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Bioinformatics Advance Access published online on November 3, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm529
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Synthetic Microarray Data Generation with RANGE and NEMO

James Long 1,* and Mitchell Roth 2

1Biotechnology Computing Research Group, University of Alaska Fairbanks, PO Box 757000, Fairbanks AK, 99775. jlong{at}alaska.edu
2Department of Computer Science, University of Alaska Fairbanks, PO Box 756670, Fairbanks AK, 99775. ffmgr{at}uaf.edu

*To whom correspondence should be addressed. Mr. James Long, E-mail: jlong{at}alaska.edu


  Abstract

Motivation: For testing and sensitivity analysis purposes, it is beneficial to have known transcription networks of sufficient size and variability during development of microarray data and network deconvolution algorithms. Description of such networks in a simple language translatable to Systems Biology Markup Language would allow generation of model data for the networks.

Results: Described herein is software (RANGE: RAndom Network GEnerator) to generate large random transcription networks in the NEMO (NEtwork MOtif) language. NEMO is recognized by a grammar for transcription network motifs using lex and yacc to output Systems Biology Markup Language models for either specified or randomized gene input functions. These models of known networks may be input to a biochemical simulator, allowing the generation of synthetic microarray data.

Availability: http://range.sourceforge.net

Associate Editor: Dr. Olga Troyanskaya

Received on August 14, 2007; revised on October 12, 2007; accepted on October 14, 2007

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