%0 Journal Article %J Genome Res %D 2009 %T A transcription factor affinity-based code for mammalian transcription initiation. %A Megraw, Molly %A Pereira, Fernando %A Jensen, Shane T %A Ohler, Uwe %A Hatzigeorgiou, Artemis G %K Base Composition %K Databases, Genetic %K DNA %K Gene Expression Regulation %K Genome, Human %K Humans %K Promoter Regions, Genetic %K RNA Polymerase II %K TATA Box %K Transcription Factors %K Transcription Initiation Site %K Transcription, Genetic %X

The recent arrival of large-scale cap analysis of gene expression (CAGE) data sets in mammals provides a wealth of quantitative information on coding and noncoding RNA polymerase II transcription start sites (TSS). Genome-wide CAGE studies reveal that a large fraction of TSS exhibit peaks where the vast majority of associated tags map to a particular location ( approximately 45%), whereas other active regions contain a broader distribution of initiation events. The presence of a strong single peak suggests that transcription at these locations may be mediated by position-specific sequence features. We therefore propose a new model for single-peaked TSS based solely on known transcription factors (TFs) and their respective regions of positional enrichment. This probabilistic model leads to near-perfect classification results in cross-validation (auROC = 0.98), and performance in genomic scans demonstrates that TSS prediction with both high accuracy and spatial resolution is achievable for a specific but large subgroup of mammalian promoters. The interpretable model structure suggests a DNA code in which canonical sequence features such as TATA-box, Initiator, and GC content do play a significant role, but many additional TFs show distinct spatial biases with respect to TSS location and are important contributors to the accurate prediction of single-peak transcription initiation sites. The model structure also reveals that CAGE tag clusters distal from annotated gene starts have distinct characteristics compared to those close to gene 5'-ends. Using this high-resolution single-peak model, we predict TSS for approximately 70% of mammalian microRNAs based on currently available data.

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%B Genome Res %V 19 %P 644-56 %8 2009 Apr %G eng %N 4 %R 10.1101/gr.085449.108