DECODE: A Deep-learning Framework for Condensing Enhancers and Refining Boundaries with Large-scale Functional Assays

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Publication Date: 7/25/2021

Event: ISMB/ECCB 2021

Reference: pp. i280–i288, 2021

Authors: Zhanlin Chen, Yale University; Jing Zhang, University of California; Jason Liu, Yale University; Yi Dai, University of California; Donghoon Lee, The Icahn School of Medicine at Mount Sinai; Martin Renqiang Min, NEC Laboratories America, Inc.; Min Xu, Carnegie Mellon University; Mark Gerstein, Yale University, The Icahn School of Medicine at Mount Sinai

Abstract: MotivationMapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have implemented enhancer discovery as a binary classification problem without accurate boundary detection, producing low-resolution annotations with superfluous regions and reducing the statistical power for downstream analyses (e.g. causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays (DECODE). First, we employed direct enhancer-activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution, we implemented a weakly supervised object detection framework for enhancer localization with precise boundary detection (to a 10 bp resolution) using Gradient-weighted Class Activation Mapping.ResultsOur DECODE binary classifier outperformed a state-of-the-art enhancer prediction method by 24% in transgenic mouse validation. Furthermore, the object detection framework can condense enhancer annotations to only 13% of their original size, and these compact annotations have significantly higher conservation scores and genome-wide association study variant enrichments than the original predictions. Overall, DECODE is an effective tool for enhancer classification and precise localization.

Publication Link: https://academic.oup.com/bioinformatics/article/37/Supplement_1/i280/6319698