The power of the application of bioinformatics across multiplepublicly available transcriptomic data sets was explored. Using 19 human and mouse circadian transcriptomic data sets, we found that NR1D1 and NR1D2 which encode heme- responsive nuclear receptors are the most rhythmic tran- scripts across sleep conditions and tissues suggesting that they are at the core of circadian rhythm generation. Analyzes of human transcriptomic data show that a core set of transcripts related to processes including immune function, glucocorticoid signalling, and lipid metabolism is rhythmically expressed independently of the sleep-wake cycle. We also identify key transcripts associated with transcription and translation that are disrupted by sleep manipulations, and through network analysis identify putative mechanisms underlying the adverse health outcomes associated with sleep disruption, such as diabetes and cancer. Comparative bioinformatics applied toexisting and future data sets will be a powerful tool for the identification of core circadian- andsleep-dependent molecules.