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Home
Inferring Gene-regulatory Networks Using Epigenomic Priors.

Home
Inferring Gene-regulatory Networks Using Epigenomic Priors.

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Inferring gene-regulatory networks using epigenomic priors.

Thomas E Bartlett¹, Melodie Li¹, Chenyu Song¹

¹Department of Statistical Science, University College London, London, UK.

|April 9, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Integrating epigenomic prior networks improves gene regulatory network (GRN) inference accuracy. DNA methylation data effectively builds these networks, revealing more candidate transcriptional factor cis-regulations for genes.

Keywords:

Biocomputational method Epigenetics data processing in systems biology gene network

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Pattern-based Search of Epigenomic Data Using GeNemo

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Published on: October 8, 2017

Area of Science:

Genomics
Epigenetics
Systems Biology

Background:

Gene regulatory networks (GRNs) control cellular functions.
Accurate GRN inference is crucial for understanding gene regulation.
Epigenomic data offers potential for improving GRN inference.

Purpose of the Study:

To enhance the accuracy of in-silico GRN structure inference.
To develop and validate a methodology using epigenomic prior networks.
To explore the utility of different epigenomic data types for prior network construction.

Main Methods:

Developed a computational methodology integrating epigenomic prior networks into GRN inference.
Re-analyzed diverse epigenomic datasets (scRNA-seq, DNA methylation, chromatin accessibility, histone modifications) from 12 studies.

Compared the effectiveness of DNA methylation and chromatin accessibility data for building epigenomic prior networks.

Main Results:

The proposed methodology significantly improved in-silico GRN inference accuracy.
DNA methylation data proved highly effective for inferring epigenomic prior networks, mirroring known structures.
Inferring prior networks from DNA methylation data identified approximately eight times more candidate TF cis-regulations compared to chromatin accessibility data.
Application to human embryonic development and breast cancer risk datasets revealed biologically relevant differential cis-regulation patterns.

Conclusions:

Epigenomic prior networks substantially enhance GRN inference accuracy.
DNA methylation data is a powerful resource for constructing epigenomic prior networks.
The methodology provides a robust framework for generating hypotheses about gene regulation and epigenomic changes in development and disease.