New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

Assessing the Phylogenetic Utility of DNA Barcoding Using the New Zealand Cicada Genus Kikihia

DNA Barcoding (Hebert et al. 2003) has the potential to revolutionize the process of identifying and cataloguing biodiversity; however, significant controversy surrounds some of the proposed applications. In the seven years since DNA barcoding was introduced, the Web of Science records more than 600 studies that have weighed the pros and cons of this procedure. Unfortunately, the scientific community has been unable to come to any consensus on what threshold to use to differentiate species or even whether the barcoding region provides enough information to serve as an accurate species identification tool. The purpose of my thesis is to analyze mitochondrial DNA (mtDNA) barcoding’s potential to identify known species and provide a well-resolved phylogeny for the New Zealand cicada genus Kikihia. In order to do this, I created a phylogenetic tree for species in the genus Kikihia based solely on the barcoding region and compared it to a phylogeny previously created by Marshall et al. (2008) that benefits from information from other mtDNA and nuclear genes as well as species-specific song data. I determined how well the barcoding region delimits species that have been recognized based on morphology and song. In addition, I looked at the effect of sampling on the success of barcoding studies. I analyzed subsets of a larger, more densely sampled dataset for the Kikihia Muta Group to determine which aspects of my sampling strategy led to the most accurate identifications. Since DNA barcoding would by definition have problems in diagnosing hybrid individuals, I studied two species (K. “murihikua” and K. angusta) that are known to hybridize. Individuals that were not obvious hybrids (determined by morphology) were selected for the case study. Phylogenetic analysis of the barcoding region revealed insights into the reasons these two species could not be successfully differentiated using barcoding alone.

GMM Based Inference with Standard Stratified Samples when the Aggregate Shares are Known

We show how to do efficient moment based inference using the generalized method of moments (GMM) when data is collected by standard stratified sampling and the maintained assumption is that the aggregate shares are known.

Moment Based Inference with Stratified Data

Many datasets used by economists and other social scientists are collected by stratified sampling. The sampling scheme used to collect the data induces a probability distribution on the observed sample that differs from the target or underlying distribution for which inference is to be made. If this effect is not taken into account, subsequent statistical inference can be seriously biased. This paper shows how to do efficient semiparametric inference in moment restriction models when data from the target population is collected by three widely used sampling schemes: variable probability sampling, multinomial sampling, and standard stratified sampling.

Bayesian inference and its application to meta-analysis of epidemiologic data

In Part One, the foundations of Bayesian inference are reviewed, and the technicalities of the Bayesian method are illustrated. Part Two applies the Bayesian meta-analysis program, the Confidence Profile Method (CPM), to clinical trial data and evaluates the merits of using Bayesian meta-analysis for overviews of clinical trials.^ The Bayesian method of meta-analysis produced similar results to the classical results because of the large sample size, along with the input of a non-preferential prior probability distribution. These results were anticipated through explanations in Part One of the mechanics of the Bayesian approach. ^

(Table S2) Cell concentrations of individual and combined Alexandrium spp. and phylogenetic groups as obtained by qPCR assay and Utermöhl counts, as well as particulate PSP toxin concentrations from discrete water depths

Molecular methods provide promising tools for routine detection and quantification of toxic microalgae in plankton samples. To this end, novel TaqMan minor groove binding probes and primers targeting the small (SSU) or large (LSU) ribosomal subunit (rRNA) were developed for two species of the marine dinoflagellate genus Alexandrium (A. minutum, A. tamutum) and for three groups/ribotypes of the A. tamarense species complex: Group I/North American (NA), Group II/Mediterranean (ME) and Group III/Western European (WE). Primers and probes for real-time quantitative PCR (qPCR) were species-specific and highly efficient when tested in qPCR assays for cross-validation with pure DNA from cultured Alexandrium strains. Suitability of the qPCR assays as molecular tools for the detection and estimation of relative cell abundances of Alexandrium species and groups was evaluated from samples of natural plankton assemblages along the Scottish east coast. The results were compared with inverted microscope cell counts (Utermöhl technique) of Alexandrium spp. and associated paralytic shellfish poisoning (PSP) toxin concentrations. The qPCR assays indicated that A. tamarense (Group I) and A. tamutum were the most abundant Alexandrium taxa and both were highly positively correlated with PSP toxin content of plankton samples. Cells of A. tamarense (Group III) were present at nearly all stations but in low abundance. Alexandrium minutum and A. tamarense (Group II) cells were not detected in any of the samples, thereby arguing for their absence from the specific North Sea region, at least at the time of the survey. The sympatric occurrence of A. tamarense Group I and Group III gives further support to the hypothesis that the groups/ribotypes of the A. tamarense species complex are cryptic species rather than variants belonging to the same species.

Thalassiosira weissflogii attachment assay and phylogenetic affiliation of bacterial isolates from samples of the German Wadden Sea

Aggregation of algae, mainly diatoms, is an important process in marine systems leading to the settling of particulate organic carbon predominantly in the form of marine snow. Exudation products of phytoplankton form transparent exopolymer particles (TEP), which acts as the glue for particle aggregation. Heterotrophic bacteria interacting with phytoplankton may influence TEP formation and phytoplankton aggregation. This bacterial impact has not been explored in detail. We hypothesized that bacteria attaching to Thalassiosira weissflogii might interact in a yet-to-be determined manner, which could impact TEP formation and aggregate abundance. The role of individual T. weissflogii-attaching and free-living new bacterial isolates for TEP production and diatom aggregation was investigated in vitro. T. weissflogii did not aggregate in axenic culture, and striking differences in aggregation dynamics and TEP abundance were observed when diatom cultures were inoculated with either diatom-attaching or free-living bacteria. The data indicated that free-living bacteria might not influence aggregation whereas bacteria attaching to diatom cells may increase aggregate formation. Interestingly, photosynthetically inactivated T. weissflogii cells did not aggregate regardless of the presence of bacteria. Comparison of aggregate formation, TEP production, aggregate sinking velocity and solid hydrated density revealed remarkable differences. Both, photosynthetically active T. weissflogii and specific diatom-attaching bacteria were required for aggregation. It was concluded that interactions between heterotrophic bacteria and diatoms increased aggregate formation and particle sinking and thus may enhance the efficiency of the biological pump.

Desing and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications—it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ‘Activity Monitor’ has been designed and implemented: a personal health-persuasive application that provides feedback on the user’s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user’s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.d

Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones, by means of the common factors following a multiplicative seasonal VARIMA(p, d, q) × (P, D, Q)s model. Additionally, a bootstrap procedure that does not need a backward representation of the model is proposed to be able to make inference for all the parameters in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing enhanced coverage of forecasting intervals. A challenging application is provided. The new proposed model and a bootstrap scheme are applied to an innovative subject in electricity markets: the computation of long-term point forecasts and prediction intervals of electricity prices. Several appendices with technical details, an illustrative example, and an additional table are available online as Supplementary Materials.

Automatic Inference of Determinacy and Mutual Exclusion for Logic Programs Using Mode and Type Analyses

We propose an analysis for detecting procedures and goals that are deterministic (i.e., that produce at most one solution at most once),or predicates whose clause tests are mutually exclusive (which implies that at most one of their clauses will succeed) even if they are not deterministic. The analysis takes advantage of the pruning operator in order to improve the detection of mutual exclusion and determinacy. It also supports arithmetic equations and disequations, as well as equations and disequations on terms,for which we give a complete satisfiability testing algorithm, w.r.t. available type information. Information about determinacy can be used for program debugging and optimization, resource consumption and granularity control, abstraction carrying code, etc. We have implemented the analysis and integrated it in the CiaoPP system, which also infers automatically the mode and type information that our analysis takes as input. Experiments performed on this implementation show that the analysis is fairly accurate and efficient.