Predicting enhancer-promoter interaction from genomic sequence with deep neural networks
By Shashank Singh, Yang Yang, Barnab'as P'oczos, Jian Ma. 2016
153 citations recorded by the source corpus.
BackgroundIn the human genome, distal enhancers are involved in regulating target genes through proximal promoters by forming enhancer-promoter interactions. Although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions.MethodsHere we report a new computational method (named “SPEID”) using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given.ResultsOur results across six different cell types demonstrate that SPEID is effective in predicting enhancer-promoter interactions as compared to state-of-the-art methods that only use information from a single cell type. As a proof-of-principle, we also applied SPEID to identify somatic non-coding mutations in melanoma samples that may have reduced enhancer-promoter interactions in tumor genomes.ConclusionsThis work demonstrates that deep learning models can help reveal that sequence-based features alone are sufficient to reliably predict enhancer-promoter interactions genome-wide.
Subfields: Molecular Biology
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In Molecular Biology
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Citations in the corpus
- Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Most deep learning studies in this area currently predict the 1D location of enhancers, but modeling 3D chromatin conformations, enhancer-promoter interactions [180], and enhancer-target gene interactions will be critical for understanding transcriptional regulation.”
- Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “promoter interactions from genomic sequence [79]; prediction of DNA-binding residues in proteins [80], global transcription start prediction [81]; and improved prediction of the impact of non-coding variants on DNA methylation [63, 81].”
- Identifying viruses from metagenomic data using deep learning (2020, background): “…predicting chromatin accessibility [26], calling genetic variants from millions of short reads in NGS samples [27], predicting methylation quantitative trait loci [28], identifying evolutionary conserved sequences [29], and identifying enhancers and promoters [30] and their interactions [31].”