Abstract
The human genome project has opened up a new page in scientific history. To this end, a variety of techniques such as microarray has evolved to monitor the transcript abundance for all of the organism’s genes rapidly and efficiently. Behind the massive numbers produced by these techniques, which amount to hundreds of data points for thousands or tens of thousands of genes, there hides an immense amount of biological information. The importance of microarray data analysis lies in presenting functional annotations and classifications. The process of the functional classifications is conducted as follows. The first step is to cluster gene expression data. Cluster 3.0 and Java Treeview are widely used open-source programs to group together genes with similar pattern of expressions, and to provide a computational and graphical environment for analyzing data from DNA microarray experiments, or other genomic datasets. Clustered genes can later be decoded by Bulk Gene Searching Systems in Java (BGSSJ). BGSSJ is an XML-based Java application that systemizes lists of interesting genes and proteins for biological interpretation in the context of the gene ontology. Gene ontology gathers information for molecular function, biological processes, and cellular components with a number of different organisms. In this chapter, in terms of how to use Cluster 3.0 and Java Treeview for microarray data clustering, and BGSSJ for functional classification are explained in detail.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yue, H., Eastman, P. S., Wang, B. B., et al. (2001) An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression. Nucleic Acids Res. 29, E41.
Ideker, T., Thorsson, V., Ranish, J. A., et al. (2001) Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929–934.
Hughes, T. R., Marton, M. J., Jones, A. R., et al. (2000) Functional discovery via a compendium of expression profiles. Cell 102, 109–126.
Seo, J., Kim, M., and Kim, J. (2000) Identification of novel genes differentially expressed in PMA-induced HL-60 cells using cDNA microarrays. Mol. Cells 10, 733–739.
Naour, F. L., Hohenkirk, L., Grolleau, A., et al. (2001) Profiling changes in gene expression during differentiation and maturation of monocyte-derived dendritic cells using both oligonucleotide microarrays and proteomics. J. Biol. Chem. 276, 17,920–17,931.
Eisen, M. B., Spellman, P. T., Brown, P. O., and Botstein, D. (1998) Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14,863–14,868.
Luo, X., Ding, L., Xu, J., Williams, R. S., and Chegini, N. (2005) Leiomyoma and myometrial gene expression profiles and their responses to gonadotropin-releasing hormone analog therapy. Endocrinology 146, 1074–1096.
Sadlier, D. M., Connolly, S. B., Kieran, N. E., et al. (2004) Sequential extracellular matrix-focused and baited-global cluster analysis of serial transcriptomic profiles identifies candidate modulators of renal tubulointerstitial fibrosis in murine adriamycin-induced nephropathy. J. Biol. Chem. 279, 29,670–29,680.
Ptitsyn, A. (2004) Class discovery analysis of the lung cancer gene expression data. DNA Cell Biol. 23, 715–721.
Bernard, P. S. and Wittwer, C. T. (2002) Real-time PCR technology for cancer diagnostics. Clin. Chem. 48, 1178–1185.
Ramaswamy, S., Tamayo, P., Rifkin, R., et al. (2001) Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. USA 98, 15,149–15,154.
De Hoon, M. J. L., Imoto, S., Nolan, J., and Miyano, S. (2004) Open source clustering software. Bioinformatics 20, 1453–1454.
Saldanha, A. J. (2004) Java Treeview—extensible visualization of microarray data. Bioinformatics 20, 3246–3248.
Juan, H. F., Lin, J. Y., Chang, W. H., et al. (2002) Biomic study of human myeloid leukemia cells differentiation to macrophages using DNA array, proteomic, and bioinformatic analytical methods. Electrophoresis 23, 2490–2504.
Draghici, S. (2003) Functional analysis and biological interpretation of microarray data, in Data Analysis Tools for DNA Microarrays, CRC, Boca Raton, FL, pp. 363–382.
Zheng, X., Ravatn, R., Lin, Y., et al. (2002) Gene expression of TPA induced differentiation in HL-60 cells by DNA microarray analysis. Nucleic Acids Res. 30, 4489–4499.
Al-Shahrour, F., Diaz-Uriarte, R., and Dopazo, J. (2004) FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 20, 578–580.
Mateos, A., Herrero, J., Tamames, J., and Dopazo, J. (2002) Supervised neural networks for clustering conditions in DNA array data after reducing noise by clustering gene expression profiles, in Methods of Microarray Data Analysis II, (Lin, S. and Johnson, K., eds.), Kluwer Academic Publishers, Boston, MA, pp. 91–103.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Humana Press Inc., Totowa, NJ
About this protocol
Cite this protocol
Juan, HF., Huang, HC. (2007). Bioinformatics. In: Rampal, J.B. (eds) Microarrays. Methods in Molecular Biology, vol 382. Humana Press. https://doi.org/10.1007/978-1-59745-304-2_25
Download citation
DOI: https://doi.org/10.1007/978-1-59745-304-2_25
Publisher Name: Humana Press
Print ISBN: 978-1-58829-944-4
Online ISBN: 978-1-59745-304-2
eBook Packages: Springer Protocols