Ginkgo: spatially-explicit simulator of complex phylogeographic histories

We present Ginkgo, a software package for agent-based, forward-time simulations of genealogies of multiple unlinked loci from diploid populations. Ginkgo simulates the evolution of one or more species on a spatially explicit landscape of cells. The user of the software can specify the geographical and environmental characteristics of the landscape, and these properties can change according to a prespecified schedule. The geographical elements modelled include the arrangement of cells and movement rates between particular cells. Each species has a function that can calculate a fitness score for any combination of an individual organism's phenotype and environmental characteristics. The user can control the number of fitness factors (the dimensionality of the cell-specific fitness factors and the individuals phenotypic vectors) and the weighting of each of these dimensions in the fitness calculation. Cell-specific fitness trait optima can be specified across the landscape to mimic differences in habitat. In addition to their differing fitness functions, species can differ in terms of their vagility and fecundity. Genealogies and occurrence data can be produced at any time during the simulation in NEXUS and ESRI Ascii Grid formats, respectively.     

Mapping of quantitative trait loci based on growth models

An approach called growth model-based mapping (GMM) of quantitative trait loci (QTLs) is proposed in this paper. The principle of the approach is to fit the growth curve of each individual or line with a theoretical or empirical growth model at first and then map QTLs based on the estimated growth parameters with the method of multiple-trait composite interval mapping. In comparison with previously proposed approaches of QTL mapping based on growth data, GMM has several advantages: (1) it can greatly reduce the amount of phenotypic data for QTL analysis and thus alleviate the burden of computation, particularly when permutation tests or simulation are performed to estimate significance thresholds; (2) it can efficiently analyze unbalanced phenotype data because both balanced and unbalanced data can be used for fitting growth models; and (3) it may potentially help us to better understand the genetic basis of quantitative trait development because the parameters in a theoretical growth model may often have clear biological meanings. A practical example of rice leaf-age development is presented to demonstrate the utility of GMM.     

Wohin steuert die Taxonomie?

In taxonomy, the organisms may be grouped into species according to different criteria, e.g. according to phenotypic and genetic characteristics or according to reproductive connections. In nature, there is no universal unit that can be called “species”, because different biological mechanisms are responsible for how individuals are grouped together or delimitated from each other. Different species concepts do not necessarily define the same entity existing in nature. One and the same individual can be assigned to different species depending on the species concept. In the last two decades, the barcode taxonomy has played a dominant role. A major advantage of the barcode taxonomy is the time‐saving automated mass detection of species without the need for taxonomically trained experts. Especially in evolutionarily young species, however, there are considerable discrepancies between the classification according to the barcode concept and the classification according to reproductive communities or phenotypic characteristics. The barcode species must therefore be understood as a species in addition to other species concepts.     

Detection of closely linked multiple quantitative trait loci using a genetic algorithm

The existence of a quantitative trait locus (QTL) is usually tested using the likelihood of the quantitative trait on the basis of phenotypic character data plus the recombination fraction between QTL and flanking markers. When doing this, the likelihood is calculated for all possible locations on the linkage map. When multiple QTL are suspected close by, it is impractical to calculate the likelihood for all possible combinations of numbers and locations of QTL. Here, we propose a genetic algorithm (GA) for the heuristic solution of this problem. GA can globally search the optimum by improving the "genotype" with alterations called "recombination" and "mutation." The "genotype" of our GA is the number and location of QTL. The "fitness" is a function based on the likelihood plus Akaike's information criterion (AIC), which helps avoid false-positive QTL. A simulation study comparing the new method with existing QTL mapping packages shows the advantage of the new GA. The GA reliably distinguishes multiple QTL located in a single marker interval.     

SDAR: a practical tool for graphical analysis of two-dimensional data

ABSTRACT: BACKGROUND: Two-dimensional data needs to be processed and analysed in almost any experimental laboratory. Some tasks in this context may be performed with generic software such as spreadsheet programs which are available ubiquitously, others may require more specialised software that requires paid licences. Additionally, more complex software packages typically require more time by the individual user to understand and operate. Practical and convenient graphical data analysis software in Java with a user-friendly interface are rare. RESULTS: We have developed SDAR, a Java application to analyse two-dimensional data with an intuitive graphical user interface. A smart ASCII parser allows import of data into SDAR without particular format requirements. The centre piece of SDAR is the Java class GraphPanel which provides methods for generic tasks of data visualisation. Data can be manipulated and analysed with respect to the most common operations experienced in an experimental biochemical laboratory. Images of the data plots can be generated in SVG-, TIFF- or PNG-format. Data exported by SDAR is annotated with commands compatible with the Grace software. CONCLUSION: Since SDAR is implemented in Java, it is truly cross-platform compatible. The software is easy to install, and very convenient to use judging by experience in our own laboratories. It is freely available to academic users at http://www.structuralchemistry.org/pcsb/. To download SDAR, users will be asked for their name, institution and email address. A manual, as well as the source code of the GraphPanel class can also be downloaded from this site.     

Assessing adaptive phenotypic plasticity by means of conditional strategies from empirical data: the latent environmental threshold model

Conditional strategies are the most common form of discrete phenotypic plasticity. In a conditional strategy, the phenotype expressed by an organism is determined by the difference between an environmental cue and a threshold, both of which may vary among individuals. The environmental threshold model (ETM) has been proposed as a mean to understand the evolution of conditional strategies, but has been surprisingly seldom applied to empirical studies. A hindrance for the application of the ETM is that often, the proximate cue triggering the phenotypic expression and the individual threshold are not measurable, and can only be assessed using a related observable cue. We describe a new statistical model that can be applied in this common situation. The Latent ETM (LETM) allows for a measurement error in the phenotypic expression of the individual environmental cue and a purely genetically determined threshold. We show that coupling our model with quantitative genetic methods allows an evolutionary approach including an estimation of the heritability of conditional strategies. We evaluate the performance of the LETM with a simulation study and illustrate its utility by applying it to empirical data on the size-dependent smolting process for stream-dwelling Atlantic salmon juveniles.     

Spatial capture–recapture models allowing Markovian transience or dispersal

Spatial capture–recapture (SCR) models are a relatively recent development in quantitative ecology, and they are becoming widely used to model density in studies of animal populations using camera traps, DNA sampling and other methods which produce spatially explicit individual encounter information. One of the core assumptions of SCR models is that individuals possess home ranges that are spatially stationary during the sampling period. For many species, this assumption is unlikely to be met and, even for species that are typically territorial, individuals may disperse or exhibit transience at some life stages. In this paper we first conduct a simulation study to evaluate the robustness of estimators of density under ordinary SCR models when dispersal or transience is present in the population. Then, using both simulated and real data, we demonstrate that such models can easily be described in the BUGS language providing a practical framework for their analysis, which allows us to evaluate movement dynamics of species using capture–recapture data. We find that while estimators of density are extremely robust, even to pathological levels of movement (e.g., complete transience), the estimator of the spatial scale parameter of the encounter probability model is confounded with the dispersal/transience scale parameter. Thus, use of ordinary SCR models to make inferences about density is feasible, but interpretation of SCR model parameters in relation to movement should be avoided. Instead, when movement dynamics are of interest, such dynamics should be parameterized explicitly in the model.     

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard.Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.     

Sex,competition and mimicry: an eco-evolutionary model reveals unexpected impacts of ecological interactions on the evolution of phenotypes in sympatry

Phenotypic evolution in sympatric species can be strongly impacted by species interactions, either mutualistic or antagonistic. Heterospecific reproductive behaviours between sympatric species have been shown to favour phenotypic divergence of traits used as sexual cues. Those traits may also be involved in local adaptation or in other types of species interactions and, as a result, undergo complex evolutions across sympatric species. Here we focus on mimicry and study how reproductive interference may impair phenotypic convergence between species with various levels of defence. We use a deterministic model assuming two sympatric species where individuals can display two different warning colour patterns. This eco-evolutionary model explores how ecological interactions shape phenotypic evolution within sympatric species. We investigate the effect of 1) the opposing density-dependent selections exerted on colour patterns by predation and reproductive behaviour and 2) the impact of relative species and phenotype abundances on the fitness costs faced by each individual depending on their species and phenotype. Our model shows that reproductive interference may limit the convergent effect of mimetic interactions and may promote phenotypic divergence between Müllerian mimics. The divergent and convergent evolution of traits also strongly depends on the relative species and phenotype abundances and levels of trophic competition, highlighting how the eco-evolutionary feedbacks between phenotypic evolution and species abundances may result in strikingly different evolutionary routes.     

Benchmarking currently available SELDI‐TOF MS preprocessing techniques

SELDI protein profiling experiments can be used as a first step in studying the pathogenesis of various diseases such as cancer. There are a plethora of software packages available for doing the preprocessing of SELDI data, each with many options and written from different signal processing perspectives, offering many researchers choices they may not have the background or desire to make. Moreover, several studies have shown that mistakes in the preprocessing of the data can bias the biological interpretation of the study. For this reason, we conduct a large scale evaluation of available signal processing techniques to establish which are most effective. We use data generated from a standard, published simulation engine so that “truth” is known. We select the top algorithms by considering two logical performance metrics, and give our recommendations for research directions that are likely to be most promising. There is considerable opportunity for future contributions improving the signal processing of SELDI spectra.     

PC‐ORD version 5: A user‐friendly toolbox for ecologists

Recently, version 5 of PC‐ORD, one of the major commercial software packages for multivariate ecological community data analyses, was released. The new version offers a whole range of techniques and methods for analyses of ecological data. It includes modules for different types of ordination and classification, as well as other exploratory techniques such as species‐area curve analysis and indicator species analysis. Data are stored in spreadsheets and can be easily manipulated in various ways. In essence, version 5 of PC‐ORD offers the user a full toolbox for exploration and analysis of ecological data, packed in a user‐friendly environment.     

A new bayesian method for fitting evolutionary models to comparative data with intraspecific variation

Phylogenetic comparative methods that incorporate intraspecific variability are relatively new and, so far, not especially widely used in empirical studies. In the present short article we will describe a new Bayesian method for fitting evolutionary models to comparative data that incorporates intraspecific variability. This method differs from an existing likelihood-based approach in that it requires no a priori inference about species means and variances; rather it takes phenotypic values from individuals and a phylogenetic tree as input, and then samples species means and variances, along with the parameters of the evolutionary model, from their joint posterior probability distribution. One of the most novel and intriguing attributes of this approach is that jointly sampling the species means with the evolutionary model parameters means that the model and tree can influence our estimates of species mean trait values, not just the reverse. In the present implementation, we first apply this method to the most widely used evolutionary model for continuously valued phenotypic trait data (Brownian motion). However, the general approach has broad applicability, which we illustrate by also fitting the λ model, another simple model for quantitative trait evolution on a phylogeny. We test our approach via simulation and by analyzing two empirical datasets obtained from the literature. Finally, we have implemented the methods described herein in a new function for the R statistical computing environment, and this function will be distributed as part of the 'phytools' R library.     

Human influences on rates of phenotypic change in wild animal populations

Human activities can expose populations to dramatic environmental perturbations, which may then precipitate adaptive phenotypic change. We ask whether or not phenotypic changes associated with human-disturbed (anthropogenic) contexts are greater than those associated with more 'natural' contexts. Our meta-analysis is based on more than 3000 rates of phenotypic change in 68 'systems', each representing a given species in a particular geographical area. We find that rates of phenotypic change are greater in anthropogenic contexts than in natural contexts. This difference may be influenced by phenotypic plasticity - because it was evident for studies of wild-caught individuals (which integrate both genetic and plastic effects) but not for common-garden or quantitative genetic studies (which minimize plastic effects). We also find that phenotypic changes in response to disturbance can be remarkably abrupt, perhaps again because of plasticity. In short, humans are an important agent driving phenotypic change in contemporary populations. Although these changes sometimes have a genetic basis, our analyses suggest a particularly important contribution from phenotypic plasticity.     

Within-species variation and measurement error in phylogenetic comparative methods

Most phylogenetically based statistical methods for the analysis of quantitative or continuously varying phenotypic traits assume that variation within species is absent or at least negligible, which is unrealistic for many traits. Within-species variation has several components. Differences among populations of the same species may represent either phylogenetic divergence or direct effects of environmental factors that differ among populations (phenotypic plasticity). Within-population variation also contributes to within-species variation and includes sampling variation, instrument-related error, low repeatability caused by fluctuations in behavioral or physiological state, variation related to age, sex, season, or time of day, and individual variation within such categories. Here we develop techniques for analyzing phylogenetically correlated data to include within-species variation, or "measurement error" as it is often termed in the statistical literature. We derive methods for (i) univariate analyses, including measurement of "phylogenetic signal," (ii) correlation and principal components analysis for multiple traits, (iii) multiple regression, and (iv) inference of "functional relations," such as reduced major axis (RMA) regression. The methods are capable of incorporating measurement error that differs for each data point (mean value for a species or population), but they can be modified for special cases in which less is known about measurement error (e.g., when one is willing to assume something about the ratio of measurement error in two traits). We show that failure to incorporate measurement error can lead to both biased and imprecise (unduly uncertain) parameter estimates. Even previous methods that are thought to account for measurement error, such as conventional RMA regression, can be improved by explicitly incorporating measurement error and phylogenetic correlation. We illustrate these methods with examples and simulations and provide Matlab programs.     

Thermal acclimation modulates the impacts of temperature and enrichment on trophic interaction strengths and population dynamics

Global change affects individual phenotypes and biotic interactions, which can have cascading effects up to the ecosystem level. However, the role of environmentally induced phenotypic plasticity in species interactions is poorly understood, leaving a substantial gap in our knowledge of the impacts of global change on ecosystems. Using a cladoceran–dragonfly system, we experimentally investigated the effects of thermal acclimation, acute temperature change and enrichment on predator functional response and metabolic rate. Using our experimental data, we next parameterized a population dynamics model to determine the consequences of these effects on trophic interaction strength and food‐chain stability. We found that (1) predation and metabolic rates of the dragonfly larvae increase with acute warming, (2) warm‐acclimated larvae have a higher maximum predation rate than cold‐acclimated ones, and (3) long‐term interaction strength increases with enrichment but decreases with both acclimation and acute temperatures. Overall, our experimental results show that thermal acclimation can buffer negative impacts of environmental change on predators and increase food‐web stability and persistence. We conclude that the effect of acclimation and, more generally, phenotypic plasticity on trophic interactions should not be overlooked if we aim to understand the effects of climate change and enrichment on species interaction strength and food‐web stability.     

Harnessing gene expression to identify the genetic basis of drug resistance

The advent of cost‐effective genotyping and sequencing methods have recently made it possible to ask questions that address the genetic basis of phenotypic diversity and how natural variants interact with the environment. We developed Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits), a statistical method that integrates genotype, gene expression and phenotype data to automatically build models that both predict complex quantitative phenotypes and identify genes that actively influence these traits. Camelot integrates genotype and gene expression data, both generated under a reference condition, to predict the response to entirely different conditions. We systematically applied our algorithm to data generated from a collection of yeast segregants, using genotype and gene expression data generated under drug‐free conditions to predict the response to 94 drugs and experimentally confirmed 14 novel gene–drug interactions. Our approach is robust, applicable to other phenotypes and species, and has potential for applications in personalized medicine, for example, in predicting how an individual will respond to a previously unseen drug.     

Scientific workflow management in proteomics

Data processing in proteomics can be a challenging endeavor, requiring extensive knowledge of many different software packages, all with different algorithms, data format requirements, and user interfaces. In this article we describe the integration of a number of existing programs and tools in Taverna Workbench, a scientific workflow manager currently being developed in the bioinformatics community. We demonstrate how a workflow manager provides a single, visually clear and intuitive interface to complex data analysis tasks in proteomics, from raw mass spectrometry data to protein identifications and beyond.     

The information system design for the Flora North America Program

The Flora North America Program will create a computer data bank of taxonomic information about the vascular plants of North America north of Mexico. The system, which will serve a broad range of users, will provide for the acquisition, editing, and storage of data and the preparation of a variety of products. It will grow and evolve over a long period of time, continually incorporating changes to data and new categories of information as they become available. The system will utilize an existing computer program package, the IBM Generalized Information System (GIS), for creating, maintaining, querying, and generating reports from its files of data. In addition to a file of taxonomic, ecological, and geographic data about each taxon, the system will build and maintain a series of authority or dictionary files to control system content. The first major system product will be a published flora, generated by computer. Many other catalogs and indexes will be prepared and specific questions may be asked of the data bank to meet individual user needs for information.     

A novel and versatile computational tool to model translation

MOTIVATION: Much is now known about the mechanistic details of gene translation. There are also rapid advances in high-throughput technologies to determine quantitative aspects of the system. As a consequence-realistic and system-wide simulation models of translation are now feasible. Such models are also needed as devices to integrate a large volume of highly fragmented data known about translation. Software: In this application note, we present a novel, highly efficient software tool to model translation. The tool represents the main aspects of translation. Features include a representation of exhaustible tRNA pools, ribosome-ribosome interactions and differential initiation rates for different mRNA species. The tool is written in Java, and is hence portable and can be parameterized for any organism. AVAILABILITY: The model can be obtained from the authors or directly downloaded from the authors' home-page (http://goo.gl/JUWvI).