Development and evaluation of software for applied clinical genomics

High-throughput next-generation DNA sequencing has evolved rapidly over the past 20 years. The Human Genome Project published its first draft of the human genome in 2000 at an enormous cost of 3 billion dollars, and was an international collaborative effort that spanned more than a decade. Subsequent technological innovations have decreased that cost by six orders of magnitude down to a thousand dollars, while throughput has increased by over 100 times to a current delivery of gigabase of data per run. In bioinformatics, significant efforts to capitalize on the new capacities have produced software for the identification of deviations from the reference sequence, including single nucleotide variants, short insertions/deletions, and more complex chromosomal characteristics such as copy number variations and translocations. Clinically, hospitals are starting to incorporate sequencing technology as part of exploratory projects to discover underlying causes of diseases with suspected genetic etiology, and to provide personalized clinical decision support based on patients’ genetic predispositions. As with any new large-scale data, a need has emerged for mechanisms to translate knowledge from computationally oriented informatics specialists to the clinically oriented users who interact with it. In the genomics field, the complexity of the data, combined with the gap in perspectives and skills between computational biologists and clinicians, present an unsolved grand challenge for bioinformaticians to translate patient genomic information to facilitate clinical decision-making. This doctoral thesis focuses on a comparative design analysis of clinical decision support systems and prototypes interacting with patient genomes under various sectors of healthcare to ultimately improve the treatment and well-being of patients. Through a combination of usability methodologies across multiple distinct clinical user groups, the thesis highlights reoccurring domain-specific challenges and introduces ways to overcome the roadblocks for translation of next-generation sequencing from research laboratory to a multidisciplinary hospital environment. To improve the interpretation efficiency of patient genomes and informed by the design analysis findings, a novel computational approach to prioritize exome variants based on automated appraisal of patient phenotypes is introduced. Finally, the thesis research incorporates applied genome analysis via clinical collaborations to inform interface design and enable mastery of genome analysis.Science, Faculty ofGraduat