Next Article in Journal
Exercise Training-Induced Changes in MicroRNAs: Beneficial Regulatory Effects in Hypertension, Type 2 Diabetes, and Obesity
Next Article in Special Issue
Insect Transcription Factors: A Landscape of Their Structures and Biological Functions in Drosophila and beyond
Previous Article in Journal
Methylation-Based Classification of Cervical Squamous Cell Carcinoma into Two New Subclasses Differing in Immune-Related Gene Expression
Previous Article in Special Issue
Role of Overexpressed Transcription Factor FOXO1 in Fatal Cardiovascular Septal Defects in Patau Syndrome: Molecular and Therapeutic Strategies
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Int. J. Mol. Sci. 2018, 19(11), 3609; https://doi.org/10.3390/ijms19113609

Causal Transcription Regulatory Network Inference Using Enhancer Activity as a Causal Anchor

1
Division of Developmental Biology, The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG Scotland, UK
2
Division of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG Scotland, UK
3
Computational Biology Unit, Department of Informatics, University of Bergen, DataBlokk, 5th Floor, Thormohlensgt 55, N-5008 Bergen, Norway
4
Computational Biology Unit, Department of Clinical Science, University of Bergen, DataBlokk, 5th Floor, Thormohlensgt 55, N-5008 Bergen, Norway
*
Author to whom correspondence should be addressed.
Received: 18 September 2018 / Revised: 5 November 2018 / Accepted: 8 November 2018 / Published: 15 November 2018
Full-Text   |   PDF [7358 KB, uploaded 15 November 2018]   |  

Abstract

Transcription control plays a crucial role in establishing a unique gene expression signature for each of the hundreds of mammalian cell types. Though gene expression data have been widely used to infer cellular regulatory networks, existing methods mainly infer correlations rather than causality. We developed statistical models and likelihood-ratio tests to infer causal gene regulatory networks using enhancer RNA (eRNA) expression information as a causal anchor and applied the framework to eRNA and transcript expression data from the FANTOM Consortium. Predicted causal targets of transcription factors (TFs) in mouse embryonic stem cells, macrophages and erythroblastic leukaemia overlapped significantly with experimentally-validated targets from ChIP-seq and perturbation data. We further improved the model by taking into account that some TFs might act in a quantitative, dosage-dependent manner, whereas others might act predominantly in a binary on/off fashion. We predicted TF targets from concerted variation of eRNA and TF and target promoter expression levels within a single cell type, as well as across multiple cell types. Importantly, TFs with high-confidence predictions were largely different between these two analyses, demonstrating that variability within a cell type is highly relevant for target prediction of cell type-specific factors. Finally, we generated a compendium of high-confidence TF targets across diverse human cell and tissue types. View Full-Text
Keywords: transcription regulation; gene expression; causal inference; enhancer activity transcription regulation; gene expression; causal inference; enhancer activity
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Vipin, D.; Wang, L.; Devailly, G.; Michoel, T.; Joshi, A. Causal Transcription Regulatory Network Inference Using Enhancer Activity as a Causal Anchor. Int. J. Mol. Sci. 2018, 19, 3609.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top