Computer Sciences Commons^™

Creation Of A Digital Storage System For Genome Sequencing Metadata, Jacquelin W. Olexa

Undergraduate Theses, Professional Papers, and Capstone Artifacts

As the field of computational genomics continues to expand in both potential and application, it is now more imperative than ever to ensure that massive genetic sequencing datasets are properly stored in an accessible manner. This project sought to establish a practical, user-friendly, secure system for a genomics research lab (the Good Lab; thegoodlab.org) at the University of Montana. A MySQL database and connected web application was ruled the best configuration to maximize utility and accessibility for the lab’s researchers. Building the logical framework for the database, creating the server, and sourcing data occurred over several months. The dataset ranged …

Model-Based Deep Autoencoders For Clustering Single-Cell Rna Sequencing Data With Side Information, Xiang Lin

Dissertations

Clustering analysis has been conducted extensively in single-cell RNA sequencing (scRNA-seq) studies. scRNA-seq can profile tens of thousands of genes' activities within a single cell. Thousands or tens of thousands of cells can be captured simultaneously in a typical scRNA-seq experiment. Biologists would like to cluster these cells for exploring and elucidating cell types or subtypes. Numerous methods have been designed for clustering scRNA-seq data. Yet, single-cell technologies develop so fast in the past few years that those existing methods do not catch up with these rapid changes and fail to fully fulfil their potential. For instance, besides profiling transcription …

Machine Learning And Network Embedding Methods For Gene Co-Expression Networks, Niloofar Aghaieabiane

Dissertations

High-throughput technologies such as DNA microarrays and RNA-seq are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed into Gene Co-expression Networks (GCNs). GCNs are analyzed to discover gene modules. GCN construction and analysis is a well-studied topic, for nearly two decades. While new types of sequencing and the corresponding data are now available, the software package WGCNA and its most recent variants are still widely used, contributing to biological discovery.

The discovery of biologically significant modules of genes from raw expression data is …

Development Of Graphical Models And Statistical Physics Motivated Approaches To Genomic Investigations, Yashwanth Lagisetty

Dissertations & Theses (Open Access)

Identifying genes involved in disease pathology has been a goal of genomic research since the early days of the field. However, as technology improves and the body of research grows, we are faced with more questions than answers. Among these is the pressing matter of our incomplete understanding of the genetic underpinnings of complex diseases. Many hypotheses offer explanations as to why direct and independent analyses of variants, as done in genome-wide association studies (GWAS), may not fully elucidate disease genetics. These range from pointing out flaws in statistical testing to invoking the complex dynamics of epigenetic processes. In the …

Ensemble Protein Inference Evaluation, Kyle Lee Lucke

Graduate Student Theses, Dissertations, & Professional Papers

The Protein inference problem is becoming an increasingly important tool that aids in the characterization of complex proteomes and analysis of complex protein samples. In bottom-up shotgun proteomics experiments the metrics for evaluation (like AUC and calibration error) are based on an often imperfect target-decoy database. These metrics make the inherent assumption that all of the proteins in the target set are present in the sample being analyzed. In general, this is not the case, they are typically a mix of present and absent proteins. To objectively evaluate inference methods, protein standard datasets are used. These datasets are special in …

Machine Learning Applications For Drug Repurposing, Hansaim Lim

Dissertations, Theses, and Capstone Projects

The cost of bringing a drug to market is astounding and the failure rate is intimidating. Drug discovery has been of limited success under the conventional reductionist model of one-drug-one-gene-one-disease paradigm, where a single disease-associated gene is identified and a molecular binder to the specific target is subsequently designed. Under the simplistic paradigm of drug discovery, a drug molecule is assumed to interact only with the intended on-target. However, small molecular drugs often interact with multiple targets, and those off-target interactions are not considered under the conventional paradigm. As a result, drug-induced side effects and adverse reactions are often neglected …

Machine Learning With Digital Signal Processing For Rapid And Accurate Alignment-Free Genome Analysis: From Methodological Design To A Covid-19 Case Study, Gurjit Singh Randhawa

Electronic Thesis and Dissertation Repository

In the field of bioinformatics, taxonomic classification is the scientific practice of identifying, naming, and grouping of organisms based on their similarities and differences. The problem of taxonomic classification is of immense importance considering that nearly 86% of existing species on Earth and 91% of marine species remain unclassified. Due to the magnitude of the datasets, the need exists for an approach and software tool that is scalable enough to handle large datasets and can be used for rapid sequence comparison and analysis. We propose ML-DSP, a stand-alone alignment-free software tool that uses Machine Learning and Digital Signal Processing to …

Comparative Analysis Of Metabolic Pathways Of Bacteria Used In Fermented Food, Keanu Hoang, Kiran Bastola

Theses/Capstones/Creative Projects

This study presents a novel methodology for analyzing metabolic pathways. Utilizing KEGG REST API through a Biopython package and file parser, data about whether or not a bacteria has an enzyme or not was extracted. The results found that differences in metabolic pathway enrichment values follow along the lines of genera and pathway type. In particular, bacteria found in food spoilage and commercial nitrogen fixing products had high values of enrichment.

Meta-Analysis Of Biological Research Literature, Evan Suggs

Honors Theses

Comparative studies have been powerful tools in generating a broad understanding about the evolution of animal social systems but they currently rely on the slow, manual process of reading thousands of abstracts and papers from research databases. A web application was created for researchers conducting a comparative survey, in order to speed up their research. This web application automates the retrieval of research papers and their selection process. Using previously obtained data sets on the orders Artiodacytla and Lagomorph, a machine learning application was created to classify the papers. These techniques and tools should greatly increase the speed at which …

Effective Statistical Energy Function Based Protein Un/Structure Prediction, Avdesh Mishra

University of New Orleans Theses and Dissertations

Proteins are an important component of living organisms, composed of one or more polypeptide chains, each containing hundreds or even thousands of amino acids of 20 standard types. The structure of a protein from the sequence determines crucial functions of proteins such as initiating metabolic reactions, DNA replication, cell signaling, and transporting molecules. In the past, proteins were considered to always have a well-defined stable shape (structured proteins), however, it has recently been shown that there exist intrinsically disordered proteins (IDPs), which lack a fixed or ordered 3D structure, have dynamic characteristics and therefore, exist in multiple states. Based on …

High-Performance Computing Frameworks For Large-Scale Genome Assembly, Sayan Goswami

LSU Doctoral Dissertations

Genome sequencing technology has witnessed tremendous progress in terms of throughput and cost per base pair, resulting in an explosion in the size of data. Typical de Bruijn graph-based assembly tools demand a lot of processing power and memory and cannot assemble big datasets unless running on a scaled-up server with terabytes of RAMs or scaled-out cluster with several dozens of nodes. In the first part of this work, we present a distributed next-generation sequence (NGS) assembler called Lazer, that achieves both scalability and memory efficiency by using partitioned de Bruijn graphs. By enhancing the memory-to-disk swapping and reducing the …

Bayesian Analytical Approaches For Metabolomics : A Novel Method For Molecular Structure-Informed Metabolite Interaction Modeling, A Novel Diagnostic Model For Differentiating Myocardial Infarction Type, And Approaches For Compound Identification Given Mass Spectrometry Data., Patrick J. Trainor

Electronic Theses and Dissertations

Metabolomics, the study of small molecules in biological systems, has enjoyed great success in enabling researchers to examine disease-associated metabolic dysregulation and has been utilized for the discovery biomarkers of disease and phenotypic states. In spite of recent technological advances in the analytical platforms utilized in metabolomics and the proliferation of tools for the analysis of metabolomics data, significant challenges in metabolomics data analyses remain. In this dissertation, we present three of these challenges and Bayesian methodological solutions for each. In the first part we develop a new methodology to serve a basis for making higher order inferences in metabolomics, …

Region Based Gene Expression Via Reanalysis Of Publicly Available Microarray Data Sets., Ernur Saka

Electronic Theses and Dissertations

A DNA microarray is a high-throughput technology used to identify relative gene expression. One of the most widely used platforms is the Affymetrix® GeneChip® technology which detects gene expression levels based on probe sets composed of a set of twenty-five nucleotide probes designed to hybridize with specific gene targets. Given a particular Affymetrix® GeneChip® platform, the design of the probes is fixed. However, the method of analysis is dynamic in nature due to the ability to annotate and group probes into uniquely defined groupings. This is particularly important since publicly available repositories of microarray datasets, such as ArrayExpress and NCBI’s …

Efficient Reduced Bias Genetic Algorithm For Generic Community Detection Objectives, Aditya Karnam Gururaj Rao

Theses

The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by …

Computational Modelling Of Human Transcriptional Regulation By An Information Theory-Based Approach, Ruipeng Lu

Electronic Thesis and Dissertation Repository

ChIP-seq experiments can identify the genome-wide binding site motifs of a transcription factor (TF) and determine its sequence specificity. Multiple algorithms were developed to derive TF binding site (TFBS) motifs from ChIP-seq data, including the entropy minimization-based Bipad that can derive both contiguous and bipartite motifs. Prior studies applying these algorithms to ChIP-seq data only analyzed a small number of top peaks with the highest signal strengths, biasing their resultant position weight matrices (PWMs) towards consensus-like, strong binding sites; nor did they derive bipartite motifs, disabling the accurate modelling of binding behavior of dimeric TFs.

This thesis presents a novel …

Recurrent Neural Networks And Their Applications To Rna Secondary Structure Inference, Devin Willmott

Theses and Dissertations--Mathematics

Recurrent neural networks (RNNs) are state of the art sequential machine learning tools, but have difficulty learning sequences with long-range dependencies due to the exponential growth or decay of gradients backpropagated through the RNN. Some methods overcome this problem by modifying the standard RNN architecure to force the recurrent weight matrix W to remain orthogonal throughout training. The first half of this thesis presents a novel orthogonal RNN architecture that enforces orthogonality of W by parametrizing with a skew-symmetric matrix via the Cayley transform. We present rules for backpropagation through the Cayley transform, show how to deal with the Cayley …

Machine Learning Based Protein Sequence To (Un)Structure Mapping And Interaction Prediction, Sumaiya Iqbal

University of New Orleans Theses and Dissertations

Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a cell that …

Network Analytics For The Mirna Regulome And Mirna-Disease Interactions, Joseph Jayakar Nalluri

Theses and Dissertations

miRNAs are non-coding RNAs of approx. 22 nucleotides in length that inhibit gene expression at the post-transcriptional level. By virtue of this gene regulation mechanism, miRNAs play a critical role in several biological processes and patho-physiological conditions, including cancers. miRNA behavior is a result of a multi-level complex interaction network involving miRNA-mRNA, TF-miRNA-gene, and miRNA-chemical interactions; hence the precise patterns through which a miRNA regulates a certain disease(s) are still elusive. Herein, I have developed an integrative genomics methods/pipeline to (i) build a miRNA regulomics and data analytics repository, (ii) create/model these interactions into networks and use optimization techniques, motif …

K-Mer Analysis Pipeline For Classification Of Dna Sequences From Metagenomic Samples, Russell Kaehler

Graduate Student Theses, Dissertations, & Professional Papers

Biological sequence datasets are increasing at a prodigious rate. The volume of data in these datasets surpasses what is observed in many other fields of science. New developments wherein metagenomic DNA from complex bacterial communities is recovered and sequenced are producing a new kind of data known as metagenomic data, which is comprised of DNA fragments from many genomes. Developing a utility to analyze such metagenomic data and predict the sample class from which it originated has many possible implications for ecological and medical applications. Within this document is a description of a series of analytical techniques used to process …

Novel Computational Methods For Transcript Reconstruction And Quantification Using Rna-Seq Data, Yan Huang

Theses and Dissertations--Computer Science

The advent of RNA-seq technologies provides an unprecedented opportunity to precisely profile the mRNA transcriptome of a specific cell population. It helps reveal the characteristics of the cell under the particular condition such as a disease. It is now possible to discover mRNA transcripts not cataloged in existing database, in addition to assessing the identities and quantities of the known transcripts in a given sample or cell. However, the sequence reads obtained from an RNA-seq experiment is only a short fragment of the original transcript. How to recapitulate the mRNA transcriptome from short RNA-seq reads remains a challenging problem. We …

De Novo Protein Structure Modeling And Energy Function Design, Lin Chen

Computer Science Theses & Dissertations

The two major challenges in protein structure prediction problems are (1) the lack of an accurate energy function and (2) the lack of an efficient search algorithm. A protein energy function accurately describing the interaction between residues is able to supervise the optimization of a protein conformation, as well as select native or native-like structures from numerous possible conformations. An efficient search algorithm must be able to reduce a conformational space to a reasonable size without missing the native conformation. My PhD research studies focused on these two directions.

A protein energy function—the distance and orientation dependent energy function of …

Computational Analysis Of Gene Expression And Connectivity Patterns In The Convoluted Structures Of Mouse Cerebellum, Tao Zeng

Computer Science Theses & Dissertations

One significant difference between evolved mammalian brains and other species is that mammalian brains exhibit increasingly convoluted structures in the cerebral cortex. Groove and ridge shaped structures named gyri and sulci expand surface area of cerebral cortex, making more functions possible. Prior studies using neuroimaging techniques such as dMRI and DTI have revealed that neural fibers are heavily connected to gyri comparing to those connected to sulci, such macro-scale experiments indicates that gyri are involved in large scale information processing while sulci process information locally. However, molecular and cellar level evidences, namely, gene expression pattern and its resulting neuronal connectivity …

Epistasis In Predator-Prey Relationships, Iuliia Inozemtseva

Electronic Theses and Dissertations

Epistasis is the interaction between two or more genes to control a single phenotype. We model epistasis of the prey in a two-locus two-allele problem in a basic predator- prey relationship. The resulting model allows us to examine both population sizes as well as genotypic and phenotypic frequencies. In the context of several numerical examples, we show that if epistasis results in an undesirable or desirable phenotype in the prey by making the particular genotype more or less susceptible to the predator or dangerous to the predator, elimination of undesirable phenotypes and then genotypes occurs.

A Novel Computational Framework For Transcriptome Analysis With Rna-Seq Data, Yin Hu

Theses and Dissertations--Computer Science

The advance of high-throughput sequencing technologies and their application on mRNA transcriptome sequencing (RNA-seq) have enabled comprehensive and unbiased profiling of the landscape of transcription in a cell. In order to address the current limitation of analyzing accuracy and scalability in transcriptome analysis, a novel computational framework has been developed on large-scale RNA-seq datasets with no dependence on transcript annotations. Directly from raw reads, a probabilistic approach is first applied to infer the best transcript fragment alignments from paired-end reads. Empowered by the identification of alternative splicing modules, this framework then performs precise and efficient differential analysis at automatically detected …

The Maximum Clique Problem: Algorithms, Applications, And Implementations, John David Eblen

Doctoral Dissertations

Computationally hard problems are routinely encountered during the course of solving practical problems. This is commonly dealt with by settling for less than optimal solutions, through the use of heuristics or approximation algorithms. This dissertation examines the alternate possibility of solving such problems exactly, through a detailed study of one particular problem, the maximum clique problem. It discusses algorithms, implementations, and the application of maximum clique results to real-world problems. First, the theoretical roots of the algorithmic method employed are discussed. Then a practical approach is described, which separates out important algorithmic decisions so that the algorithm can be easily …

Smart Sequence Similarity Search (S⁴) System, Zhuo Chen

Theses Digitization Project

Sequence similarity searching is commonly used to help clarify the biochemical and physiological features of newly discovered genes or proteins. An efficient similarity search relies on the choice of tools and their associated subprograms and numerous parameter settings. To assist researchers in selecting optimal programs and parameter settings for efficient sequence similarity searches, the web-based expert system, Smart Sequence Similarity Search (S4) was developed.