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NATbox: a network analysis toolbox in R.

Shweta S Chavan1, Michael A Bauer, Marco Scutari

  • 1UALR/UAMS Joint Bioinformatics Program, University of Arkansas at Little Rock, Arkansas, USA. schavan@uams.edu

BMC Bioinformatics
|October 9, 2009
PubMed
Summary
This summary is machine-generated.

NATbox is an R-based GUI tool for analyzing functional relationships (FRs) in gene expression data using Bayesian structure learning. It simplifies complex network analysis for biologists, offering visualization and data imputation features.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Functional relationships (FRs) are crucial for understanding gene interactions and biological pathways.
  • Bayesian structure learning (BSL) techniques effectively model FRs from gene expression profiles.
  • There is a need for user-friendly tools to apply BSL for discovering undocumented FRs and signaling mechanisms.

Purpose of the Study:

  • To develop NATbox, a graphical user interface (GUI) in R for modeling and analyzing FRs.
  • To integrate a suite of BSL algorithms and statistical tools within NATbox.
  • To facilitate the analysis of FRs from gene expression profiles into acyclic networks.

Main Methods:

  • Developed a menu-driven, open-source GUI in R, named NATbox.
  • Integrated BSL algorithms for modeling FRs and network structure from gene expression data.
  • Included functionalities for data imputation, robust dependency identification via bootstrapping, and network visualization with topological analysis.

Main Results:

  • NATbox enables modeling of FRs and network structures using various BSL algorithms.
  • The tool visualizes FRs as acyclic graphs and allows for topological property investigation.
  • NATbox incorporates options for retrieving FRs from literature and using them as structural priors in BSL.

Conclusions:

  • NATbox offers a user-friendly GUI for FR modeling and analysis from gene expression data.
  • The open-source nature enhances reproducibility, transparency, and sustainability.
  • Ideal for researchers with minimal programming experience, serving as a teaching tool in systems biology.