Machine learning polymer models of three-dimensional chromatin organization in human lymphoblastoid cells.

ABSTRACT: We present machine learning models of human genome three-dimensional structure that combine one dimensional (linear) sequence specificity, epigenomic information, and transcription factor binding profiles, with the polymer-based biophysical simulations in order to explain the extensive long-range chromatin looping observed in ChIA-PET experiments for lymphoblastoid cells. Random Forest, Gradient Boosting Machine (GBM), and Deep Learning models were constructed and evaluated, when predicting high-resolution interactions within Topologically Associating Domains (TADs). The predicted interactions are consistent with the experimental long-read ChIA-PET interactions mediated by CTCF and RNAPOL2 for GM12878 cell line. The contribution of sequence information and chromatin state defined by epigen

SUBMITTER: Al Bkhetan Z 

PROVIDER: S-EPMC6800180 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature