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Nucleic Acids Res. 2019 Jun 4;47(10):e60. doi: 10.1093/nar/gkz167.

DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning.

Li W1,2,3, Wong WH2,3, Jiang R1.

Author information

1
MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Department of Automation, Tsinghua University, Beijing 100084, China.
2
Department of Statistics, Stanford University, Stanford, CA 94305, USA.
3
Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.

Abstract

Interactions between regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanisms. Hi-C technique has been developed for genome-wide detection of chromatin contacts. However, unless extremely deep sequencing is performed on a very large number of input cells, which is technically limited and expensive, current Hi-C experiments do not have high enough resolution to resolve contacts between regulatory elements. Here, we develop DeepTACT, a bootstrapping deep learning model, to integrate genome sequences and chromatin accessibility data for the prediction of chromatin contacts between regulatory elements. DeepTACT can infer not only promoter-enhancer interactions, but also promoter-promoter interactions. In tests based on promoter capture Hi-C data, DeepTACT shows better performance over existing methods. DeepTACT analysis also identifies a class of hub promoters, which are correlated with transcriptional activation across cell lines, enriched in housekeeping genes, functionally related to fundamental biological processes, and capable of reflecting cell similarity. Finally, the utility of chromatin contacts in the study of human diseases is illustrated by the association of IFNA2 to coronary artery disease via an integrative analysis of GWAS data and interactions predicted by DeepTACT.

PMID:
30869141
PMCID:
PMC6547469
DOI:
10.1093/nar/gkz167
[Indexed for MEDLINE]
Free PMC Article

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