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Transcription factors (TFs) control gene expression by binding to genomic DNA in a sequence-specific manner. Mutations in TF binding sites are increasingly found to be associated with human disease, yet we currently lack robust methods to predict these sites. Here, we developed a versatile maximum likelihood framework named No Read Left Behind (NRLB) that infers a biophysical model of protein-DNA recognition across the full affinity range from a library of in vitro selected DNA binding sites. NRLB predicts human Max homodimer binding in near-perfect agreement with existing low-throughput measurements. It can capture the specificity of the p53 tetramer and distinguish multiple binding modes within a single sample. Additionally, we confirm that newly identified low-affinity enhancer binding sites are functional in vivo, and that their contribution to gene expression matches their predicted affinity. Our results establish a powerful paradigm for identifying protein binding sites and interpreting gene regulatory sequences in eukaryotic genomes.

Transcription factors (TF) play an essential role in the cell as locus- and condition-specific recruiters of transcriptional machinery or chromatin-modifying complexes. However, predicting the in vivo profile of TF occupancy along the genome, which depends on complex interactions with other chromatin-associated proteins, from the DNA sequence remains a major challenge. Through careful reanalysis of ChIP-chip data for 138 TFs obtained in rich media, we were able to classify the upstream promoter regions of S. cerevisiae into 15 distinct chromatin types. One of these encompasses 5% of all promoters and is unique in that it is highly occupied by (essentially) all TFs expressed in rich media. These "hotspots" of TF occupancy are strongly nucleosome-depleted and preferentially targeted by chromatin-remodeling complexes and the origin-of-replication complex (ORC). They are also the only chromatin type enriched for predicted Rap1p and Pdr1p binding sites, which we found to work cooperatively with AAA/TTT motifs, known to affect local DNA structure, to reduce nucleosome occupancy. Taken together, our results reveal and characterize a new type of local chromatin structure in yeast., Comment: This paper has been withdrawn by the author due to a crucial error in the text

We have developed a method for interpreting genomic tiling array data, implemented as the program TranscriptionDetector. Probed loci expressed above background are identified by combining replicates in a way that makes minimal assumptions about the data. We performed medium-resolution Anopheles gambiae tiling array experiments and found extensive transcription of both coding and non-coding regions. Our method also showed improved detection of transcriptional units when applied to high-density tiling array data for ten human chromosomes.