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You searched for +publisher:"University of Washington" +contributor:("Ruzzo, Walter L"). Showing records 1 – 3 of 3 total matches.

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University of Washington

1. Jones, Daniel Caleb. The analysis of RNA-Seq experiments using approximate likelihood.

Degree: PhD, 2021, University of Washington

The analysis of mRNA transcript abundance with RNA-Seq is a central tool in molecular biology research, but often analyses fail to account for the uncertainty in these estimates, which can be significant, especially when trying to disentangle isoforms or duplicated genes. Preserving uncertainty necessitates a full probabilistic model of the all the sequencing reads which quickly becomes intractable, as experiments can consist of billions of reads. To overcome these limitations, we propose a new method of approximating the likelihood function of a sparse mixture model, using a technique we call the Polya tree transformation. We demonstrate that substituting this approximation for the real thing achieves most of the benefits with a fraction of the computational costs, leading to more accurate detection of differential transcript expression. Advisors/Committee Members: Ruzzo, Walter L. (advisor).

Subjects/Keywords: likelihood approximation; RNA-Seq; variational inference; Computer science; Bioinformatics; Computer science and engineering

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APA (6th Edition):

Jones, D. C. (2021). The analysis of RNA-Seq experiments using approximate likelihood. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/46770

Chicago Manual of Style (16th Edition):

Jones, Daniel Caleb. “The analysis of RNA-Seq experiments using approximate likelihood.” 2021. Doctoral Dissertation, University of Washington. Accessed May 08, 2021. http://hdl.handle.net/1773/46770.

MLA Handbook (7th Edition):

Jones, Daniel Caleb. “The analysis of RNA-Seq experiments using approximate likelihood.” 2021. Web. 08 May 2021.

Vancouver:

Jones DC. The analysis of RNA-Seq experiments using approximate likelihood. [Internet] [Doctoral dissertation]. University of Washington; 2021. [cited 2021 May 08]. Available from: http://hdl.handle.net/1773/46770.

Council of Science Editors:

Jones DC. The analysis of RNA-Seq experiments using approximate likelihood. [Doctoral Dissertation]. University of Washington; 2021. Available from: http://hdl.handle.net/1773/46770

2. Tseng, Huei-Hun Elizabeth. Discovery and Applications of Bacterial Noncoding RNAs.

Degree: PhD, 2013, University of Washington

Noncoding RNAs (ncRNAs) are functional transcripts that do not code for proteins. Many of them play indispensible roles in the cell. For example, the ribosomal RNAs make up the ribosome that is the factory for making proteins and riboswitches bind to small metabolites in the cell and regulate gene expression. Computational discovery of ncRNAs is challenging, however, because ncRNAs evolve rapidly on the nucleotide level while preserving secondary structure. In the first part of this thesis, we develop two clustering algorithms that are robust to weak sequence homology signals and are applicable on the genomic scale. We show that both algorithms can recover most known ncRNA families and as few as 5 homologous sequences are needed to predict a strong motif. In the second part of the thesis, we investigate whether secondary structure in- formation improves maximum likelihood tree inference for ncRNAs. An accurate phylogenetic tree has important biological and clinical applications: it can be used to infer the function of novel organisms and understand the evolutionary history of species. We show that using structure information, a more realistic gap model, and a maximum likelihood approach improves phylogenetic tree inference. In the third part of the thesis, we develop a method for profiling human gut microbial communities using high-throughput sequencing. Our method works on Illumina short reads and does not require assembly or taxonomic identification. We show that it can differentiate between the gut microbiota of healthy individuals at low sequencing depth, making it a cost-effective screening tool for large population studies. In the final part of the thesis, we use a standard additions experiment to examine sequencing bias and errors in Illumina HiSeq. We identify features associated with systematic errors and develop an error correction pipeline. We show that our method reduces base errors and produces better species diversity estimates. Advisors/Committee Members: Ruzzo, Walter L (advisor).

Subjects/Keywords:

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Tseng, H. E. (2013). Discovery and Applications of Bacterial Noncoding RNAs. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/22012

Chicago Manual of Style (16th Edition):

Tseng, Huei-Hun Elizabeth. “Discovery and Applications of Bacterial Noncoding RNAs.” 2013. Doctoral Dissertation, University of Washington. Accessed May 08, 2021. http://hdl.handle.net/1773/22012.

MLA Handbook (7th Edition):

Tseng, Huei-Hun Elizabeth. “Discovery and Applications of Bacterial Noncoding RNAs.” 2013. Web. 08 May 2021.

Vancouver:

Tseng HE. Discovery and Applications of Bacterial Noncoding RNAs. [Internet] [Doctoral dissertation]. University of Washington; 2013. [cited 2021 May 08]. Available from: http://hdl.handle.net/1773/22012.

Council of Science Editors:

Tseng HE. Discovery and Applications of Bacterial Noncoding RNAs. [Doctoral Dissertation]. University of Washington; 2013. Available from: http://hdl.handle.net/1773/22012


University of Washington

3. Earls, John Carl. Quantifying wellness and disease with personal, dense, dynamic data clouds.

Degree: PhD, 2021, University of Washington

Precision Medicine, where medical treatment is guided by deep molecular knowledge of the individual, has gained momentum in recent years. Rapid advancement in biological measurement technologies such as genome sequencing, mass spectrometry, protein capture assays, microfluidics and quantified-self devices provide an unprecedented opportunity to quantify, explain, and affect each person's health. The key challenge now is how to utilize these new capabilities to maximize wellness and prevent disease. These developments are concurrent with and aided by the increased availability of robust data analytic tools and cheap, scalable computation. In this dissertation, I present three steps taken to advance Precision Medicine. I present the first large multi-omic wellness study, where information from these -omics were integrated and used to provide personalized wellness guidance through a trained wellness coach. I present a holistic and modifiable wellness marker based on aging, generated from longitudinal multi-omic data. Finally, I apply systems approaches with dense phenotypic longitudinal data to profiling cancer, highlighting one approach to personalized 'N of 1' medicine. The research I present in this dissertation has led to the formation of two companies, so far. Advisors/Committee Members: Price, Nathan D (advisor), Ruzzo, Walter L (advisor).

Subjects/Keywords: Computational Biology; Multi-omics; Systems Biology; Computer science; Bioinformatics; Medicine; Computer science and engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Earls, J. C. (2021). Quantifying wellness and disease with personal, dense, dynamic data clouds. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/46767

Chicago Manual of Style (16th Edition):

Earls, John Carl. “Quantifying wellness and disease with personal, dense, dynamic data clouds.” 2021. Doctoral Dissertation, University of Washington. Accessed May 08, 2021. http://hdl.handle.net/1773/46767.

MLA Handbook (7th Edition):

Earls, John Carl. “Quantifying wellness and disease with personal, dense, dynamic data clouds.” 2021. Web. 08 May 2021.

Vancouver:

Earls JC. Quantifying wellness and disease with personal, dense, dynamic data clouds. [Internet] [Doctoral dissertation]. University of Washington; 2021. [cited 2021 May 08]. Available from: http://hdl.handle.net/1773/46767.

Council of Science Editors:

Earls JC. Quantifying wellness and disease with personal, dense, dynamic data clouds. [Doctoral Dissertation]. University of Washington; 2021. Available from: http://hdl.handle.net/1773/46767

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