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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Reverse engineering transcriptional gene networks.

Vincenzo Belcastro1, Diego di Bernardo

  • 1Faculty of Engineering, University of Naples "Federico II", Naples, Italy.

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Summary
This summary is machine-generated.

This guide details inferring gene networks from gene expression data. It covers data collection, mutual information calculation, and network module discovery for gene function analysis.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Gene networks represent complex biological interactions.
  • Understanding these networks is crucial for deciphering gene function and disease mechanisms.
  • Existing methods for gene network inference require comprehensive, step-by-step guidance.

Purpose of the Study:

  • To provide a detailed, practical guide for inferring gene networks from gene expression profiles.
  • To explain the process from data acquisition to network module identification.
  • To introduce the Netview web-tool for practical application.

Main Methods:

  • Data acquisition from public repositories to build a gene expression profile compendium.
  • Discretization of gene expression profiles and computation of mutual information (MI) between gene pairs.
  • Application of pruning strategies, Boolean adjacency matrix construction, and clustering algorithms for module detection.

Main Results:

  • A systematic workflow for gene network inference is presented.
  • The method allows for the identification of modules (communities) within the gene network.
  • Case studies demonstrate the utility of the generated gene networks.

Conclusions:

  • The described step-by-step approach facilitates gene network inference from gene expression data.
  • The inferred gene networks serve as a valuable tool for generating hypotheses on gene function and analyzing gene signatures.
  • The Netview web-tool offers a practical solution for implementing these network inference methods.