Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks.

ABSTRACT: Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) mo

SUBMITTER: Dibaeinia P 

PROVIDER: S-EPMC8501998 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature