Bioinformatics: microarray data clustering and functional classification

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, whic...

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Bibliographic Details
Published in:Methods in molecular biology (Clifton, N.J.) Vol. 382; p. 405
Main Authors: Juan, Hsueh-Fen, Huang, Hsuan-Cheng
Format: Journal Article
Language:English
Published: United States 2007
Subjects:
Carcinogens - pharmacology
Cluster Analysis
Computational Biology - methods
Gene Expression Profiling
Humans
Leukemia, Myeloid - classification
Leukemia, Myeloid - drug therapy
Leukemia, Myeloid - genetics
Oligonucleotide Array Sequence Analysis - methods
Pattern Recognition, Automated
Software
Tetradecanoylphorbol Acetate - pharmacology
Tumor Cells, Cultured - drug effects
ISSN:1064-3745
Online Access:Get more information
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Summary: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.
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ISSN:1064-3745
DOI:10.1007/978-1-59745-304-2_25