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NPJ systems biology and applications
12 09, 2021;7 (1): 45.

Constructing gene regulatory networks using epigenetic data.

Abhijeet Rajendra Sonawane, Dawn L DeMeo, John Quackenbush, Kimberly Glass
Author Information
  1. Abhijeet Rajendra Sonawane: Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA. ORCID
  2. Dawn L DeMeo: Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
  3. John Quackenbush: Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA. ORCID
  4. Kimberly Glass: Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA. kimberly.glass@channing.harvard.edu. ORCID
PMID: 34887443 DOI: 10.1038/s41540-021-00208-3

Abstract

The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell's epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER's predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.

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Grants

  1. K25 HL133599/NHLBI NIH HHS
  2. S10 RR026772/NCRR NIH HHS
  3. R35 CA220523/NCI NIH HHS
  4. P01 HL132825/NHLBI NIH HHS
  5. P01 HL114501/NHLBI NIH HHS

MeSH Term

Chromatin Immunoprecipitation
Computational Biology
Epigenesis, Genetic
Gene Regulatory Networks
Transcription Factors

Chemicals

Transcription Factors
Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't
Article has an altmetric score of 2

Word Cloud

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