The tissue-specific pattern of mRNA expression can indicate important clues about gene function. High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale. Toward this end, we have designed custom arrays that interrogate the expression of the vast majority of protein-encoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues. The resulting data set provides the expression patterns for thousands of predicted genes, as well as known and poorly characterized genes, from mice and humans. We have explored this data set for global trends in gene expression, evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns indicative of chromosomal organization of transcription. We describe hundreds of regions of correlated transcription and show that some are subject to both tissue and parental allele-specific expression, suggesting a link between spatial expression and imprinting.

In mammals, circadian control of physiology and behavior is driven by a master pacemaker located in the suprachiasmatic nuclei (SCN) of the hypothalamus. We have used gene expression profiling to identify cycling transcripts in the SCN and in the liver. Our analysis revealed approximately 650 cycling transcripts and showed that the majority of these were specific to either the SCN or the liver. Genetic and genomic analysis suggests that a relatively small number of output genes are directly regulated by core oscillator components. Major processes regulated by the SCN and liver were found to be under circadian regulation. Importantly, rate-limiting steps in these various pathways were key sites of circadian control, highlighting the fundamental role that circadian clocks play in cellular and organismal physiology.

High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority of these experiments have focused on specific biological processes and perturbations. Here, we have generated and analyzed gene expression from a set of samples spanning a broad range of biological conditions. Specifically, we profiled gene expression from 91 human and mouse samples across a diverse array of tissues, organs, and cell lines. Because these samples predominantly come from the normal physiological state in the human and mouse, this dataset represents a preliminary, but substantial, description of the normal mammalian transcriptome. We have used this dataset to illustrate methods of mining these data, and to reveal insights into molecular and physiological gene function, mechanisms of transcriptional regulation, disease etiology, and comparative genomics. Finally, to allow the scientific community to use this resource, we have built a free and publicly accessible website (http:͞͞ expression.gnf.org) that integrates data visualization and curation of current gene annotations.