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opencc-zeroDec 2013View details →

Data from: System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of Soybean (Glycine max)

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN.

Data from: Recommendations for using msBayes to incorporate uncertainty in selecting an ABC model prior: a response to Oaks et al.

Prior specification is an essential component of parameter estimation and model comparison in Approximate Bayesian computation (ABC). Oaks et al. present a simulation-based power analysis of msBayes and conclude that msBayes has low power to detect genuinely random divergence times across taxa, and suggest the cause is Lindley's paradox. Although the predictions are similar, we show that their findings are more fundamentally explained by insufficient prior sampling that arises with poorly chosen wide priors that critically undersample nonsimultaneous divergence histories of high likelihood. In a reanalysis of their data on Philippine Island vertebrates, we show how this problem can be circumvented by expanding upon a previously developed procedure that accommodates uncertainty in prior selection using Bayesian model averaging. When these procedures are used, msBayes supports recent divergences without support for synchronous divergence in the Oaks et al. data and we further present a simulation analysis that demonstrates that msBayes can have high power to detect asynchronous divergence under narrower priors for divergence time. Our findings highlight the need for exploration of plausible parameter space and prior sampling efficiency for ABC samplers in high dimensions. We discus potential improvements to msBayes and conclude that when used appropriately with model averaging, msBayes remains an effective and powerful tool.

Data from: Suitability of Laurentian Great Lakes for invasive species based on global species distribution models and local habitat

Efficient management and prevention of species invasions requires accurate prediction of where species of concern can arrive and persist. Species distribution models provide one way to identify potentially suitable habitat by developing the relationship between climate variables and species occurrence data. However, these models when applied to freshwater invasions are complicated by two factors. The first is that the range expansions that typically occur as part of the invasion process violate standard species distribution model assumptions of data stationarity. Second, predicting potential range of freshwater aquatic species is complicated by the reliance on terrestrial climate measurements to develop occurrence relationships for species that occur in aquatic environments. To overcome these obstacles, we combined a recently developed algorithm for species distribution modeling—range bagging—with newly available aquatic habitat-specific information from the North American Great Lakes region to predict suitable habitat for three potential invasive species: golden mussel, killer shrimp, and northern snakehead. Range bagging may more accurately predict relative suitability than other methods because it focuses on the limits of the species environmental tolerances rather than central tendency or "typical" cases. Overlaying the species distribution model output with aquatic habitat-specific data then allowed for more specific predictions of areas with high suitability. Our results indicate there is suitable habitat for northern snakehead in the Great Lakes, particularly shallow coastal habitats in the lower four Great Lakes where literature suggests they will favor areas of wetland and submerged aquatic vegetation. These coastal areas also offer the highest suitability for golden mussel, but our models suggest they are marginal habitats. Globally, the Great Lakes provide the closest match to the currently invaded range of killer shrimp, but they appear to pose an intermediate risk to the region. Range bagging provided reliable predictions when assessed either by a standard test set or by tests for spatial transferability, with golden mussel being the most difficult to accurately predict. Our approach illustrates the strength of combining multiple sources of data, while reiterating the need for increased measurement of freshwater habitat at high spatial resolutions to improve the ability to predict potential invasive species.

Data from: The chinchilla as a novel animal model of pregnancy

Several parameters are important when choosing the most appropriate animal to model human obstetrics, including gestation period, number of fetuses per gestation and placental structure. The domesticated long-tailed chinchilla (Chinchilla lanigera) is a well-suited and appropriate animal model of pregnancy that often will carry only one offspring and has a long gestation period of 105–115 days. Furthermore, the chinchilla placenta is of the haemomonochorial labyrinthine type and is therefore comparable to the human villous haemomonochorial placenta. This proof-of-concept study demonstrated the feasibility in laboratory settings, and demonstrated the potential of the pregnant chinchilla as an animal model for obstetric research and its potential usefulness for non-invasive measurements in the placenta. We demonstrate measurements of the placental and fetal metabolism (demonstrated in vivo by hyperpolarized MRI and in vitro by qPCR analyses), placental vessels (demonstrated ex vivo by contrast-enhanced CT angiography) and overall anatomy (demonstrated in vivo by whole-body CT).

Data from: Antagonistic versus non-antagonistic models of balancing selection: characterizing the relative timescales and hitchhiking effects of partial selective sweeps

Antagonistically selected alleles-–those with opposing fitness effects between sexes, environments, or fitness components-–represent an important component of additive genetic variance in fitness-related traits, with stably balanced polymorphisms often hypothesized to contribute to observed quantitative genetic variation. Balancing selection hypotheses imply that intermediate-frequency alleles disproportionately contribute to genetic variance of life-history traits and fitness. Such alleles may also associate with population genetic footprints of recent selection, including reduced genetic diversity and inflated linkage disequilibrium at linked, neutral sites. Here, we compare the evolutionary dynamics of different balancing selection models, and characterize the evolutionary timescale and hitchhiking effects of partial selective sweeps generated under antagonistic versus nonantagonistic (e.g., overdominant and frequency-dependent selection) processes. We show that the evolutionary timescales of partial sweeps tend to be much longer, and hitchhiking effects are drastically weaker, under scenarios of antagonistic selection. These results predict an interesting mismatch between molecular population genetic and quantitative genetic patterns of variation. Balanced, antagonistically selected alleles are expected to contribute more to additive genetic variance for fitness than alleles maintained by classic, nonantagonistic mechanisms. Nevertheless, classical mechanisms of balancing selection are much more likely to generate strong population genetic signatures of recent balancing selection.

Data from: An inconvenient truth about xylem resistance to embolism in the model species for refilling Laurus nobilis L.

Direct, non-invasive X-ray microtomography and optical technique observations applied in stems and leaves of intact seedlings revealed that laurel is highly resistant to drought-induced xylem embolism. Contrary to what has been brought forward, daily cycles of embolism formation and refilling are unlikely to occur in this species and to explain how it copes with drought. There has been considerable controversy regarding xylem embolism resistance for long-vesselled angiosperm species and particularly for the model species for refilling (Laurus nobilis L.). The purpose of this study was to resolve the hydraulic properties of this species by documenting vulnerability curves of different organs in intact plants. Here, we applied a direct, non-invasive method to visualize xylem embolism in stems and leaves of intact laurel seedlings up to 2-m tall using X-ray microtomography (microCT) observations and the optical vulnerability technique. These approaches were coupled with complementary centrifugation measurements performed on 1-m long branches sampled from adult trees and compared with additional microCT analyses carried out on 80-cm cut branches. Direct observations of embolism spread during desiccation of intact laurels revealed that 50% loss of xylem conductivity (Ψ50) was reached at − 7.9 ± 0.5 and − 8.4 ± 0.3 MPa in stems and leaves, respectively, while the minimum xylem water potentials measured in the field were − 4.2 MPa during a moderate drought season. Those findings reveal that embolism formation is not routine in Laurus nobilis contrary to what has been previously reported. These Ψ50 values were close to those based on the flow-centrifuge technique (− 9.2 ± 0.2 MPa), but at odds with microCT observations of cut branches (− 4.0 ± 0.5 MPa). In summary, independent methods converge toward the same conclusion that laurel is highly resistant to xylem embolism regardless its development stage. Under typical growth conditions without extreme drought events, this species maintains positive hydraulic safety margin, while daily cycles of embolism formation and refilling are unlikely to occur in this species.

opencc-zeroDec 2014View details →

Data from: Genome-wide gene-associated microsatellite markers for the model invasive ascidian, Ciona intestinalis species complex

The vase tunicate, Ciona intestinalis species complex, has become a good model for ecological and evolutionary studies, especially those focusing on microevolution associated with rapidly changing environments. However, genome-wide genetic markers are still lacking. Here we characterized a large set of genome-wide gene-associated microsatellite markers for C. intestinalis spA (= C. robusta). Bioinformatic analysis identified 4654 microsatellites from expressed sequence tags (ESTs), 2126 of which successfully assigned to chromosomes were selected for further analysis. Based on the distribution evenness on chromosomes, function annotation and suitability for primer design, we chose 545 candidate microsatellites for further characterization. After amplification validation and variation assessment, 218 loci were polymorphic in at least one of the two populations collected from the coast of Arenys de Mar, Spain (N = 24 - 48) and Cape Town, South Africa (N = 24 - 33). The number of alleles, observed heterozygosity and expected heterozygosity ranged from two to 11, 0 to 0.833 and 0.021 to 0.818, and from two to 10, 0 to 0.879 and 0.031 to 0.845 for the Spanish and African populations, respectively. When all microsatellites were tested for cross-species utility, only 60 loci (25.8%) could be successfully amplified and all loci were polymorphic in C. intestinalis spB. A high level of genome-wide polymorphism is likely responsible for the low transferability. The large set of microsatellite markers characterized here is expected to provide a useful genome-wide resource for ecological and evolutionary studies using C. intestinalis as a model.

Data from: Differential temperature sensitivity of synaptic and firing processes in a neural mass model of epileptic discharges explains heterogeneous response of experimental epilepsy to focal brain cooling

Experiments with drug-induced epilepsy in rat brains and epileptic human brain region reveal that focal cooling can suppress epileptic discharges without affecting the brain's normal neurological function. Findings suggest a viable treatment for intractable epilepsy cases via an implantable cooling device. However, precise mechanisms by which cooling suppresses epileptic discharges are still not clearly understood. Cooling experiments in vitro presented evidence of reduction in neurotransmitter release from presynaptic terminals and loss of dendritic spines at post-synaptic terminals offering a possible synaptic mechanism. We show that termination of epileptic discharges is possible by introducing a homogeneous temperature factor in a neural mass model which attenuates the post-synaptic impulse responses of the neuronal populations. This result however may be expected since such attenuation leads to reduced post-synaptic potential and when the effect on inhibitory interneurons is less than on excitatory interneurons, frequency of firing of pyramidal cells is consequently reduced. While this is observed in cooling experiments in vitro, experiments in vivo exhibit persistent discharges during cooling but suppressed in magnitude. This leads us to conjecture that reduction in the frequency of discharges may be compensated through intrinsic excitability mechanisms. Such compensatory mechanism is modelled using a reciprocal temperature factor in the firing response function in the neural mass model. We demonstrate that the complete model can reproduce attenuation of both magnitude and frequency of epileptic discharges during cooling. The compensatory mechanism suggests that cooling lowers the average and the variance of the distribution of threshold potential of firing across the population. Bifurcation study with respect to the temperature parameters of the model reveals how heterogeneous response of epileptic discharges to cooling (termination or suppression only) is exhibited. Possibility of differential temperature effects on post-synaptic potential generation of different populations is also explored.

Data from: A model of urban scaling laws based on distance dependent interactions

Socio-economic related properties of a city grow faster than a linear relationship with the population, in a log–log plot, the so-called superlinear scaling. Conversely, the larger a city, the more efficient it is in the use of its infrastructure, leading to a sublinear scaling on these variables. In this work, we addressed a simple explanation for those scaling laws in cities based on the interaction range between the citizens and on the fractal properties of the cities. To this purpose, we introduced a measure of social potential which captured the influence of social interaction on the economic performance and the benefits of amenities in the case of infrastructure offered by the city. We assumed that the population density depends on the fractal dimension and on the distance-dependent interactions between individuals. The model suggests that when the city interacts as a whole, and not just as a set of isolated parts, there is improvement of the socio-economic indicators. Moreover, the bigger the interaction range between citizens and amenities, the bigger the improvement of the socio-economic indicators and the lower the infrastructure costs of the city. We addressed how public policies could take advantage of these properties to improve cities development, minimizing negative effects. Furthermore, the model predicts that the sum of the scaling exponents of social-economic and infrastructure variables are 2, as observed in the literature. Simulations with an agent-based model are confronted with the theoretical approach and they are compatible with the empirical evidences.

Data from: Compressor map regression modelling based on partial least squares

In this work, two kinds of partial least squares modelling methods are applied to predict a compressor map: one uses a Power function polynomial as the basis function (PLSO), and the other uses a trigonometric function polynomial (PLSN). To demonstrate the potential capabilities of PLSO and PLSN for a typical interpolated prediction and extrapolated prediction, they are compared with two other classical data-driven modelling methods, namely, the look-up table and artificial neural network. PLSO and PLSN are also compared to each other. The results show that PLSO and PLSN have a better prediction performance than the look-up table and the artificial neural network, especially for the extrapolated prediction. At the same time, the computational time is also decreased sharply. Compared with PLSO, PLSN is characterized with higher prediction accuracy and shorter computational time than PLSO. It can be expected that PLSN can be time-saving and improve the accuracy of a thermodynamic model of a diesel engine.

opencc-zeroDec 2014View details →

Data from: A new method to scan genomes for introgression in a secondary contact model

Secondary contact between divergent populations or incipient species may result in the exchange and introgression of genomic material. We develop a simple DNA sequence measure, called Gmin, which is designed to identify genomic regions experiencing introgression in a secondary contact model. Gmin is defined as the ratio of the minimum between-population number of nucleotide differences in a genomic window to the average number of between-population differences. Although it is conceptually simple, one advantage of Gmin is that it is computationally inexpensive relative to model-based methods for detecting gene flow and it scales easily to the level of whole-genome analysis. We compare the sensitivity and specificity of Gmin to those of the widely used index of population differentiation, FST, and suggest a simple statistical test for identifying genomic outliers. Extensive computer simulations demonstrate that Gmin has both greater sensitivity and specificity for detecting recent introgression than does FST. Furthermore, we find that the sensitivity of Gmin is robust with respect to both the population mutation and recombination rates. Finally, a scan of Gmin across the X chromosome of Drosophila melanogaster identifies candidate regions of introgression between sub-Saharan African and cosmopolitan populations that were previously missed by other methods. These results show that Gmin is a biologically straightforward, yet powerful, alternative to FST, as well as to more computationally intensive model-based methods for detecting gene flow.

Data from: Modelling the spread of innovation in wild birds

We apply three plausible algorithms in agent-based computer simulations to recent experiments on social learning in wild birds. Although some of the phenomena are simulated by all three learning algorithms, several manifestations of social conformity bias are simulated by only the approximate majority (AM) algorithm, which has roots in chemistry, molecular biology and theoretical computer science. The simulations generate testable predictions and provide several explanatory insights into the diffusion of innovation through a population. The AM algorithm's success raises the possibility of its usefulness in studying group dynamics more generally, in several different scientific domains. Our differential-equation model matches simulation results and provides mathematical insights into the dynamics of these algorithms.

opencc-zeroDec 2014View details →

Data from: SNP discovery in non-model organisms: strand-bias and base-substitution errors reduce conversion rates

Single nucleotide polymorphisms (SNPs) have become the marker of choice for genetic studies in organisms of conservation, commercial or biological interest. Most SNP discovery projects in nonmodel organisms apply a strategy for identifying putative SNPs based on filtering rules that account for random sequencing errors. Here, we analyse data used to develop 4723 novel SNPs for the commercially important deep-sea fish, orange roughy (Hoplostethus atlanticus), to assess the impact of not accounting for systematic sequencing errors when filtering identified polymorphisms when discovering SNPs. We used SAMtools to identify polymorphisms in a velvet assembly of genomic DNA sequence data from seven individuals. The resulting set of polymorphisms were filtered to minimize 'bycatch'—polymorphisms caused by sequencing or assembly error. An Illumina Infinium SNP chip was used to genotype a final set of 7714 polymorphisms across 1734 individuals. Five predictors were examined for their effect on the probability of obtaining an assayable SNP: depth of coverage, number of reads that support a variant, polymorphism type (e.g. A/C), strand-bias and Illumina SNP probe design score. Our results indicate that filtering out systematic sequencing errors could substantially improve the efficiency of SNP discovery. We show that BLASTX can be used as an efficient tool to identify single-copy genomic regions in the absence of a reference genome. The results have implications for research aiming to identify assayable SNPs and build SNP genotyping assays for nonmodel organisms.

Data from: Model selection and parameter inference in phylogenetics using nested sampling

Bayesian inference methods rely on numerical algorithms for both model selection and parameter inference. In general, these algorithms require a high computational effort to yield reliable estimates. One of the major challenges in phylogenetics is the estimation of the marginal likelihood. This quantity is commonly used for comparing different evolutionary models, but its calculation, even for simple models, incurs high computational cost. Another interesting challenge relates to the estimation of the posterior distribution. Often, long Markov chains are required to get sufficient samples to carry out parameter inference, especially for tree distributions. In general, these problems are addressed separately by using different procedures. Nested sampling (NS) is a Bayesian computation algorithm which provides the means to estimate marginal likelihoods together with their uncertainties, and to sample from the posterior distribution at no extra cost. The methods currently used in phylogenetics for marginal likelihood estimation lack in practicality due to their dependence on many tuning parameters and their inability of most implementations to provide a direct way to calculate the uncertainties associated with the estimates, unlike NS. In this paper, we introduce NS to phylogenetics. Its performance is analysed under different scenarios and compared to established methods. We conclude that NS is a competitive and attractive algorithm for phylogenetic inference. An implementation is available as a package for BEAST 2 under the LGPL licence, accessible at https://github.com/BEAST2-Dev/nested-sampling.

Data from: Trait-based modeling of multi-host pathogen transmission: plant-pollinator networks

Epidemiological models for multi-host pathogen systems often classify individuals taxonomically and use species-specific parameter values, but in species-rich communities, that approach may require intractably many parameters. Trait-based epidemiological models offer a potential solution, but have not accounted for within-species trait variation or between-species trait overlap. Here, we propose and study trait-based models with host and vector communities represented as trait distributions without regard to species identity. To illustrate this approach, we develop SIS models for disease spread in plant-pollinator networks with continuous trait distributions. We model trait-dependent contact rates in two common scenarios: nested networks, and specialized plant-pollinator interactions based on trait matching. We find that disease spread in plant-pollinator networks is impacted the most by selective pollinators, universally attractive flowers, and co-specialized plant-pollinator pairs. When extreme pollinator traits are rare, pollinators with common traits are most important for disease spread, whereas when extreme flower traits are rare, flowers with uncommon traits impact disease spread the most. Greater nestedness and specialization both typically promote disease persistence. Given recent pollinator declines caused in part by pathogens, we discuss how trait-based models could inform conservation strategies for wild and managed pollinators. Furthermore, while we have applied our model to pollinators and pathogens, its framework is general and can be transferred to any kind of species interactions, in any community.

Data from: De novo transcriptomic analyses for non-model organisms: an evaluation of methods across a multi-species data set

High-throughput sequencing (HTS) is revolutionizing biological research by enabling scientists to quickly and cheaply query variation at a genomic scale. Despite the increasing ease of obtaining such data, using these data effectively still poses notable challenges, especially for those working with organisms without a high-quality reference genome. For every stage of analysis – from assembly to annotation to variant discovery – researchers have to distinguish technical artefacts from the biological realities of their data before they can make inference. In this work, I explore these challenges by generating a large de novo comparative transcriptomic data set data for a clade of lizards and constructing a pipeline to analyse these data. Then, using a combination of novel metrics and an externally validated variant data set, I test the efficacy of my approach, identify areas of improvement, and propose ways to minimize these errors. I find that with careful data curation, HTS can be a powerful tool for generating genomic data for non-model organisms.

opencc-zeroDec 2015View details →

Data from: Opsins have evolved under the permanent heterozygote model: insights from phylotranscriptomics of Odonata

Gene duplication plays a central role in adaptation to novel environments by providing new genetic material for functional divergence and evolution of biological complexity. Several evolutionary models have been proposed for gene duplication to explain how new gene copies are preserved by natural selection, but these models have rarely been tested using empirical data. Opsin proteins, when combined with a chromophore, form a photopigment that is responsible for the absorption of light, the first step in the phototransduction cascade. Adaptive gene duplications have occurred many times within the animal opsins' gene family, leading to novel wavelength sensitivities. Consequently, opsins are an attractive choice for the study of gene duplication evolutionary models. Odonata (dragonflies and damselflies) have the largest opsin repertoire of any insect currently known. Additionally, there is tremendous variation in opsin copy number between species, particularly in the long-wavelength-sensitive (LWS) class. Using comprehensive phylotranscriptomic and statistical approaches, we tested various evolutionary models of gene duplication. Our results suggest that both the blue-sensitive (BS) and LWS opsin classes were subjected to strong positive selection that greatly weakens after multiple duplication events, a pattern that is consistent with the permanent heterozygote model. Due to the immense interspecific variation and duplicability potential of opsin genes among odonates, they represent a unique model system to test hypotheses regarding opsin gene duplication and diversification at the molecular level.

Data from: Allometric convergence in savanna trees and implications for plant scaling models in variable ecosystems

Theoretical models of allometric scaling provide frameworks for understanding and predicting how and why the morphology and function of organisms vary with scale. It remains unclear, however, if the predictions of 'universal' scaling models for vascular plants hold across diverse species in variable environments. Phenomena such as competition and disturbance may drive allometric scaling relationships away from theoretical predictions based on an optimized tree. Here, we use a hierarchical Bayesian approach to calculate tree-specific, species-specific, and 'global' (i.e. interspecific) scaling exponents for several allometric relationships using tree- and branch-level data harvested from three savanna sites across a rainfall gradient in Mali, West Africa. We use these exponents to provide a rigorous test of three plant scaling models (Metabolic Scaling Theory (MST), Geometric Similarity, and Stress Similarity) in savanna systems. For the allometric relationships we evaluated (diameter vs. length, aboveground mass, stem mass, and leaf mass) the empirically calculated exponents broadly overlapped among species from diverse environments, except for the scaling exponents for length, which increased with tree cover and density. When we compare empirical scaling exponents to the theoretical predictions from the three models we find MST predictions are most consistent with our observed allometries. In those situations where observations are inconsistent with MST we find that departure from theory corresponds with expected tradeoffs related to disturbance and competitive interactions. We hypothesize savanna trees have greater length-scaling exponents than predicted by MST due to an evolutionary tradeoff between fire escape and optimization of mechanical stability and internal resource transport. Future research on the drivers of systematic allometric variation could reconcile the differences between observed scaling relationships in variable ecosystems and those predicted by ideal models such as MST.

Data from: The critical role of local refugia in postglacial colonization of Chinese pine: joint inferences from DNA analyses, pollen records, and species distribution modeling

The importance of long-distance migration from low to high latitudes relative to local spread from northern refugia after the Last Glacial Maximum (LGM) remains a focus of debate for many temperate tree species. We assessed the dynamics of Chinese pine (Pinus tabulaeformis), a widespread species endemic to northern China, since the LGM by integrating cytoplasmic DNA data, mapped pollen records and ecological niche modeling. Genetic variation among 544 individuals from 50 populations spanning the entire natural species range revealed eight genetic clusters with distinct geographic distribution, indicating glacial lineages likely originating from multiple local microrefugia. Palynological evidence suggested that the northernmost part of the natural distribution originated from local postglacial spread. Niche modeling indicated high probability of the species being present in the area of the Loess Plateau and coastal areas north of the Yangtze River during the LGM. The three lines of evidence jointly suggest that the species persisted through the last glaciation in the mountains surrounding the Loess Plateau of northern China and that the current distribution of the species originated primarily from the spread of local refugial populations, instead of long-distance migration. These results cast doubt on the notion that Chinese pine migrated from areas south of the Yangtze River and underscore the importance of northern refugia.