Next generation sequencing technology provides a powerful tool for measuring gene expression (mRNA) levels in the form of RNA-sequence data. Method development for identifying differentially expressed (DE) genes from RNA-seq data, which frequently includes many low-count integers and can exhibit severe overdispersion relative to Poisson or binomial 1

Some strains of the foliar pathogen Pseudomonas syringae are adapted for growth and survival on leaf surfaces and in the leaf interior. Global transcriptome profiling was used to evaluate if these two habitats offer distinct environments for bacteria and thus present distinct driving forces for adaptation. The transcript profiles of Pseudomonas syringae pv. syringae B728a support a model in which leaf surface, or epiphytic, sites specifically favor flagellar motility, swarming motility based on 3-(3-hydroxyalkanoyloxy)alkanoic acid surfactant production, chemosensing, and chemotaxis, indicating active relocation primarily on the leaf surface. Epiphytic sites also promote high transcript levels for phenylalanine degradation, which may help counteract phenylpropanoidbased defenses before leaf entry. In contrast, intercellular, or apoplastic, sites favor the high-level expression of genes for GABA metabolism (degradation of these genes would attenuate GABA repression of virulence) and the synthesis of phytotoxins, two additional secondary metabolites, and syringolin A. These findings support roles for these compounds in virulence, including a role for syringolin A in suppressing defense responses beyond stomatal closure. A comparison of the transcriptomes from in planta cells and from cells exposed to osmotic stress, oxidative stress, and iron and nitrogen limitation indicated that water availability, in particular, was limited in both leaf habitats but was more severely limited in the apoplast than on the leaf surface under the conditions tested. These findings contribute to a coherent model of the adaptations of this widespread bacterial phytopathogen to distinct habitats within its host.endophyte | epiphyte | phyllosphere

Typically, F 1 -hybrids are more vigorous than their homozygous, genetically distinct parents, a phenomenon known as heterosis. In the present study, the transcriptomes of the reciprocal maize (Zea mays L.) hybrids B733Mo17 and Mo173B73 and their parental inbred lines B73 and Mo17 were surveyed in primary roots, early in the developmental manifestation of heterotic root traits. The application of statistical methods and a suitable experimental design established that 34,233 (i.e., 86%) of all high-confidence maize genes were expressed in at least one genotype. Nearly 70% of all expressed genes were differentially expressed between the two parents and 42%-55% of expressed genes were differentially expressed between one of the parents and one of the hybrids. In both hybrids,~10% of expressed genes exhibited nonadditive gene expression. Consistent with the dominance model (i.e., complementation) for heterosis, 1124 genes that were expressed in the hybrids were expressed in only one of the two parents. For 65 genes, it could be shown that this was a consequence of complementation of genomic presence/absence variation. For dozens of other genes, alleles from the inactive inbred were activated in the hybrid, presumably via interactions with regulatory factors from the active inbred. As a consequence of these types of complementation, both hybrids expressed more genes than did either parental inbred. Finally, in hybrids,~14% of expressed genes exhibited allele-specific expression (ASE) levels that differed significantly from the parental-inbred expression ratios, providing further evidence for interactions of regulatory factors

Innovations in sequencing technologies have allowed biologists to make incredible advances in understanding biological systems. As experience grows, researchers increasingly recognize that analyzing the wealth of data provided by these new sequencing platforms requires careful attention to detail for robust results. Thus far, much of the scientific Communit’s focus for use in bacterial genomics has been on evaluating genome assembly algorithms and rigorously validating assembly program performance. Missing, however, is a focus on critical evaluation of variant callers for these genomes. Variant calling is essential for comparative genomics as it yields insights into nucleotide-level organismal differences. Variant calling is a multistep process with a host of potential error sources that may lead to incorrect variant calls. Identifying and resolving these incorrect calls is critical for bacterial genomics to advance. The goal of this review is to provide guidance on validating algorithms and pipelines used in variant calling for bacterial genomics. First, we will provide an overview of the variant calling procedures and the potential sources of error associated with the methods. We will then identify appropriate datasets for use in evaluating algorithms and describe statistical methods for evaluating algorithm performance. As variant calling moves from basic research to the applied setting, standardized methods for performance evaluation and reporting are required; it is our hope that this review provides the groundwork for the development of these standards.