First step toward gene expression data integration: transcriptomic data acquisition with COMMAND>_.

BACKGROUND: Exploring cellular responses to stimuli using extensive gene expression profiles has become a routine procedure performed on a daily basis. Raw and processed data from these studies are available on public databases but the opportunity to fully exploit such rich datasets is limited due to the large heterogeneity of data formats. In recent years, several approaches have been proposed to effectively integrate gene expression data for analysis and exploration at a broader level. Despite the different goals and approaches towards gene expression data integration, the first step is common to any proposed method: data acquisition. Although it is seemingly straightforward to extract valuable information from a set of downloaded files, things can rapidly get complicated, especially as the number of experiments grows. Transcriptomic datasets are deposited in public databases with little regard to data format and thus retrieving raw data might become a challenging task. While for RNA-seq experiments such problem is partially mitigated by the fact that raw reads are generally available on databases such as the NCBI SRA, for microarray experiments standards are not equally well established, or enforced during submission, and thus a multitude of data formats has emerged. RESULTS: COMMAND>_ is a specialized tool meant to simplify gene expression data acquisition. It is a flexible multi-user web-application that allows users to search and download gene expression experiments, extract only the relevant information from experiment files, re-annotate microarray platforms, and present data in a simple and coherent data model for subsequent analysis. CONCLUSIONS: COMMAND>_ facilitates the creation of local datasets of gene expression data coming from both microarray and RNA-seq experiments and may be a more efficient tool to build integrated gene expression compendia. COMMAND>_ is free and open-source software, including publicly available tutorials and documentation.

COLOMBOS v3.0: leveraging gene expression compendia for cross-species analyses.

COLOMBOS is a database that integrates publicly available transcriptomics data for several prokaryotic model organisms. Compared to the previous version it has more than doubled in size, both in terms of species and data available. The manually curated condition annotation has been overhauled as well, giving more complete information about samples' experimental conditions and their differences. Functionality-wise cross-species analyses now enable users to analyse expression data for all species simultaneously, and identify candidate genes with evolutionary conserved expression behaviour. All the expression-based query tools have undergone a substantial improvement, overcoming the limit of enforced co-expression data retrieval and instead enabling the return of more complex patterns of expression behaviour. COLOMBOS is freely available through a web application at http://colombos.net/. The complete database is also accessible via REST API or downloadable as tab-delimited text files.

Dual RNA-Seq of Lysobacter capsici AZ78 - Phytophthora infestans interaction shows the implementation of attack strategies by the bacterium and unsuccessful oomycete defense responses.

Biological interactions in the microbial communities of the rhizosphere continuously shape the gene expression patterns of each individual microorganism. A dual RNA-Seq approach was applied to obtain a comprehensive overview of the molecular mechanisms activated during the interaction between the biocontrol rhizobacterium Lysobacter capsici AZ78 and the soilborne phytopathogenic oomycete Phytophthora infestans. The RNA-Seq transcriptional profile of L. capsici AZ78 was characterized by up-regulation of genes concerned in the biogenesis of type 4 pilus and lytic enzymes, involved, respectively, in host colonization and subsequent attack of the P. infestans cell wall. The activation of detoxification processes allowed L. capsici AZ78 to overcome the attempted defense processes of P. infestans. Moreover, the genes involved in antibiotic biosynthesis were up-regulated in L. capsici AZ78 and caused cell death in P. infestans, with the activation of putative apoptotic processes. The consequences of P. infestans cell death resulted in the down-regulation of primary metabolic pathways, such as carbohydrates, nucleic acids and protein metabolisms. Overall, the mechanism of action of L. capsici AZ78 was related to parasitism and predatory activities that cause the death of P. infestans.

Molecular analysis of the early interaction between the grapevine flower and Botrytis cinerea reveals that prompt activation of specific host pathways leads to fungus quiescence.

Grape quality and yield can be impaired by bunch rot, caused by the necrotrophic fungus Botrytis cinerea. Infection often occurs at flowering, and the pathogen stays quiescent until fruit maturity. Here, we report a molecular analysis of the early interaction between B. cinerea and Vitis vinifera flowers, using a controlled infection system, confocal microscopy and integrated transcriptomic and metabolic analysis of the host and the pathogen. Flowers from fruiting cuttings of the cultivar Pinot Noir were infected with green fluorescent protein (GFP)-labelled B. cinerea and studied at 24 and 96 hours post-inoculation (h.p.i.). We observed that penetration of the epidermis by B. cinerea coincided with increased expression of genes encoding cell-wall-degrading enzymes, phytotoxins and proteases. Grapevine responded with a rapid defence reaction involving 1193 genes associated with the accumulation of antimicrobial proteins, polyphenols, reactive oxygen species and cell wall reinforcement. At 96 h.p.i., the reaction appears largely diminished both in the host and in the pathogen. Our data indicate that the defence responses of the grapevine flower collectively are able to restrict invasive fungal growth into the underlying tissues, thereby forcing the fungus to enter quiescence until the conditions become more favourable to resume pathogenic development.

COLOMBOS v2.0: an ever expanding collection of bacterial expression compendia.

The COLOMBOS database (http://www.colombos.net) features comprehensive organism-specific cross-platform gene expression compendia of several bacterial model organisms and is supported by a fully interactive web portal and an extensive web API. COLOMBOS was originally published in PLoS One, and COLOMBOS v2.0 includes both an update of the expression data, by expanding the previously available compendia and by adding compendia for several new species, and an update of the surrounding functionality, with improved search and visualization options and novel tools for programmatic access to the database. The scope of the database has also been extended to incorporate RNA-seq data in our compendia by a dedicated analysis pipeline. We demonstrate the validity and robustness of this approach by comparing the same RNA samples measured in parallel using both microarrays and RNA-seq. As far as we know, COLOMBOS currently hosts the largest homogenized gene expression compendia available for seven bacterial model organisms.

Dissecting the effect of soil on plant phenology and berry transcriptional plasticity in two Italian grapevine varieties (Vitis vinifera L.).

Grapevine embodies a fascinating species as regards phenotypic plasticity and genotype-per-environment interactions. The terroir, namely the set of agri-environmental factors to which a variety is subjected, can influence the phenotype at the physiological, molecular, and biochemical level, representing an important phenomenon connected to the typicality of productions. We investigated the determinants of plasticity by conducting a field-experiment where all terroir variables, except soil, were kept as constant as possible. We isolated the effect of soils collected from different areas, on phenology, physiology, and transcriptional responses of skin and flesh of a red and a white variety of great economic value: Corvina and Glera. Molecular results, together with physio-phenological parameters, suggest a specific effect of soil on grapevine plastic response, highlighting a higher transcriptional plasticity of Glera in respect to Corvina and a marked response of skin compared to flesh. Using a novel statistical approach, we identified clusters of plastic genes subjected to the specific influence of soil. These findings could represent an issue of applicative value, posing the basis for targeted agricultural practices to enhance the desired characteristics for any soil/cultivar combination, to improve vineyards management for a better resource usage and to valorize vineyards uniqueness maximizing the terroir-effect.

Whole genome methylation profiles as independent markers of survival in stage IIIC melanoma patients.

BACKGROUND: The clinical course of cutaneous melanoma (CM) can differ significantly for patients with identical stages of disease, defined clinico-pathologically, and no molecular markers differentiate patients with such a diverse prognosis. This study aimed to define the prognostic value of whole genome DNA methylation profiles in stage III CM. METHODS: Genome-wide methylation profiles were evaluated by the Illumina Human Methylation 27 BeadChip assay in short-term neoplastic cell cultures from 45 stage IIIC CM patients. Unsupervised K-means partitioning clustering was exploited to sort patients into 2 groups based on their methylation profiles. Methylation patterns related to the discovered groups were determined using the nearest shrunken centroid classification algorithm. The impact of genome-wide methylation patterns on overall survival (OS) was assessed using Cox regression and Kaplan-Meier analyses. RESULTS: Unsupervised K-means partitioning by whole genome methylation profiles identified classes with significantly different OS in stage IIIC CM patients. Patients with a "favorable" methylation profile had increased OS (P = 0.001, log-rank = 10.2) by Kaplan-Meier analysis. Median OS of stage IIIC patients with a "favorable" vs. "unfavorable" methylation profile were 31.5 and 10.4 months, respectively. The 5 year OS for stage IIIC patients with a "favorable" methylation profile was 41.2% as compared to 0% for patients with an "unfavorable" methylation profile. Among the variables examined by multivariate Cox regression analysis, classification defined by methylation profile was the only predictor of OS (Hazard Ratio = 2.41, for "unfavorable" methylation profile; 95% Confidence Interval: 1.02-5.70; P = 0.045). A 17 gene methylation signature able to correctly assign prognosis (overall error rate = 0) in stage IIIC patients on the basis of distinct methylation-defined groups was also identified. CONCLUSIONS: A discrete whole-genome methylation signature has been identified as molecular marker of prognosis for stage IIIC CM patients. Its use in daily practice is foreseeable, and promises to refine the comprehensive clinical management of stage III CM patients.

A COMPASS for VESPUCCI: A FAIR Way to Explore the Grapevine Transcriptomic Landscape.

Successfully integrating transcriptomic experiments is a challenging task with the ultimate goal of analyzing gene expression data in the broader context of all available measurements, all from a single point of access. In its second major release VESPUCCI, the integrated database of gene expression data for grapevine, has been updated to be FAIR-compliant, employing standards and created with open-source technologies. It includes all public grapevine gene expression experiments from both microarray and RNA-seq platforms. Transcriptomic data can be accessed in multiple ways through the newly developed COMPASS GraphQL interface, while the expression values are normalized using different methodologies to flexibly satisfy different analysis requirements. Sample annotations are manually curated and use standard formats and ontologies. The updated version of VESPUCCI provides easy querying and analyzing of integrated grapevine gene expression (meta)data and can be seamlessly embedded in any analysis workflow or tools. VESPUCCI is freely accessible and offers several ways of interaction, depending on the specific goals and purposes and/or user expertise; an overview can be found at https://vespucci.readthedocs.io/.

Apyrase-mediated amplification of secretory IgA promotes intestinal homeostasis.

Secretory immunoglobulin A (SIgA) interaction with commensal bacteria conditions microbiota composition and function. However, mechanisms regulating reciprocal control of microbiota and SIgA are not defined. Bacteria-derived adenosine triphosphate (ATP) limits T follicular helper (Tfh) cells in the Peyer's patches (PPs) via P2X7 receptor (P2X7R) and thereby SIgA generation. Here we show that hydrolysis of extracellular ATP (eATP) by apyrase results in amplification of the SIgA repertoire. The enhanced breadth of SIgA in mice colonized with apyrase-releasing Escherichia coli influences topographical distribution of bacteria and expression of genes involved in metabolic versus immune functions in the intestinal epithelium. SIgA-mediated conditioning of bacteria and enterocyte function is reflected by differences in nutrient absorption in mice colonized with apyrase-expressing bacteria. Apyrase-induced SIgA improves intestinal homeostasis and attenuates barrier impairment and susceptibility to infection by enteric pathogens in antibiotic-induced dysbiosis. Therefore, amplification of SIgA by apyrase can be leveraged to restore intestinal fitness in dysbiotic conditions.

ROC analysis: applications to the classification of biological sequences and 3D structures.

ROC ('receiver operator characteristics') analysis is a visual as well as numerical method used for assessing the performance of classification algorithms, such as those used for predicting structures and functions from sequence data. This review summarizes the fundamental concepts of ROC analysis and the interpretation of results using examples of sequence and structure comparison. We overview the available programs and provide evaluation guidelines for genomic/proteomic data, with particular regard to applications to large and heterogeneous databases used in bioinformatics.

A Protein Classification Benchmark collection for machine learning.

Protein classification by machine learning algorithms is now widely used in structural and functional annotation of proteins. The Protein Classification Benchmark collection (http://hydra.icgeb.trieste.it/benchmark) was created in order to provide standard datasets on which the performance of machine learning methods can be compared. It is primarily meant for method developers and users interested in comparing methods under standardized conditions. The collection contains datasets of sequences and structures, and each set is subdivided into positive/negative, training/test sets in several ways. There is a total of 6405 classification tasks, 3297 on protein sequences, 3095 on protein structures and 10 on protein coding regions in DNA. Typical tasks include the classification of structural domains in the SCOP and CATH databases based on their sequences or structures, as well as various functional and taxonomic classification problems. In the case of hierarchical classification schemes, the classification tasks can be defined at various levels of the hierarchy (such as classes, folds, superfamilies, etc.). For each dataset there are distance matrices available that contain all vs. all comparison of the data, based on various sequence or structure comparison methods, as well as a set of classification performance measures computed with various classifier algorithms.

Transcriptome Profiles of Strawberry (Fragaria vesca) Fruit Interacting With Botrytis cinerea at Different Ripening Stages.

Gray mold caused by Botrytis cinerea is a major cause of economic losses in strawberry fruit production, limiting fruit shelf life and commercialization. When the fungus infects Fragaria × ananassa strawberry at flowering or unripe fruit stages, symptoms develop after an extended latent phase on ripe fruits before or after harvesting. To elucidate the growth kinetics of B. cinerea on flower/fruit and the molecular responses associated with low susceptibility of unripe fruit stages, woodland strawberry Fragaria vesca flowers and fruits, at unripe white and ripe red stages, were inoculated with B. cinerea. Quantification of fungal genomic DNA within 72 h postinoculation (hpi) showed limited fungal growth on open flower and white fruit, while on red fruit, the growth was exponential starting from 24 hpi and sporulation was observed within 48 hpi. RNA sequencing applied to white and red fruit at 24 hpi showed that a total of 2,141 genes (12.5% of the total expressed genes) were differentially expressed due to B. cinerea infection. A broad transcriptional reprogramming was observed in both unripe and ripe fruits, involving in particular receptor and signaling, secondary metabolites, and defense response pathways. Membrane-localized receptor-like kinases and nucleotide-binding site leucine-rich repeat genes were predominant in the surveillance system of the fruits, most of them being downregulated in white fruits and upregulated in red fruits. In general, unripe fruits exhibited a stronger defense response than red fruits. Genes encoding for pathogenesis-related proteins and flavonoid polyphenols as well as genes involved in cell-wall strengthening were upregulated, while cell-softening genes appeared to be switched off. As a result, B. cinerea remained quiescent in white fruits, while it was able to colonize ripe red fruits.

Dual Transcriptome and Metabolic Analysis of Vitis vinifera cv. Pinot Noir Berry and Botrytis cinerea During Quiescence and Egressed Infection.

Botrytis cinerea is an important necrotroph in vineyards. Primary infections are mostly initiated by airborne conidia from overwintered sources around bloom, then the fungus remains quiescent from bloom till maturity and egresses at ripeness. We previously described in detail the process of flower infection and quiescence initiation. Here, we complete the characterization studying the cross-talk between the plant and the fungus during pathogen quiescence and egression by an integrated transcriptomic and metabolic analysis of the host and the pathogen. Flowers from fruiting cuttings of the cv. Pinot Noir were inoculated with a GFP-labeled strain of B. cinerea at full cap-off stage, and molecular analyses were carried out at 4 weeks post inoculation (wpi, fungal quiescent state) and at 12 wpi (fungal pre-egression and egression states). The expressed fungal transcriptome highlighted that the fungus remodels its cell wall to evade plant chitinases besides undergoing basal metabolic activities. Berries responded by differentially regulating genes encoding for different PR proteins and genes involved in monolignol, flavonoid, and stilbenoid biosynthesis pathways. At 12 wpi, the transcriptome of B. cinerea in the pre-egressed samples showed that virulence-related genes were expressed, suggesting infection process was initiated. The egressed B. cinerea expressed almost all virulence and growth related genes that enabled the pathogen to colonize the berries. In response to egression, ripe berries reprogrammed different defense responses, though futile. Examples are activation of membrane localized kinases, stilbene synthases, and other PR proteins related to SA and JA-mediated responses. Our results indicated that hard-green berries defense program was capable to hamper B. cinerea growth. However, ripening associated fruit cell wall self-disassembly together with high humidity created the opportunity for the fungus to egress and cause bunch rot.

Benchmarking protein classification algorithms via supervised cross-validation.

Development and testing of protein classification algorithms are hampered by the fact that the protein universe is characterized by groups vastly different in the number of members, in average protein size, similarity within group, etc. Datasets based on traditional cross-validation (k-fold, leave-one-out, etc.) may not give reliable estimates on how an algorithm will generalize to novel, distantly related subtypes of the known protein classes. Supervised cross-validation, i.e., selection of test and train sets according to the known subtypes within a database has been successfully used earlier in conjunction with the SCOP database. Our goal was to extend this principle to other databases and to design standardized benchmark datasets for protein classification. Hierarchical classification trees of protein categories provide a simple and general framework for designing supervised cross-validation strategies for protein classification. Benchmark datasets can be designed at various levels of the concept hierarchy using a simple graph-theoretic distance. A combination of supervised and random sampling was selected to construct reduced size model datasets, suitable for algorithm comparison. Over 3000 new classification tasks were added to our recently established protein classification benchmark collection that currently includes protein sequence (including protein domains and entire proteins), protein structure and reading frame DNA sequence data. We carried out an extensive evaluation based on various machine-learning algorithms such as nearest neighbor, support vector machines, artificial neural networks, random forests and logistic regression, used in conjunction with comparison algorithms, BLAST, Smith-Waterman, Needleman-Wunsch, as well as 3D comparison methods DALI and PRIDE. The resulting datasets provide lower, and in our opinion more realistic estimates of the classifier performance than do random cross-validation schemes. A combination of supervised and random sampling was used to construct model datasets, suitable for algorithm comparison.

Discovering Causal Relationships in Grapevine Expression Data to Expand Gene Networks. A Case Study: Four Networks Related to Climate Change.

In recent years the scientific community has been heavily engaged in studying the grapevine response to climate change. Final goal is the identification of key genetic traits to be used in grapevine breeding and the setting of agronomic practices to improve climatic resilience. The increasing availability of transcriptomic studies, describing gene expression in many tissues and developmental, or treatment conditions, have allowed the implementation of gene expression compendia, which enclose a huge amount of information. The mining of transcriptomic data represents an effective approach to expand a known local gene network (LGN) by finding new related genes. We recently published a pipeline based on the iterative application of the PC-algorithm, named NES2RA, to expand gene networks in Escherichia coli and Arabidopsis thaliana. Here, we propose the application of this method to the grapevine transcriptomic compendium Vespucci, in order to expand four LGNs related to the grapevine response to climate change. Two networks are related to the secondary metabolic pathways for anthocyanin and stilbenoid synthesis, involved in the response to solar radiation, whereas the other two are signaling networks, related to the hormones abscisic acid and ethylene, possibly involved in the regulation of cell water balance and cuticle transpiration. The expansion networks produced by NES2RA algorithm have been evaluated by comparison with experimental data and biological knowledge on the identified genes showing fairly good consistency of the results. In addition, the algorithm was effective in retaining only the most significant interactions among the genes providing a useful framework for experimental validation. The application of the NES2RA to Vitis vinifera expression data by means of the BOINC-based implementation is available upon request (valter.cavecchia@cnr.it).

The genome sequence and transcriptome of Potentilla micrantha and their comparison to Fragaria vesca (the woodland strawberry).

Background: The genus Potentilla is closely related to that of Fragaria, the economically important strawberry genus. Potentilla micrantha is a species that does not develop berries but shares numerous morphological and ecological characteristics with Fragaria vesca. These similarities make P. micrantha an attractive choice for comparative genomics studies with F. vesca. Findings: In this study, the P. micrantha genome was sequenced and annotated, and RNA-Seq data from the different developmental stages of flowering and fruiting were used to develop a set of gene predictions. A 327 Mbp sequence and annotation of the genome of P. micrantha, spanning 2674 sequence contigs, with an N50 size of 335,712, estimated to cover 80% of the total genome size of the species was developed. The genus Potentilla has a characteristically larger genome size than Fragaria, but the recovered sequence scaffolds were remarkably collinear at the micro-syntenic level with the genome of F. vesca, its closest sequenced relative. A total of 33,602 genes were predicted, and 95.1% of bench-marking universal single-copy orthologous genes were complete within the presented sequence. Thus, we argue that the majority of the gene-rich regions of the genome have been sequenced. Conclusions: Comparisons of RNA-Seq data from the stages of floral and fruit development revealed genes differentially expressed between P. micrantha and F. vesca.The data presented are a valuable resource for future studies of berry development in Fragaria and the Rosaceae and they also shed light on the evolution of genome size and organization in this family.

Abscisic Acid Is a Major Regulator of Grape Berry Ripening Onset: New Insights into ABA Signaling Network.

Grapevine is a world-wide cultivated economically relevant crop. The process of berry ripening is non-climacteric and does not rely on the sole ethylene signal. Abscisic acid (ABA) is recognized as an important hormone of ripening inception and color development in ripening berries. In order to elucidate the effect of this signal at the molecular level, pre-véraison berries were treated ex vivo for 20 h with 0.2 mM ABA and berry skin transcriptional modulation was studied by RNA-seq after the treatment and 24 h later, in the absence of exogenous ABA. This study highlighted that a small amount of ABA triggered its own biosynthesis and had a transcriptome-wide effect (1893 modulated genes) characterized by the amplification of the transcriptional response over time. By comparing this dataset with the many studies on ripening collected within the grapevine transcriptomic compendium Vespucci, an extended overlap between ABA- and ripening modulated gene sets was observed (71% of the genes), underpinning the role of this hormone in the regulation of berry ripening. The signaling network of ABA, encompassing ABA metabolism, transport and signaling cascade, has been analyzed in detail and expanded based on knowledge from other species in order to provide an integrated molecular description of this pathway at berry ripening onset. Expression data analysis was combined with in silico promoter analysis to identify candidate target genes of ABA responsive element binding protein 2 (VvABF2), a key upstream transcription factor of the ABA signaling cascade which is up-regulated at véraison and also by ABA treatments. Two transcription factors, VvMYB143 and VvNAC17, and two genes involved in protein degradation, Armadillo-like and Xerico-like genes, were selected for in vivo validation by VvABF2-mediated promoter trans-activation in tobacco. VvNAC17 and Armadillo-like promoters were induced by ABA via VvABF2, while VvMYB143 responded to ABA in a VvABF2-independent manner. This knowledge of the ABA cascade in berry skin contributes not only to the understanding of berry ripening regulation but might be useful to other areas of viticultural interest, such as bud dormancy regulation and drought stress tolerance.

Surface-antigen expression profiling of B cell chronic lymphocytic leukemia: from the signature of specific disease subsets to the identification of markers with prognostic relevance.

Studies of gene expression profiling have been successfully used for the identification of molecules to be employed as potential prognosticators. In analogy with gene expression profiling, we have recently proposed a novel method to identify the immunophenotypic signature of B-cell chronic lymphocytic leukemia subsets with different prognosis, named surface-antigen expression profiling. According to this approach, surface marker expression data can be analysed by data mining tools identical to those employed in gene expression profiling studies, including unsupervised and supervised algorithms, with the aim of identifying the immunophenotypic signature of B-cell chronic lymphocytic leukemia subsets with different prognosis. Here we provide an overview of the overall strategy employed for the development of such an "outcome class-predictor" based on surface-antigen expression signatures. In addition, we will also discuss how to transfer the obtained information into the routine clinical practice by providing a flow-chart indicating how to select the most relevant antigens and build-up a prognostic scoring system by weighing each antigen according to its predictive power. Although referred to B-cell chronic lymphocytic leukemia, the methodology discussed here can be also useful in the study of diseases other than B-cell chronic lymphocytic leukemia, when the purpose is to identify novel prognostic determinants.

Complete genome sequence of Bacillus amyloliquefaciens subsp. plantarum S499, a rhizobacterium that triggers plant defences and inhibits fungal phytopathogens.

Bacillus amyloliquefaciens subsp. plantarum S499 is a plant beneficial rhizobacterium with a good antagonistic potential against phytopathogens through the release of active secondary metabolites. Moreover, it can induce systemic resistance in plants by producing considerable amounts of surfactins. The complete genome sequence of B. amyloliquefaciens subsp. plantarum S499 includes a circular chromosome of 3,927,922bp and a plasmid of 8,008bp. A remarkable abundance in genomic regions of putative horizontal origin emerged from the analysis. Furthermore, we highlighted the presence of genes involved in the establishment of interactions with the host plants at the root level and in the competition with other soil-borne microorganisms. More specifically, genes related to the synthesis of amylolysin, amylocyclicin, and butirosin were identified. These antimicrobials were not known before to be part of the antibiotic arsenal of the strain. The information embedded in the genome will support the upcoming studies regarding the application of B. amyloliquefaciens isolates as plant-growth promoters and biocontrol agents.

The Lysobacter capsici AZ78 Genome Has a Gene Pool Enabling it to Interact Successfully with Phytopathogenic Microorganisms and Environmental Factors.

Lysobacter capsici AZ78 has considerable potential for biocontrol of phytopathogenic microorganisms. However, lack of information about genetic cues regarding its biological characteristics may slow down its exploitation as a biofungicide. In order to obtain a comprehensive overview of genetic features, the L. capsici AZ78 genome was sequenced, annotated and compared with the phylogenetically related pathogens Stenotrophomonas malthophilia K729a and Xanthomonas campestris pv. campestris ATCC 33913. Whole genome comparison, supported by functional analysis, indicated that L. capsici AZ78 has a larger number of genes responsible for interaction with phytopathogens and environmental stress than S. malthophilia K729a and X. c. pv. campestris ATCC 33913. Genes involved in the production of antibiotics, lytic enzymes and siderophores were specific for L. capsici AZ78, as well as genes involved in resistance to antibiotics, environmental stressors, fungicides and heavy metals. The L. capsici AZ78 genome did not encompass genes involved in infection of humans and plants included in the S. malthophilia K729a and X. c. pv. campestris ATCC 33913 genomes, respectively. The L. capsici AZ78 genome provides a genetic framework for detailed analysis of other L. capsici members and the development of novel biofungicides based on this bacterial strain.