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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Pathway activity analysis of bulk and single-cell RNA-Seq data

Jenkins, David 21 February 2019 (has links)
Gene expression profiling can produce effective biomarkers that can provide additional information beyond other approaches for characterizing disease. While these approaches are typically performed on standard bulk RNA sequencing data, new methods for RNA sequencing of individual cells have allowed these approaches to be applied at the resolution of a single cell. As these methods enter the mainstream, there is an increased need for user-friendly software that allows researchers without experience in bioinformatics to apply these techniques. In this thesis, I have developed new, user-friendly data resources and software tools to allow researchers to use gene expression signatures in their own datasets. Specifically, I created the Single Cell Toolkit, a user-friendly and interactive toolkit for analyzing single-cell RNA sequencing data and used this toolkit to analyze the pathway activity levels in breast cancer cells before and after cancer therapy. Next, I created and validated a set of activated oncogenic growth factor receptor signatures in breast cancer, which revealed additional heterogeneity within public breast cancer cell line and patient sample RNA sequencing datasets. Finally, I created an R package for rapidly profiling TB samples using a set of 30 existing tuberculosis gene signatures. I applied this tool to look at pathway differences in a dataset of tuberculosis treatment failure samples. Taken together, the results of these studies serve as a set of user-friendly software tools and data sets that allow researchers to rapidly and consistently apply pathway activity methods across RNA sequencing samples.

Knowledge transfer: what, how, and why

Chin, Si-Chi 01 May 2013 (has links)
People learn from prior experiences. We first learn how to use a spoon and then know how to use a different size of spoon. We first learn how to sew and then learn how to embroider. Transferring knowledge from one situation to another related situation often increases the speed of learning. This observation is relevant to human learning, as well as machine learning. This thesis focuses on the problem of knowledge transfer -- an area of study in machine learning. The goal of knowledge transfer is to train a system to recognize and apply knowledge acquired from previous tasks to new tasks or new domains. An effective knowledge transfer system facilitates learning processes for novel tasks, where little information is available. For example, the ability to transfer knowledge from a model that identifies writers born in the U.S. to identify writers born in Kiribati, a much lesser known country, would increase the speed of learning to identify writers born in Kiribati from scratch. In this thesis, we investigate three dimensions of knowledge transfer: what, how, and why. We present and elaborate on these questions: What type of knowledge to transfer? How to transfer knowledge across entities? Why a certain pattern of knowledge transfer is observed? We first propose Segmented Transfer -- a novel knowledge transfer model -- to identify and learn from the most informative partitions from prior tasks. The proposed model is applied to Wikipedia vandalism detection problem and to entity search and retrieval problem and improves the predictions. Based on the foundation of knowledge transfer and network theory, we propose Knowledge Transfer Network (KTN), a novel type of network describing transfer learning relationships among problems. KTN is not only a knowledge representation, but also a framework to select an effective and efficient ensemble of learners to improve a predictive model. This novel type of network provides insights on identifying ontological connections that were initially obscured. For example, we may observe knowledge transfer occurs among dissimilar tasks, such as transferring from using a knife and fork to using chopsticks.

Functional interpretation of high-resolution multi-omic data using molecular interaction networks

Blum, Benjamin Coburn 16 June 2021 (has links)
Advances in instrumentation and sample preparation techniques enable evermore in-depth molecular profiling to catalyze exciting research into complex biological processes. Current platforms survey biomolecular classes with varying depth. While sequencing is near comprehensive, and even enabled at single cell resolution, challenges remain in global metabolite surveys primarily due to the increased chemical diversity relative to other “omics” data types. At the same time, metabolism and the interaction of diverse biomolecules are increasingly recognized as vitally important components of many disease processes. Presented here is work describing the development and use of molecular interaction subnetworks for the functional interpretation of multi-omic data. Metabolic pathway-centric subnetworks for functional inference with protein or gene derived global profiling data were created from the integration of disparate network models: Protein- protein interaction (PPI) networks and metabolic models. The subnetworks were shown to increase mapping between metabolic pathways and the proteome, and the subnetwork- derived analysis shows dramatic improvement over primary enzymes alone with direct metabolomic experimental measurements for validation of pathway findings. We illustrate the functional utility of integrating PPI data with metabolic models by finding network modules previously but independently implicated in disease. Specifically, the analysis reveals abundance increases in known oncogenes in response to changes in breast cancer metabolism. Additionally, we reveal cellular mechanisms related to metabolic stress observed in patient sera following viral SARS-CoV-2 infection, and metabolic changes in a model of heart disease, where the characteristic muscle fibers make in-depth proteomic profiling difficult. Functional network models were additionally used to compare the response of varying cell lines in response to viral infection, showing significant context- specific differences. All of these findings demonstrate the importance of functional models to help interpret multi-omic data. The implications of revealing the connections between metabolism and protein subnetwork rewiring may be profound; for example, suggesting metabolic pathway activity may be as important a biomarker as mutation status in cancer. This research points to a means of practically inferring metabolic state from proteomic data. We further describe the release of our open-source software to accelerate integrative multi-omic analysis in the broader research community. / 2023-06-16T00:00:00Z

Noncoding RNA-Involved Interactions for Cancer Prognosis: A Prostate Cancer Study

Wang, Leying 08 October 2020 (has links)
No description available.

Integrative 'Omics Approach to Investigate Relationship Between COPD and Lung Cancer

Skander, Dannielle 28 August 2019 (has links)
No description available.

Single cell analysis and methods to characterize peripheral blood immune cell types in disease and aging

Karagiannis, Tanya Theodora 18 February 2022 (has links)
In the past decade, RNA-sequencing (RNA-seq)-based genome-wide expression studies have contributed to major advances in understanding human biology and disease. However, for heterogeneous tissues such as peripheral blood, RNA-sequencing masks the expression of different populations of cells that may be important in understanding different conditions and disease progression. With the advent of single cell RNA-sequencing (scRNA-seq), it has become possible to study the gene expression of each single cell and to explore cellular heterogeneity in the context of disease and under the influence of medications or other substances. In this dissertation, I will present three projects that demonstrate how single cell sequencing methods can be used to characterize novel changes in the peripheral immune system in human disease and aging. I will also describe novel methodological approaches I created to analyze cell type composition and gene expression level changes. First, I investigated the cell type specific changes due to opioid use in human peripheral blood. Utilizing single cell transcriptomic methods, I identified a genome-wide suppression of antiviral gene expression across immune cell types of chronic opioid users, and similarly under acute exposure to morphine. Second, I investigated the immune cell type specific changes of gene expression and composition in the context of human aging and longevity. I developed novel approaches to measure and compare overall cell type composition between samples, and identified significant overall differences in immune cell type composition, including pro-inflammatory cell populations, between extreme longevity and younger ages. In addition, I generated cell type-specific signatures associated with longevity after accounting for age-related changes that demonstrate an upregulation in immune response and metabolic processes important in the activation of immune cells in extreme long lived individuals compared to normally aging individuals. Finally, I investigated whether aging of the immune system is accelerated in opioid-dependent individuals. I utilized the unique aging signatures generated in the aging project and discovered higher expression of aging signatures in specific cell types of opioid-dependent individuals, suggesting chronic opioid use causes premature aging of the immune system that may contribute to the increased susceptibility to infections in these individuals. / 2023-02-18T00:00:00Z

FRANz: reconstruction of wild multi-generation pedigrees

Riester, Markus, Stadler, Peter F., Klemm, Konstantin 07 November 2018 (has links)
We present a software package for pedigree reconstruction in natural populations using co-dominant genomic markers such as microsatellites and single nucleotide polymorphisms (SNPs). If available, the algorithm makes use of prior information such as known relationships (sub-pedigrees) or the age and sex of individuals. Statistical confidence is estimated by Markov Chain Monte Carlo (MCMC) sampling. The accuracy of the algorithm is demonstrated for simulated data as well as an empirical dataset with known pedigree. The parentage inference is robust even in the presence of genotyping errors.

Frequent Subgraph Mining Analysis of GPCR Activation

Mishra, Satyakam 21 June 2021 (has links)
No description available.

Investigation of HIV-TB co-infection through analysis of the potential impact of host genetic variation on host-pathogen protein interactions

Heekes, Alexa Storme 29 August 2022 (has links) (PDF)
HIV and Mycobacterium tuberculosis (Mtb) co-infection causes treatment and diagnostic difficulties, which places a major burden on health care systems in settings with high prevalence of both infectious diseases, such as South Africa. Human genetic variation adds further complexity, with variants affecting disease susceptibility and response to treatment. The identification of variants in African populations is affected by reference mapping bias, especially in complex regions like the Major Histocompatibility Complex (MHC), which plays an important role in the immune response to HIV and Mtb infection. We used a graph-based approach to identify novel variants in the MHC region within African samples without mapping to the canonical reference genome. We generated a host-pathogen functional interaction network made up of inter- and intraspecies protein interactions, gene expression during co-infection, drug-target interactions, and human genetic variation. Differential expression and network centrality properties were used to prioritise proteins that may be important in co-infection. Using the interaction network we identified 28 human proteins that interact with both pathogens (”bridge” proteins). Network analysis showed that while MHC proteins did not have significantly higher centrality measures than non-MHC proteins, bridge proteins had significantly shorter distance to MHC proteins. Proteins that were significantly differentially expressed during co-infection or contained variants clinically-associated with HIV or TB also had significantly stronger network properties. Finally, we identified common and consequential variants within prioritised proteins that may be clinically-associated with HIV and TB. The integrated network was extensively annotated and stored in a graph database that enables rapid and high throughput prioritisation of sets of genes or variants, facilitates detailed investigations and allows network-based visualisation.

An African Genome Variation Database and its applications in human diversity and health

Todt, Davis 22 March 2022 (has links)
African genomes exhibit the highest levels of sequence and haplotype diversity of all extant human populations. A combination of historical as well as geographical factors have contributed toward the high level of genetic diversity in Ancestral populations in Africa. Additionally, a series of concomitant migration events out of Africa, with founder populations harbouring only a subset of this genetic variation, have contributed to the relatively lower genetic diversity observed in non-Africans. Population genetic studies have refined our understanding of human evolutionary history and clinical genomic studies have resulted in improved patient outcomes. However, despite the increased throughput and decreased cost afforded from next-generation sequencing (NGS) and despite the relatively higher genetic variation in Africans, relatively little of the genomic data currently available is representative of diverse African populations. This may result in adverse outcomes in the context of minority populations with little representation in clinical databases. Given the under-representation of African genetic variation and the importance of highlighting and further characterizing it, the objectives of this project were to design, develop and deploy a proof of concept database and web application for the storage, analysis and visualization of African genetic variant data – the African Genome Variation Database (AGVD). The AGVD was developed according to software industry design standards. The project also explored available genomic tools and databases in order to leverage existing software solutions where suitable. Additionally, relevant data sets were identified for use during testing and validation of the pilot phase of the project. To this end, the open access 1000 Genomes Project phase 3 dataset was selected and the genotypes for several chromosomes were loaded into the AGVD. The AGVD leverages the scalable, performant, and open source genomics engine OpenCGA for data storage and analysis. A custom front-end web application was developed by applying a novel approach to render and serve static Vue JS assets from the Python Flask microframework. The web application supports rich data search and filtering operations of loaded variants and allows end-users to visualize annotations of genomic loci and allele change, variant type, associated gene and transcript consequences, clinical significance, and allele frequency information for all annotated cohorts in a highly interactive manner. A bespoke REST API also supports future analytical functionality. The AGVD has demonstrated proof of concept in the secure and scalable storage and visualization of African genomic data, providing a viable solution for H3ABioNet to further extend in future iterations of the project and a valuable resource for researchers to explore African genetic variation.

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