Skip Navigation


Bioinformatics Advance Access originally published online on November 3, 2007
Bioinformatics 2008 24(1):132-134; doi:10.1093/bioinformatics/btm529
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrowOA All Versions of this Article:
24/1/132    most recent
btm529v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Google Scholar
Right arrow Articles by Long, J.
Right arrow Articles by Roth, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Long, J.
Right arrow Articles by Roth, M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 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 and 2Department of Computer Science, University of Alaska Fairbanks, PO Box 756670, Fairbanks, AK 99775, USA

*To whom correspondence should be addressed.


  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

Contact: jlong{at}alaska.edu

Associate Editor: Olga Troyanskaya

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

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Nucleic Acids ResHome page
J. Carrera, G. Rodrigo, and A. Jaramillo
Model-based redesign of global transcription regulation
Nucleic Acids Res., April 1, 2009; 37(5): e38 - e38.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.