AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data.

AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data. Research Abstract Details 

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AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data. Abstract Text:

Guoqing Lu,The V Nguyen,Yuannan Xia,Michael Fromm,

BACKGROUND: DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously. With the advance of microarray technology, the challenge issue becomes how to analyze a large amount of microarray data and make biological sense of them. Affymetrix GeneChips are widely used microarrays, where a variety of statistical algorithms have been explored and used for detecting significant genes in the experiment. These methods rely solely on the quantitative data, i.e., signal intensity; however, qualitative data are also important parameters in detecting differentially expressed genes. RESULTS: AffyMiner is a tool developed for detecting differentially expressed genes in Affymetrix GeneChip microarray data and for associating gene annotation and gene ontology information with the genes detected. AffyMiner consists of the functional modules, GeneFinder for detecting significant genes in a treatment versus control experiment and GOTree for mapping genes of interest onto the Gene Ontology (GO) space; and interfaces to run Cluster, a program for clustering analysis, and GenMAPP, a program for pathway analysis. AffyMiner has been used for analyzing the GeneChip data and the results were presented in several publications. CONCLUSION: AffyMiner fills an important gap in finding differentially expressed genes in Affymetrix GeneChip microarray data. AffyMiner effectively deals with multiple replicates in the experiment and takes into account both quantitative and qualitative data in identifying significant genes. AffyMiner reduces the time and effort needed to compare data from multiple arrays and to interpret the possible biological implications associated with significant changes in a gene's expression.

AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data. Publishing Authors By Initials

G Lu,TV Nguyen,Y Xia,M Fromm,

For similar information science: computing methodologies: software: user-computer interface research abstracts see: information science: computing methodologies: software: user-computer interface research

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AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data. Journal Published:

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: BMC bioinformatics

VOLUME: 7 Suppl 4

Page Numbers: S26

Journal Abbreviation: BMC Bioinformatics

ISSN: 1471-2105

DAY: 12

MONTH: 12

YEAR: 2006

AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data. Information

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LANGUAGE: eng

NlmUniqueID: 100965194

AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data. Keywords Mesh Terms:

KEYWORDS: User-Computer Interface

MESH TERMS: methods

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Grant and Affiliation Information for AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data.

AFFILIATION: Department of Biology, University of Nebraska, Omaha, NE 68182, USA. glu3@mail.unomaha.edu

Country: England

AGENCY: United States NCRR

GRANT: P20 RR16469

ACRONYM: RR

MEDLINETA: BMC Bioinformatics

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