A large-scale analysis of the human and mouse transcriptomes was conducted using gene expression profiling across 91 samples from diverse tissues, organs, and cell lines. This dataset represents a preliminary but comprehensive description of the normal mammalian transcriptome. The study aimed to reveal insights into gene function, transcriptional regulation, disease etiology, and comparative genomics. A publicly accessible website (http://expression.gnf.org) was developed to allow users to query the data. The study used high-throughput gene expression analysis to construct a resource similar to multiple-tissue Northern blots for thousands of genes. It included 46 human and 45 mouse tissues, providing a broad range of expression data. The dataset was used to identify tissue-specific genes, which were defined as genes expressed in one tissue but not in others. This approach helped identify genes with specific physiological roles. The study also identified genes with potential disease relevance, such as those overexpressed in prostate cancer. These genes were compared to normal tissues to identify potential markers for disease. Additionally, the study compared gene expression profiles of mouse and human orthologs to understand the conservation of gene function between species. The integration of gene expression data with sequence homology-based annotation provided a more complete description of gene function. This approach was used to identify differentially expressed genes in families such as GPCRs and kinases, which may have therapeutic relevance. The study also identified potential regulatory elements in the promoter regions of genes, including those related to pituitary-specific gene expression. The analysis revealed that half of all mouse and human orthologs have a correlation in their expression patterns of 0.6 or better, indicating a degree of conservation in gene function between species. However, some genes showed divergent expression patterns, suggesting different physiological roles in mice and humans. The study highlights the importance of large-scale transcriptomic data in understanding gene function and disease mechanisms. The publicly available database and website provide a valuable resource for researchers to explore gene expression patterns and identify potential therapeutic targets. The findings contribute to the broader understanding of mammalian gene function and the development of new biomedical research approaches.A large-scale analysis of the human and mouse transcriptomes was conducted using gene expression profiling across 91 samples from diverse tissues, organs, and cell lines. This dataset represents a preliminary but comprehensive description of the normal mammalian transcriptome. The study aimed to reveal insights into gene function, transcriptional regulation, disease etiology, and comparative genomics. A publicly accessible website (http://expression.gnf.org) was developed to allow users to query the data. The study used high-throughput gene expression analysis to construct a resource similar to multiple-tissue Northern blots for thousands of genes. It included 46 human and 45 mouse tissues, providing a broad range of expression data. The dataset was used to identify tissue-specific genes, which were defined as genes expressed in one tissue but not in others. This approach helped identify genes with specific physiological roles. The study also identified genes with potential disease relevance, such as those overexpressed in prostate cancer. These genes were compared to normal tissues to identify potential markers for disease. Additionally, the study compared gene expression profiles of mouse and human orthologs to understand the conservation of gene function between species. The integration of gene expression data with sequence homology-based annotation provided a more complete description of gene function. This approach was used to identify differentially expressed genes in families such as GPCRs and kinases, which may have therapeutic relevance. The study also identified potential regulatory elements in the promoter regions of genes, including those related to pituitary-specific gene expression. The analysis revealed that half of all mouse and human orthologs have a correlation in their expression patterns of 0.6 or better, indicating a degree of conservation in gene function between species. However, some genes showed divergent expression patterns, suggesting different physiological roles in mice and humans. The study highlights the importance of large-scale transcriptomic data in understanding gene function and disease mechanisms. The publicly available database and website provide a valuable resource for researchers to explore gene expression patterns and identify potential therapeutic targets. The findings contribute to the broader understanding of mammalian gene function and the development of new biomedical research approaches.