Integrative prediction of gene expression with chromatin accessibility and conformation data
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 enha...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2020-02-01
|
Series: | Epigenetics & Chromatin |
Subjects: | |
Online Access: | https://doi.org/10.1186/s13072-020-0327-0 |
id |
doaj-6af9954ace1f4ca99906d7c4ef964481 |
---|---|
record_format |
Article |
spelling |
doaj-6af9954ace1f4ca99906d7c4ef9644812021-02-07T12:25:08ZengBMCEpigenetics & Chromatin1756-89352020-02-0113111710.1186/s13072-020-0327-0Integrative prediction of gene expression with chromatin accessibility and conformation dataFlorian Schmidt0Fabian Kern1Marcel H. Schulz2High-throughput Genomics & Systems Biology, Cluster of Excellence on Multimodal Computing and InteractionHigh-throughput Genomics & Systems Biology, Cluster of Excellence on Multimodal Computing and InteractionHigh-throughput Genomics & Systems Biology, Cluster of Excellence on Multimodal Computing and InteractionAbstract 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. Results We have extended our $$\textsc{TEPIC}$$ TEPIC 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. 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.https://doi.org/10.1186/s13072-020-0327-0Machine learningChromatin accessibilityDNase1-seqChromatin conformationGene regulationHiC |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Florian Schmidt Fabian Kern Marcel H. Schulz |
spellingShingle |
Florian Schmidt Fabian Kern Marcel H. Schulz Integrative prediction of gene expression with chromatin accessibility and conformation data Epigenetics & Chromatin Machine learning Chromatin accessibility DNase1-seq Chromatin conformation Gene regulation HiC |
author_facet |
Florian Schmidt Fabian Kern Marcel H. Schulz |
author_sort |
Florian Schmidt |
title |
Integrative prediction of gene expression with chromatin accessibility and conformation data |
title_short |
Integrative prediction of gene expression with chromatin accessibility and conformation data |
title_full |
Integrative prediction of gene expression with chromatin accessibility and conformation data |
title_fullStr |
Integrative prediction of gene expression with chromatin accessibility and conformation data |
title_full_unstemmed |
Integrative prediction of gene expression with chromatin accessibility and conformation data |
title_sort |
integrative prediction of gene expression with chromatin accessibility and conformation data |
publisher |
BMC |
series |
Epigenetics & Chromatin |
issn |
1756-8935 |
publishDate |
2020-02-01 |
description |
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. Results We have extended our $$\textsc{TEPIC}$$ TEPIC 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. 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. |
topic |
Machine learning Chromatin accessibility DNase1-seq Chromatin conformation Gene regulation HiC |
url |
https://doi.org/10.1186/s13072-020-0327-0 |
work_keys_str_mv |
AT florianschmidt integrativepredictionofgeneexpressionwithchromatinaccessibilityandconformationdata AT fabiankern integrativepredictionofgeneexpressionwithchromatinaccessibilityandconformationdata AT marcelhschulz integrativepredictionofgeneexpressionwithchromatinaccessibilityandconformationdata |
_version_ |
1724281222187712512 |