MERRIN: MEtabolic regulation rule INference from time series data.

MOTIVATION: Many techniques have been developed to infer Boolean regulations from a prior knowledge network (PKN) and experimental data. Existing methods are able to reverse-engineer Boolean regulations for transcriptional and signaling networks, but they fail to infer regulations that control metabolic networks. RESULTS: We present a novel approach to infer Boolean rules for metabolic regulation from time-series data and a PKN. Our method is based on a combination of answer set programming and linear programming. By solving both combinatorial and linear arithmetic constraints, we generate candidate Boolean regulations that can reproduce the given data when coupled to the metabolic network. We evaluate our approach on a core regulated metabolic network and show how the quality of the predictions depends on the available kinetic, fluxomics or transcriptomics time-series data. AVAILABILITY AND IMPLEMENTATION: Software available at https://github.com/bioasp/merrin. SUPPLEMENTARY INFORMATION: Supplementary data are available at https://doi.org/10.5281/zenodo.6670164.

A multi-organ metabolic model of tomato predicts plant responses to nutritional and genetic perturbations.

Predicting and understanding plant responses to perturbations require integrating the interactions between nutritional sources, genes, cell metabolism, and physiology in the same model. This can be achieved using metabolic modeling calibrated by experimental data. In this study, we developed a multi-organ metabolic model of a tomato (Solanum lycopersicum) plant during vegetative growth, named Virtual Young TOmato Plant (VYTOP) that combines genome-scale metabolic models of leaf, stem and root and integrates experimental data acquired from metabolomics and high-throughput phenotyping of tomato plants. It is composed of 6,689 reactions and 6,326 metabolites. We validated VYTOP predictions on five independent use cases. The model correctly predicted that glutamine is the main organic nutrient of xylem sap. The model estimated quantitatively how stem photosynthetic contribution impacts exchanges between the different organs. The model was also able to predict how nitrogen limitation affects vegetative growth and the metabolic behavior of transgenic tomato lines with altered expression of core metabolic enzymes. The integration of different components, such as a metabolic model, physiological constraints, and experimental data, generates a powerful predictive tool to study plant behavior, which will be useful for several other applications, such as plant metabolic engineering or plant nutrition.

MtEFD and MtEFD2: Two transcription factors with distinct neofunctionalization in symbiotic nodule development.

Rhizobium-legume nitrogen-fixing symbiosis involves the formation of a specific organ, the root nodule, which provides bacteria with the proper cellular environment for atmospheric nitrogen fixation. Coordinated differentiation of plant and bacterial cells is an essential step of nodule development, for which few transcriptional regulators have been characterized. Medicago truncatula ETHYLENE RESPONSE FACTOR REQUIRED FOR NODULE DIFFERENTIATION (MtEFD) encodes an APETALA2/ETHYLENE RESPONSIVE FACTOR (ERF) transcription factor, the mutation of which leads to both hypernodulation and severe defects in nodule development. MtEFD positively controls a negative regulator of cytokinin signaling, the RESPONSE REGULATOR 4 (MtRR4) gene. Here we showed that that the Mtefd-1 mutation affects both plant and bacterial endoreduplication in nodules, as well as the expression of hundreds of genes in young and mature nodules, upstream of known regulators of symbiotic differentiation. MtRR4 expressed with the MtEFD promoter complemented Mtefd-1 hypernodulation but not the nodule differentiation phenotype. Unexpectedly, a nonlegume homolog of MtEFD, AtERF003 in Arabidopsis (Arabidopsis thaliana), could efficiently complement both phenotypes of Mtefd-1, in contrast to the MtEFD paralog MtEFD2 expressed in the root and nodule meristematic zone. A domain swap experiment showed that MtEFD2 differs from MtEFD by its C-terminal fraction outside the DNA binding domain. Furthermore, clustered regularly interspaced short palindromic repeats-CRISPR associated protein 9 (CRISPR-Cas9) mutagenesis of MtEFD2 led to a reduction in the number of nodules formed in Mtefd-1, with downregulation of a set of genes, including notably NUCLEAR FACTOR-YA1 (MtNF-YA1) and MtNF-YB16, which are essential for nodule meristem establishment. We, therefore, conclude that nitrogen-fixing symbiosis recruited two proteins originally expressed in roots, MtEFD and MtEFD2, with distinct functions and neofunctionalization processes for each of them.

The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution.

The domesticated sunflower, Helianthus annuus L., is a global oil crop that has promise for climate change adaptation, because it can maintain stable yields across a wide variety of environmental conditions, including drought. Even greater resilience is achievable through the mining of resistance alleles from compatible wild sunflower relatives, including numerous extremophile species. Here we report a high-quality reference for the sunflower genome (3.6 gigabases), together with extensive transcriptomic data from vegetative and floral organs. The genome mostly consists of highly similar, related sequences and required single-molecule real-time sequencing technologies for successful assembly. Genome analyses enabled the reconstruction of the evolutionary history of the Asterids, further establishing the existence of a whole-genome triplication at the base of the Asterids II clade and a sunflower-specific whole-genome duplication around 29 million years ago. An integrative approach combining quantitative genetics, expression and diversity data permitted development of comprehensive gene networks for two major breeding traits, flowering time and oil metabolism, and revealed new candidate genes in these networks. We found that the genomic architecture of flowering time has been shaped by the most recent whole-genome duplication, which suggests that ancient paralogues can remain in the same regulatory networks for dozens of millions of years. This genome represents a cornerstone for future research programs aiming to exploit genetic diversity to improve biotic and abiotic stress resistance and oil production, while also considering agricultural constraints and human nutritional needs.

Rhizobium leguminosarum symbiovar viciae strains are natural wheat endophytes that can stimulate root development.

Although rhizobia that establish a nitrogen-fixing symbiosis with legumes are also known to promote growth in non-legumes, studies on rhizobial associations with wheat roots are scarce. We searched for Rhizobium leguminosarum symbiovar viciae (Rlv) strains naturally competent to endophytically colonize wheat roots. We isolated 20 strains from surface-sterilized wheat roots and found a low diversity of Rlv compared to that observed in the Rlv species complex. We tested the ability of a subset of these Rlv for wheat root colonization when co-inoculated with other Rlv. Only a few strains, including those isolated from wheat roots, and one strain isolated from pea nodules, were efficient in colonizing roots in co-inoculation conditions, while all the strains tested in single strain inoculation conditions were found to colonize the surface and interior of roots. Furthermore, Rlv strains isolated from wheat roots were able to stimulate root development and early arbuscular mycorrhizal fungi colonization. These responses were strain and host genotype dependent. Our results suggest that wheat can be an alternative host for Rlv; nevertheless, there is a strong competition between Rlv strains for wheat root colonization. In addition, we showed that Rlv are endophytic wheat root bacteria with potential ability to modify wheat development.

Sunflower resistance to multiple downy mildew pathotypes revealed by recognition of conserved effectors of the oomycete Plasmopara halstedii.

Over the last 40 years, new sunflower downy mildew isolates (Plasmopara halstedii) have overcome major gene resistances in sunflower, requiring the identification of additional and possibly more durable broad-spectrum resistances. Here, 354 RXLR effectors defined in silico from our new genomic data were classified in a network of 40 connected components sharing conserved protein domains. Among 205 RXLR effector genes encoding conserved proteins in 17 P. halstedii pathotypes of varying virulence, we selected 30 effectors that were expressed during plant infection as potentially essential genes to target broad-spectrum resistance in sunflower. The transient expression of the 30 core effectors in sunflower and in Nicotiana benthamiana leaves revealed a wide diversity of targeted subcellular compartments, including organelles not so far shown to be targeted by oomycete effectors such as chloroplasts and processing bodies. More than half of the 30 core effectors were able to suppress pattern-triggered immunity in N. benthamiana, and five of these induced hypersensitive responses (HR) in sunflower broad-spectrum resistant lines. HR triggered by PhRXLRC01 co-segregated with Pl22 resistance in F3 populations and both traits localized in 1.7 Mb on chromosome 13 of the sunflower genome. Pl22 resistance was physically mapped on the sunflower genome recently sequenced, unlike all the other downy mildew resistances published so far. PhRXLRC01 and Pl22 are proposed as an avirulence/resistance gene couple not previously described in sunflower. Core effector recognition is a successful strategy to accelerate broad-spectrum resistance gene identification in complex crop genomes such as sunflower.

MetExplore: collaborative edition and exploration of metabolic networks.

Metabolism of an organism is composed of hundreds to thousands of interconnected biochemical reactions responding to environmental or genetic constraints. This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers an all-in-one online solution composed of interactive tools for metabolic network curation, network exploration and omics data analysis. In particular, it is possible to curate and annotate metabolic networks in a collaborative environment. The network exploration is also facilitated in MetExplore by a system of interactive tables connected to a powerful network visualization module. Finally, the contextualization of metabolic elements in the network and the calculation of over-representation statistics make it possible to interpret any kind of omics data. MetExplore is a sustainable project maintained since 2010 freely available at https://metexplore.toulouse.inra.fr/metexplore2/.

Computational analysis of the Plasmodiophora brassicae genome: mitochondrial sequence description and metabolic pathway database design.

Plasmodiophora brassicae is an obligate biotrophic pathogenic protist responsible for clubroot, a root gall disease of Brassicaceae species. In addition to the reference genome of the P. brassicae European e3 isolate and the draft genomes of Canadian or Chinese isolates, we present the genome of eH, a second European isolate. Refinement of the annotation of the eH genome led to the identification of the mitochondrial genome sequence, which was found to be bigger than that of Spongospora subterranea, another plant parasitic Plasmodiophorid phylogenetically related to P. brassicae. New pathways were also predicted, such as those for the synthesis of spermidine, a polyamine up-regulated in clubbed regions of roots. A P. brassicae pathway genome database was created to facilitate the functional study of metabolic pathways in transcriptomics approaches. These available tools can help in our understanding of the regulation of P. brassicae metabolism during infection and in response to diverse constraints.

MetExploreViz: web component for interactive metabolic network visualization.

SUMMARY: MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context. AVAILABILITY AND IMPLEMENTATION: Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc/.

A Resource Allocation Trade-Off between Virulence and Proliferation Drives Metabolic Versatility in the Plant Pathogen Ralstonia solanacearum.

Bacterial pathogenicity relies on a proficient metabolism and there is increasing evidence that metabolic adaptation to exploit host resources is a key property of infectious organisms. In many cases, colonization by the pathogen also implies an intensive multiplication and the necessity to produce a large array of virulence factors, which may represent a significant cost for the pathogen. We describe here the existence of a resource allocation trade-off mechanism in the plant pathogen R. solanacearum. We generated a genome-scale reconstruction of the metabolic network of R. solanacearum, together with a macromolecule network module accounting for the production and secretion of hundreds of virulence determinants. By using a combination of constraint-based modeling and metabolic flux analyses, we quantified the metabolic cost for production of exopolysaccharides, which are critical for disease symptom production, and other virulence factors. We demonstrated that this trade-off between virulence factor production and bacterial proliferation is controlled by the quorum-sensing-dependent regulatory protein PhcA. A phcA mutant is avirulent but has a better growth rate than the wild-type strain. Moreover, a phcA mutant has an expanded metabolic versatility, being able to metabolize 17 substrates more than the wild-type. Model predictions indicate that metabolic pathways are optimally oriented towards proliferation in a phcA mutant and we show that this enhanced metabolic versatility in phcA mutants is to a large extent a consequence of not paying the cost for virulence. This analysis allowed identifying candidate metabolic substrates having a substantial impact on bacterial growth during infection. Interestingly, the substrates supporting well both production of virulence factors and growth are those found in higher amount within the plant host. These findings also provide an explanatory basis to the well-known emergence of avirulent variants in R. solanacearum populations in planta or in stressful environments.

A Laser Dissection-RNAseq Analysis Highlights the Activation of Cytokinin Pathways by Nod Factors in the Medicago truncatula Root Epidermis.

Nod factors (NFs) are lipochitooligosaccharidic signal molecules produced by rhizobia, which play a key role in the rhizobium-legume symbiotic interaction. In this study, we analyzed the gene expression reprogramming induced by purified NF (4 and 24 h of treatment) in the root epidermis of the model legume Medicago truncatula Tissue-specific transcriptome analysis was achieved by laser-capture microdissection coupled to high-depth RNA sequencing. The expression of 17,191 genes was detected in the epidermis, among which 1,070 were found to be regulated by NF addition, including previously characterized NF-induced marker genes. Many genes exhibited strong levels of transcriptional activation, sometimes only transiently at 4 h, indicating highly dynamic regulation. Expression reprogramming affected a variety of cellular processes, including perception, signaling, regulation of gene expression, as well as cell wall, cytoskeleton, transport, metabolism, and defense, with numerous NF-induced genes never identified before. Strikingly, early epidermal activation of cytokinin (CK) pathways was indicated, based on the induction of CK metabolic and signaling genes, including the CRE1 receptor essential to promote nodulation. These transcriptional activations were independently validated using promoter:ß-glucuronidase fusions with the MtCRE1 CK receptor gene and a CK response reporter (TWO COMPONENT SIGNALING SENSOR NEW). A CK pretreatment reduced the NF induction of the EARLY NODULIN11 (ENOD11) symbiotic marker, while a CK-degrading enzyme (CYTOKININ OXIDASE/DEHYDROGENASE3) ectopically expressed in the root epidermis led to increased NF induction of ENOD11 and nodulation. Therefore, CK may play both positive and negative roles in M. truncatula nodulation.

TrypanoCyc: a community-led biochemical pathways database for Trypanosoma brucei.

The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about the enzymes that are active in particular cells under particular conditions and as technologies advance to allow detailed measurements of the cellular metabolome. Metabolic network databases are of increasing importance in allowing us to contextualise data sets emerging from transcriptomic, proteomic and metabolomic experiments. Here we present a dynamic database, TrypanoCyc (http://www.metexplore.fr/trypanocyc/), which describes the generic and condition-specific metabolic network of Trypanosoma brucei, a parasitic protozoan responsible for human and animal African trypanosomiasis. In addition to enabling navigation through the BioCyc-based TrypanoCyc interface, we have also implemented a network-based representation of the information through MetExplore, yielding a novel environment in which to visualise the metabolism of this important parasite.

An integrated analysis of plant and bacterial gene expression in symbiotic root nodules using laser-capture microdissection coupled to RNA sequencing.

Rhizobium-induced root nodules are specialized organs for symbiotic nitrogen fixation. Indeterminate-type nodules are formed from an apical meristem and exhibit a spatial zonation which corresponds to successive developmental stages. To get a dynamic and integrated view of plant and bacterial gene expression associated with nodule development, we used a sensitive and comprehensive approach based upon oriented high-depth RNA sequencing coupled to laser microdissection of nodule regions. This study, focused on the association between the model legume Medicago truncatula and its symbiont Sinorhizobium meliloti, led to the production of 942 million sequencing read pairs that were unambiguously mapped on plant and bacterial genomes. Bioinformatic and statistical analyses enabled in-depth comparison, at a whole-genome level, of gene expression in specific nodule zones. Previously characterized symbiotic genes displayed the expected spatial pattern of expression, thus validating the robustness of our approach. We illustrate the use of this resource by examining gene expression associated with three essential elements of nodule development, namely meristem activity, cell differentiation and selected signaling processes related to bacterial Nod factors and redox status. We found that transcription factor genes essential for the control of the root apical meristem were also expressed in the nodule meristem, while the plant mRNAs most enriched in nodules compared with roots were mostly associated with zones comprising both plant and bacterial partners. The data, accessible on a dedicated website, represent a rich resource for microbiologists and plant biologists to address a variety of questions of both fundamental and applied interest.

Telling metabolic stories to explore metabolomics data: a case study on the yeast response to cadmium exposure.

MOTIVATION: The increasing availability of metabolomics data enables to better understand the metabolic processes involved in the immediate response of an organism to environmental changes and stress. The data usually come in the form of a list of metabolites whose concentrations significantly changed under some conditions, and are thus not easy to interpret without being able to precisely visualize how such metabolites are interconnected. RESULTS: We present a method that enables to organize the data from any metabolomics experiment into metabolic stories. Each story corresponds to a possible scenario explaining the flow of matter between the metabolites of interest. These scenarios may then be ranked in different ways depending on which interpretation one wishes to emphasize for the causal link between two affected metabolites: enzyme activation, enzyme inhibition or domino effect on the concentration changes of substrates and products. Equally probable stories under any selected ranking scheme can be further grouped into a single anthology that summarizes, in a unique subnetwork, all equivalently plausible alternative stories. An anthology is simply a union of such stories. We detail an application of the method to the response of yeast to cadmium exposure. We use this system as a proof of concept for our method, and we show that we are able to find a story that reproduces very well the current knowledge about the yeast response to cadmium. We further show that this response is mostly based on enzyme activation. We also provide a framework for exploring the alternative pathways or side effects this local response is expected to have in the rest of the network. We discuss several interpretations for the changes we see, and we suggest hypotheses that could in principle be experimentally tested. Noticeably, our method requires simple input data and could be used in a wide variety of applications. AVAILABILITY AND IMPLEMENTATION: The code for the method presented in this article is available at http://gobbolino.gforge.inria.fr.

Identification and phylogenetic analyses of VASt, an uncharacterized protein domain associated with lipid-binding domains in Eukaryotes.

BACKGROUND: Several regulators of programmed cell death (PCD) in plants encode proteins with putative lipid-binding domains. Among them, VAD1 is a regulator of PCD propagation harboring a GRAM putative lipid-binding domain. However the function of VAD1 at the subcellular level is unknown and the domain architecture of VAD1 has not been analyzed in details. RESULTS: We analyzed sequence conservation across the plant kingdom in the VAD1 protein and identified an uncharacterized VASt (VAD1 Analog of StAR-related lipid transfer) domain. Using profile hidden Markov models (profile HMMs) and phylogenetic analysis we found that this domain is conserved among eukaryotes and generally associates with various lipid-binding domains. Proteins containing both a GRAM and a VASt domain include notably the yeast Ysp2 cell death regulator and numerous uncharacterized proteins. Using structure-based phylogeny, we found that the VASt domain is structurally related to Bet v1-like domains. CONCLUSION: We identified a novel protein domain ubiquitous in Eukaryotic genomes and belonging to the Bet v1-like superfamily. Our findings open perspectives for the functional analysis of VASt-containing proteins and the characterization of novel mechanisms regulating PCD.

Algorithms and complexity of enumerating minimal precursor sets in genome-wide metabolic networks.

MOTIVATION: In the context of studying whole metabolic networks and their interaction with the environment, the following question arises: given a set of target metabolites T and a set of possible external source metabolites , which are the minimal subsets of that are able to produce all the metabolites in T. Such subsets are called the minimal precursor sets of T. The problem is then whether we can enumerate all of them efficiently. RESULTS: We propose a new characterization of precursor sets as the inputs of reaction sets called factories and an efficient algorithm to decide if a set of sources is precursor set of T. We show proofs of hardness for the problems of finding a precursor set of minimum size and of enumerating all minimal precursor sets T. We propose two new algorithms which, despite the hardness of the enumeration problem, allow to enumerate all minimal precursor sets in networks with up to 1000 reactions. AVAILABILITY: Source code and datasets used in our benchmarks are freely available for download at http://sites.google.com/site/pitufosoftware/download. CONTACT: vicente77@gmail.com, pvmilreu@gmail.com or marie-france.sagot@inria.fr.

Insights into the metabolic specificities of pathogenic strains from the Ralstonia solanacearum species complex.

All the strains grouped under the species Ralstonia solanacearum represent a species complex responsible for many diseases on agricultural crops throughout the world. The strains have different lifestyles and host range. Here, we investigated whether specific metabolic pathways contribute to strain diversification. To this end, we carried out systematic comparisons on 11 strains representing the diversity of the species complex. We reconstructed the metabolic network of each strain from its genome sequence and looked for the metabolic pathways differentiating the different reconstructed networks and, by extension, the different strains. Finally, we conducted an experimental validation by determining the metabolic profile of each strain with the Biolog technology. Results revealed that the metabolism is conserved between strains, with a core metabolism composed of 82% of the pan-reactome. The three species composing the species complex could be distinguished according to the presence/absence of some metabolic pathways, in particular, one involving salicylic acid degradation. Phenotypic assays revealed that the trophic preferences on organic acids and several amino acids such as glutamine, glutamate, aspartate, and asparagine are conserved between strains. Finally, we generated mutants lacking the quorum-sensing-dependent regulator PhcA in four diverse strains, and we showed that the phcA-dependent trade-off between growth and production of virulence factors is conserved across the R. solanacearum species complex. IMPORTANCE Ralstonia solanacearum is one of the most important threats to plant health worldwide, causing disease on a very large range of agricultural crops such as tomato or potato. Behind the R. solanacearum name are hundreds of strains with different host range and lifestyle, classified into three species. Studying the differences between strains allows to better apprehend the biology of the pathogens and the specificity of some strains. None of the published genomic comparative studies have focused on the metabolism of the strains so far. We developed a new bioinformatic pipeline to build high-quality metabolic networks and used a combination of metabolic modeling and high-throughput phenotypic Biolog microplates to look for the metabolic differences between 11 strains across the three species. Our study revealed that genes encoding enzymes are overall conserved, with few variations between strains. However, more variations were observed when considering substrate usage. These variations probably result from regulation rather than the presence or absence of enzymes in the genome.

Exploration of the core metabolism of symbiotic bacteria.

BACKGROUND: A large number of genome-scale metabolic networks is now available for many organisms, mostly bacteria. Previous works on minimal gene sets, when analysing host-dependent bacteria, found small common sets of metabolic genes. When such analyses are restricted to bacteria with similar lifestyles, larger portions of metabolism are expected to be shared and their composition is worth investigating. Here we report a comparative analysis of the small molecule metabolism of symbiotic bacteria, exploring common and variable portions as well as the contribution of different lifestyle groups to the reduction of a common set of metabolic capabilities. RESULTS: We found no reaction shared by all the bacteria analysed. Disregarding those with the smallest genomes, we still do not find a reaction core, however we did find a core of biochemical capabilities. While obligate intracellular symbionts have no core of reactions within their group, extracellular and cell-associated symbionts do have a small core composed of disconnected fragments. In agreement with previous findings in Escherichia coli, their cores are enriched in biosynthetic processes whereas the variable metabolisms have similar ratios of biosynthetic and degradation reactions. Conversely, the variable metabolism of obligate intracellular symbionts is enriched in anabolism. CONCLUSION: Even when removing the symbionts with the most reduced genomes, there is no core of reactions common to the analysed symbiotic bacteria. The main reason is the very high specialisation of obligate intracellular symbionts, however, host-dependence alone is not an explanation for such absence. The composition of the metabolism of cell-associated and extracellular bacteria shows that while they have similar needs in terms of the building blocks of their cells, they have to adapt to very distinct environments. On the other hand, in obligate intracellular bacteria, catabolism has largely disappeared, whereas synthetic routes appear to have been selected for depending on the nature of the symbiosis. As more genomes are added, we expect, based on our simulations, that the core of cell-associated and extracellular bacteria continues to diminish, converging to approximately 60 reactions.

Transcriptome database resource and gene expression atlas for the rose.

BACKGROUND: For centuries roses have been selected based on a number of traits. Little information exists on the genetic and molecular basis that contributes to these traits, mainly because information on expressed genes for this economically important ornamental plant is scarce. RESULTS: Here, we used a combination of Illumina and 454 sequencing technologies to generate information on Rosa sp. transcripts using RNA from various tissues and in response to biotic and abiotic stresses. A total of 80714 transcript clusters were identified and 76611 peptides have been predicted among which 20997 have been clustered into 13900 protein families. BLASTp hits in closely related Rosaceae species revealed that about half of the predicted peptides in the strawberry and peach genomes have orthologs in Rosa dataset. Digital expression was obtained using RNA samples from organs at different development stages and under different stress conditions. qPCR validated the digital expression data for a selection of 23 genes with high or low expression levels. Comparative gene expression analyses between the different tissues and organs allowed the identification of clusters that are highly enriched in given tissues or under particular conditions, demonstrating the usefulness of the digital gene expression analysis. A web interface ROSAseq was created that allows data interrogation by BLAST, subsequent analysis of DNA clusters and access to thorough transcript annotation including best BLAST matches on Fragaria vesca, Prunus persica and Arabidopsis. The rose peptides dataset was used to create the ROSAcyc resource pathway database that allows access to the putative genes and enzymatic pathways. CONCLUSIONS: The study provides useful information on Rosa expressed genes, with thorough annotation and an overview of expression patterns for transcripts with good accuracy.

MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks.

High-throughput metabolomic experiments aim at identifying and ultimately quantifying all metabolites present in biological systems. The metabolites are interconnected through metabolic reactions, generally grouped into metabolic pathways. Classical metabolic maps provide a relational context to help interpret metabolomics experiments and a wide range of tools have been developed to help place metabolites within metabolic pathways. However, the representation of metabolites within separate disconnected pathways overlooks most of the connectivity of the metabolome. By definition, reference pathways cannot integrate novel pathways nor show relationships between metabolites that may be linked by common neighbours without being considered as joint members of a classical biochemical pathway. MetExplore is a web server that offers the possibility to link metabolites identified in untargeted metabolomics experiments within the context of genome-scale reconstructed metabolic networks. The analysis pipeline comprises mapping metabolomics data onto the specific metabolic network of an organism, then applying graph-based methods and advanced visualization tools to enhance data analysis. The MetExplore web server is freely accessible at http://metexplore.toulouse.inra.fr.