Physical Sciences and Mathematics Commons^™

Filter-Based Multiscale Entropy Analysis Of Complex Physiological Time Series, Liang Zhao

Dissertations - ALL

The multiscale entropy (MSE) has been widely and successfully used in analyzing the complexity of physiologic time series. In this thesis, we re-interpret the averaging process in MSE as filtering a time series by a filter of a piecewise constant type. From this viewpoint, we introduce the {\it filter-based multiscale entropy} (FME) which filters a time series by filters to generate its multiple frequency components and then compute the {\it blockwise} entropy of the resulting components. By choosing filters adapted to the feature of a given time series, FME is able to better capture its multiscale information and to provide …

Demonstration Of A Targeted Proteome Characterization Approach For Examining Specific Metabolic Pathways In Complex Bacterial Systems, Adam Justin Martin

Masters Theses

Multiple Reaction Monitoring (MRM) is a powerful tandem mass spectrometry (MS/MS) tool frequently implemented in proteomic studies to provide targeted analysis of proteins and peptides. The selectivity that MRM delivers is so strong that it provides the quadrupole mass spectrometers (QQQ), on which it is commonly employed, with pertinence to proteomic studies that they would otherwise lack for their relatively low resolution. Additionally, this increased level of selectivity is sufficient enough to supplant complicated fractionation techniques, additional dimensions of chromatography, and 24 hour long MS/MS experiments in simplistic biological samples. But there is a deficiency of evidence to determine the …

Estimation Of Variation For High-Throughput Molecular Biological Experiments With Small Sample Size, Danni Yu

Open Access Dissertations

Motivation: In the quantification of molecular components, a large variation can affect and even potentially mislead the biological conclusions. Meanwhile, the high-throughput experiments often involve a small number of samples due to the limitation of cost and time. In such cases, the stochastic information may dominate the outcome of an experiment because there may not be enough samples to present the true biological information. It is challenging to distinguish the changes in phenotype from the stochastic variation.

Methods: Since the biological molecules have been quantified with different technologies, different statistical methods are required. Focusing on three types of important high-throughput …

Statistical Models For Gene And Transcripts Quantification And Identification Using Rna-Seq Technology, Han Wu

Open Access Dissertations

RNA-Seq has emerged as a powerful technique for transcriptome study. As much as the improved sensitivity and coverage, RNA-Seq also brings challenges for data analysis. The massive amount of sequence reads data, excessive variability, uncertainties, and bias and noises stemming from multiple sources all make the analysis of RAN-Seq data difficult. Despite much progress, RNA-Seq data analysis still has much room for improvement, especially on the quantification of gene and transcript expression levels. The quantification of gene expression level is a direct inference problem, whereas the quantification of the transcript expression level is an indirect problem, because the label of …

Modeling Leafhopper Populations And Their Role In Transmitting Plant Diseases., Ji Ruan

Electronic Thesis and Dissertation Repository

This M.Sc. thesis focuses on the interactions between crops and leafhoppers.

Firstly, a general delay differential equations system is proposed, based on the infection age structure, to investigate disease dynamics when disease latencies are considered. To further the understanding on the subject, a specific model is then introduced. The basic reproduction numbers $\cR_0$ and $\cR_1$ are identified and their threshold properties are discussed. When $\cR_0 < 1$, the insect-free equilibrium is globally asymptotically stable. When $\cR_0 > 1$ and $\cR_1 < 1$, the disease-free equilibrium exists and is locally asymptotically stable. When $\cR_1>1$, the disease will persist.

Secondly, we derive another general delay differential equations system to examine how different life stages of leafhoppers affect crops. The basic reproduction numbers $\cR_0$ is determined: when …

A Mathematical Model And Numerical Method For Thermoelectric Dna Sequencing, Liwei Shi

Doctoral Dissertations

DNA sequencing is the process of determining the precise order of nucleotide bases, adenine, guanine, cytosine, and thymine within a DNA molecule. It includes any method or technology that is used to determine the order of the four bases in a strand of DNA. The advent of rapid DNA sequencing methods has greatly accelerated biological and medical research and discovery. Thermoelectric DNA sequencing is a novel method to sequence DNA by measuring the heat that is released when DNA polymerase inserts a deoxyribonucleoside triphosphate into a growing DNA strand. The thermoelectric device for this project is composed of four parts: …

Performance Comparison Of Five Rna-Seq Alignment Tools, Yuanpeng Lu

Theses

Aligning millions of short reads to a reference genome is a critical task in high throughput sequencing. In recent years, a large number of mapping algorithms have been developed, all of which have in common that they align a vast number of reads to genomic or transcriptomic sequences. RNA-Seq data is discrete in nature, therefore with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. To provide guidance in the choice of alignment algorithms, five different alignment tools for RNA-Seq data are evaluated. In order to compare the …

Polyaseeker: A Computational Framework For Identifying Polyadenylation Cleavage Site From Rna-Seq, Xiao Ling

Theses

Alternative polyadenylation (APA) of mRNA plays a crucial role for post-transcriptional gene regulation. Recently, advances in next generation sequencing technology have made it possible to efficiently characterize the transcriptome and identify the 3’end of polyadenylated RNAs. However, no comprehensive bioi nformatic pipelines have fulfilled this goal. The PolyASeeker, a computational framework for identifying polyadenylation cleavage sites from RNA-Seq data is proposed in this thesis. By using the simulated RNA-seq dataset, a novel method is developed to evaluate the performance of the proposed framework versus the traditional A-stretch approach, and compute accurate Precisions and Recalls that previous estimation could not get. …

A Gpu Program To Compute Snp-Snp Interactions In Genome-Wide Association Studies, Srividya Ramakrishnan

Theses

With the recent advances in the next generation sequencing technologies, short read sequences of human genome are made more accessible. Paired end sequencing of short reads is currently the most sensitive method for detecting somatic mutations that arise during tumor development. In this study, a novel approach to optimize the detection of structural variants using a new short read alignment program is presented.

Pairwise interaction effects of the Single Nucleotide Polymorphisms (SNPs) have proven to uncover the underlying complex disease traits. Computing the disease risk based on the interaction effects of SNPs on a case - control study is a …

Genome Wide Search For Pseudo Knotted Non-Coding Rnas, Meghana S. Vasavada

Theses

Non-coding RNAs (ncRNAs) are the functional RNA molecules that are involved in many biological processes including gene regulation, chromosome replication and RNA modification. Searching genomes using computational methods has become an important asset for prediction and annotation of ncRNAs. To annotate an individual genome for a specific family of ncRNAs, a computational tool is interpreted to scan through the genome and align its sequence segments to some structure model for the ncRNA family. With the recent advances in detecting an ncRNA in the genome, heuristic techniques are designed to perform an accurate search and sequence-structure alignment. This study uses a …

Rna-Sequence Analysis Of Human Melanoma Cells, Jharna Miya

Theses

RNA-sequencing refers to the use of high throughput sequencing technologies that are used to sequence cDNA in order to get the complete information of a sample’s RNA content. The objective of this study is to analyze this data in different aspects and to characterize gene expression. Besides this characterization, the data was also used to investigate the effect of sequencing depth on gene expression measurements.

This research focuses on quantitative measurement of expression levels of genes and their transcripts. In this study, complementary DNA fragments of cultured human melanoma cells are sequenced and a total of 139,501,106 million 200-bp reads …

Development Of Novel Methods To Minimize The Impact Of Sequencing Errors In The Next-Generation Sequencing Data Analysis, Xiaofeng Zheng

Dissertations & Theses (Open Access)

Next-generation sequencing (NGS) technology has become a prominent tool in biological and biomedical research. However, NGS data analysis, such as de novo assembly, mapping and variants detection is far from maturity, and the high sequencing error-rate is one of the major problems. .

To minimize the impact of sequencing errors, we developed a highly robust and efficient method, MTM, to correct the errors in NGS reads. We demonstrated the effectiveness of MTM on both single-cell data with highly non-uniform coverage and normal data with uniformly high coverage, reflecting that MTM’s performance does not rely on the coverage of the sequencing …

Compound Identification Using Penalized Linear Regression., Ruiqi Liu

Electronic Theses and Dissertations

In this study, we propose a new method for compound identification using penalized linear regression. Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. In the context of the linear regression, the response variable is an experimental mass spectrum (i.e., query) and all the compounds in the reference library are the independent variables. However, the number of compounds in the reference library is much larger than the range of m/z values so that the data become high dimensional data with suffering from singularity. For …

Towards Personalized Medicine Using Systems Biology And Machine Learning, Calin Voichita

Wayne State University Dissertations

The rate of acquiring biological data has greatly surpassed our ability to interpret it. At the same time, we have started to understand that evolution of many diseases such as cancer, are the results of the interplay between the disease itself and the immune system of the host. It is now well accepted that cancer is not a single disease, but a “complex collection of distinct genetic diseases united by common hallmarks”. Understanding the differences between such disease subtypes is key not only in providing adequate treatments for known subtypes but also identifying new ones. These unforeseen disease subtypes are …

Computational Approaches To Anti-Toxin Therapies And Biomarker Identification, Rebecca Jane Swett

Wayne State University Dissertations

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data.

Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and …

Improved Efficiency Of Rna Secondary Structure Prediction Using Distributed Computing, Gerardo A. Cardenas

Open Access Theses & Dissertations

The rapidly growing amounts of available biomolecular sequence data, which may represent information from small gene fragments to large complete genomes, have led to the a great need for powerful computational resources for data analysis and storage. With the decoding of the human and other genomes, RNA secondary structure prediction has become an important area of interest in biology and medicine because they help in understanding the mechanisms of many biological processes such as gene regulation and viral replication, and in designing RNA-based therapies to treat various diseases. Due to the complexity of their algorithms, many existing and upcoming computational …

Automatic Elucidation Of Gpi Molecular Structures With Grid Computing Technology, Juan Clemente Aguilar Bonavides

Open Access Theses & Dissertations

Glycosylphosphatidylinositol (GPI)-anchored proteins are involved in many biological processes and are of medical importance. The identification and analysis of the entire collection of free and protein-linked GPIs within an organism (i.e., GPIomics) requires highly sensitive instruments. At present, liquid chromatography-tandem mass spectrometry (LC-MS/MS or -MSⁿ) is the most efficient laboratory technique for these tasks. As a typical MSⁿ experiment produces hundreds of thousands of spectra, the data analysis creates a major bottleneck in high-throughput GPIomic projects. Yet, no computational tool for characterizing the chemical structures of GPI is available to date. We propose a library-search algorithm to …

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 …