PT  - JOURNAL ARTICLE
AU  - Payam Dibaeinia
AU  - Saurabh Sinha
TI  - Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks
AID  - 10.1101/2021.03.01.433444
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.03.01.433444
4099  - http://biorxiv.org/content/early/2021/03/02/2021.03.01.433444.short
4100  - http://biorxiv.org/content/early/2021/03/02/2021.03.01.433444.full
AB  - 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) model, called CoNSEPT, that learns enhancer “grammar” in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference. CoNSEPT is freely available at: https://github.com/PayamDiba/CoNSEPT.Competing Interest StatementThe authors have declared no competing interest.