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Synthetic microarray data generation with RANGE and NEMO.

James Long1, Mitchell Roth

  • 1Biotechnology Computing Research Group, University of Alaska Fairbanks, PO Box 757000, Fairbanks, AK, USA. jlong@alaska.edu

Bioinformatics (Oxford, England)
|November 6, 2007
PubMed
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Researchers developed RAndom Network GEnerator (RANGE) software to create large, random transcription networks. This tool generates Systems Biology Markup Language models for synthetic microarray data, aiding algorithm development and network analysis.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Developing and validating microarray data analysis and network deconvolution algorithms requires well-characterized transcription networks.
  • Generating synthetic data from known networks is crucial for testing and sensitivity analysis.

Purpose of the Study:

  • To introduce software for generating large, random transcription networks.
  • To enable the creation of Systems Biology Markup Language models from these networks for data simulation.

Main Methods:

  • The RAndom Network GEnerator (RANGE) software utilizes the NEMO (NEtwork MOtif) language.
  • A grammar, processed by lex and yacc, translates transcription network motifs into Systems Biology Markup Language.
  • The software supports both specified and randomized gene input functions.

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Main Results:

  • RANGE generates large random transcription networks.
  • The software outputs Systems Biology Markup Language models representing these networks.
  • These models can be used to generate synthetic microarray data via biochemical simulation.

Conclusions:

  • RANGE provides a valuable tool for creating synthetic biological network data.
  • The generated data facilitates the development and testing of computational algorithms for biological network analysis.