Abstract
The MicroCore toolkit is a suite of analysis programs for microarray and proteomics data that is open source and programmed exclusively in Java. MicroCore provides a flexible and extensible environment for the interpretation of functional genomics data through visualization. The first version of the application (downloadable from the MicroCore website: http://www.ucl.ac.uk/oncology/MicroCore/microcore.htm), implements two programs—PIMs (protein interaction maps) and MicroExpress—and is soon to be followed by an extended version which will also feature a fuzzy k-means clustering application and a Java-based R plug-in for microarray analysis. PIMs and MicroExpress provide a simple yet powerful way of graphically relating large quantities of expression data from multiple experiments to cellular pathways and biological processes in a statistically meaningful way.
Full Text
The Full Text of this article is available as a PDF (167.9 KB).
Selected References
These references are in PubMed. This may not be the complete list of references from this article.
- Boguski Mark S., McIntosh Martin W. Biomedical informatics for proteomics. Nature. 2003 Mar 13;422(6928):233–237. doi: 10.1038/nature01515. [DOI] [PubMed] [Google Scholar]
- Dahlquist Kam D., Salomonis Nathan, Vranizan Karen, Lawlor Steven C., Conklin Bruce R. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet. 2002 May;31(1):19–20. doi: 10.1038/ng0502-19. [DOI] [PubMed] [Google Scholar]
- Dudoit Sandrine, Gentleman Robert C., Quackenbush John. Open source software for the analysis of microarray data. Biotechniques. 2003 Mar;Suppl:45–51. [PubMed] [Google Scholar]
- Hucka M., Finney A., Sauro H. M., Bolouri H., Doyle J. C., Kitano H., Arkin A. P., Bornstein B. J., Bray D., Cornish-Bowden A. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics. 2003 Mar 1;19(4):524–531. doi: 10.1093/bioinformatics/btg015. [DOI] [PubMed] [Google Scholar]
- Lipshutz R. J., Fodor S. P., Gingeras T. R., Lockhart D. J. High density synthetic oligonucleotide arrays. Nat Genet. 1999 Jan;21(1 Suppl):20–24. doi: 10.1038/4447. [DOI] [PubMed] [Google Scholar]
- Ross D. T., Scherf U., Eisen M. B., Perou C. M., Rees C., Spellman P., Iyer V., Jeffrey S. S., Van de Rijn M., Waltham M. Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet. 2000 Mar;24(3):227–235. doi: 10.1038/73432. [DOI] [PubMed] [Google Scholar]
- Scherf U., Ross D. T., Waltham M., Smith L. H., Lee J. K., Tanabe L., Kohn K. W., Reinhold W. C., Myers T. G., Andrews D. T. A gene expression database for the molecular pharmacology of cancer. Nat Genet. 2000 Mar;24(3):236–244. doi: 10.1038/73439. [DOI] [PubMed] [Google Scholar]
- Slonim Donna K. From patterns to pathways: gene expression data analysis comes of age. Nat Genet. 2002 Dec;32 (Suppl):502–508. doi: 10.1038/ng1033. [DOI] [PubMed] [Google Scholar]
- Sturn Alexander, Quackenbush John, Trajanoski Zlatko. Genesis: cluster analysis of microarray data. Bioinformatics. 2002 Jan;18(1):207–208. doi: 10.1093/bioinformatics/18.1.207. [DOI] [PubMed] [Google Scholar]
- Valafar Faramarz. Pattern recognition techniques in microarray data analysis: a survey. Ann N Y Acad Sci. 2002 Dec;980:41–64. doi: 10.1111/j.1749-6632.2002.tb04888.x. [DOI] [PubMed] [Google Scholar]