
Transcription factor-binding sites and the cis-regulatory modules they compose are central determinants of gene expression. We previously showed that binding site motifs and modules in proximal promoters can be used to predict a significant portion of mammalian tissue-specific transcription. Here, we report on a systematic analysis of promoters controlling tissue-specific expression in heart, kidney, liver, pancreas, skeletal muscle, testis and CD4 T cells, for both human and mouse. We integrated multiple sources of expression data to compile sets of transcripts with strong evidence for tissue-specific regulation. The analysis of the promoters corresponding to these sets produced a catalog of predicted tissue-specific motifs and modules, and cis-regulatory elements. Predicted regulatory interactions are supported by statistical evidence, and provide a foundation for targeted experiments that will improve our understanding of tissue-specific regulatory networks. In a broader context, methods used to construct the catalog provide a model for the analysis of genomic regions that regulate differentially expressed genes.
CD4-Positive T-Lymphocytes, Male, 570, databases, Statistics, Nonparametric, Mice, Report, organs types and functions, Databases, Genetic, Animals, Humans, human, RNA, Messenger, Regulatory Elements, Transcriptional, Muscle, Skeletal, Promoter Regions, Genetic, Pancreas, mouse, Oligonucleotide Array Sequence Analysis, database optimization, Gene Expression Profiling, Myocardium, Computational Biology, Sequence Analysis, DNA, Systems Integration, Liver, transcription, Algorithms
CD4-Positive T-Lymphocytes, Male, 570, databases, Statistics, Nonparametric, Mice, Report, organs types and functions, Databases, Genetic, Animals, Humans, human, RNA, Messenger, Regulatory Elements, Transcriptional, Muscle, Skeletal, Promoter Regions, Genetic, Pancreas, mouse, Oligonucleotide Array Sequence Analysis, database optimization, Gene Expression Profiling, Myocardium, Computational Biology, Sequence Analysis, DNA, Systems Integration, Liver, transcription, Algorithms
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