the activation of nitrogen fixation are regulated by systemic N signaling in the mutant, explaining why its hypernodulation phenotype is not associated with higher nitrogen fixation of the whole plant. We show that the control of transcriptome reprogramming of nodule formation by systemic N signaling requires other pathway(s) that parallel the SUNN/CLE (CLAVATA3/EMBRYO SURROUNDING REGION-LIKE PEPTIDES) pathway.

Additional file of Comparative transcriptomics of drought responses in Populus: a meta-analysis of genome-wide expression profiling in mature leaves and root apices across two genotypes

Additional file 9:(pdf file) Validation of microarray results by RT-qPCR. The Log 2 ratios were obtained either by RT-qPCR (a, b: -ΔΔCt) or by array analysis (c, d: intensity ratio). We compared the expression patterns of 4 selected genes in mature leaves (a, c) and of 5 selected genes in root apices (b, d). Gene models are given in Additional file 8. -ΔΔCt was calculated with PP2A as the housekeeping gene. (PDF 104 KB)

Authors’ original file for figure 1

Additional file 8:(pdf file) Primer sequences used for RT-qPCR validation. (PDF 113 KB)

In the context of finite mixture models one considers the problem of classifying as many observations as possible in the classes of interest while controlling the classification error rate in these same classes. Similar to what is done in the framework of statistical test theory, different type I and type II-like classification error rates can be defined, along with their associated optimal rules, where optimality is defined as minimizing type II error rate while controlling type I error rate at some nominal level. It is first shown that finding an optimal classification rule boils down to searching an optimal region in the observation space where to apply the classical Maximum A Posteriori (MAP) rule. Depending on the misclassification rate to be controlled, the shape of the optimal region is provided, along with a heuristic to compute the optimal classification rule in practice. In particular, a multiclass FDR-like optimal rule is defined and compared to the thresholded MAP rules that is used in most applications. It is shown on both simulated and real datasets that the FDR-like optimal rule may be significantly less conservative than the thresholded MAP rule.

Additional file 7:(pdf file) ABA-mediated drought response in poplar. Based on the literature and sequence homology with arabidopsis, putative ABA-related genes involved in drought response were identified. These genes were assigned to ABA biosynthesis (A, G), ABA-mediated signalling pathway (B, D), and response to ABA stimulus (C) as well as to cell rescue/detoxification process settled in response to ABA-mediated ROS production (H). Putative interactions with ABA-independent signalling pathway are shown (E). Genes (The Populus genome v1.1) and source are given in a table. Supporting literature [23, 42–60, 68, 76–86, 88, 91, 92, 109–113]. (PDF 25 KB)

Additional file 2:(pdf file) Functional category enrichment analysis (SEA) among differentially expressed genes. For clarity, only the most enriched GO terms and their P- values are given for each pair comparison (treated vs respective control). SEA were performed independently for up and down regulated genes (no Log 2 ratio cut-off, P < 0.05). (PDF 288 KB)

SummaryPhytoplasmas inhabit phloem sieve elements and cause abnormal growth and altered sugar partitioning. But how they interact with phloem functions is not clearly known. The phloem responses were investigated in tomato infected by ‘Candidatus Phytoplasma solani’, at the beginning of the symptomatic stage of infection, both in symptomatic and asymptomatic leaves, the first symptoms appearing in the sink top leaf at the stem apex. Antisense lines impaired in the phloem sucrose transporters SUT1 and SUT2 were included. The infection in source leaves was not associated with symptoms. In the symptomatic, sink leaf, yellowing and leaf curling was associated with higher starch accumulation and expression of defense genes. The transcriptional analysis of symptomatic leaf midribs indicated that transcript levels for genes acting in the glycolysis and peroxisome metabolism in infected plants differed from these in non-infected plants. Phytoplasma multiplied actively in at least three additional lower leaves although they were symptomless, with no sign of activation of defense markers, although the rate of exudation of sucrose from these symptomless, source leaves was lower for infected plants. A few metabolites in phloem-enriched exudate were affected by the infection, such as glycolate and aspartate, and some of them were also affected in the control SUT1- and SUT2- antisense lines, in which sucrose retrieval or release in the sieve elements are impaired. A metabolic switch could explain the delivery of more glycolate into the sieve elements of infected plants. The findings suggest a link between sugar transport and redox homeostasis.One sentence summaryAn impairment of sucrose retrieval and release in the sieve elements occurs during phytoplasma infection, associated with changes in sugar and peroxisome metabolism

The effect of systemic signaling of whole-plant N demand on mature N2-fixing nodules was investigated. Sugar allocation and hormones are candidates to integrate symbiotic nodules to whole-plant nutritional demand., In symbiotic root nodules of legumes, terminally differentiated rhizobia fix atmospheric N2 producing an NH4+ influx that is assimilated by the plant. The plant, in return, provides photosynthates that fuel the symbiotic nitrogen acquisition. Mechanisms responsible for the adjustment of the symbiotic capacity to the plant N demand remain poorly understood. We have investigated the role of systemic signaling of whole-plant N demand on the mature N2-fixing nodules of the model symbiotic association Medicago truncatula/Sinorhizobium using split-root systems. The whole-plant N-satiety signaling rapidly triggers reductions of both N2 fixation and allocation of sugars to the nodule. These responses are associated with the induction of nodule senescence and the activation of plant defenses against microbes, as well as variations in sugars transport and nodule metabolism. The whole-plant N-deficit responses mirror these changes: a rapid increase of sucrose allocation in response to N-deficit is associated with a stimulation of nodule functioning and development resulting in nodule expansion in the long term. Physiological, transcriptomic, and metabolomic data together provide evidence for strong integration of symbiotic nodules into whole-plant nitrogen demand by systemic signaling and suggest roles for sugar allocation and hormones in the signaling mechanisms.

International audience; Thousands of lncRNAs with ecotype-specific expression, including two that likely regulate primary root growth, are potentially linked to the evolution of regulatory mechanisms among ecotypes.Root architecture varies widely between species; it even varies between ecotypes of the same species, despite strong conservation of the coding portion of their genomes. By contrast, noncoding RNAs evolve rapidly between ecotypes and may control their differential responses to the environment, since several long noncoding RNAs (lncRNAs) are known to quantitatively regulate gene expression. Roots from ecotypes Columbia and Landsbergerectaof Arabidopsis (Arabidopsis thaliana) respond differently to phosphate starvation. Here, we compared transcriptomes (mRNAs, lncRNAs, and small RNAs) of root tips from these two ecotypes during early phosphate starvation. We identified thousands of lncRNAs that were largely conserved at the DNA level in these ecotypes. In contrast to coding genes, many lncRNAs were specifically transcribed in one ecotype and/or differentially expressed between ecotypes independent of phosphate availability. We further characterized these ecotype-related lncRNAs and studied their link with small interfering RNAs. Our analysis identified 675 lncRNAs differentially expressed between the two ecotypes, including antisense RNAs targeting key regulators of root-growth responses. Misregulation of several lincRNAs showed that at least two ecotype-related lncRNAs regulate primary root growth in ecotype Columbia. RNA-sequencing analysis following deregulation of lncRNA NPC48 revealed a potential link with root growth and transport functions. This exploration of the noncoding transcriptome identified ecotype-specific lncRNA-mediated regulation in root apexes. The noncoding genome may harbor further mechanisms involved in ecotype adaptation of roots to different soil environments.

International audience

The control of gynecium development in Arabidopsis thaliana by the auxin response factor ETTIN (ETT) correlates with a reduction in the methylesterification of cell-wall pectins and a decrease in cell-wall stiffness in the valve tissues of the ovary. Here, we determine the list of genes rapidly regulated following the in-vivo activation of an ETT fusion protein, and show these to be significantly enriched in genes encoding cell-wall proteins, including several pectin methylesterases (PMEs) and pectin methylesterase inhibitors (PMEIs). We also perform a genome-wide scan for potential ETT-binding sites, and incorporate the results of this procedure into a comparison of datasets, derived using four distinct methods, to identify genes regulated directly or indirectly by ETT. We conclude from our combined analyses that PMEIs are likely to be key actors that mediate the regulation of gynecium development by ETT, while ETT may simultaneously regulate PMEs to prevent exaggerated developmental effects from the regulation of PMEIs. We also postulate the existence of one or more rapidly-acting intermediate factors in the transcriptional regulation of PMEs and PMEIs by ETT.

International audience; The control of gynecium development in Arabidopsis thaliana by the auxin response factor ETTIN (ETT) correlates with a reduction in the methylesterification of cell-wall pectins and a decrease in cell-wall stiffness in the valve tissues of the ovary. Here, we determine the list of genes rapidly regulated following the in-vivo activation of an ETT fusion protein, and show these to be significantly enriched in genes encoding cell-wall proteins, including several pectin methylesterases (PMEs) and pectin methylesterase inhibitors (PMEIs). We also perform a genome-wide scan for potential ETT-binding sites, and incorporate the results of this procedure into a comparison of datasets, derived using four distinct methods, to identify genes regulated directly or indirectly by ETT. We conclude from our combined analyses that PMEIs are likely to be key actors that mediate the regulation of gynecium development by ETT, while ETT may simultaneously regulate PMEs to prevent exaggerated developmental effects from the regulation of PMEIs. We also postulate the existence of one or more rapidly-acting intermediate factors in the transcriptional regulation of PMEs and PMEIs by ETT.

International audience; Understanding the molecular mechanisms controlling the accumulation of grain storage proteins in response to nitrogen (N) and sulfur (S) nutrition is essential to improve cereal grain nutritional and functional properties. Here, we studied the grain transcriptome and metabolome responses to postanthesis N and S supply for the diploid wheat einkorn (Triticum monococcum). During grain filling, 848 transcripts and 24 metabolites were differentially accumulated in response to N and S availability. The accumulation of total free amino acids per grain and the expression levels of 241 genes showed significant modifications during most of the grain filling period and were upregulated in response to S deficiency. Among them, 24 transcripts strongly responded to S deficiency and were identified in coexpression network analyses as potential coordinators of the grain response to N and S supply. Sulfate transporters and genes involved in sulfate and Met metabolism were upregulated, suggesting regulation of the pool of free amino acids and of the grain N-to-S ratio. Several genes highlighted in this study might limit the impact of S deficiency on the accumulation of grain storage proteins.Sulfur deficiency in einkorn grain leads to upregulation of genes acting in sulfur transport and metabolism that may coordinate the pool of free amino acids necessary for storage protein synthesis.

Additional file 8 Suppl. Fig. 6. Proportion of linear model (LM, top row) and neural network (NN, bottom row) predictions with a R2 above (left column) or below (right column) the 95% confidence interval computed from the predictions with the random sets of genes for each gene set (there is no neural network model computed for the Complete BLUP full set).

International audience; Prostate cancer (PCa) immunotherapy has shown limited efficacy so far, even in advanced-stage cancers. The success rate of PCa immunotherapy might be improved by approaches more adapted to the immunobiology of the disease. The objective of this study was to perform a multi-omics analysis to identify immune genes associated with PCa progression to better characterize PCa immunobiology and propose new immunotherapeutic targets. mRNA, miRNA, methylation, copy number aberration, and single nucleotide variant datasets from The Cancer Genome Atlas PRAD cohort were analyzed after filtering for genes associated with immunity. Sparse partial least squares-discriminant analyses were performed to identify features associated with biochemical recurrence (BCR) in each type of omics data. Selected features predicted BCR with a balanced error rate (BER) of 0.20 to 0.51 in single-omics and of 0.05 in multi-omics analyses. Amongst features associated with BCR were genes from the Immunoglobulin Ig-like Receptor (LILR) family which are immune checkpoints with immunotherapeutic potential. Using Multivariate INTegrative (MINT) analysis, the association of five LILR genes with BCR was quantified in a combination of three RNA-seq datasets and confirmed with Kaplan-Meier analysis in both these and in an independent RNA-seq dataset. Finally, immunohistochemistry showed that a high number of LILRB1 positive cells within the tumors predicted long-term adverse outcomes. Thus, tumors characterized by abnormal expression of LILR genes have an elevated risk of recurring after definitive local therapy. The immunotherapeutic potential of these regulators to stimulate the immune response against PCa should be evaluated in pre-clinical models.

Additional file 5 Suppl. Fig. 3. Relationship between Spearman’s correlations between module-trait (y-axis) and gene significance-kME (x-axis).

Additional file 10 Suppl. Fig. 8. Predictions scores on test sets for increasing numbers of the peripheral genes. Violin and boxplots of prediction R2 for the LM Ridge algorithm and for increasing sizes of the peripheral genes set (in brown) and the peripheral NG genes set (in orange), used for the predictions (in percent of the full set).

Additional file 4 Suppl. Fig. 2. Traits hierarchical ascendant clustering dendrogram. Clustering was performed from the correlations between traits with Ward method ("Ward.D2") by the R package pvclust. Approximately Unbiased (au, in red) and Bootstrap Probability (bp, in green) p-values indicated the degree of belief associated with clusters. Highly supported modules are framed by a red square, grouping (a) the mean sample diameter with the two circumference traits, (b) the S/G ratios with glucose composition, (c) the two C5/C6 together, and (d) the H/G ratios.

Additional file 3 Suppl. Fig. 1. PCA score plots on gene expression data. Each plot represents the distribution of the individuals on the 2 first axes of the PCA (representing 17,7% of the variation), colored by class of various experimental factors (Xylem and cambium scraper, extractor and extraction method, population, sequencing column, line and plate, the growth rate at harvest, sampling date, time, temperature, solar radiation, humidity and wind speed). Cofactors related to weather are presented in the 6 lower plots.

Additional file 7 Suppl. Fig. 5. Histograms of the centrality scores for the genes selected by Boruta at different p-values thresholds. Repartition of selected genes within the following gene sets is hilighted, with core in blue, peripheral NG in orange, peripheral in brown and other (NA) in black. Four p-value thhresolds for Boruta selections were considered: 0.01, 0.05, 0.1 and 0.2.

Additional file 6 Suppl. Fig. 4. Histograms of the centrality scores without (top) or with (bottom) the grey group. Core, peripheral and peripheral without grey sets are represented respectively by the blue, dark orange and orange bars. Random sets are distributed across the histogram and do not appear on this figure. Distribution of genes clustered in the grey module is represented by the grey bars, white bars are for other genes.

Additional file 9 Suppl. Fig. 7. Difference of prediction scores between algorithms (top) and sets (bottom). On the top panel, the difference between LM and NN prediction scores for the core (in blue), random (in grey), peripheral (in brown), peripheral (in orange) and Boruta gene sets (in green). On the bottom panel, the LM differences are in red and the NN differences in turquoise and the color filling the bar represents the difference between core and peripheral genes in brown, core and peripheral NG in orange and between the random sets in grey. For the random pairs, error bars represent the first and third quartiles of the differences between pairs of randomized sets and the bar corresponds to the median.

Background RNAseq is nowadays the method of choice for transcriptome analysis. In the last decades, a high number of statistical methods, and associated bioinformatics tools, for RNAseq analysis were developed. More recently, statistical studies realized neutral comparison studies using benchmark datasets, shedding light on the most appropriate approaches for RNAseq data analysis. Nevertheless, performing an RNAseq analysis remains a challenge for the biologists. Results DiCoExpress is a workspace implemented in R that includes methods chosen based on their performance in neutral comparisons studies. DiCoExpress uses the pre-existing R packages as well as FactoMineR, edgeR and coseq, to perform quality control, differential, and co-expression analysis of RNAseq data. Users can perform the full analysis, providing a mapped read expression data file and a file containing the information on the experimental design. Following the quality control step, the user can move on to the differential expression analysis performed using generalized linear models with no effort thanks to the automated contrast writing function. DiCoExpress proposes a list of comparisons based on the experimental design, and the user needs only to choose the one(s) of interest for his research question. A co-expression analysis is implemented using the coseq package. Identified co-expression clusters are automatically analyzed for enrichment of annotations provided by the user, and several result outputs proposed. We used DiCoExpress to analyze a publicly available Bra ssica napus L. RNAseq dataset on the transcriptional response to silicon treatment in plant roots and mature leaves. This dataset, including two biological factors and three replicates for each condition, allowed us to demonstrate in a tutorial all the features of DiCoExpress. Conclusions DiCoExpress is an R workspace to allow users without advanced statistical knowledge and programming skills to perform a full RNAseq analysis from quality controls to co-expression analysis through differential analysis based on contrasts inside generalized linear models . Hence, with DiCoExpress, the user can focus on the statistical modeling of gene expression according to the experimental design and on the interpretation of the results of such analysis in biological terms.