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Predicting enhancer-promoter interaction from genomic sequence with deep neural networks
Shashank Singh1, Yang Yang2, Barnabás Póczos1
1Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Quantitative Biology (Beijing, China)
|June 11, 2021
Summary
Deep learning models can predict enhancer-promoter interactions using only DNA sequence information. This computational method, SPEID, accurately identifies these crucial gene-regulatory interactions across cell types.
Area of Science:
- Genomics
- Computational Biology
- Molecular Biology
Background:
- Distal enhancers regulate target genes via enhancer-promoter interactions in the human genome.
- Genome-wide experimental methods identify potential enhancer-promoter interactions, but sequence-level guidance remains unclear.
Purpose of the Study:
- To develop a computational method predicting enhancer-promoter interactions using only sequence-based features.
- To assess the sufficiency of sequence information for predicting enhancer-promoter interactions.
Main Methods:
- Developed SPEID, a deep learning model for predicting enhancer-promoter interactions.
- Utilized sequence-based features from putative enhancer and promoter locations.
Main Results:
- SPEID effectively predicts enhancer-promoter interactions across six cell types.
- Demonstrated superior performance compared to single-cell-type methods.
- Applied SPEID to identify potential somatic non-coding mutations affecting enhancer-promoter interactions in melanoma.
Conclusions:
- Sequence-based features alone are sufficient for reliable genome-wide prediction of enhancer-promoter interactions.
- Deep learning models can effectively elucidate these regulatory relationships.
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