SIRIUS: Dual, Symmetric, Interactive Dimension Reductions.

Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights.

Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics.

Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However, these algorithmic combinations are often ad hoc or disconnected, working independently and in parallel rather than integrating some degree of interdependence. A number of design decisions must be addressed when employing dimension reduction and clustering algorithms concurrently in a visualization system, including the selection of each algorithm, the order in which they are processed, and how to present and interact with the resulting projection. This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualization system that makes use of both families of algorithms.

Expert-Guided Generative Topographical Modeling with Visual to Parametric Interaction.

Introduced by Bishop et al. in 1996, Generative Topographic Mapping (GTM) is a powerful nonlinear latent variable modeling approach for visualizing high-dimensional data. It has shown useful when typical linear methods fail. However, GTM still suffers from drawbacks. Its complex parameterization of data make GTM hard to fit and sensitive to slight changes in the model. For this reason, we extend GTM to a visual analytics framework so that users may guide the parameterization and assess the data from multiple GTM perspectives. Specifically, we develop the theory and methods for Visual to Parametric Interaction (V2PI) with data using GTM visualizations. The result is a dynamic version of GTM that fosters data exploration. We refer to the new version as V2PI-GTM. In this paper, we develop V2PI-GTM in stages and demonstrate its benefits within the context of a text mining case study.

A system to automatically classify and name any individual genome-sequenced organism independently of current biological classification and nomenclature.

A broadly accepted and stable biological classification system is a prerequisite for biological sciences. It provides the means to describe and communicate about life without ambiguity. Current biological classification and nomenclature use the species as the basic unit and require lengthy and laborious species descriptions before newly discovered organisms can be assigned to a species and be named. The current system is thus inadequate to classify and name the immense genetic diversity within species that is now being revealed by genome sequencing on a daily basis. To address this lack of a general intra-species classification and naming system adequate for today's speed of discovery of new diversity, we propose a classification and naming system that is exclusively based on genome similarity and that is suitable for automatic assignment of codes to any genome-sequenced organism without requiring any phenotypic or phylogenetic analysis. We provide examples demonstrating that genome similarity-based codes largely align with current taxonomic groups at many different levels in bacteria, animals, humans, plants, and viruses. Importantly, the proposed approach is only slightly affected by the order of code assignment and can thus provide codes that reflect similarity between organisms and that do not need to be revised upon discovery of new diversity. We envision genome similarity-based codes to complement current biological nomenclature and to provide a universal means to communicate unambiguously about any genome-sequenced organism in fields as diverse as biodiversity research, infectious disease control, human and microbial forensics, animal breed and plant cultivar certification, and human ancestry research.

A Dirichlet process model for classifying and forecasting epidemic curves.

BACKGROUND: A forecast can be defined as an endeavor to quantitatively estimate a future event or probabilities assigned to a future occurrence. Forecasting stochastic processes such as epidemics is challenging since there are several biological, behavioral, and environmental factors that influence the number of cases observed at each point during an epidemic. However, accurate forecasts of epidemics would impact timely and effective implementation of public health interventions. In this study, we introduce a Dirichlet process (DP) model for classifying and forecasting influenza epidemic curves. METHODS: The DP model is a nonparametric Bayesian approach that enables the matching of current influenza activity to simulated and historical patterns, identifies epidemic curves different from those observed in the past and enables prediction of the expected epidemic peak time. The method was validated using simulated influenza epidemics from an individual-based model and the accuracy was compared to that of the tree-based classification technique, Random Forest (RF), which has been shown to achieve high accuracy in the early prediction of epidemic curves using a classification approach. We also applied the method to forecasting influenza outbreaks in the United States from 1997-2013 using influenza-like illness (ILI) data from the Centers for Disease Control and Prevention (CDC). RESULTS: We made the following observations. First, the DP model performed as well as RF in identifying several of the simulated epidemics. Second, the DP model correctly forecasted the peak time several days in advance for most of the simulated epidemics. Third, the accuracy of identifying epidemics different from those already observed improved with additional data, as expected. Fourth, both methods correctly classified epidemics with higher reproduction numbers (R) with a higher accuracy compared to epidemics with lower R values. Lastly, in the classification of seasonal influenza epidemics based on ILI data from the CDC, the methods' performance was comparable. CONCLUSIONS: Although RF requires less computational time compared to the DP model, the algorithm is fully supervised implying that epidemic curves different from those previously observed will always be misclassified. In contrast, the DP model can be unsupervised, semi-supervised or fully supervised. Since both methods have their relative merits, an approach that uses both RF and the DP model could be beneficial.

Semantics of directly manipulating spatializations.

When high-dimensional data is visualized in a 2D plane by using parametric projection algorithms, users may wish to manipulate the layout of the data points to better reflect their domain knowledge or to explore alternative structures. However, few users are well-versed in the algorithms behind the visualizations, making parameter tweaking more of a guessing game than a series of decisive interactions. Translating user interactions into algorithmic input is a key component of Visual to Parametric Interaction (V2PI) [13]. Instead of adjusting parameters, users directly move data points on the screen, which then updates the underlying statistical model. However, we have found that some data points that are not moved by the user are just as important in the interactions as the data points that are moved. Users frequently move some data points with respect to some other 'unmoved' data points that they consider as spatially contextual. However, in current V2PI interactions, these points are not explicitly identified when directly manipulating the moved points. We design a richer set of interactions that makes this context more explicit, and a new algorithm and sophisticated weighting scheme that incorporates the importance of these unmoved data points into V2PI.

Allelic variation in two distinct Pseudomonas syringae flagellin epitopes modulates the strength of plant immune responses but not bacterial motility.

The bacterial flagellin (FliC) epitopes flg22 and flgII-28 are microbe-associated molecular patterns (MAMPs). Although flg22 is recognized by many plant species via the pattern recognition receptor FLS2, neither the flgII-28 receptor nor the extent of flgII-28 recognition by different plant families is known. Here, we tested the significance of flgII-28 as a MAMP and the importance of allelic diversity in flg22 and flgII-28 in plant-pathogen interactions using purified peptides and a Pseudomonas syringae ∆fliC mutant complemented with different fliC alleles. The plant genotype and allelic diversity in flg22 and flgII-28 were found to significantly affect the plant immune response, but not bacterial motility. The recognition of flgII-28 is restricted to a number of solanaceous species. Although the flgII-28 peptide does not trigger any immune response in Arabidopsis, mutations in both flg22 and flgII-28 have FLS2-dependent effects on virulence. However, the expression of a tomato allele of FLS2 does not confer to Nicotiana benthamiana the ability to detect flgII-28, and tomato plants silenced for FLS2 are not altered in flgII-28 recognition. Therefore, MAMP diversification is an effective pathogen virulence strategy, and flgII-28 appears to be perceived by an as yet unidentified receptor in the Solanaceae, although it has an FLS2-dependent virulence effect in Arabidopsis.

Nonagricultural reservoirs contribute to emergence and evolution of Pseudomonas syringae crop pathogens.

While the existence of environmental reservoirs of human pathogens is well established, less is known about the role of nonagricultural environments in emergence, evolution, and spread of crop pathogens. Here, we analyzed phylogeny, virulence genes, host range, and aggressiveness of Pseudomonas syringae strains closely related to the tomato pathogen P. syringae pv. tomato (Pto), including strains isolated from snowpack and streams. The population of Pto relatives in nonagricultural environments was estimated to be large and its diversity to be higher than that of the population of Pto and its relatives on crops. Ancestors of environmental strains, Pto, and other genetically monomorphic crop pathogens were inferred to have frequently recombined, suggesting an epidemic population structure for P. syringae. Some environmental strains have repertoires of type III-secreted effectors very similar to Pto, are almost as aggressive on tomato as Pto, but have a wider host range than typical Pto strains. We conclude that crop pathogens may have evolved through a small number of evolutionary events from a population of less aggressive ancestors with a wider host range present in nonagricultural environments.

Visual to Parametric Interaction (V2PI).

Typical data visualizations result from linear pipelines that start by characterizing data using a model or algorithm to reduce the dimension and summarize structure, and end by displaying the data in a reduced dimensional form. Sensemaking may take place at the end of the pipeline when users have an opportunity to observe, digest, and internalize any information displayed. However, some visualizations mask meaningful data structures when model or algorithm constraints (e.g., parameter specifications) contradict information in the data. Yet, due to the linearity of the pipeline, users do not have a natural means to adjust the displays. In this paper, we present a framework for creating dynamic data displays that rely on both mechanistic data summaries and expert judgement. The key is that we develop both the theory and methods of a new human-data interaction to which we refer as " Visual to Parametric Interaction" (V2PI). With V2PI, the pipeline becomes bi-directional in that users are embedded in the pipeline; users learn from visualizations and the visualizations adjust to expert judgement. We demonstrate the utility of V2PI and a bi-directional pipeline with two examples.

The plant pathogen Pseudomonas syringae pv. tomato is genetically monomorphic and under strong selection to evade tomato immunity.

Recently, genome sequencing of many isolates of genetically monomorphic bacterial human pathogens has given new insights into pathogen microevolution and phylogeography. Here, we report a genome-based micro-evolutionary study of a bacterial plant pathogen, Pseudomonas syringae pv. tomato. Only 267 mutations were identified between five sequenced isolates in 3,543,009 nt of analyzed genome sequence, which suggests a recent evolutionary origin of this pathogen. Further analysis with genome-derived markers of 89 world-wide isolates showed that several genotypes exist in North America and in Europe indicating frequent pathogen movement between these world regions. Genome-derived markers and molecular analyses of key pathogen loci important for virulence and motility both suggest ongoing adaptation to the tomato host. A mutational hotspot was found in the type III-secreted effector gene hopM1. These mutations abolish the cell death triggering activity of the full-length protein indicating strong selection for loss of function of this effector, which was previously considered a virulence factor. Two non-synonymous mutations in the flagellin-encoding gene fliC allowed identifying a new microbe associated molecular pattern (MAMP) in a region distinct from the known MAMP flg22. Interestingly, the ancestral allele of this MAMP induces a stronger tomato immune response than the derived alleles. The ancestral allele has largely disappeared from today's Pto populations suggesting that flagellin-triggered immunity limits pathogen fitness even in highly virulent pathogens. An additional non-synonymous mutation was identified in flg22 in South American isolates. Therefore, MAMPs are more variable than expected differing even between otherwise almost identical isolates of the same pathogen strain.

Reconstructing host range evolution of bacterial plant pathogens using Pseudomonas syringae pv. tomato and its close relatives as a model.

Several lines of evidence suggest that highly virulent bacterial human pathogens evolved from less virulent wider host range animal pathogens since human migration out of Africa. To investigate evolution of host specificity of bacterial plant pathogens, here we report a molecular evolutionary analysis of the model plant pathogen Pseudomonas syringae pv. tomato DC3000 and of close relatives that are pathogens of a diverse set of crop plants. Extensive host range tests on five different plant species were performed. Combining phylogenetic data with host range data, a reconstruction of host range of all putative ancestors was performed. In particular, the hypothesis was tested that highly virulent narrow host range pathogens of today's crops grown in monoculture evolved from ancestors with wider host range that were adapted to natural mixed plant communities of pre-agricultural times. We found support for this hypothesis in individual clades. However, reconstruction of host range of the most recent common ancestor of all analyzed strains was not conclusive. Based on the obtained results we stress the importance of including pathogens from wild plants when reconstructing the evolution of plant pathogenic bacteria.

Arm-specific dynamics of chromosome evolution in malaria mosquitoes.

BACKGROUND: The malaria mosquito species of subgenus Cellia have rich inversion polymorphisms that correlate with environmental variables. Polymorphic inversions tend to cluster on the chromosomal arms 2R and 2L but not on X, 3R and 3L in Anopheles gambiae and homologous arms in other species. However, it is unknown whether polymorphic inversions on homologous chromosomal arms of distantly related species from subgenus Cellia nonrandomly share similar sets of genes. It is also unclear if the evolutionary breakage of inversion-poor chromosomal arms is under constraints. RESULTS: To gain a better understanding of the arm-specific differences in the rates of genome rearrangements, we compared gene orders and established syntenic relationships among Anopheles gambiae, Anopheles funestus, and Anopheles stephensi. We provided evidence that polymorphic inversions on the 2R arms in these three species nonrandomly captured similar sets of genes. This nonrandom distribution of genes was not only a result of preservation of ancestral gene order but also an outcome of extensive reshuffling of gene orders that created new combinations of homologous genes within independently originated polymorphic inversions. The statistical analysis of distribution of conserved gene orders demonstrated that the autosomal arms differ in their tolerance to generating evolutionary breakpoints. The fastest evolving 2R autosomal arm was enriched with gene blocks conserved between only a pair of species. In contrast, all identified syntenic blocks were preserved on the slowly evolving 3R arm of An. gambiae and on the homologous arms of An. funestus and An. stephensi. CONCLUSIONS: Our results suggest that natural selection favors specific gene combinations within polymorphic inversions when distant species are exposed to similar environmental pressures. This knowledge could be useful for the discovery of genes responsible for an association of inversion polymorphisms with phenotypic variations in multiple species. Our data support the chromosomal arm specificity in rates of gene order disruption during mosquito evolution. We conclude that the distribution of breakpoint regions is evolutionary conserved on slowly evolving arms and tends to be lineage-specific on rapidly evolving arms.

Genome mapping and characterization of the Anopheles gambiae heterochromatin.

BACKGROUND: Heterochromatin plays an important role in chromosome function and gene regulation. Despite the availability of polytene chromosomes and genome sequence, the heterochromatin of the major malaria vector Anopheles gambiae has not been mapped and characterized. RESULTS: To determine the extent of heterochromatin within the An. gambiae genome, genes were physically mapped to the euchromatin-heterochromatin transition zone of polytene chromosomes. The study found that a minimum of 232 genes reside in 16.6 Mb of mapped heterochromatin. Gene ontology analysis revealed that heterochromatin is enriched in genes with DNA-binding and regulatory activities. Immunostaining of the An. gambiae chromosomes with antibodies against Drosophila melanogaster heterochromatin protein 1 (HP1) and the nuclear envelope protein lamin Dm0 identified the major invariable sites of the proteins' localization in all regions of pericentric heterochromatin, diffuse intercalary heterochromatin, and euchromatic region 9C of the 2R arm, but not in the compact intercalary heterochromatin. To better understand the molecular differences among chromatin types, novel Bayesian statistical models were developed to analyze genome features. The study found that heterochromatin and euchromatin differ in gene density and the coverage of retroelements and segmental duplications. The pericentric heterochromatin had the highest coverage of retroelements and tandem repeats, while intercalary heterochromatin was enriched with segmental duplications. We also provide evidence that the diffuse intercalary heterochromatin has a higher coverage of DNA transposable elements, minisatellites, and satellites than does the compact intercalary heterochromatin. The investigation of 42-Mb assembly of unmapped genomic scaffolds showed that it has molecular characteristics similar to cytologically mapped heterochromatin. CONCLUSIONS: Our results demonstrate that Anopheles polytene chromosomes and whole-genome shotgun assembly render the mapping and characterization of a significant part of heterochromatic scaffolds a possibility. These results reveal the strong association between characteristics of the genome features and morphological types of chromatin. Initial analysis of the An. gambiae heterochromatin provides a framework for its functional characterization and comparative genomic analyses with other organisms.

Genome landscape and evolutionary plasticity of chromosomes in malaria mosquitoes.

BACKGROUND: Nonrandom distribution of rearrangements is a common feature of eukaryotic chromosomes that is not well understood in terms of genome organization and evolution. In the major African malaria vector Anopheles gambiae, polymorphic inversions are highly nonuniformly distributed among five chromosomal arms and are associated with epidemiologically important adaptations. However, it is not clear whether the genomic content of the chromosomal arms is associated with inversion polymorphism and fixation rates. METHODOLOGY/PRINCIPAL FINDINGS: To better understand the evolutionary dynamics of chromosomal inversions, we created a physical map for an Asian malaria mosquito, Anopheles stephensi, and compared it with the genome of An. gambiae. We also developed and deployed novel Bayesian statistical models to analyze genome landscapes in individual chromosomal arms An. gambiae. Here, we demonstrate that, despite the paucity of inversion polymorphisms on the X chromosome, this chromosome has the fastest rate of inversion fixation and the highest density of transposable elements, simple DNA repeats, and GC content. The highly polymorphic and rapidly evolving autosomal 2R arm had overrepresentation of genes involved in cellular response to stress supporting the role of natural selection in maintaining adaptive polymorphic inversions. In addition, the 2R arm had the highest density of regions involved in segmental duplications that clustered in the breakpoint-rich zone of the arm. In contrast, the slower evolving 2L, 3R, and 3L, arms were enriched with matrix-attachment regions that potentially contribute to chromosome stability in the cell nucleus. CONCLUSIONS/SIGNIFICANCE: These results highlight fundamental differences in evolutionary dynamics of the sex chromosome and autosomes and revealed the strong association between characteristics of the genome landscape and rates of chromosomal evolution. We conclude that a unique combination of various classes of genes and repetitive DNA in each arm, rather than a single type of repetitive element, is likely responsible for arm-specific rates of rearrangements.

PAMDB, a multilocus sequence typing and analysis database and website for plant-associated microbes.

Although there are adequate DNA sequence differences among plant-associated and plant-pathogenic bacteria to facilitate molecular approaches for their identification, identification at a taxonomic level that is predictive of their phenotype is a challenge. The problem is the absence of a taxonomy that describes genetic variation at a biologically relevant resolution and of a database containing reference strains for comparison. Moreover, molecular evolution, population genetics, ecology, and epidemiology of many plant-pathogenic and plant-associated bacteria are still poorly understood. To address these challenges, a database with web interface was specifically designed for plant-associated and plant-pathogenic microorganisms. The Plant-Associated Microbes Database (PAMDB) comprises, thus far, data from multilocus sequence typing and analysis (MLST/MLSA) studies of Acidovorax citrulli, Pseudomonas syringae, Ralstonia solanacearum, and Xanthomonas spp. Using data deposited in PAMDB, a robust phylogeny of Xanthomonas axonopodis and related bacteria has been inferred, and the diversity existing in the Xanthomonas genus and in described Xanthomonas spp. has been compared with the diversity in P. syringae and R. solanacearum. Moreover, we show how PAMDB makes it easy to distinguish between different pathogens that cause almost identical diseases. The scalable design of PAMDB will make it easy to add more plant pathogens in the future.

Modeling the spread of infectious disease using genetic information within a marked branching process.

Accurate assessment of disease dynamics requires a quantification of many unknown parameters governing disease transmission processes. While infection control strategies within hospital settings are stringent, some disease will be propagated due to human interactions (patient-to-patient or patient-to-caregiver-to-patient). In order to understand infectious transmission rates within the hospital, it is necessary to isolate the amount of disease that is endemic to the outside environment. While discerning the origins of disease is difficult when using ordinary spatio-temporal data (locations and time of disease detection), genotypes that are common to pathogens, with common sources, aid in distinguishing nosocomial infections from independent arrivals of the disease. The purpose of this study was to demonstrate a Bayesian modeling procedure for identifying nosocomial infections, and quantify the rate of these transmissions. We will demonstrate our method using a 10-year history of Morexella catarhallis. Results will show the degree to which pathogen-specific, genotypic information impacts inferences about the nosocomial rate of infection.

The evolutionary forest algorithm.

MOTIVATION: Gene genealogies offer a powerful context for inferences about the evolutionary process based on presently segregating DNA variation. In many cases, it is the distribution of population parameters, marginalized over the effectively infinite-dimensional tree space, that is of interest. Our evolutionary forest (EF) algorithm uses Monte Carlo methods to generate posterior distributions of population parameters. A novel feature is the updating of parameter values based on a probability measure defined on an ensemble of histories (a forest of genealogies), rather than a single tree. RESULTS: The EF algorithm generates samples from the correct marginal distribution of population parameters. Applied to actual data from closely related fruit fly species, it rapidly converged to posterior distributions that closely approximated the exact posteriors generated through massive computational effort. Applied to simulated data, it generated credible intervals that covered the actual parameter values in accordance with the nominal probabilities. AVAILABILITY: A C++ implementation of this method is freely accessible at http://www.isds.duke.edu/~scl13

Likelihoods from summary statistics: recent divergence between species.

We describe an importance-sampling method for approximating likelihoods of population parameters based on multiple summary statistics. In this first application, we address the demographic history of closely related members of the Drosophila pseudoobscura group. We base the maximum-likelihood estimation of the time since speciation and the effective population sizes of the extant and ancestral populations on the pattern of nucleotide variation at DPS2002, a noncoding region tightly linked to a paracentric inversion that strongly contributes to reproductive isolation. Consideration of summary statistics rather than entire nucleotide sequences permits a compact description of the genealogy of the sample. We use importance sampling first to propose a genealogical and mutational history consistent with the observed array of summary statistics and then to correct the likelihood with the exact probability of the history determined from a system of recursions. Analysis of a subset of the data, for which recursive computation of the exact likelihood was feasible, indicated close agreement between the approximate and exact likelihoods. Our results for the complete data set also compare well with those obtained through Metropolis-Hastings sampling of fully resolved genealogies of entire nucleotide sequences.