Neil D. Clarke

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Transcription factors (TFs) and their specific interactions with targets are crucial for specifying gene-expression programs. To gain insights into the transcriptional regulatory networks in embryonic stem (ES) cells, we use chromatin immunoprecipitation coupled with ultra-high-throughput DNA sequencing (ChIP-seq) to map the locations of 13(More)
Three distinct cell types are present within the 64-cell stage mouse blastocyst. We have investigated cellular development up to this stage using single-cell expression analysis of more than 500 cells. The 48 genes analyzed were selected in part based on a whole-embryo analysis of more than 800 transcription factors. We show that in the morula, blastomeres(More)
The sequence specificity of DNA-binding proteins is the primary mechanism by which the cell recognizes genomic features. Here, we describe systematic determination of yeast transcription factor DNA-binding specificities. We obtained binding specificities for 112 DNA-binding proteins representing 19 distinct structural classes. One-third of the binding(More)
BACKGROUND Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the(More)
The human body contains thousands of unique cell types, each with specialized functions. Cell identity is governed in large part by gene transcription programs, which are determined by regulatory elements encoded in DNA. To identify regulatory elements active in seven cell lines representative of diverse human cell types, we used DNase-seq and FAIRE-seq(More)
Sequence motifs that are potentially recognized by DNA-binding proteins occur far more often in genomic DNA than do observed in vivo protein-DNA interactions. To determine how chromatin influences the utilization of particular DNA-binding sites, we compared the in vivo genome-wide binding location of the yeast transcription factor Leu3 to the binding(More)
We have developed a computational model that predicts the probability of transcription factor binding to any site in the genome. GOMER (generalizable occupancy model of expression regulation) calculates binding probabilities on the basis of position weight matrices, and incorporates the effects of cooperativity and competition by explicit calculation of(More)
The derivation of human ES cells (hESCs) from human blastocysts represents one of the milestones in stem cell biology. The full potential of hESCs in research and clinical applications requires a detailed understanding of the genetic network that governs the unique properties of hESCs. Here, we report a genome-wide RNA interference screen to identify genes(More)
A major question in transcription factor (TF) biology is why a TF binds to only a small fraction of motif eligible binding sites in the genome. Using the estrogen receptor-α as a model system, we sought to explicitly define parameters that determine TF-binding site selection. By examining 12 genetic and epigenetic parameters, we find that an energetically(More)
With each round of CASP (Critical Assessment of Techniques for Protein Structure Prediction), automated prediction servers have played an increasingly important role. Today, most protein structure prediction approaches in some way depend on automated methods for fold recognition or model building. The accuracy of server predictions has significantly(More)