| Summary: | The first chapter of this thesis addresses a common problem in genomics experiments: interpreting a resulting "hit list" of interesting genes. We present work on an approach for summarizing and exploring "hit lists" that makes use of the large amount of gene expression data in public repositories such as the Gene Expression Omnibus. We compare the query list with datasets that we have analyzed for differential expression of genes. Studies that have similarities to the given hit list yield additional insights, help contextualize studies, and serve as a basis for future meta-analysis. A conceptually similar problem that we addressed is the classification or clustering of datasets based on patterns of differential expression. Both problems required a method for determining distances between datasets based on rankings of genes. We tested and benchmarked several methods using manually annotated datasets. The method that performed best according to our evaluation process is based on Kendall's Tau top-k distance. We investigated potential sources of confounds, finding that the largest challenge may be posed by the high prevalence of certain gene expression patterns. These highly prevalent patterns tended to dominate search results. Nonetheless, we demonstrated the effectiveness of this approach in a case study. In the second chapter, we investigated the role of microRNAs in the context of major depression and suicide. We profiled microRNA and messenger RNA levels in post-mortem prefrontal cortex and hippocampus brain tissue of depressed suicides, suicides, and controls. In the prefrontal cortex, we found miR-1202 to be down-regulated in suicides versus controls, and LCT (lactase enzyme) was up-regulated in suicides or depressed suicides compared to controls. The former result was independently confirmed using quantitative PCR. While further study is needed, our results have the potential to provide insight into molecular changes in the brains of depressed and suicidal individuals.
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