StORF-Reporter: finding genes between genes.

Large regions of prokaryotic genomes are currently without any annotation, in part due to well-established limitations of annotation tools. For example, it is routine for genes using alternative start codons to be misreported or completely omitted. Therefore, we present StORF-Reporter, a tool that takes an annotated genome and returns regions that may contain missing CDS genes from unannotated regions. StORF-Reporter consists of two parts. The first begins with the extraction of unannotated regions from an annotated genome. Next, Stop-ORFs (StORFs) are identified in these unannotated regions. StORFs are open reading frames that are delimited by stop codons and thus can capture those genes most often missing in genome annotations. We show this methodology recovers genes missing from canonical genome annotations. We inspect the results of the genomes of model organisms, the pangenome of Escherichia coli, and a set of 5109 prokaryotic genomes of 247 genera from the Ensembl Bacteria database. StORF-Reporter extended the core, soft-core and accessory gene collections, identified novel gene families and extended families into additional genera. The high levels of sequence conservation observed between genera suggest that many of these StORFs are likely to be functional genes that should now be considered for inclusion in canonical annotations.

No one tool to rule them all: prokaryotic gene prediction tool annotations are highly dependent on the organism of study.

MOTIVATION: The biases in CoDing Sequence (CDS) prediction tools, which have been based on historic genomic annotations from model organisms, impact our understanding of novel genomes and metagenomes. This hinders the discovery of new genomic information as it results in predictions being biased towards existing knowledge. To date, users have lacked a systematic and replicable approach to identify the strengths and weaknesses of any CDS prediction tool and allow them to choose the right tool for their analysis. RESULTS: We present an evaluation framework (ORForise) based on a comprehensive set of 12 primary and 60 secondary metrics that facilitate the assessment of the performance of CDS prediction tools. This makes it possible to identify which performs better for specific use-cases. We use this to assess 15 ab initio- and model-based tools representing those most widely used (historically and currently) to generate the knowledge in genomic databases. We find that the performance of any tool is dependent on the genome being analysed, and no individual tool ranked as the most accurate across all genomes or metrics analysed. Even the top-ranked tools produced conflicting gene collections, which could not be resolved by aggregation. The ORForise evaluation framework provides users with a replicable, data-led approach to make informed tool choices for novel genome annotations and for refining historical annotations. AVAILABILITY AND IMPLEMENTATION: Code and datasets for reproduction and customisation are available at https://github.com/NickJD/ORForise. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An eco-epidemiological modeling approach to investigate dilution effect in two different tick-borne pathosystems.

Disease (re)emergence appears to be driven by biodiversity decline and environmental change. As a result, it is increasingly important to study host-pathogen interactions within the context of their ecology and evolution. The dilution effect is the concept that higher biodiversity decreases pathogen transmission. It has been observed especially in zoonotic vector-borne pathosystems, yet evidence against it has been found. In particular, it is still debated how the community (dis)assembly assumptions and the degree of generalism of vectors and pathogens affect the direction of the biodiversity-pathogen transmission relationship. The aim of this study was to use empirical data and mechanistic models to investigate dilution mechanisms in two rodent-tick-pathogen systems differing in their vector degree of generalism. A community was assembled to include ecological interactions that expand from purely additive to purely substitutive. Such systems are excellent candidates to analyze the link between vector ecology, community (dis)assembly dynamics, and pathogen transmission. To base our mechanistic models on empirical data, rodent live-trapping, including tick sampling, was conducted in Wales across two seasons for three consecutive years. We have developed a deterministic single-vector, multi-host compartmental model that includes ecological relationships with non-host species, uniquely integrating theoretical and observational approaches. To describe pathogen transmission across a gradient of community diversity, the model was populated with parameters describing five different scenarios differing in ecological complexity; each based around one of the pathosystems: Ixodes ricinus (generalist tick)-Borrelia burgdorferi and I. trianguliceps (small mammals specialist tick)-Babesia microti. The results suggested that community composition and interspecific dynamics affected pathogen transmission with different dilution outcomes depending on the vector degree of generalism. The model provides evidence that dilution and amplification effects are not mutually exclusive in the same community but depend on vector ecology and the epidemiological output considered (i.e., the "risk" of interest). In our scenarios, more functionally diverse communities resulted in fewer infectious rodents, supporting the dilution effect. In the pathosystem with generalist vector we identified a hump shaped relationship between diversity and infections in hosts, while for that characterized by specialist tick, this relationship was more complex and more dependent upon specific parameter values.

Inference of the Arabidopsis lateral root gene regulatory network suggests a bifurcation mechanism that defines primordia flanking and central zones.

A large number of genes involved in lateral root (LR) organogenesis have been identified over the last decade using forward and reverse genetic approaches in Arabidopsis thaliana. Nevertheless, how these genes interact to form a LR regulatory network largely remains to be elucidated. In this study, we developed a time-delay correlation algorithm (TDCor) to infer the gene regulatory network (GRN) controlling LR primordium initiation and patterning in Arabidopsis from a time-series transcriptomic data set. The predicted network topology links the very early-activated genes involved in LR initiation to later expressed cell identity markers through a multistep genetic cascade exhibiting both positive and negative feedback loops. The predictions were tested for the key transcriptional regulator AUXIN RESPONSE FACTOR7 node, and over 70% of its targets were validated experimentally. Intriguingly, the predicted GRN revealed a mutual inhibition between the ARF7 and ARF5 modules that would control an early bifurcation between two cell fates. Analyses of the expression pattern of ARF7 and ARF5 targets suggest that this patterning mechanism controls flanking and central zone specification in Arabidopsis LR primordia.

Linear discriminant analysis reveals differences in root architecture in wheat seedlings related to nitrogen uptake efficiency.

Root architecture impacts water and nutrient uptake efficiency. Identifying exactly which root architectural properties influence these agronomic traits can prove challenging. In this paper, approximately 300 wheat (Triticum aestivum) plants were divided into four groups using two binary classifications, high versus low nitrogen uptake efficiency (NUpE), and high versus low nitrate in the growth medium. The root system architecture for each wheat plant was captured using 16 quantitative variables. The multivariate analysis tool, linear discriminant analysis, was used to construct composite variables, each a linear combination of the original variables, such that the score of the plants on the new variables showed the maximum between-group variability. The results show that the distribution of root-system architecture traits differs between low- and high-NUpE plants and, less strongly, between low-NUpE plants grown on low versus high nitrate media.

Lateral root morphogenesis is dependent on the mechanical properties of the overlaying tissues.

In Arabidopsis, lateral root primordia (LRPs) originate from pericycle cells located deep within the parental root and have to emerge through endodermal, cortical, and epidermal tissues. These overlaying tissues place biomechanical constraints on the LRPs that are likely to impact their morphogenesis. This study probes the interplay between the patterns of cell division, organ shape, and overlaying tissues on LRP morphogenesis by exploiting recent advances in live plant cell imaging and image analysis. Our 3D/4D image analysis revealed that early stage LRPs exhibit tangential divisions that create a ring of cells corralling a population of rapidly dividing cells at its center. The patterns of division in the latter population of cells during LRP morphogenesis are not stereotypical. In contrast, statistical analysis demonstrated that the shape of new LRPs is highly conserved. We tested the relative importance of cell division pattern versus overlaying tissues on LRP morphogenesis using mutant and transgenic approaches. The double mutant aurora1 (aur1) aur2 disrupts the pattern of LRP cell divisions and impacts its growth dynamics, yet the new organ's dome shape remains normal. In contrast, manipulating the properties of overlaying tissues disrupted LRP morphogenesis. We conclude that the interaction with overlaying tissues, rather than the precise pattern of divisions, is most important for LRP morphogenesis and optimizes the process of lateral root emergence.

Mechanical modelling quantifies the functional importance of outer tissue layers during root elongation and bending.

Root elongation and bending require the coordinated expansion of multiple cells of different types. These processes are regulated by the action of hormones that can target distinct cell layers. We use a mathematical model to characterise the influence of the biomechanical properties of individual cell walls on the properties of the whole tissue. Taking a simple constitutive model at the cell scale which characterises cell walls via yield and extensibility parameters, we derive the analogous tissue-level model to describe elongation and bending. To accurately parameterise the model, we take detailed measurements of cell turgor, cell geometries and wall thicknesses. The model demonstrates how cell properties and shapes contribute to tissue-level extensibility and yield. Exploiting the highly organised structure of the elongation zone (EZ) of the Arabidopsis root, we quantify the contributions of different cell layers, using the measured parameters. We show how distributions of material and geometric properties across the root cross-section contribute to the generation of curvature, and relate the angle of a gravitropic bend to the magnitude and duration of asymmetric wall softening. We quantify the geometric factors which lead to the predominant contribution of the outer cell files in driving root elongation and bending.

Accelerating the domestication of a bioenergy crop: identifying and modelling morphological targets for sustainable yield increase in Miscanthus.

To accelerate domestication of Miscanthus, an important energy crop, 244 replicated genotypes, including two different species and their hybrids, were analysed for morphological traits and biomass yield over three growing seasons following an establishment phase of 2 years in the largest Miscanthus diversity trial described to date. Stem and leaf traits were selected that contributed both directly and indirectly to total harvested biomass yield, and there was variation in all traits measured. Morphological diversity within the population was correlated with dry matter yield (DMY) both as individual traits and in combination, in order to determine the respective contributions of the traits to biomass accumulation and to identify breeding targets for yield improvement. Predictive morphometric analysis was possible at year 3 within Miscanthus sinensis genotypes but not between M. sinensis, Miscanthus sacchariflorus, and interspecific hybrids. Yield is a complex trait, and no single simple trait explained more than 33% of DMY, which varied from 1 to 5297 g among genotypes within this trial. Associating simple traits increased the power of the morphological data to predict yield to 60%. Trait variety, in combination, enabled multiple ideotypes, thereby increasing the potential diversity of the crop for multiple growth locations and end uses. Both triploids and interspecific hybrids produced the highest mature yields, indicating that there is significant heterosis to be exploited within Miscanthus that might be overlooked in early selection screens within years 1-3. The potential for optimizing biomass yield by selecting on the basis of morphology is discussed.

Xyloglucan endotransglucosylase/hydrolase (XTH) overexpression affects growth and cell wall mechanics in etiolated Arabidopsis hypocotyls.

Growth and biomechanics of etiolated hypocotyls from Arabidopsis thaliana lines overexpressing xyloglucan endotransglucosylase/hydrolase AtXTH18, AtXTH19, AtXTH20, and PttXET16-34 were studied. Overexpression of AtXTH18, AtXTH19, and AtXTH20 stimulated growth of hypocotyls, while PttXET16-34 overexpression did not show this effect. In vitro extension of frozen/thawed hypocotyls measured by a constant-load extensiometer started from a high-amplitude initial deformation followed by a slow time-dependent creep. Creep of growing XTH-overexpressing (OE) hypocotyls was more linear in time compared with the wild type at pH 5.0, reflecting their higher potential for long-term extension. XTH-OE plants deposited 65-84% more cell wall material per hypocotyl cross-sectional area than wild-type plants. As a result, their wall stress under each external load was lower than in the wild-type. Growing XTH-OE hypocotyls had higher values of initial deformation·stress(-1) compared with the wild type. Plotting creep rates for each line under different loads against the respective wall stress values gave straight lines. Their slopes and intercepts with the abscissa correspond to ϕ (in vitro cell wall extensibility) and y (in vitro cell wall yield threshold) values characterizing cell wall material properties. The wall material in XTH-OE lines was more pliant than in the wild type due to lower y values. In contrast, the acid-induced wall extension in vitro resulted from increasing ϕ values. Thus, three factors contributed to the XTH-OE-stimulated growth in Arabidopsis hypocotyls: their more linear creep, higher values of initial deformation·stress(-1), and lower y values.

The circadian clock rephases during lateral root organ initiation in Arabidopsis thaliana.

The endogenous circadian clock enables organisms to adapt their growth and development to environmental changes. Here we describe how the circadian clock is employed to coordinate responses to the key signal auxin during lateral root (LR) emergence. In the model plant, Arabidopsis thaliana, LRs originate from a group of stem cells deep within the root, necessitating that new organs emerge through overlying root tissues. We report that the circadian clock is rephased during LR development. Metabolite and transcript profiling revealed that the circadian clock controls the levels of auxin and auxin-related genes including the auxin response repressor IAA14 and auxin oxidase AtDAO2. Plants lacking or overexpressing core clock components exhibit LR emergence defects. We conclude that the circadian clock acts to gate auxin signalling during LR development to facilitate organ emergence.

Robust Bayesian clustering for replicated gene expression data.

Experimental scientific data sets, especially biology data, usually contain replicated measurements. The replicated measurements for the same object are correlated, and this correlation must be carefully dealt with in scientific analysis. In this paper, we propose a robust Bayesian mixture model for clustering data sets with replicated measurements. The model aims not only to accurately cluster the data points taking the replicated measurements into consideration, but also to find the outliers (i.e., scattered objects) which are possibly required to be studied further. A tree-structured variational Bayes (VB) algorithm is developed to carry out model fitting. Experimental studies showed that our model compares favorably with the infinite Gaussian mixture model, while maintaining computational simplicity. We demonstrate the benefits of including the replicated measurements in the model, in terms of improved outlier detection rates in varying measurement uncertainty conditions. Finally, we apply the approach to clustering biological transcriptomics mRNA expression data sets with replicated measurements.

Identifying biological landmarks using a novel cell measuring image analysis tool: Cell-o-Tape.

BACKGROUND: The ability to quantify the geometry of plant organs at the cellular scale can provide novel insights into their structural organization. Hitherto manual methods of measurement provide only very low throughput and subjective solutions, and often quantitative measurements are neglected in favour of a simple cell count. RESULTS: We present a tool to count and measure individual neighbouring cells along a defined file in confocal laser scanning microscope images. The tool allows the user to extract this generic information in a flexible and intuitive manner, and builds on the raw data to detect a significant change in cell length along the file. This facility can be used, for example, to provide an estimate of the position of transition into the elongation zone of an Arabidopsis root, traditionally a location sensitive to the subjectivity of the experimenter. CONCLUSIONS: Cell-o-tape is shown to locate cell walls with a high degree of accuracy and estimate the location of the transition feature point in good agreement with human experts. The tool is an open source ImageJ/Fiji macro and is available online.

Statistical evaluation of transcriptomic data generated using the Affymetrix one-cycle, two-cycle and IVT-Express RNA labelling protocols with the Arabidopsis ATH1 microarray.

BACKGROUND: Microarrays are a powerful tool used for the determination of global RNA expression. There is an increasing requirement to focus on profiling gene expression in tissues where it is difficult to obtain large quantities of material, for example individual tissues within organs such as the root, or individual isolated cells. From such samples, it is difficult to produce the amount of RNA required for labelling and hybridisation in microarray experiments, thus a process of amplification is usually adopted. Despite the increasing use of two-cycle amplification for transcriptomic analyses on the Affymetrix ATH1 array, there has been no report investigating any potential bias in gene representation that may occur as a result. RESULTS: Here we compare transcriptomic data generated using Affymetrix one-cycle (standard labelling protocol), two-cycle (small-sample protocol) and IVT-Express protocols with the Affymetrix ATH1 array using Arabidopsis root samples. Results obtained with each protocol are broadly similar. However, we show that there are 35 probe sets (of a total of 22810) that are misrepresented in the two-cycle data sets. Of these, 33 probe sets were classed as mis-amplified when comparisons of two independent publicly available data sets were undertaken. CONCLUSIONS: Given the unreliable nature of the highlighted probes, we caution against using data associated with the corresponding genes in analyses involving transcriptomic data generated with two-cycle amplification protocols. We have shown that the Affymetrix IVT-E labelling protocol produces data with less associated bias than the two-cycle protocol, and as such, would recommend this kit for new experiments that involve small samples.