TY - JOUR AU - Kumar, Sunil AU - Bucher, Philipp PY - 2016 DA - 2016/01/11 TI - Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features JO - BMC Bioinformatics SP - S4 VL - 17 IS - 1 AB - Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often not very effective in predicting in vivo site occupancy and in any case could not explain cell-type specific binding events. Recent reports show that chromatin accessibility, nucleosome occupancy and specific histone post-translational modifications greatly influence TF site occupancy in vivo. In this work, we use machine-learning methods to build predictive models and assess the relative importance of different sequence-intrinsic and chromatin features in the TF-to-target-site recruitment process. SN - 1471-2105 UR - https://doi.org/10.1186/s12859-015-0846-z DO - 10.1186/s12859-015-0846-z ID - Kumar2016 ER -