02835nas a2200337 4500008004100000022001400041245008700055210006900142260001300211300001100224490000700235520183700242653002102079653002302100653000802123653003102131653001802162653001102180653003002191653002202221653001302243653002602256653003402282653002702316100001802343700002202361700002102383700001502404700003002419856004802449 2009 eng d a1088-905100aA transcription factor affinity-based code for mammalian transcription initiation.0 atranscription factor affinitybased code for mammalian transcript c2009 Apr a644-560 v193 a
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.
10aBase Composition10aDatabases, Genetic10aDNA10aGene Expression Regulation10aGenome, Human10aHumans10aPromoter Regions, Genetic10aRNA Polymerase II10aTATA Box10aTranscription Factors10aTranscription Initiation Site10aTranscription, Genetic1 aMegraw, Molly1 aPereira, Fernando1 aJensen, Shane, T1 aOhler, Uwe1 aHatzigeorgiou, Artemis, G uhttp://megraw.cgrb.oregonstate.edu/node/326