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TMREC: A Database of Transcription Factor and MiRNA Regulatory Cascades in Human Diseases

Shuyuan Wang1, Wei Li2, Baofeng Lian3

  • 1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, P. R. China.

Plos One
|May 2, 2015
PubMed

Insights

Researchers developed the TMREC database to map regulatory cascades involving transcription factors (TFs) and microRNAs (miRNAs) in human diseases. This resource aids in understanding gene regulation, disease origins, and identifying new therapeutic targets.

Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology
  • Systems Biology

Background:

  • Combinatorial regulation by transcription factors (TFs) and microRNAs (miRNAs) is crucial for biological processes.
  • Dysregulation of these regulatory elements is linked to human diseases.
  • A gap exists in comprehensive resources detailing TF-miRNA regulatory cascades in disease contexts.

Purpose of the Study:

  • To establish the Transcription Factor and microRNA Regulatory Cascade (TMREC) database.
  • To provide a resource for analyzing TF-miRNA interactions in relation to human diseases.
  • To facilitate the understanding of gene expression regulation and disease etiology.

Main Methods:

  • Integration of curated transcriptional and post-transcriptional regulatory data.
  • Construction of a comprehensive TF-miRNA regulatory network.
  • Identification of linear regulatory paths using Breadth First Search (BFS) traversal.
  • Association analysis of regulatory cascades with known disease-related genes and miRNAs.

Main Results:

  • The TMREC database contains 74,248 regulatory cascades and 25,194 cascade clusters.
  • These involve 412 TFs, 266 miRNAs, and are associated with 545 human diseases.
  • The database quantifies the association strength between regulatory cascades and diseases.

Conclusions:

  • TMREC serves as a valuable resource for experimental biologists.
  • It enables comprehensive analysis of gene expression regulation and disease mechanisms.
  • The database supports the prediction of novel therapeutic targets for human diseases.

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