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Activation of cell-surface and intracellular receptor-mediated immunity results in rapid transcriptional reprogramming that underpins disease resistance. However, the mechanisms by which co-activation of both immune systems lead to transcriptional changes are not clear. Here, we combine RNA-seq and ATAC-seq to define changes in gene expression and chromatin accessibility. Activation of cell-surface or intracellular receptor-mediated immunity, or both, increases chromatin accessibility at induced defence genes. Analysis of ATAC-seq and RNA-seq data combined with publicly available information on transcription factor DNA-binding motifs enabled comparison of individual gene regulatory networks activated by cell-surface or intracellular receptor-mediated immunity, or by both. These results and analyses reveal overlapping and conserved transcriptional regulatory mechanisms between the two immune systems. [ABSTRACT FROM AUTHOR]

In plants, nucleotide-binding domain and leucine-rich repeat (NLR)-containing proteins can form receptor networks to confer hypersensitive cell death and innate immunity. One class of NLRs, known as NLR required for cell death (NRCs), are central nodes in a complex network that protects against multiple pathogens and comprises up to half of the NLRome of solanaceous plants. Given the prevalence of this NLR network, we hypothesised that pathogens convergently evolved to secrete effectors that target NRC activities. To test this, we screened a library of 165 bacterial, oomycete, nematode, and aphid effectors for their capacity to suppress the cell death response triggered by the NRC-dependent disease resistance proteins Prf and Rpi-blb2. Among 5 of the identified suppressors, 1 cyst nematode protein and 1 oomycete protein suppress the activity of autoimmune mutants of NRC2 and NRC3, but not NRC4, indicating that they specifically counteract a subset of NRC proteins independently of their sensor NLR partners. Whereas the cyst nematode effector SPRYSEC15 binds the nucleotide-binding domain of NRC2 and NRC3, the oomycete effector AVRcap1b suppresses the response of these NRCs via the membrane trafficking-associated protein NbTOL9a (Target of Myb 1-like protein 9a). We conclude that plant pathogens have evolved to counteract central nodes of the NRC immune receptor network through different mechanisms. Coevolution with pathogen effectors may have driven NRC diversification into functionally redundant nodes in a massively expanded NLR network. Plant pathogens have evolved to counteract their hosts' immune systems. A screen for pathogen effectors that suppress sensor NLR-mediated cell death in tobacco identifies effectors from a cyst nematode and an oomycete that suppress the NRC branch of the immune network to inhibit the immune-related cell death response. [ABSTRACT FROM AUTHOR]

Background: Plant pathogens cause billions of dollars of crop loss every year and are a major threat to global food security. Effector proteins are the tools such pathogens use to infect the cell, predicting effectors de novo from sequence is difficult because of the heterogeneity of the sequences. We hypothesised that deep learning classifiers based on Convolutional Neural Networks would be able to identify effectors and deliver new insights. Results: We created a training set of manually curated effector sequences from PHI-Base and used these to train a range of model architectures for classifying bacteria, fungal and oomycete sequences. The best performing classifiers had accuracies from 93 to 84%. The models were tested against popular effector detection software on our own test data and data provided with those models. We observed better performance from our models. Specifically our models showed greater accuracy and lower tendencies to call false positives on a secreted protein negative test set and a greater generalisability. We used GRAD-CAM activation map analysis to identify the sequences that activated our CNN-LSTM models and found short but distinct N-terminal regions in each taxon that was indicative of effector sequences. No motifs could be observed in these regions but an analysis of amino acid types indicated differing patterns of enrichment and depletion that varied between taxa. Conclusions: Small training sets can be used effectively to train highly accurate and sensitive deep learning models without need for the operator to know anything other than sequence and without arbitrary decisions made about what sequence features or physico-chemical properties are important. Biological insight on subsequences important for classification can be achieved by examining the activations in the model [ABSTRACT FROM AUTHOR]

Summary: The plant immune system involves detection of pathogens via both cell‐surface and intracellular receptors. Both receptor classes can induce transcriptional reprogramming that elevates disease resistance. To assess differential gene expression during plant immunity, we developed and deployed quantitative sequence capture (CAP‐I). We designed and synthesized biotinylated single‐strand RNA bait libraries targeted to a subset of defense genes, and generated sequence capture data from 99 RNA‐seq libraries. We built a data processing pipeline to quantify the RNA‐CAP‐I‐seq data, and visualize differential gene expression. Sequence capture in combination with quantitative RNA‐seq enabled cost‐effective assessment of the expression profile of a specified subset of genes. Quantitative sequence capture is not limited to RNA‐seq or any specific organism and can potentially be incorporated into automated platforms for high‐throughput sequencing. [ABSTRACT FROM AUTHOR]

Expansion of gene families facilitates robustness and evolvability of biological processes but impedes functional genetic dissection of signalling pathways. To address this, quantitative analysis of single cell responses can help characterize the redundancy within gene families. We developed high‐throughput quantitative imaging of stomatal closure, a response of plant guard cells, and performed a reverse genetic screen in a group of Arabidopsis mutants to five stimuli. Focussing on the intersection between guard cell signalling and the endomembrane system, we identified eight clusters based on the mutant stomatal responses. Mutants generally affected in stomatal closure were mostly in genes encoding SNARE and SCAMP membrane regulators. By contrast, mutants in RAB5 GTPase genes played specific roles in stomatal closure to microbial but not drought stress. Together with timed quantitative imaging of endosomes revealing sequential patterns in FLS2 trafficking, our imaging pipeline can resolve non‐redundant functions of the RAB5 GTPase gene family. Finally, we provide a valuable image‐based tool to dissect guard cell responses and outline a genetic framework of stomatal closure. High‐throughput imaging at cellular and subcellular scales enables genetic dissection of plant physiological responses to biotic and abiotic stresses. We developed the software tool StomataMeasurer allowing the identification and measurements of stomatal apertures in an automated fashion. Using Arabidopsis mutant screening, we demonstrate that StomataMeasurer is suitable to analyse stomatal patterning and behaviours in response to five stimuli. [ABSTRACT FROM AUTHOR]

Summary: The tomato PROCERA gene encodes a DELLA protein, and loss‐of‐function mutations derepress growth. We used CRISPR/Cas9 and a single guide RNAs (sgRNA) to target mutations to the PROCERADELLA domain, and recovered several loss‐of‐function mutations and a dominant dwarf mutation that carries a deletion of one amino acid in the DELLA domain. This is the first report of a dominant dwarf PROCERA allele. This allele retains partial responsiveness to exogenously applied gibberellin. Heterozygotes show an intermediate phenotype at the seedling stage, but adult heterozygotes are as dwarfed as homozygotes. [ABSTRACT FROM AUTHOR]

High throughput confocal imaging poses challenges in the computational image analysis of complex subcellular structures such as the microtubule cytoskeleton. Here, we developed CellArchitect, an automated image analysis tool that quantifies changes to subcellular patterns illustrated by microtubule markers in plants. We screened microtubule-targeted herbicides and demonstrate that high throughput confocal imaging with integrated image analysis by CellArchitect can distinguish effects induced by the known herbicides indaziflam and trifluralin. The same platform was used to examine 6 other compounds with herbicidal activity, and at least 3 different effects induced by these compounds were profiled. We further show that CellArchitect can detect subcellular patterns tagged by actin and endoplasmic reticulum markers. Thus, the platform developed here can be used to automate image analysis of complex subcellular patterns for purposes such as herbicide discovery and mode of action characterisation. The capacity to use this tool to quantitatively characterize cellular responses lends itself to application across many areas of biology. [ABSTRACT FROM AUTHOR]

Plants recognize pathogen-associated molecular patterns (PAMPs) via cell surface-localized pattern recognition receptors (PRRs), leading to PRR-triggered immunity (PTI). The Arabidopsis cytoplasmic kinase BIK1 is a downstream substrate of several PRR complexes. How plant PTI is negatively regulated is not fully understood. Here, we identify the protein phosphatase PP2C38 as a negative regulator of BIK1 activity and BIK1-mediated immunity. PP2C38 dynamically associates with BIK1, as well as with the PRRs FLS2 and EFR, but not with the co-receptor BAK1. PP2C38 regulates PAMP-induced BIK1 phosphorylation and impairs the phosphorylation of the NADPH oxidase RBOHD by BIK1, leading to reduced oxidative burst and stomatal immunity. Upon PAMP perception, PP2C38 is phosphorylated on serine 77 and dissociates from the FLS2/EFR-BIK1 complexes, enabling full BIK1 activation. Together with our recent work on the control of BIK1 turnover, this study reveals another important regulatory mechanism of this central immune component. [ABSTRACT FROM AUTHOR]

Background: To cope with the ever-increasing amount of sequence data generated in the field of genomics, the demand for efficient and fast database searches that drive functional and structural annotation in both large- and small-scale genome projects is on the rise. The tools of the BLAST+ suite are the most widely employed bioinformatic method for these database searches. Recent trends in bioinformatics application development show an increasing number of JavaScript apps that are based on modern frameworks such as Node.js. Until now, there is no way of using database searches with the BLAST+ suite from a Node.js codebase. Results: We developed blastjs, a Node.js library that wraps the search tools of the BLAST+ suite and thus allows to easily add significant functionality to any Node.js-based application. Conclusion: blastjs is a library that allows the incorporation of BLAST+ functionality into bioinformatics applications based on JavaScript and Node.js. The library was designed to be as user-friendly as possible and therefore requires only a minimal amount of code in the client application. The library is freely available under the MIT license at https://github.com/teammaclean/blastjs. [ABSTRACT FROM AUTHOR]

1. Herbivory often induces systemic plant responses that affect the host choice of subsequent herbivores, either deterring or attracting them, with implications for the performance of both herbivore and host plant. Combining measures of herbivore movement and consumption can efficiently provide insights into the induced plant responses that are most important for determining choice behaviour. 2. The preferences of two frugivorous stink bug species, Nezara viridula and Euschistus servus between cotton plants left undamaged or damaged by Helicoverpa zea and Heliothis virescens larvae were investigated. A novel consumer movement model was used to investigate if attraction rates or leaving rates determined preferences. Stink bug consumption rates were measured using salivary sheath flanges. Finally, the systemic induction of selected phenolic-based and terpenoid secondary metabolites were measured from heliothine herbivory on developing cotton bolls, to investigate if they explained stink bug feeding responses. 3. Heliothine herbivory did not affect the N. viridula feeding preference. However, we found opposing effects of H. zea and H. virescens herbivory on the behaviour of E. servus. Avoidance of H. zea-damaged plants is not obviously related to phenolic or terpenoid induction in cotton bolls; whereas a preference for H. virescens-damaged plants may be related to reductions in chlorogenic acid in boll carpel walls. 4. The present results highlight the inferential power of measuring both consumer movement and consumption in preference experiments and combining behavioural responses with phytochemical responses. Furthermore, while plant-mediated interactions among herbivorous insects are well studied, interactions among frugivorous species specifically have been poorly documented. [ABSTRACT FROM AUTHOR]

The article offers information on the Fraxinus Facebook based game.

Motivation: Single nucleotide polymorphism (SNP) discovery is an important preliminary for understanding genetic variation. With current sequencing methods, we can sample genomes comprehensively. SNPs are found by aligning sequence reads against longer assembled references. De Bruijn graphs are efficient data structures that can deal with the vast amount of data from modern technologies. Recent work has shown that the topology of these graphs captures enough information to allow the detection and characterization of genetic variants, offering an alternative to alignment-based methods. Such methods rely on depth-first walks of the graph to identify closing bifurcations. These methods are conservative or generate many false-positive results, particularly when traversing highly inter-connected (complex) regions of the graph or in regions of very high coverage.Results: We devised an algorithm that calls SNPs in converted De Bruijn graphs by enumerating 2k + 2 cycles. We evaluated the accuracy of predicted SNPs by comparison with SNP lists from alignment-based methods. We tested accuracy of the SNP calling using sequence data from 16 ecotypes of Arabidopsis thaliana and found that accuracy was high. We found that SNP calling was even across the genome and genomic feature types. Using sequence-based attributes of the graph to train a decision tree allowed us to increase accuracy of SNP calls further. Together these results indicate that our algorithm is capable of finding SNPs accurately in complex sub-graphs and potentially comprehensively from whole genome graphs.Availability and implementation: The source code for a C++ implementation of our algorithm is available under the GNU Public Licence v3 at: https://github.com/danmaclean/2kplus2. The datasets used in this study are available at the European Nucleotide Archive, reference ERP00565, http://www.ebi.ac.uk/ena/data/view/ERP000565Contact: dan.maclean@tsl.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Background Analysis of mutants isolated from forward-genetic screens has revealed key components of several plant signalling pathways. Mapping mutations by position, either using classical methods or whole genome high-throughput sequencing (HTS), largely relies on the analysis of genome-wide polymorphisms in F2 recombinant populations. Combining bulk segregant analysis with HTS has accelerated the identification of causative mutations and has been widely adopted in many research programmes. A major advantage of HTS is the ability to perform bulk segregant analysis after back-crossing to the parental line rather than outcrossing to a polymorphic plant ecotype, which reduces genetic complexity and avoids issues with phenotype penetrance in different ecotypes. Plotting the positions of homozygous polymorphisms in a mutant genome identifies areas of low recombination and is an effective way to detect molecular linkage to a phenotype of interest. Results We describe the use of single nucleotide polymorphism (SNP) density plots as a mapping strategy to identify and refine chromosomal positions of causative mutations from screened plant populations. We developed a web application called CandiSNP that generates density plots from user-provided SNP data obtained from HTS. Candidate causative mutations, defined as SNPs causing non-synonymous changes in annotated coding regions are highlighted on the plots and listed in a table. We use data generated from a recent mutant screen in the model plant Arabidopsis thaliana as proof-of-concept for the validity of our tool. Conclusions CandiSNP is a user-friendly application that will aid in novel discoveries from forwardgenetic mutant screens. It is particularly useful for analysing HTS data from bulked backcrossed mutants, which contain fewer polymorphisms than data generated from out-crosses. The web-application is freely available online at http://candisnp.tsl.ac.uk. [ABSTRACT FROM AUTHOR]

RenSeq is a NB- LRR (nucleotide binding-site leucine-rich repeat) gene-targeted, Resistance gene enrichment and sequencing method that enables discovery and annotation of pathogen resistance gene family members in plant genome sequences. We successfully applied RenSeq to the sequenced potato Solanum tuberosum clone DM, and increased the number of identified NB- LRRs from 438 to 755. The majority of these identified R gene loci reside in poorly or previously unannotated regions of the genome. Sequence and positional details on the 12 chromosomes have been established for 704 NB- LRRs and can be accessed through a genome browser that we provide. We compared these NB- LRR genes and the corresponding oligonucleotide baits with the highest sequence similarity and demonstrated that ~80% sequence identity is sufficient for enrichment. Analysis of the sequenced tomato S. lycopersicum 'Heinz 1706' extended the NB- LRR complement to 394 loci. We further describe a methodology that applies RenSeq to rapidly identify molecular markers that co-segregate with a pathogen resistance trait of interest. In two independent segregating populations involving the wild Solanum species S. berthaultii ( Rpi-ber2) and S. ruiz-ceballosii ( Rpi-rzc1), we were able to apply Ren Seq successfully to identify markers that co-segregate with resistance towards the late blight pathogen Phytophthora infestans. These SNP identification workflows were designed as easy-to-adapt Galaxy pipelines. [ABSTRACT FROM AUTHOR]

Summary [ABSTRACT FROM AUTHOR]

Biotrophic eukaryotic plant pathogens require a living host for their growth and form an intimate haustorial interface with parasitized cells. Evolution to biotrophy occurred independently in fungal rusts and powdery mildews, and in oomycete white rusts and downy mildews. Biotroph evolution and molecular mechanisms of biotrophy are poorly understood. It has been proposed, but not shown, that obligate biotrophy results from (i) reduced selection for maintenance of biosynthetic pathways and (ii) gain of mechanisms to evade host recognition or suppress host defence. Here we use Illumina sequencing to define the genome, transcriptome, and gene models for the obligate biotroph oomycete and Arabidopsis parasite, Albugo laibachii. A. laibachii is a member of the Chromalveolata, which incorporates Heterokonts (containing the oomycetes), Apicomplexa (which includes human parasites like Plasmodium falciparum and Toxoplasma gondii), and four other taxa. From comparisons with other oomycete plant pathogens and other chromalveolates, we reveal independent loss of molybdenum-cofactor-requiring enzymes in downy mildews, white rusts, and the malaria parasite P. falciparum. Biotrophy also requires ''effectors'' to suppress host defence; we reveal RXLR and Crinkler effectors shared with other oomycetes, and also discover and verify a novel class of effectors, the ''CHXCs'', by showing effector delivery and effector functionality. Our findings suggest that evolution to progressively more intimate association between host and parasite results in reduced selection for retention of certain biosynthetic pathways, and particularly reduced selection for retention of molybdopterinrequiring biosynthetic pathways. These mechanisms are not only relevant to plant pathogenic oomycetes but also to human pathogens within the Chromalveolata. [ABSTRACT FROM AUTHOR]

Membrane compartmentalization and trafficking within and between cells is considered an essential cellular property of higher eukaryotes. We established a high-throughput imaging method suitable for the quantitative detection of membrane compartments at subcellular resolution in intact epidermal tissue. Whole Arabidopsis (Arabidopsis thaliana) cotyledon leaves were subjected to quantitative confocal laser microscopy using automated image acquisition, computational pattern recognition, and quantification of membrane compartments. This revealed that our method is sensitive and reliable to detect distinct endomembrane compartments. We applied quantitative confocal laser microscopy to a transgenic line expressing GFP-2xFYVE as a marker for endosomal compartments during biotic or abiotic stresses, and detected markedly quantitative adaptations in response to changing environments. Using a transgenic line expressing the plasma membrane-resident syntaxin GFP-PEN1, we quantified the pathogen-inducible extracellular accumulation of this fusion protein at fungal entry sites. Our protocol provides a platform to study the quantitative and dynamic changes of endomembrane trafficking, and potential adaptations of this machinery to physiological stress. [ABSTRACT FROM AUTHOR]

This work describes a parallel implementation of a distinctly different concept in self-organizing maps (SOMs)—processing of the input as a whole, in parallel, via neurons attracted to cover regions of the input space rather than single input instances. The algorithm is analyzed from the perspective of its parallelism and a simple multi-machine concept is utilized to demonstrate the parallel nature of the neural network architecture. The nature of the classic SOM (Ozdzynski, P., Lin, A., Liljeholm, M. and Beatty, J. (2002) A parallel implementation of Kohonen's self-organizing map algorithm: performance and scalability. Neurocomputing, 44–46, 567–571) algorithm displays some parallel characteristics, but contains several bottlenecks, which prevent a true parallel execution. Most SOM parallel implementations either deal with expensive hardware that requires considerable algorithm adaptation, or run multiple instances of SOM on multiple machines and choose the most relevant, or adopt controller–worker architecture with the controller machine presenting a significant bottleneck in the parallelism. Our algorithm (Valova, I., Szer, D., Gueorguieva, G. and Buer, A. (2005) A parallel growing architecture for self-organizing maps with unsupervised learning. Neurocomputing, 68C, 177–195), which we eventually named ParaSOM, reveals no need for significant adaptation to be ported to parallelism and the analysis and implementations we discuss illustrate this quality. This work presents two designs for parallelizing our algorithm and analyzes both while demonstrating the easiness in the transition to a parallel paradigm. We have illustrated the performance gain of the proposed implementation and compared it with other similar methods, from the perspectives of both parallelism and nature of the mapping algorithm. [ABSTRACT FROM PUBLISHER]

Onion is a major vegetable crop in the world. However, various plant diseases, including sour skin caused by Burkholderia cepacia, pose a great threat to the onion industry by reducing shelf-life and are responsible for significant postharvest losses in both conventional and controlled atmosphere (CA) storage. This study investigated a new sensing approach to detect sour skin using a gas sensor array and the support vector machine (SVM). Sour skin infected onions were put in a concentration chamber for headspace accumulation and measured three to six days after inoculation. Principal component analysis (PCA) score plots showed two distinct clusters formed by healthy and sour skin infected onions. The MANOVA statistical test further proved the hypothesis that the responses of the gas sensor array to healthy onion bulbs and sour skin infected onion bulbs are significantly different ( P < 0.0001). The support vector machine was employed for the classification model development. The study was undertaken in two phases: model training and cross-validation within the training datasets and model validation using new datasets. The performances of three feature selection schemes were compared using the trained SVM model. The classification results showed that although the six-sensor scheme (with 81% sensor reduction) had a slightly lower correct classification rate in the training phase, it significantly outperformed its counterparts in the validation phase (85% vs. 69% and 67%). This result proved that effective feature selection strategy could improve the discrimination power of the gas sensor array. This study demonstrated the feasibility of using a gas sensor array coupled with the SVM for the detection of sour skin in sweet onion bulbs. Early detection of sour skin will help reduce postharvest losses and secondary spread of bacteria in storage. [ABSTRACT FROM AUTHOR]

This study investigates oxidative stress and bioindicators of reproductive function in wild white sucker (Catostomus commersoni) collected from environments receiving pulp and paper mill effluent discharges in northern Ontario. Samples were collected over an eight-year period adjacent to three pulp and paper mills using a variety of processing and bleaching techniques. Fish collected downstream of pulp and paper mills within the Moose River basin exhibited elevated hepatic and gonadal 2-thiobarbituric acid reactive substances (TBARS), the presence of which is indicative of oxidative stress in these tissues. Within the Jackfish Bay system, exposure to pulp and paper mill effluent did not elevate hepatic or gonadal TBARS. Hepatic cytochrome P4501A activity (CYP1A) and fatty acyl-CoA oxidase (FAO) activities were frequently increased in livers of Moose River basin fish exposed to pulp and paper mill effluent, while lower activities of both enzymes were found within fish from the Jackfish Bay system. This suggests that oxidative stress may be related to CYP1A and FAO activities. Within the Moose River system, increases in measures of oxidative stress (TBARS, FAO) were generally coincident with decreased levels of 17 β-estradiol; however, testosterone was often lower in Jackfish Bay system fish without any commensurate changes in oxidative stress. The suite of reproductive and oxidative stress parameters measured in this study varied between seasons and mills suggesting responses to effluent are dynamic and effects are complicated by different receiving environments. The relationship between gonad size, gonadal oxidative stress, and circulating plasma steroids remains unclear. [ABSTRACT FROM AUTHOR]

Motivation: bio-samtools is a Ruby language interface to SAMtools, the highly popular library that provides utilities for manipulating high-throughput sequence alignments in the Sequence Alignment/Map format. Advances in Ruby, now allow us to improve the analysis capabilities and increase bio-samtools utility, allowing users to accomplish a large amount of analysis using a very small amount of code. bio-samtools can also be easily developed to include additional SAMtools methods and hence stay current with the latest SAMtools releases. Results: We have added new Ruby classes for the MPileup and Variant Call Format (VCF) data formats emitted by SAMtools and introduced more analysis methods for variant analysis, including alternative allele calculation and allele frequency calling for SNPs. Our new implementation of biosamtools also ensures that all the functionality of the SAMtools library is now supported and that bio-samtools can be easily extended to include future changes in SAMtools. bio-samtools 2 also provides methods that allow the user to directly produce visualization of alignment data [ABSTRACT FROM AUTHOR]

Summary: Biogem provides a software development environment for the Ruby programming language, which encourages community-based software development for bioinformatics while lowering the barrier to entry and encouraging best practices.Biogem, with its targeted modular and decentralized approach, software generator, tools and tight web integration, is an improved general model for scaling up collaborative open source software development in bioinformatics.Availability: Biogem and modules are free and are OSS. Biogem runs on all systems that support recent versions of Ruby, including Linux, Mac OS X and Windows. Further information at http://www.biogems.info. A tutorial is available at http://www.biogems.info/howto.htmlContact: bonnal@ingm.org [ABSTRACT FROM AUTHOR]

Background Assay for Transposase-Accessible Chromatin (ATAC)-cap-seq is a high-throughput sequencing method that combines ATAC-seq with targeted nucleic acid enrichment of precipitated DNA fragments. There are increased analytical difficulties arising from working with a set of regions of interest that may be small in number and biologically dependent. Common statistical pipelines for RNA sequencing might be assumed to apply but can give misleading results on ATAC-cap-seq data. A tool is needed to allow a nonspecialist user to quickly and easily summarize data and apply sensible and effective normalization and analysis. Results We developed atacR to allow a user to easily analyze their ATAC enrichment experiment. It provides comprehensive summary functions and diagnostic plots for studying enriched tag abundance. Application of between-sample normalization is made straightforward. Functions for normalizing based on user-defined control regions, whole library size, and regions selected from the least variable regions in a dataset are provided. Three methods for detecting differential abundance of tags from enriched methods are provided, including bootstrap t, Bayes factor, and a wrapped version of the standard exact test in the edgeR package. We compared the precision, recall, and F -score of each detection method on resampled datasets at varying replicate, significance threshold, and genes changed and found that the Bayes factor method had the greatest overall detection power, though edgeR was slightly stronger in simulations with lower numbers of genes changed. Conclusions Our package allows a nonspecialist user to easily and effectively apply methods appropriate to the analysis of ATAC-cap-seq in a reproducible manner. The package is implemented in pure R and is fully interoperable with common workflows in Bioconductor. [ABSTRACT FROM AUTHOR]

Extended abstract of a paper presented at Microscopy and Microanalysis 2010 in Portland, Oregon, USA, August 1 – August 5, 2010. [ABSTRACT FROM PUBLISHER]

Puccinia monoica is a spectacular plant parasitic rust fungus that triggers the formation of flower-like structures (pseudoflowers) in its Brassicaceae host plant Boechera stricta. Pseudoflowers mimic in shape, color, nectar and scent co-occurring and unrelated flowers such as buttercups. They act to attract insects thereby aiding spore dispersal and sexual reproduction of the rust fungus. Although much ecological research has been performed on P. monoica-induced pseudoflowers, this system has yet to be investigated at the molecular or genomic level. To date, the molecular alterations underlying the development of pseudoflowers and the genes involved have not been described. To address this, we performed gene expression profiling to reveal 256 plant biological processes that are significantly altered in pseudoflowers. Among these biological processes, plant genes involved in cell fate specification, regulation of transcription, reproduction, floral organ development, anthocyanin (major floral pigments) and terpenoid biosynthesis (major floral volatile compounds) were down-regulated in pseudoflowers. In contrast, plant genes involved in shoot, cotyledon and leaf development, carbohydrate transport, wax biosynthesis, cutin transport and L-phenylalanine metabolism (pathway that results in phenylethanol and phenylacetaldehyde volatile production) were up-regulated. These findings point to an extensive reprogramming of host genes by the rust pathogen to induce floral mimicry. We also highlight 31 differentially regulated plant genes that are enriched in the biological processes mentioned above, and are potentially involved in the formation of pseudoflowers. This work illustrates the complex perturbations induced by rust pathogens in their host plants, and provides a starting point for understanding the molecular mechanisms of pathogen-induced floral mimicry. [ABSTRACT FROM AUTHOR]

This study investigated the effects of exposure to waterborne perfluorooctane sulfonate (PFOS) on oxidative stress and reproductive endpoints in fish. Exposures utilized species commonly used in toxicological testing, including the fathead minnow (Pimephales promelas) and rainbow trout (Oncorhynchus mykiss), as well as relatively insensitive taxa such as creek chub (Semotilus atromaculatus), spottail shiner (Notropis hudsonius), and white sucker (Catostomus commersoni). In all fish species, short-term (14-28 d) exposure to PFOS produced only modest mortality at concentrations consistent with environmental spill scenarios. However, PFOS consistently increased hepatic fatty acyl--CoA oxidase activity and increased oxidative damage, as quantified using the 2-thiobarbituric acid--reactive substances assay. Plasma testosterone, 11-ketotestosterone, and 17ß-estradiol titers were often elevated with PFOS exposure. Vitellogenin, the egg yolk precursor protein, was occasionally altered in the plasma with PFOS exposure, but responses varied with maturity. Oviposition frequency and egg deposition in fathead minnow were not significantly impaired with PFOS exposure, despite a trend toward progressive impairment with increasing exposure concentrations. Although short-term PFOS exposure produced significant impacts on biochemical and reproductive endpoints in fish at concentrations consistent with environmental spills, the impact of long-term exposure to environmentally relevant concentrations of PFOS is unclear. [ABSTRACT FROM AUTHOR]

Presents the poem "Hideo Nakamura," by Dan MacLean.