Doctor of Philosophy, The Ohio State University, 2018, Evolution, Ecology and Organismal Biology

Most broadly, this study aimed to develop a better understanding of how organisms evolve novel functions and traits, and examine how seemingly complex adaptive trait syndromes can convergently evolve. As an ideal example of this, the carnivorous plants were chosen. This polyphyletic grouping contains taxa derived from multiple independent evolutionary origins, in at least five plant orders, and has resulted in striking convergence of niche and morphology. First, a database study was performed, with the goal of understanding the evolutionary trends that impact carnivorous plants as a whole. Using carnivorous and non-carnivorous plant genomes available from GenBank. An a priori list of Gene Ontology-coded functions implicated in plant carnivory by earlier studies was constructed via literature review. Experimental and control samples were tested for statistical overrepresentation of these functions. It was found that, while some functions were significant in some taxa, there was no overall shared signal of plant carnivory, with each taxon presumably having selected for a different subset of these functions. Next, analyses were performed that targeted Sarracenia alata specifically. A reference genome for S. alata was assembled using PacBio, Illumina, and BioNano data and annotated using MAKER-P with additional preliminary database filtration. From these, it was found that Sarracenia alata possesses significant and substantial overrepresentation of genes with functions associated with plant carnivory, at odds with the hypothesis that the plant primarily relies on symbioses. Finally, pitcher fluid was collected from S. alata in the field. RNA was extracted from the fluid, sequenced via Illumina, and assembled with Trinity. Sequences were sorted into host plant and microbiome based on BLAST match to the S. alata reference genome. It was found that, while S. alata contributes two-thirds of the transcripts, these encode no digestive enzymes and a very limited set o (open full item for complete abstract)

Committee: Bryan Carstens Ph.D. (Advisor); Marymegan Daly Ph.D. (Committee Member); Zakee Sabree Ph.D. (Committee Member); Andrea Wolfe Ph.D. (Committee Member) Subjects: Bioinformatics; Biology; Botany

Master of Science, University of Akron, 2011, Computer Science

A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins.

Committee: Zhong-Hui Duan Dr. (Advisor); Chien-Chung Chan Dr. (Committee Member); Yingcai Xiao Dr. (Committee Member) Subjects: Bioinformatics; Computer Science

Master of Science (MS), Bowling Green State University, 2023, Biological Sciences

All epithelia are characterized by keratins, which make up a type of intermediate filament (IF). In epithelial tumors, which account for the majority of clinical cancers, the loss of cytoskeletal integration is considered one of the first alterations in epithelial metaplasia. This may have something to do with the expression of keratins or rearrangement of keratin filaments. In this study, I employed shotgun proteomic analysis and bioinformatic tools to identify proteins that interact with keratin filaments and thus may contribute to the disintegration of cytoskeleton. Using four airway epithelial cell lines in culture, I confirmed they highly expressed Keratin 14 (K14) and its obligatory partners, Keratin 5 (K5) or Keratin 6A (K6A). This suggests that the predominant IF is made up of K14 paired with K5/K6A. Although samples were enriched in keratin-associated proteins by immunoprecipitation (IP) with an antibody directed against K14 and K17, additional keratins not specifically targeted were also captured. Proteomic analysis revealed a list of non-keratin proteins enriched by IP. Some were associated with actin and microtubules, 23 and 6 proteins, respectively. Most of these were not linearly related to keratin content by abundance, but the motor protein, dynein I heavy chain, showed a Pearson correlation coefficient (CC) of -0.84 with keratin. Similarly, of 54 proteins associated with focal adhesions, intercellular junctions, or membranes, only septin-9 had a CC suggesting its abundance tracked with that of keratins. Finally, I analyzed IP-specific proteins that were cytosolic or had unknown subcellular distribution. A CC of -0.91 was found for one of these proteins, namely 26S proteasome regulatory subunit 8 (Psmc5). Further investigation and validation of the dataset was done by GO Enrichment Analysis. Using a subset of proteins highly concentrated by IP, compared to controls, I found the GO functions predicted were intracellular transport, (open full item for complete abstract)

Committee: Carol Heckman Ph.D (Committee Chair); Michael Geusz Ph.D (Committee Member); Xiaohong Tan Ph.D (Committee Member) Subjects: Bioinformatics; Biology; Biomedical Research; Cellular Biology; Molecular Biology; Oncology

Doctor of Philosophy, Miami University, 2019, Botany

Applications of clustering and visualization approaches are essential in uncovering biological insights from the large and complex genomic datasets. The ability to efficiently cluster large sets of nucleotide sequences can aid in performing many genomics tasks, such as the taxonomic assignment of metagenomics reads, identification of sequencing errors, and exploring virus genome variations. Effective visualization approaches are essential in interpreting the complex biological processes associated with the differentially expressed genes obtained from transcriptomics studies. In this dissertation a novel prime number-based feature extraction approach was proposed with applications in nucleotide sequence clustering. The feasibility of the proposed approach was explored by incorporating the approach as a filter into the nucleotide clustering tool PEACE (Parallel Environment for Assembly and Clustering of Gene Expression) and testing it on sequencing reads and virus genomes. The filter was effective in accelerating the clustering of Influenza A virus segment 4 and Dengue virus genomic sequences. The utility of the prime number-based feature extraction approach was further explored by using it to develop a self organizing map-based tool for clustering Influenza A virus segment 4 sequences. Additionally, network-based visualization methods were utilized to uncover the biological processes associated with the retinal pigment epithelium (RPE) reprogramming during chicken retina regeneration, using transcriptomic data. The findings associated with this study will aid to better understand the clustering and the visualization of genomic data. Chapter 1 of this dissertation provides an introduction to the usage of clustering and visualization approaches in genomics. Chapter 2 provides the details of the study performed to investigate the feasibility of the proposed filter in accelerating PEACE clustering. Chapter 3 gives in details of the network-based visualization approaches (open full item for complete abstract)

Committee: Chun Liang (Advisor); Bruce Cochrane (Committee Member); Richard Moore (Committee Member); Meixia Zhao (Committee Member); Dhananjai Rao (Committee Member) Subjects: Bioinformatics; Biology; Botany

Doctor of Philosophy, The Ohio State University, 2014, Horticulture and Crop Science

Flower longevity is a genetically programmed event that ends in flower senescence. Flowers can last from several hours to several months, based on flower type and environmental factors. For many flowers, particularly those that are ethylene-sensitive, longevity is greatly reduced after pollination. Cellular components are disassembled and nutrients are remobilized during senescence, which reduces the net energy expenditures of floral structures. The goal of this research is to identify the genes that can be targeted to extent shelf life by inhibiting pollination-induced senescence. Identifying and characterizing regulatory shelf-life genes will enable breeders to incorporate specific alleles that improve post production quality into ethylene-sensitive crops. Petunia × hybrida is particularly amenable to flower longevity studies because of its large floral organs, predictable flower senescence timing, and importance in the greenhouse industry. A general approach to gene functional analysis involves reducing gene expression and observing the resulting phenotype. Viruses, such as tobacco rattle virus (TRV), can be used to induce gene silencing in plants like petunia. We optimized several parameters that improved virus-induced gene silencing (VIGS) in petunia by increasing the consistency and efficiency of silencing. They included applying inocula to wounded apical meristems, growing petunias at temperatures of 20 °C day/18 °C night, utilizing the cultivar `Picobella Blue', and inoculating plants at three or four weeks after sowing. As a control for VIGS experiments, an empty vector is frequently used, but severe TRV symptoms often lead to death in petunia. We developed a control construct, which contained a fragment of the green florescent protein. This construct eliminated all severe viral symptoms and served as a better control. This optimized protocol and control construct enabled us to silence many genes and screen for phenotypic results within a few months. (open full item for complete abstract)

Committee: Michelle Jones (Advisor); Feng Qu (Committee Member); Eric Stockinger (Committee Member); Esther van der Knaap (Committee Member) Subjects: Bioinformatics; Biology; Cellular Biology; Horticulture; Molecular Biology; Plant Biology; Plant Pathology; Plant Sciences; Pollen; Virology

Master of Computer Science, Miami University, 2010, Computer Science and Systems Analysis

Similarity measurement is an important notion. In the context of ontologies, similarity measures are used to determine how similar one concept is to another. Because graph models have been used to represent ontologies, a variety of algorithms have been proposed for calculating the similarity between the graph nodes which represent ontological concepts. This thesis overviews existing ontological similarity measures and investigates mathematically and experimentally a wide range of these measures. The objective is not to assess performance to a gold-standard of similarity judgment but to develop a better understanding of the relationships among these measures through comparing their results when applied to the Gene Ontology. The experimental results show that some ontological similarity measures, especially information content-based measures, are highly correlated. The results of experiments comparing corpus-based to ontology-based information content measures for the Gene Ontology support previous experimental results using WordNet which demonstrated little difference between the two approaches.

Committee: Valerie Cross PhD (Advisor); Alton Sanders PhD (Committee Member); Eric Bachmann PhD (Committee Member) Subjects: Computer Science

Master of Computer Science, Miami University, 2009, Computer Science and Systems Analysis

The Gene Ontology (GO) provides a terminology to describe biological concepts and their relationships in a consistent and species-independent manner. Recent research has begun exploring the use of formal concept analysis (FCA) on annotated biological objects such as genes and gene products to discover the similarity and relationships among them. This thesis has produced a generic FCA software tool that creates a variety of concept lattices and incorporates the structure of the ontological terminology used for annotations. The user can tailor the creation of the concept lattices based on characteristics of the annotations and view a 3D visualization of the results. Numerous querying capabilities for exploring the resulting concept lattices are also included. Real-world data including a breast-cancer gene list from the University of Cincinnati's Childrens' Hospital and Medical Center and a well-known set of gene products GPD194 from the research literature are used to evaluate the FCA software.

Committee: Valerie Cross PhD (Advisor); Alton Sanders PhD (Committee Member); Michael Zmuda PhD (Committee Member) Subjects: Computer Science

Doctor of Philosophy, Case Western Reserve University, 2009, EECS - Computer and Information Sciences

With the advent of high-throughput experimental and genome sequencing technologies, the amounts of produced biological data and the related literature have increased dramatically. A significant portion of the produced biological data has revealed genotypic features of many model organisms. An outstanding problem presently is to map the characterized genotypic features of organisms to their phenotypic properties with the ultimate goal of making high-impact scientific discoveries in areas including diagnosing/curing diseases, engineering genomes, and inventing drugs. To this end, three major challenges concerning the management and analysis of the available data are: (i) high volume (e.g., thousands of genes, millions of publications), (ii) increasing diversity (e.g., genes, pathways, metabolic profiles), and (iii) high complexity (e.g., hierarchical organization of entities, graph structures, text/image data). Hence, efficient and effective biological data analysis and mining tools that can keep up with the increasing biological data production rate are highly desirable. In this thesis, we study four biological data mining and analysis problems towards having a better understanding of the underlying biological phenomena. Our contributions address distinct keystones on the path from genotype (e.g., genes and their functionality annotations) to phenotype (e.g., metabolite concentration level changes, physiological conditions). More specifically, at the textual-knowledge level, we investigate automated functionality annotations of individual genomic entities from biomedical articles through text mining. Next, at the annotation (ontology) level, we study how functional annotations of individual genomic entities form templates in the context of their pathways with applications on pathway mining and categorization. Then, we generalize the problem of discovering frequent pathway functionality templates into a purely computer science problem, namely, that of mining taxonomy- (open full item for complete abstract)

Committee: Gultekin Ozsoyoglu PhD (Advisor); Mark Adams PhD (Committee Member); Mehmet Koyuturk PhD (Committee Member); Jing Li PhD (Committee Member); Meral Ozsoyoglu PhD (Committee Member) Subjects: Bioinformatics; Computer Science

Doctor of Philosophy, University of Akron, 2011, Biomedical Engineering

In the post-genomic world, one of the most important and challenging problems is to understand protein-protein interactions (PPIs) on a large scale. They are integral to the underlying mechanisms of most of the fundamental cellular processes. A number of experimental methods such as protein affinity chromatography, affinity blotting, and immunoprecipitation have traditionally helped in detecting PPIs on a small scale. Recently, high-throughput methods have made available an increasing amount of PPI data. However, this data contains a significant amount of erroneous information in the form of false positives and false negatives and shows little overlap among PPIs pooled from different methods, thus severely limiting their reliability. Because of such limitations, computational predictions are emerging to narrow down the set of putative PPIs. In this dissertation, a novel computational PPI predictor was devised to predict PPIs with high accuracy. The PPI predictor integrates a number of proteomic features derived from biological databases. The features chosen for the purpose of this research were gene expression, gene ontology, MIPS functions, sequence patterns such as motifs and domains, and protein essentiality. While these features have little or no correlation with each other, they share some degree of relationship with the ability of proteins to interact with each other. Therefore, novel feature specific approaches were devised to characterize that relationship. Text mining and network topology based approaches were also studied. Gold Standard data comprising of high confidence PPIs and non-PPIs was used as evidence of interaction or lack thereof. The predictive power of the individual features was integrated using Bayesian methods. The average accuracy, based on 10-fold cross-validation, was found to be 0.9396. Since all the features are computed on the proteomic scale, the Bayesian integration yields likelihood values for all possible combinations of prot (open full item for complete abstract)

Committee: Dr. Dale H. Mugler (Advisor); Dr. Daniel B. Sheffer (Committee Member); Dr. George C. Giakos (Committee Member); Dr. Amy Milsted (Committee Member); Dr. Daniel L. Ely (Committee Member) Subjects: Bioinformatics; Biomedical Engineering; Biostatistics; Computer Science; Molecular Biology

Master of Science, University of Akron, 2008, Computer Science

The human genome project and numerous other genome projects have produced a large and ever increasing amount of sequence data. One of the main research challenges in the post-genomic era is to understand the relationship between the nucleotide sequences of genes and the functions of the proteins they encode. The objective of this thesis is to develop an automated protein function prediction system that is based on a set of homologous proteins and gene ontology categories. A novel measure based on a set of best local alignments is used to identify the homologues. The biological functions of the homologous proteins are characterized with gene ontology annotations. The protein function prediction is performed based on data mining models using decision trees. The models are trained and tested using the complete proteome of model organism yeast. The results show that the prediction accuracy depends on individual functional groups of proteins. There is a general trend of decreased model accuracy with the level of a group on the gene ontology graph, but the accuracy at a fix level varies from group to group. The prediction accuracy varies from group to group, no obvious accuracy changes from one level to another. These variations of accuracy illustrate certain limitations of sequence-based protein function prediction methods. But the fundamental assumption used in this thesis, similar amino acid sequences implying similar biological functions, is largely valid. The prediction results based on the proteome of yeast indicate that the accuracies for most of the functional groups are over 75%. We conclude that the decision tree model can be used as a preliminary tool for protein function prediction although the prediction results need to be verified through other means.

Committee: Zhong-Hui Duan (Advisor) Subjects: Bioinformatics