Phylogenetic simulation of promoter evolution: estimation and modeling of binding site turnover events and assessment of their impact on alignment tools

Phylogenetic simulation of promoter evolution were used to analyze functional site turnover in regulatory sequences

Integration of Alignment and Phylogeny in the Whole-Genome Era

With the development of new sequencing techniques, whole genomes of many species have become available. This huge amount of data gives rise to new opportunities and challenges. These new sequences provide valuable information on relationships among species, e.g. genome recombination and conservation. One of the principal ways to investigate such information is multiple sequence alignment (MSA). Currently, there is large amount of MSA data on the internet, such as the UCSC genome database, but how to effectively use this information to solve classical and new problems is still an area lacking of exploration. In this thesis, we explored how to use this information in four problems, i.e. sequence orthology search problem, multiple alignment improvement problem, short read mapping problem, and genome rearrangement inference problem. For the first problem, we developed a EM algorithm to iteratively align a query with a multiple alignment database with the information from a phylogeny relating the query species and the species in the multiple alignment. We also infer the query\u27s location in the phylogeny. We showed that by doing alignment and phylogeny inference together, we can improve the accuracies for both problems. For the second problem, we developed an optimization algorithm to iteratively refine the multiple alignment quality. Experiment results showed our algorithm is very stable in term of resulting alignments. The results showed that our method is more accurate than existing methods, i.e. Mafft, Clustal-O, and Mavid, on test data from three sets of species from the UCSC genome database. For the third problem, we developed a model, PhyMap, to align a read to a multiple alignment allowing mismatches and indels. PhyMap computes local alignments of a query sequence against a fixed multiple-genome alignment of closely related species. PhyMap uses a known phylogenetic tree on the species in the multiple alignment to improve the quality of its computed alignments while also estimating the placement of the query on this tree. Both theoretical computation and experiment results show that our model can differentiate between orthologous and paralogous alignments better than other popular short read mapping tools (BWA, BOWTIE and BLAST). For the fourth problem, we gave a simple genome recombination model which can express insertions, deletions, inversions, translocations and inverted translocations on aligned genome segments. We also developed an MCMC algorithm to infer the order of the query segments. We proved that using any Euclidian metrics to measure distance between two sequence orders in the tree optimization goal function will lead to a degenerated solution where the inferred order will be the order of one of the leaf nodes. We also gave a graph-based formulation of the problem which can represent the probability distribution of the order of the query sequences

A Collapsing Method for Efficient Recovery of Optimal Edges

In this thesis we present a novel algorithm, HyperCleaning*, for effectively inferring phylogenetic trees. The method is based on the quartet method paradigm and is guaranteed to recover the best supported edges of the underlying phylogeny based on the witness quartet set. This is performed efficiently using a collapsing mechanism that employs memory/time tradeoff to ensure no loss of information. This enables HyperCleaning* to solve the relaxed version of the Maximum-Quartet-Consistency problem feasibly, thus providing a valuable tool for inferring phylogenies using quartet based analysis

Phylogenetic shadowing using a model selection process

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Methodological approaches for studying the microbial ecology of drinking water distribution systems

The study of the microbial ecology of drinking water distribution systems (DWDS) has traditionally been based on culturing organisms from bulk water samples. The development and application of molecular methods has supplied new tools for examining the microbial diversity and activity of environmental samples, yielding new insights into the microbial community and its diversity within these engineered ecosystems. In this review, the currently available methods and emerging approaches for characterising microbial communities, including both planktonic and biofilm ways of life, are critically evaluated. The study of biofilms is considered particularly important as it plays a critical role in the processes and interactions occurring at the pipe wall and bulk water interface. The advantages, limitations and usefulness of methods that can be used to detect and assess microbial abundance, community composition and function are discussed in a DWDS context. This review will assist hydraulic engineers and microbial ecologists in choosing the most appropriate tools to assess drinking water microbiology and related aspects

De Novo Transcription Factor Binding Site Discovery: A Machine Learning And Model Selection Approach

Computational methods have been widely applied to the problem of predicting regulatory elements. Many tools have been proposed. Each has taken a different approach and has been based on different underlying sets of assumptions, frequently similar to those of other tools. To date, the accuracy of each individual tool has been relatively poor. Noting that different tools often report different results, common practice is to analyze a given set of regulatory regions using more than one tool and to manually compare the results. Recently, ensemble approaches have been proposed that automate the execution of a set of tools and aggregate the results. This has been seen to provide some improvement but is still handled in an ad hoc manner since tool outputs are often in dissimilar formats. Another approach to improve accuracy has been to investigate the objective functions currently in use and identify additional informational statistics to incorporate into them. As a result of this investigation, one statistical measure of positional specificity has been demonstrated to be informative. In this context, this thesis explores the application of three simple models for the positional distribution of transcription factor binding sites (TFBS) to the problem of TFBS discovery. As alternate measures of positional specificity, log-likelihood ratios for the three models are calculated and treated as features to classify TFBSs as biologically relevant or irrelevant. As a verification step, randomly generated positional distributions are analyzed to demonstrate the robustness and accuracy of the log-likelihood ratios at classifying data from known distributions using a simple classifier. To improve classification accuracy, a support vector machine (SVM) approach is used. Subsequently, randomly generated sequences seeded with TFBSs at positions chosen to conform to one of the three models are analyzed as an additional verification step. Finally, two types of sets of real regulatory region sequences are analyzed. First, results consistent with the literature are obtained in three cases for genes experimentally determined to be co-expressed during mouse thymocyte maturation, and a novel role is predicted for three families of TFBSs in single positive (SP) T-cells. Second, the mouse and human ―real‖ sets from Tompa et al’s ―Assessment of Computational Motif Discovery Tools‖ are analyzed, and the results are reported

Does Positive Selection Drive Transcription Factor Binding Site Turnover? A Test with Drosophila Cis-Regulatory Modules

Transcription factor binding site(s) (TFBS) gain and loss (i.e., turnover) is a well-documented feature of cis-regulatory module (CRM) evolution, yet little attention has been paid to the evolutionary force(s) driving this turnover process. The predominant view, motivated by its widespread occurrence, emphasizes the importance of compensatory mutation and genetic drift. Positive selection, in contrast, although it has been invoked in specific instances of adaptive gene expression evolution, has not been considered as a general alternative to neutral compensatory evolution. In this study we evaluate the two hypotheses by analyzing patterns of single nucleotide polymorphism in the TFBS of well-characterized CRM in two closely related Drosophila species, Drosophila melanogaster and Drosophila simulans. An important feature of the analysis is classification of TFBS mutations according to the direction of their predicted effect on binding affinity, which allows gains and losses to be evaluated independently along the two phylogenetic lineages. The observed patterns of polymorphism and divergence are not compatible with neutral evolution for either class of mutations. Instead, multiple lines of evidence are consistent with contributions of positive selection to TFBS gain and loss as well as purifying selection in its maintenance. In discussion, we propose a model to reconcile the finding of selection driving TFBS turnover with constrained CRM function over long evolutionary time