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Integrative prediction of gene expression with chromatin accessibility and conformation data.
- Source :
-
Epigenetics & chromatin [Epigenetics Chromatin] 2020 Feb 06; Vol. 13 (1), pp. 4. Date of Electronic Publication: 2020 Feb 06. - Publication Year :
- 2020
-
Abstract
- Background: Enhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter-enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organization of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability.<br />Results: We have extended our [Formula: see text] framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We designed a novel machine learning approach that allows the prioritization of TFs binding to distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer-promoter loops involving YY1 in different cell lines.<br />Conclusion: We present a novel approach that can be used to prioritize TFs involved in distal and promoter-proximal regulatory events by integrating chromatin accessibility, conformation, and gene expression data. We show that the integration of chromatin conformation data can improve gene expression prediction and aids model interpretability.
- Subjects :
- Binding Sites
Chromatin genetics
HCT116 Cells
HeLa Cells
Human Umbilical Vein Endothelial Cells metabolism
Humans
Jurkat Cells
K562 Cells
Machine Learning
Protein Binding
Transcription Factors chemistry
Transcription Factors metabolism
Chromatin chemistry
Chromatin Assembly and Disassembly
Enhancer Elements, Genetic
Genomics methods
Subjects
Details
- Language :
- English
- ISSN :
- 1756-8935
- Volume :
- 13
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Epigenetics & chromatin
- Publication Type :
- Academic Journal
- Accession number :
- 32029002
- Full Text :
- https://doi.org/10.1186/s13072-020-0327-0