abstract: Next-generation sequencing is a powerful tool for detecting genetic variation. How-ever, it is also error-prone, with error rates that are much larger than mutation rates. This can make mutation detection difficult; and while increasing sequencing depth can often help, sequence-specific errors and other non-random biases cannot be de- tected by increased depth. The problem of accurate genotyping is exacerbated when there is not a reference genome or other auxiliary information available. I explore several methods for sensitively detecting mutations in non-model or- ganisms using an example Eucalyptus melliodora individual. I use the structure of the tree to find bounds on its somatic mutation rate and evaluate several algorithms for variant calling. I find that conventional methods are suitable if the genome of a close relative can be adapted to the study organism. However, with structured data, a likelihood framework that is aware of this structure is more accurate. I use the techniques developed here to evaluate a reference-free variant calling algorithm. I also use this data to evaluate a k-mer based base quality score recalibrator (KBBQ), a tool I developed to recalibrate base quality scores attached to sequencing data. Base quality scores can help detect errors in sequencing reads, but are often inaccurate. The most popular method for correcting this issue requires a known set of variant sites, which is unavailable in most cases. I simulate data and show that errors in this set of variant sites can cause calibration errors. I then show that KBBQ accurately recalibrates base quality scores while requiring no reference or other information and performs as well as other methods. Finally, I use the Eucalyptus data to investigate the impact of quality score calibra- tion on the quality of output variant calls and show that improved base quality score calibration increases the sensitivity and reduces the false positive rate of a variant calling algorithm. / Dissertation/Thesis / Doctoral Dissertation Molecular and Cellular Biology 2020

The United States health care expenditures are higher than any other developed country. Due to this, physician payment reform is moving from fee-for-service (FFS) to a value-based model, with a focus on prevention and quality. The purpose of this quantitative study was to measure the effect of a series of data delivery interventions associated with the quality scorecards and which method increased the quality scores at a medical teaching practice in Tennessee. Data were gathered via the Physician Quality Scorecard, an internally developed instrument. Each quarter, a different data delivery intervention was performed, and scorecard data were analyzed for comparison. The study population included all living faculty and resident physicians who practiced medicine between quality years 2018-2020. Statistical procedures included one-way ANOVA, independent t-test, and Pearson correlation coefficient. Data analyses revealed that the data delivery intervention of email only was more likely than other interventions to yield the most positive change in quality scores in the years 2018-2020. However, the classroom training data delivery method generated the most positive change and email only generated the least positive change in the quality year 2019 only. The quality year 2018, yielded the best quality year overall. It is important to note that data collected in 2020 may have limitations due to the COVID-19 pandemic.

In the automotive sector, engine testing generates vast data volumes that are mainly beneficial to requesting engineers. However, these tests are often not revisited for further analysis due to inconsistent data quality and a lack of structured assessment methods. Moreover, the absence of a tailored knowledge discovery process hinders effective preprocessing, transformation, analytics, and visualization of data, restricting the potential for historical data insights. Another challenge arises from the heterogeneous nature of test structures, resulting in varying measurements, data types, and contextual requirements across different engine test datasets. This thesis aims to overcome these obstacles by introducing a specialized knowledge discovery approach for the distinctive Multiple Inconsistent Heterogeneous Data (MIHData) format characteristic of engine testing. The proposed methods include adapting data quality assessment and reporting, classifying engine types through compositional features, employing modified dendrogram similarity measures for classification, performing customized feature extraction, transformation, and structuring, generating and manipulating synthetic images to enhance data visualization, and applying adapted list-based indexing for multivariate engine test summary data searches. The thesis demonstrates how these techniques enable exploratory analysis, visualization, and classification, presenting a practical framework to extract meaningful insights from historical data within the engineering domain. The ultimate objective is to facilitate the reuse of past data resources, contributing to informed decision-making processes and enhancing comprehension within the automotive industry. Through its focus on data quality, heterogeneity, and knowledge discovery, this research establishes a foundation for optimized utilization of historical Engine Test Data (ETD) for improved insights. / Soroptimist International Bradford