Quantitative modeling of transcription factor binding specificities using DNA shape

ABSTRACT: Accurate predictions of the DNA binding specificities of transcription factors (TFs) are necessary for understanding gene regulatory mechanisms. Traditionally, predictive models are built based on nucleotide sequence features. Here, we employed three- dimensional DNA shape information obtained on a high-throughput basis to integrate intuitive DNA structural features into the modeling of TF binding specificities using support vector regression. We performed quantitative predictions of DNA binding specificities, using the DREAM5 dataset for 65 mouse TFs and genomic-context protein binding microarray data for three human basic helix-loop-helix TFs. DNA shape-augmented models compared favorably with sequence-based models for these predictions. Although both k-mer and DNA shape features encoded

ORGANISM(S): Homo sapiens

SUBMITTER: Raluca Gordan 

PROVIDER: E-GEOD-59845 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress