A temporal precedence based clustering method for gene expression microarray data

Background  

Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not.     

A method to determine rates and patterns of variability in ecological communities

It is well known that ecological communities are spatially and temporally dynamic. Quantifying temporal variability in ecological communities is challenging, however, especially for time-series data sets of less than 40 measurement intervals. In this paper, we describe a method to quantify temporal variability in multispecies communities over time frames of 10–40 measurement intervals. Our approach is a community-level extension of autocorrelation analysis, but we use Euclidean distance to measure similarity of community samples at increasing time lags rather than the correlation coefficient. Regressing Euclidean distances versus increasing time lags yields a measure of the rate and nature of community change over time. We demonstrate the method with empirical data sets from shortgrass steppe, old-field succession and zooplankton dynamics in lakes, and we investigate properties of the analysis using simulation models. Results indicate that time-lag analysis provides a useful quantitative measurement of the rate and pattern of temporal dynamics in communities over time frames that are too short for more traditional autocorrelation approaches.     

Is plant temporal beta diversity of field margins related to changes in management practices?

Field margins have considerable ecological significance in agriculture-dominated landscapes by supporting biodiversity and associated services. However, agricultural changes during mid-20th century led to their drastic loss with a serious threat for biodiversity. Using time-series data, we aimed to get better insights into processes underlying plant patterns of field margins through time by i) quantifying plant temporal beta diversity components, ii) assessing whether the observed changes in plant communities can be related to changes in management practices applied to field margins. During the springs of 1994, 1998 and 2001, we surveyed plant communities and management practices of the same 116 field margins in three contrasted landscapes. We estimated temporal beta diversity in plant communities and partitioned it into its two dissimilarity resultant components, accounting for replacement of species (i.e. turnover) and for the nested gain or loss of species (i.e. nestedness). We then tested whether the observed changes in plant communities between 1994 and 1998 and, between 1998 and 2001 were related to changes in management practices using linear models. Plant communities of field margins exhibited strong temporal beta diversity dominated by turnover. Temporal turnover in plant communities was partly related to changes in management practices, i.e., a decrease of grazing concomitant to an increase of herbicide spraying. However, relationships were not consistent between all landscape contexts nor time period, suggesting that other unmeasured deterministic or stochastic processes could be driving the observed plant patterns. Taken together, our results suggest that maintaining a wide diversity of field margins with contrasted management contribute to maintaining plant diversity at a landscape scale. They underline the value of investigating plant temporal diversity patterns using time-series data and thus, the need to develop long-term studies making it possible to understand ecological processes shaping plant communities in agricultural landscapes.     

Biotic homogenisation and differentiation as directional change in beta diversity: synthesising driver–response relationships to develop conceptual models across ecosystems

Biotic homogenisation is defined as decreasing dissimilarity among ecological assemblages sampled within a given spatial area over time. Biotic differentiation, in turn, is defined as increasing dissimilarity over time. Overall, changes in the spatial dissimilarities among assemblages (termed ‘beta diversity’) is an increasingly recognised feature of broader biodiversity change in the Anthropocene. Empirical evidence of biotic homogenisation and biotic differentiation remains scattered across different ecosystems. Most meta-analyses quantify the prevalence and direction of change in beta diversity, rather than attempting to identify underlying ecological drivers of such changes. By conceptualising the mechanisms that contribute to decreasing or increasing dissimilarity in the composition of ecological assemblages across space, environmental managers and conservation practitioners can make informed decisions about what interventions may be required to sustain biodiversity and can predict potential biodiversity outcomes of future disturbances. We systematically reviewed and synthesised published empirical evidence for ecological drivers of biotic homogenisation and differentiation across terrestrial, marine, and freshwater realms to derive conceptual models that explain changes in spatial beta diversity. We pursued five key themes in our review: (i) temporal environmental change; (ii) disturbance regime; (iii) connectivity alteration and species redistribution; (iv) habitat change; and (v) biotic and trophic interactions. Our first conceptual model highlights how biotic homogenisation and differentiation can occur as a function of changes in local (alpha) diversity or regional (gamma) diversity, independently of species invasions and losses due to changes in species occurrence among assemblages. Second, the direction and magnitude of change in beta diversity depends on the interaction between spatial variation (patchiness) and temporal variation (synchronicity) of disturbance events. Third, in the context of connectivity and species redistribution, divergent beta diversity outcomes occur as different species have different dispersal characteristics, and the magnitude of beta diversity change associated with species invasions also depends strongly on alpha and gamma diversity prior to species invasion. Fourth, beta diversity is positively linked with spatial environmental variability, such that biotic homogenisation and differentiation occur when environmental heterogeneity decreases or increases, respectively. Fifth, species interactions can influence beta diversity via habitat modification, disease, consumption (trophic dynamics), competition, and by altering ecosystem productivity. Our synthesis highlights the multitude of mechanisms that cause assemblages to be more or less spatially similar in composition (taxonomically, functionally, phylogenetically) through time. We consider that future studies should aim to enhance our collective understanding of ecological systems by clarifying the underlying mechanisms driving homogenisation or differentiation, rather than focusing only on reporting the prevalence and direction of change in beta diversity, per se.     

Identifying anticancer peptides by using a generalized chaos game representation

We generalize chaos game representation (CGR) to higher dimensional spaces while maintaining its bijection, keeping such method sufficiently representative and mathematically rigorous compare to previous attempts. We first state and prove the asymptotic property of CGR and our generalized chaos game representation (GCGR) method. The prediction follows that the dissimilarity of sequences which possess identical subsequences but distinct positions would be lowered exponentially by the length of the identical subsequence; this effect was taking place unbeknownst to researchers. By shining a spotlight on it now, we show the effect fundamentally supports (G)CGR as a similarity measure or feature extraction technique. We develop two feature extraction techniques: GCGR-Centroid and GCGR-Variance. We use the GCGR-Centroid to analyze the similarity between protein sequences by using the datasets 9 ND5, 24 TF and 50 beta-globin proteins. We obtain consistent results compared with previous studies which proves the significance thereof. Finally, by utilizing support vector machines, we train the anticancer peptide prediction model by using both GCGR-Centroid and GCGR-Variance, and achieve a significantly higher prediction performance by employing the 3 well-studied anticancer peptide datasets.

     

Analysing ecological dynamics with relational event models: The case of biological invasions

Aims

Spatio-temporal processes play a key role in ecology, from genes to large-scale macroecological and biogeographical processes. Existing methods studying such spatio-temporally structured data either simplify the dynamic structure or the complex interactions of ecological drivers. The aim of this paper is to present a generic method for ecological research that allows analysing spatio-temporal patterns of biological processes at large spatial scales by including the time-varying variables that drive these dynamics.

Location

Global analysis at the level of 272 regions.

Methods

We introduce a method called relational event modelling (REM). REM relies on temporal interaction dynamics that encode sequences of relational events connecting a sender node to a recipient node at a specific point in time. We apply REM to the spread of alien species around the globe between 1880 and 2005, following accidental or deliberate introductions into geographical regions outside of their native range. In this context, a relational event represents the new occurrence of an alien species given its former distribution.

Results

The application of relational event models to the first reported invasions of 4835 established alien species outside of their native ranges from four major taxonomic groups enables us to unravel the main drivers of the dynamics of the spread of invasive alien species. Combining the alien species first records data with other spatio-temporal information enables us to discover which factors have been responsible for the spread of species across the globe. Besides the usual drivers of species invasions, such as trade, land use and climatic conditions, we also find evidence for species-interconnectedness in alien species spread.

Conclusions

Relational event models offer the capacity to account for the temporal sequences of ecological events such as biological invasions and to investigate how relationships between these events and potential drivers change over time.     

Diversity partitioning of Rao’s quadratic entropy

Many applications of diversity indices are only valid if they are first transformed into their equivalent number of species. These equivalent numbers of species can be multiplicatively partitioned into independent alpha, beta and gamma components, and can be formed into mathematically consistent similarity measures. The utility of beta diversity and similarity measures that incorporate information about the degree of ecological dissimilarity between species is becoming increasingly recognized. The concept of equivalent number of species is here extended to Rao’s quadratic entropy, opening the way to methods of diversity partitioning that take into account taxonomic or ecological differences between species.     

基于对齐的生物序列相似性分析

生物序列相似性(或差异性)分析是生物信息学研究的一种重要的方法。其中基于对齐的生物序列相似性分析方法,重点介绍基于隐马尔可夫模型的比较方法,并比较基于对齐的各种生物序列分析方法的优缺点。     

Mysis in the Okanagan Lake food web: a time-series analysis of interaction strengths in an invaded plankton community

Mysis introductions to the lakes of western North America have shown they are important predators on zooplankton, especially daphnids, and intercept energy flows that would otherwise be available to pelagic fishes. However, understanding of the ecological roles of Mysis within invaded communities following their establishment remains weak. We analyzed zooplankton and phytoplankton data collected from Okanagan Lake, British Columbia, within a time-series framework to evaluate the strength of ecological interactions between Mysis and the other dominant plankton. Top-down effects of Mysis in the plankton community were only detected on cyclopoid copepods and cyanophytes. Mysis dynamics were mostly driven by bottom-up effects from diatoms and from small cladocerans whose dynamics were driven primarily by the abundance of edible phytoplankton. This result supports the growing appreciation of the importance of omnivory in mysids and was consistent between the two main basins of the lake. We also analyzed published stable C and N isotope data from the plankton of Okanagan Lake with an isotope mixing model to estimate the relative importance of various potential energy sources to Mysis. This analysis supported the time-series results suggesting the importance of diatoms and small zooplankton to Mysis. However, the isotopes also suggested important resource flows from Daphnia to Mysis, an interaction not detected in the time-series analysis. Taken together, these results suggest that Mysis is a strong interactor in the Okanagan Lake food web, relying in part on energy flow through Daphnia. However, subsidies from diatoms likely decouple seasonal Mysis population dynamics from the seasonal population dynamics of Daphnia.     

MSMBuilder: Statistical Models for Biomolecular Dynamics

MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. MSMBuilder was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements.     

Spatial and temporal homogenisation of freshwater macrofaunal communities in ditches

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Ecological change predicts population dynamics and genetic diversity over 120 000 years

While ecological effects on short‐term population dynamics are well understood, their effects over millennia are difficult to demonstrate and convincing evidence is scant. Using coalescent methods, we analysed past population dynamics of three lizard species (Psammodromus hispanicus, P. edwardsianus, P. occidentalis) and linked the results with climate change data covering the same temporal horizon (120 000 years). An increase in population size over time was observed in two species, and in P. occidentalis, no change was observed. Temporal changes in temperature seasonality and the maximum temperature of the warmest month were congruent with changes in population dynamics observed for the three species and both variables affected population density, either directly or indirectly (via a life‐history trait). These results constitute the first solid link between ecological change and long‐term population dynamics. The results moreover suggest that ecological change leaves genetic signatures that can be retrospectively traced, providing evidence that ecological change is a crucial driver of genetic diversity and speciation.     

Measures of precision for dissimilarity‐based multivariate analysis of ecological communities

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity‐based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity‐based standard error (MultSE) as a useful quantity for assessing sample‐size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided.     

Environmental variability drives phytoplankton assemblage persistence in a subtropical reservoir

Temporal changes of assemblages may result from environmental variability and reflect seasonal dynamics of their ecosystem. In the subtropics, the hydrological regime is usually characterized by well‐defined wet and dry seasons, regulating discharge and influencing a series of environmental variables that affect phytoplankton persistence. Therefore, we may expect that dry seasons are environmentally more stable than wet seasons. We analysed interannual phytoplankton assemblage variability (or, inversely, persistence) in a subtropical reservoir sampled every austral summer and winter during 5 years. We tested (i) if phytoplankton assemblage structure differed between the dry (summer) and wet (winter) seasons; (ii) if assemblage persistence differed between the seasons; (iii) if assemblage persistence was related to environmental stability; and (iv) if assemblage dissimilarity increased over time. Phytoplankton assemblages differed between the summer and winter seasons. Winter indicator species were mostly Bacillariophyceae or Cryptophyceae, whereas Cyanophyceae and Chlorophyceae taxa were more frequent and abundant in summer. Assemblages in the dry season were more persistent among years than those occurring during rainy periods. Similarly, environmental variability tended to be lower among dry than among rainy seasons. The relation between the phytoplankton temporal cycle and the temporal patterns of environmental variability supports our prediction that high environmental stability results in more persistent assemblages. Assemblage dissimilarity increased as sampling years were farther apart, for both seasons. Additionally, assemblages in the rainy periods showed a more pronounced increase in dissimilarity, as their changes among years were less predictable. We found a clear temporal pattern and an increased dissimilarity over time in the phytoplankton assemblage structure. Unravelling these temporal patterns may improve our understanding of phytoplankton temporal dynamics, and may have implications for management and monitoring programs. High dissimilarity of assemblages among years, particularly among rainy periods, can obscure human impacts, and monitoring programs should take this into account.     

Differential regional response of the bushy‐tailed woodrat (Neotoma cinerea) to late Quaternary climate change

Aim To reconstruct the regional biogeographical history of the bushy‐tailed woodrat, Neotoma cinerea (Rodentia: Cricetidae), across its distribution using multiple sources of information, including genetic data, ecological niche models and the palaeorecord. Location Western North America. Methods We analysed complete cytochrome b gene (1143 bp) sequences from 182 specimens of N. cinerea using Bayesian and coalescent methods to infer phylogenetic relationships, time of major divergences, and recent demographic trends. For comparison, we developed clade‐specific ecological niche models for groups of interest and analysed spatial trends of N. cinerea in the palaeorecord as well as temporal frequency trends across strata of individual palaeomiddens. Results We found two largely allopatric clades within N. cinerea, with several regionally distinct subclades showing contrasting recent population dynamics. Niche models showed consistent habitat at the Last Glacial Maximum (LGM) and modern times in the Rocky Mountains and northern United States, while the Great Basin may have been markedly less suitable at the LGM than today. The palaeorecord showed great spatio‐temporal variability in the presence of N. cinerea, but documents broad‐scale patterns of occupancy and regional population trends. Main conclusions The Quaternary dynamics and evolutionary history of N. cinerea appear to have been shaped by both vicariant events associated with geographical barriers and the availability of suitable habitat through time. Divergence of the two major clades dates to the Pliocene–Pleistocene transition, with clades separated by the Green and Colorado rivers and northern Rocky Mountain Pleistocene glaciations. We found largely concordant genetic, niche model and palaeorecord patterns suggesting long‐term population stability in the Rocky Mountains, while extant clades in the Great Basin and far north appear to have expanded or re‐expanded into these regions relatively recently. Furthermore, disjunct haplotype distributions, regional demographic history, and historical distribution of suitable habitat suggest that the Great Basin has been a particularly dynamic region.     

Relating fitness to long‐term environmental variations in natura

Quantifying links between ecological processes and adaptation dynamics in natura remains a crucial challenge. Many studies have documented the strength, form and direction of selection, and its variations in space and time, but only a few managed to link these variations to their proximal causes. This step is, however, crucial, if we are to understand how the variation in selective pressure affects adaptive allele dynamics in natural settings. We used data from a long‐term survey (about 30 years) monitoring the adaptation to insecticides of Culex pipiens mosquitoes in Montpellier area (France), focusing on three resistance alleles of the Ester locus. We used a population genetics model taking temporal and spatial variations in selective pressure into account, to assess the quantitative relationships between variations in the proximal agent of selection (amounts of insecticide sprayed) and the fitness of resistance alleles. The response to variations in selective pressure was fast, and the alleles displayed different fitness‐to‐environment relationships: the analyses revealed that even slight changes in insecticide doses could induce changes in the strength and direction of selection, thus changing the fitness ranking of the adaptive alleles. They also revealed that selective pressures other than the insecticides used for mosquito control affected the resistance allele dynamics. These fitness‐to‐environment relationships, fast responses and continuous evolution limit our ability to predict the outcome of adaptive allele dynamics in a changing environment, but they clearly contribute to the maintenance of polymorphism in natural populations. Our study also emphasizes the necessity of long‐term surveys in evolutionary ecology.     

A new metric for evaluating the correspondence of spatial patterns in vegetation models

Aim  To present a new metric, the 'opposite and identity' (OI) index, for evaluating the correspondence between two sets of simulated time-series dynamics of an ecological variable.
Innovation  The OI index is introduced and its mathematical expression is defined using vectors to denote simulated variations of an ecological variable on the basis of the vector addition rule. The value of the OI index varies from 0 to 1 with a value 0 (or 1) indicating that compared simulations are opposite (or identical). An OI index with a value near 0.5 suggests that the difference in the amplitudes of variations between compared simulations is large. The OI index can be calculated in a grid cell, for a given biome and for time-series simulations. The OI indices calculated in each grid cell can be used to map the spatial agreement between compared simulations, allowing researchers to pinpoint the extent of agreement or disagreement between two simulations. The OI indices calculated for time-series simulations allow researchers to identify the time at which one simulation differs from another. A case study demonstrates the application and reliability of the OI index for comparing two simulated time-series dynamics of terrestrial net primary productivity in Asia from 1982 to 2000. In the case study, the OI index performs better than the correlation coefficient at accurately quantifying the agreement between two simulated time-series dynamics of terrestrial net primary productivity in Asia.
Main conclusions  The OI index provides researchers with a useful tool and multiple flexible ways to compare two simulation results or to evaluate simulation results against observed spatiotemporal data. The OI index can, in some cases, quantify the agreement between compared spatiotemporal data more accurately than the correlation coefficient because of its insensitivity to influential data and outliers and the autocorrelation of simulated spatiotemporal data.     

Gene structure conservation aids similarity based gene prediction

One of the primary tasks in deciphering the functional contents of a newly sequenced genome is the identification of its protein coding genes. Existing computational methods for gene prediction include ab initio methods which use the DNA sequence itself as the only source of information, comparative methods using multiple genomic sequences, and similarity based methods which employ the cDNA or protein sequences of related genes to aid the gene prediction. We present here an algorithm implemented in a computer program called Projector which combines comparative and similarity approaches. Projector employs similarity information at the genomic DNA level by directly using known genes annotated on one DNA sequence to predict the corresponding related genes on another DNA sequence. It therefore makes explicit use of the conservation of the exon–intron structure between two related genes in addition to the similarity of their encoded amino acid sequences. We evaluate the performance of Projector by comparing it with the program Genewise on a test set of 491 pairs of independently confirmed mouse and human genes. It is more accurate than Genewise for genes whose proteins are <80% identical, and is suitable for use in a combined gene prediction system where other methods identify well conserved and non-conserved genes, and pseudogenes.     

Spatiotemporal dynamics of α‐ and β‐diversity across topographic gradients in the herbaceous layer of an old‐growth deciduous forest

Understanding the factors that regulate biodiversity over spatial and temporal gradients is an important scientific objective with ramifications for theory and conservation. Species composition is known to vary across spatial gradients, but how this spatial variation is linked to temporal dynamics is less well studied. Our objective was to understand how Shannon (α) diversity, spatial species turnover (Bray–Curtis dissimilarity), and temporal species turnover (Bray–Curtis dissimilarity) varied with regard to three topographic gradients (aspect, slope and elevation) over one growing season. In April, June and August of 2011, the herbaceous layer was sampled in 320 1‐m² plots within Big Everidge Hollow, an old‐growth forest in southeastern Kentucky. Multiple regression models revealed that Shannon diversity was linearly related to aspect (negative) and slope (positive), but unimodally related to elevation, indicating steep, mid‐elevation, and south‐facing plots were most diverse. Distance decay analysis showed that significant spatial species turnover occurred across all three topographic gradients, but aspect and elevation had a greater influence on compositional dissimilarity than slope. Mean temporal species turnover was significantly greater (p < 0.001) between April and June (0.39 ± 0.02 SE) than between June and August (0.20 ± 0.01). April‐to‐June turnover was related to aspect (linear) and elevation (quadratic; r²= 0.23, p < 0.0001), suggesting greater temporal species turnover occurred on north‐facing and mid‐elevation plots during this period; however, June‐to‐August turnover was weakly related to slope only (positive linear; r²= 0.08, p = 0.006). Environmental heterogeneity generated by topography is one of many factors that may constrain or promote biodiversity through space and across time, and a solid understanding of these spatiotemporal patterns of diversity can benefit both conservation and ecological theory.