Format

Send to

Choose Destination
Methods Mol Biol. 2007;382:405-16. doi: 10.1007/978-1-59745-304-2_25.

Bioinformatics: microarray data clustering and functional classification.

Author information

1
Department of Life Science, Institute of Molecular and Cellular Biology, National Taiwan University, Taipei.

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.

PMID:
18220245
DOI:
10.1007/978-1-59745-304-2_25
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center