A transcription factor affinity-based code for mammalian transcription initiation.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Megraw, M; Pereira, F; Jensen, ST; Ohler, U; Hatzigeorgiou, AG

Published Date

  • April 2009

Published In

Volume / Issue

  • 19 / 4

Start / End Page

  • 644 - 656

PubMed ID

  • 19141595

Pubmed Central ID

  • 19141595

International Standard Serial Number (ISSN)

  • 1088-9051

Digital Object Identifier (DOI)

  • 10.1101/gr.085449.108

Language

  • eng

Conference Location

  • United States