<p> Key to understanding the transition that vertebrates made from water to land is determining the developmental and genomic bases for the changes. New bioinformatic tools provide an opportunity to automate the discovery, broaden the number of, and provide an evidence-based ranking for potential candidate genes. I sought to explore this potential for the fin/limb transition, using the substantial genetic and phenotypic data available in model organism databases. Model organism data was used to hypothesize candidate genes for the fin/limb transition. In addition, 131 fin/limb candidate genes from the literature were extracted and used as a basis for comparison with candidates from the model organism databases. Additionally, seven genes specific to limb and 24 genes specific to fin were identified as future fin/limb transition candidates.</p>

Thesis (Ph.D.)--University of California, San Francisco, 2008. / Source: Dissertation Abstracts International, Volume: 69-06, Section: B, page: 3531. Adviser: Kathleen M. Giacomini.

Thesis (Ph.D.)--Indiana University, Dept. of Mathematics, 2007. / Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6740. Adviser: Elizabeth A. Housworth. Title from dissertation home page (viewed May 20, 2008).

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007. / Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7040. Adviser: Steven J. Clough. Includes supplementary digital materials. Includes bibliographical references (leaves 86-98) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--Drexel University, 2009. / Includes abstract and vita. Includes bibliographical references (leaves 112-122).

<p> Whole metagenome shotgun sequencing is a powerful approach for assaying many aspects of microbial communities, including the functional and symbiotic potential of each contributing community member. The research community currently lacks tools that efficiently align DNA reads against protein references, the technique necessary for constructing functional profiles. This thesis details the creation of PALADIN&mdash;a novel modification of the Burrows-Wheeler Aligner that provides orders-of-magnitude improved efficiency by directly mapping in protein space. In addition to performance considerations, utilizing PALADIN and associated tools as the foundation of metagenomic pipelines also allows for novel characterization and downstream analysis. </p><p> The accuracy and efficiency of PALADIN were compared against existing applications that employ nucleotide or protein alignment algorithms. Using both simulated and empirically obtained reads, PALADIN consistently outperformed all compared alignment tools across a variety of metrics, mapping reads nearly 8,000 times faster than the widely utilized protein aligner, BLAST. A variety of analysis techniques were demonstrated using this data, including detecting horizontal gene transfer, performing taxonomic grouping, and generating declustered references.</p><p>

This dissertation develops and applies several statistical and computational methods to the analysis of Next Generation Sequencing (NGS) data in order to gain a better understanding of our biology. In the rest of the chapter we introduce key concepts in molecular biology, and recent technological developments that help us better understand this complex science, which, in turn, provide the foundation and motivation for the subsequent chapters. In the second chapter we present the problem of estimating gene/isoform expression at the allelic level, and different models to solve this problem. First, we describe the observed data and the computational workflow to process the data. Next, we propose frequentist and bayesian models motivated by the central dogma of molecular biology and the data generating process (DGP) for RNA-Seq. We develop EM and Gibbs sampling approaches to estimate gene and transcript-specic expression from our proposed models. Finally, we present the performance of our models in simulations and we end with the analysis of experimental RNA-Seq data at the allelic level. In the third chapter we present our paired factorial experimental design to study parentally biased gene/isoform expression in the mouse cerebellum, and dynamic changes of this pattern between young and adult stages of cerebellar development. We present a bayesian variable selection model to estimate the difference in expression between the paternal and maternal genes, while incorporating relevant factors and its interactions into the model. Next, we apply our model to our experimental data, and further on we validate our predictions using pyrosequencing follow-up experiments. We subsequently applied our model to the pyrosequencing data across multiple brain regions. Our method, combined with the validation experiments, allowed us to find novel imprinted genes, and investigate, for the first time, imprinting dynamics across brain regions and across development. In the fourth chapter we move from the controlled-experiments in mouse isogenic lines to the highly variant world of human genetics in observational studies. In this chapter we introduce a Bayesian Regression Allelic Imbalance Model, BRAIM, that estimates the imbalance coming from two major sources: cis-regulation and imprinting. We model the cis-effect as an additive effect for the heterozygous group and we model the parent-of-origin detect with a latent variable that indicates to which parent a given allele belongs. Next, we show the performance of the model under simulation scenarios, and finally we apply the model to several experiments across multiple tissues and multiple individuals. In the fifth chapter we characterize the transcriptional regulation and gene expression of in-vitro Embryonic Stem Cells (ESCs), and two-related in-vivo cells; the Inner Cell Mass (ICM) tissue, and the embryonic tissue at day 6.5. Our objective is two fold. First we would like to understand the differences in gene expression between the ESCs and their in-vivo counterpart from where these cells were derived (ICM). Second, we want to characterize the active transcriptional regulatory regions using several histone modifications and to connect such regulatory activity with gene expression. In this chapter we used several statistical and computational methods to analyze and visualize the data, and it provides a good showcase of how combining several methods of analysis we can delve into interesting developmental biology.

This dissertation presents my graduate work analyzing biological structure. My research spans three different areas, which I discuss in turn. First I present my work studying how the genome folds. The three-dimensional structure of the genome inside of the nucleus is a matter of great biological importance, yet there are many questions about just how the genetic material is folded up. To probe this, we performed Hi-C experiments to create the highest resolution dataset (to date) of genome-wide contacts in the nucleus. Analysis of this data uncovered an array of fundamental structures in the folded genome. We discovered approximately 10,000 loops in the human genome, which each bring a pair of loci far apart along the DNA strand (up to millions of basepairs away) into close proximity. We found that contiguous stretches of DNA are segregated into self-associating contact domains. These domains are associated with distinct patterns of histone marks and segregate into six nuclear subcompartments. We found that these spatial structures are deeply connected to the regulation of the genome and cell function, suggesting that understanding and characterizing the 3D structure of the genome is crucial for a complete description of biology. Second, I present my work on self-assembly. Many biological structures are formed via `bottom-up' assembly, wherein a collection of subunits assemble into a complex arrangement. In this work we developed a theory which predicts the fundamental complexity limits for these types of systems. Using an information theory framework, we calculated the capacity, the maximum amount of information that can be encoded and decoded in systems of specific interactions, giving possible future directions for improvements in experimental realizations of self-assembly. Lastly, I present work examining the statistical structure of noisy data. Experimental datasets are a combination of signal and randomness, and data analysis algorithms, such as Principal Component Analysis (PCA), all seek to extract the signal. We used random matrix theory to demonstrate that even in situations where the dataset contains too much noise for PCA to be successful, the signal can be still be recovered with the use of prior information. / Engineering and Applied Sciences - Applied Math

Real-time processing of physiological signals collected from wearable sensors that can be done with low computational power is a requirement for continuous health monitoring. Such processing involves identifying underlying physiological state x from a measured biomedical signal y, that are related stochastically: y = f(x; e) (here e is a random variable). Often the state space of x is large, and the dimensionality of y is low: if y has dimension N and S is the state space of x then |S| >> N, since the purpose is to infer a complex physiological state from minimal measurements. This makes real-time inference a challenging task. We present algorithms that address this problem by using lower dimensional approximations of the state. Our algorithms are based on two techniques often used for state dimensionality reduction: (a) decomposition where variables can be grouped into smaller sets, and (b) factorization where variables can be factored into smaller sets. The algorithms are computationally inexpensive, and permit online application. We demonstrate their use in dimensionality reduction by successfully solving two real complex problems in medicine and public safety. Motivated originally by the problem of predicting cognitive fatigue state from EEG (Chapter 1), we developed the Correlated Sparse Signal Recovery (CSSR) algorithm and successfully applied it to the problem of elimination of blink artifacts in EEG from awake subjects (Chapter 2). Finding the decomposition x = x1+ x2 into a low dimensional representation of the artifact signal x1 is a non-trivial problem and currently there are no online real-time methods accurately solve the problem for small N (dimensionality of y). By using a skew-Gaussian dictionary and a novel method to represent group statistical structure, CSSR is able to identify and remove blink artifacts even from few (e.g. 4-6) channels of EEG recordings in near real-time. The method uses a Bayesian framework. It results in more effective decomposition, as measured by spectral and entropy properties of the decomposed signals, compared to some state-of-the-art artifact subtraction and structured sparse recovery methods. CSSR is novel in structured sparsity: unlike existing group sparse methods (such as block sparse recovery) it does not rely on the assumption of a common sparsity profile. It is also a novel EEG denoising method: unlike state-of-the art artifact removal technique such as independent components analysis, it does not require manual intervention, long recordings or high density (e.g. 32 or more channels) recordings. Potentially this method of denoising is of tremendous utility to the medical community since EEG artifact removal is usually done manually, which is a lengthy tedious process requiring trained technicians and often making entire epochs of data unuseable. Identification of the artifact in itself can be used to determine some physiological state relevant from the artifact properties (for example, blink duration and frequency can be used as a marker of fatigue). A potential application of CSSR is to determine if structurally decomposed cortical EEG (i.e. non-spectral ) representation can instead be used for fatigue prediction. A new E-M based active learning algorithm for ensemble classification is presented in Chapter 3 and applied to the problem of detection of artifactual epochs based upon several criteria including the sparse features obtained from CSSR. The algorithm offers higher accuracy than existing ensemble methods for unsupervised learning such as similarity- and graph-based ensemble clustering, as well as higher accuracy and lower computational complexity than several active learning methods such as Query-by-Committee and Importance-Weighted Active Learning when tested on data comprising of noisy Gaussian mixtures. In one case we were to successfully identify artifacts with approximately 98% accuracy based upon 31-dimensional data from 700,000 epochs in a matter of seconds on a personal laptop using less than 10% active labels. This is to be compared to a maximum of 94% from other methods. As far as we know, the area of active learning for ensemble-based classification has not been previously applied to biomedical signal classification including artifact detection; it can also be applied to other medical areas, including classification of polysomnographic signals into sleep stages. Algorithms based upon state-space factorization in the case where there is unidirectional dependence amongst the dynamics groups of variables ( the "Cascade Markov Model") are presented in Chapters 4. An algorithm for estimation of factored state where dynamics follow a Markov model from observations is developed using E-M (i.e. a version of Baum-Welch algorithm on factored state spaces) and applied to real-time human gait and fall detection. The application of factored HMMs to gait and fall detection is novel; falls in the elderly are a major safety issue. Results from the algorithm show higher fall detection accuracy (95%) than that achieved with PCA based estimation (70%). In this chapter, a new algorithm for optimal control on factored Markov decision processes is derived. The algorithm, in the form of decoupled matrix differential equations, both is (i) computationally efficient requiring solution of a one-point instead of two-point boundary value problem and (ii) obviates the "curse of dimensionality" inherent in HJB equations thereby facilitating real-time solution. The algorithm may have application to medicine, such as finding optimal schedules of light exposure for correction of circadian misalignment and optimal schedules for drug intervention in patients. The thesis demonstrates development of new methods for complexity reduction in high dimensional systems and that their application solves some problems in medicine and public safety more efficiently than state-of-the-art methods. / Engineering and Applied Sciences - Applied Math

The purpose of this thesis is to build a classification algorithm using a Genome Wide Association (GWA) study. Briefly, a GWA is a case-control study using genotypes derived from DNA microarrays for thousands of people. These microarrays are able to acquire the genotypes of hundreds of thousands of Single Nucleotide Polymorphisms (SNPs) for a person at a time. In this thesis, we first describe the processes necessary to prepare the data for analysis. Next, we introduce the Naive Bayes classification algorithm and a modification so that effects of a SNP on the disease of interest are weighted by a Bayesian posterior probability of association. This thesis then uses the data from three coronary artery disease GWAs, one as a training set and two as test sets, to build and test the classifier. Finally, this thesis discusses the relevance of the results and the generalizability of this method to future studies.