The circadian clock drives daily rhythms in behavior and physiology, often in anticipation of the coming dusk or dawn. Almost all organisms possess an internal time-keeper, as it represents an adaptation to one of the most ancient selective pressures; the day-night cycle. Mounting evidence suggests the clock plays important roles in critical metabolic and signalling pathways, the sleep/wake cycle, immune function, as well as learning and memory. Perhaps more importantly, misregulation of the clock is associated with metabolic disorders, neurodegeneration, and incidence of cancer. In an effort to unlock the connections between the circadian clock and these downstream effects, researchers have searched for genes with rhytmic transcription driven by the clock. These so-called clock-controlled genes (CCGs) mediate these observed rhythms in important biological pathways. Over the past decade, researchers have searched for these CCGs using microarrays. However, with the growing popularity of high-throughput sequencing, and revelations about both the number and importance of non-coding RNAs (ncRNAs), investigators have begun to use RNA-seq for their circadian profiles. While RNA-seq has led to important findings about the circadian regulation of RNA editing, small RNAs, and epigenetic modifications, there is still much about its biases and limitations that we are still discovering. To this end, this thesis seeks to build upon this foundation and examine the use of RNA-seq for studying circadian transcription. I applied a hybrid RNA-seq, microarray approach to assay the circading transcriptome in liver, and eleven other mouse tissues. Notably, I saw that 1/3rd of ncRNAs conserved between human and mouse show rhythmic transcription. These rhythmic transcripts are strong candidates for future functional validation, and include important miRNA and snoRNA precursors. Additionaly, I found hundreds novel ncRNAs with rhythmic expression, which may provide novel CCGs. Lastly, I developped and applied a method for identifying the sources of bias in RNA-seq protocols. Taken together, this work extends our understanding of the circadian transcriptome, and the challenges associated with interpreting RNA-seq data.