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The advent of RNAseq has reshaped agriculture, personalized medicine, and the whole of molecular biology. The shared nature of RNA from species to species makes the execution of RNAseq nearly indiscernible between species. While technical aspects of RNAseq analysis have been solved, there is a massive underutilization of data due to the absence of analysts with depth of biological knowledge or biologists with a depth of computational knowledge. Ultimately this results in underutilized data and missed discoveries. By developing new and innovative analytical approaches we can more completely interpret the massive amount of data provided by a single or collective group of RNAseq experiments. To explore the potential of RNAseq, (a) classical interpretation of RNAseq data (Chapter 2) (b) the integration of global metadata and previous literature (chapter 3), (c) the intersection of multiple RNAseq datasets (Chapter 4), (d) clustering analyses to identify coexpression of genes across dissimilar tissues (Chapter 5), and (e) the integration of epidemiological and validation datasets to drive novel analyses of publicly available data (Chapter 6) are applied to variable datasets. Each approach is demonstrated as a successful and broadly applicable approach depending on the research question.