The cyclic AMP-responsive element-binding protein (CREB) is an important transcription factor that can be activated by hormonal stimulation and regulates neuronal function and development. An unbiased, global analysis of where CREB binds has not been performed. We have mapped for the first time the binding distribution of CREB along an entire human chromosome. Chromatin immunoprecipitation of CREB-associated DNA and subsequent hybridization of the associated DNA to a genomic DNA microarray containing all of the nonrepetitive DNA of human chromosome 22 revealed 215 binding sites corresponding to 192 different loci and 100 annotated potential gene targets. We found binding near or within many genes involved in signal transduction and neuronal function. We also found that only a small fraction of CREB binding sites lay near well-defined 5 ends of genes; the majority of sites were found elsewhere, including introns and unannotated regions. Several of the latter lay near novel unannotated transcriptionally active regions. Few CREB targets were found near full-length cyclic AMP response element sites; the majority contained shorter versions or close matches to this sequence. Several of the CREB targets were altered in their expression by treatment with forskolin; interestingly, both induced and repressed genes were found. Our results provide novel molecular insights into how CREB mediates its functions in humans.The cyclic AMP (cAMP)-responsive element-binding protein (CREB) is a key transcription factor that stimulates the expression of numerous genes in response to growth factors, hormones, neurotransmitters, ion fluxes, and stress signals. The CREB signaling pathway is activated by extracellular ligands that bind to cell surface receptors. Various intracellular second messengers then relay signals through kinase pathways to the nuclear resident CREB. Upon induction of the pathway, CREB is phosphorylated and activated at Ser-133. At least four types of kinases have been proposed to phosphorylate this residue: cAMP-dependent protein kinase, multiple mitogenactivated protein kinases (MAPKs), ribosome S6 kinase, Ca 2ϩ -and calmodulin-dependent kinases (CAMKs), and possibly Akt (reviewed in reference 22). Phosphorylation of CREB at Ser-133 leads to the recruitment of CREB binding protein (CBP) or its paralog p300 and subsequent transcriptional activation. A number of CREB targets have been identified, mostly through studies of individual genes (17, 26, 32). However, a full range of targets has not been explored. Given CREB's role in many important cellular processes, it is important to identify as many potential targets as possible.CREB functions either by itself or with the related family members CREM and ATF1; CREB, CREM, and ATF1 homoand heterodimerize through basic leucine zipper (bZIP) domains (27,40). A number of studies indicate that CREB is constitutively bound to chromatin even in the absence of agonists. CREB usually binds to cAMP response elements (CREs) near TATA boxes (26). However, not all DNA binding regi...

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For the ∼1% of the human genome in the ENCODE regions, only about half of the transcriptionally active regions (TARs) identified with tiling microarrays correspond to annotated exons. Here we categorize this large amount of "unannotated transcription." We use a number of disparate features to classify the 6988 novel TARs-array expression profiles across cell lines and conditions, sequence composition, phylogenetic profiles (presence/absence of syntenic conservation across 17 species), and locations relative to genes. In the classification, we first filter out TARs with unusual sequence composition and those likely resulting from cross-hybridization. We then associate some of those remaining with proximal exons having correlated expression profiles. Finally, we cluster unclassified TARs into putative novel loci, based on similar expression and phylogenetic profiles. To encapsulate our classification, we construct a Database of Active Regions and Tools (DART.gersteinlab.org). DART has special facilities for rapidly handling and comparing many sets of TARs and their heterogeneous features, synchronizing across builds, and interfacing with other resources. Overall, we find that ∼14% of the novel TARs can be associated with known genes, while ∼21% can be clustered into ∼200 novel loci. We observe that TARs associated with genes are enriched in the potential to form structural RNAs and many novel TAR clusters are associated with nearby promoters. To benchmark our classification, we design a set of experiments for testing the connectivity of novel TARs. Overall, we find that 18 of the 46 connections tested validate by RT-PCR and four of five sequenced PCR products confirm connectivity unambiguously.

A key step in genomic studies is to assess high throughput measurements across millions of markers for each participant’s DNA, either using microarrays or sequencing techniques. Accurate genotype calling is essential for downstream statistical analysis of genotype-phenotype associations, and next generation sequencing (NGS) has recently become a more common approach in genomic studies. How the accuracy of variant calling in NGS-based studies affects downstream association analysis has not, however, been studied using empirical data in which both microarrays and NGS were available. In this article, we investigate the impact of variant calling errors on the statistical power to identify associations between single nucleotides and disease, and on associations between multiple rare variants and disease. Both differential and nondifferential genotyping errors are considered. Our results show that the power of burden tests for rare variants is strongly influenced by the specificity in variant calling, but is rather robust with regard to sensitivity. By using the variant calling accuracies estimated from a substudy of a Cooperative Studies Program project conducted by the Department of Veterans Affairs, we show that the power of association tests is mostly retained with commonly adopted variant calling pipelines. An R package, GWAS.PC, is provided to accommodate power analysis that takes account of genotyping errors (http://zhaocenter.org/software/).