Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 29 for “"next-generation sequencing data"”.
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Variant Detection Using Next Generation Sequencing Data
… With recent development of cost effective next generation sequencing (NGS) technologies, it has become possible to identify novel variants with high resolutions and to identify some copy neutral variants such as inversions, which cannot be detected using microarray based technologies. …
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Computational methods for the analysis of next generation sequencing data
Recently, next generation sequencing (NGS) technology has emerged as a powerful approach and dramatically transformed biomedical research in an unprecedented scale. NGS is expected to replace the traditional hybridization-based microarray technology because of its affordable cost and high digital …
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Bayesian Integration and Modeling for Next-generation Sequencing Data Analysis
… biology currently faces challenges in a big data world with thousands of data samples across multiple disease types including cancer. The challenging problem is how to extract biologically meaningful information from large-scale genomic data. Next-generation Sequencing (NGS) can now produce …
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Cancer risk prediction with next generation sequencing data using machine learning
The use of computational biology for next generation sequencing (NGS) analysis is rapidly increasing in genomics research. However, the effectiveness of NGS data to predict disease abundance is yet unclear. This research investigates the problem in the whole exome NGS data of the chronic …
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Accurate Estimation of Isoform and Gene Expression Levels from Next Generation Sequencing Data
<p>Massively parallel transcriptome sequencing is quickly replacing microarrays as the technology of choice for performing gene expression profiling due to its wider dynamic range and digital quantitation capabilities. However, accurate estimation of expression levels from sequencing data remains …
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Implementation, adaptation and evaluation of statistical analysis techniques for next generation sequencing data
Deep sequencing is a new high‐throughput sequencing technology intended to lower the cost of DNA sequencing further than what was previously thought possible using standard methods. Analysis of sequencing data such as SAGE (serial analysis of gene expression) and microarray data has been a popular …
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Statistical methods for variant discovery and functional genomic analysis using next-generation sequencing data
The development of high-throughput next-generation sequencing (NGS) techniques produces massive amount of data, allowing the identification of biomarkers in early disease diagnosis and driving the transformation of most disciplines in biology and medicine. A greater concentration is needed in …
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Novel Algorithms and Methodology to Help Unravel Secrets that Next Generation Sequencing Data Can Tell
… an organism. It is therefore no surprise that sequencing the genome provides an invaluable tool for the scientific study of an organism. Via the inference of an evolutionary (phylogenetic) tree, DNA sequences can be used to reconstruct the evolutionary history of a set of species. DNA …
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A Quantitative Exploration of Causes of False Positive Single Nucleotide Polymorphisms in Next-Generation Sequencing Data
… has increased massively since the inception of Next-Generation Sequencing (NGS) technologies, which allow detection of large numbers of SNPs at low cost. However, both NGS data and their analysis are error-prone, which can lead to the generation of false positive (FP) SNPs. The traditional …
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Development of Novel Methods to Minimize The Impact of Sequencing Errors In The Next-Generation Sequencing Data Analysis
<p>Next-generation sequencing (NGS) technology has become a prominent tool in biological and biomedical research. However, NGS data analysis, such as <em>de novo</em> assembly, mapping and variants detection is far from maturity, and the high sequencing error-rate is one of the major problems. …
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Germline Mutation Detection In Next Generation Sequencing Data and Tp53 Mutation Carrier Probability Estimation For Li-Fraumeni Syndrome
<p>Next generation sequencing technology has been widely used in genomic analysis, but its application has been compromised by the missing true variants, especially when these variants are rare. We proposed a family-based variant calling method, FamSeq, integrating Mendelian transmission …
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Bayesian modelling and sampling strategies for ordering and clustering problems with a focus on next-generation sequencing data
… sampling strategies specifically tailored for next-generation sequencing data. Most high-throughput measurements for single-cell data are destructive, resulting in the loss of longitudinal information. I developed a new, Bayesian, way of reconstructing this information computationally, sampling …
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Return on investment and library complexity analysis for DNA sequencing
… profiles of information acquisition during DNA sequencing experiments is critical to the design and implementation of large-scale studies in medical and population genetics. One known technical challenge and cost driver in next-generation sequencing data is the occurrence of non-independent …
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On the Origin of Phenotypic Variation: Novel Technologies to Dissect Molecular Determinants of Phenotype
… algorithms, SNPseeker and SPLINTER, applied to next-generation sequencing data. The second part of my work describes the creation of a reporter system for DNA methylation for the purpose of dissecting the genetic contribution of tissue-specific patterns of DNA methylation across the genome. …
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Compressive algorithms for search and storage in biological data
Disparate biological datasets often exhibit similar well-defined structure; efficient algorithms can be designed to exploit this structure. In this doctoral thesis, we present a framework for similarity search based on entropy and fractal dimension; here, we prove that a clustered search algorithm …
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Associative Pattern Recognition for Biological Regulation Data
<p>In the last decade, bioinformatics data has been accumulated at an unprecedented rate, thanks to the advancement in sequencing technologies. Such rapid development poses both challenges and promising research topics. In this dissertation, we propose a series of associative pattern recognition …
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High-Performance Computational Genomics
Next-generation sequencing data is growing at an unprecedented rate, leading to new revelations in biology, healthcare, and medicine. Many researchers use high-level programming languages to navigate and analyze this data, but as gigabytes grow to terabytes or even petabytes, high-level languages …
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Phylogeography of Oculina Corals and their Algal Symbionts: Insights into the Origin and Expansion of Oculina Patagonica in the Mediterranean
… To do this, I have utilized nuclear markers and next-generation sequencing data for the coral host and its algal symbiont as well as environmental data. Although only recently first described from the waters of the Mediterranean, genetic, historical demographic, and fossil evidence suggests that …
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A Systems Biology Approach to Microbiology and Cancer
… through an iterative cycle composed of data generation, data analysis and mathematical modeling. Our contributions to systems biology revolve around the following two axes: - Data analysis: Two data analysis projects, which were initiated when I was a co-op at GlaxoSmithKline, are …
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