Nature Genetics
20, 19 - 23 (1998)
doi:10.1038/1670
Data management and analysis for gene expression arraysOlga Ermolaeva1, 2, Mohit Rastogi3, Kim D. Pruitt2, Gregory D. Schuler2, Michael L. Bittner1, Yidong Chen1, Richard Simon4, Paul Meltzer1, Jeffrey M. Trent1
& Mark S. Boguski2, 31
Cancer Genetics Branch, National Human Genome Research
Institute, National Institutes of Health, Bethesda,
Maryland 20892, USA. 2
National Center for Biotechnology Information, National
Library of Medicine, National Institutes of Health, Bethesda,
Maryland 20894, USA. 3
Genome Technology Branch, National Human Genome Research
Institute, National Institutes of Health, Bethesda,
Maryland 20892, USA. 4
Biometric Research Branch, National Cancer Institute,
National Institutes of Health, Bethesda, Maryland
20892, USA.
Correspondence should be addressed to Mark S. Boguski boguski@ncbi.nlm.nih.govMicroarray technology makes it possible to simultaneously study the expression
of thousands of genes during a single experiment. We have developed an information
system, ArrayDB, to manage and analyse large-scale expression data. The underlying
relational database was designed to allow flexibility in the nature and structure
of data input and also in the generation of standard or customized reports
through a web-browser interface. ArrayDB provides varied options for data
retrieval and analysis tools that should facilitate the interpretation of
complex hybridization results. A sampling of ArrayDB storage, retrieval and
analysis capabilities is available
(http://www.nhgri.nih.gov/DIR/LCG/15K/HTML/),
along with information on a set of approximately 15,000 genes used
to fabricate several widely used microarrays. Information stored in ArrayDB
is used to provide integrated gene expression reports by linking array target
sequences with NCBI's Entrez retrieval system, UniGene and KEGG pathway views.
The integration of external information resources is essential in interpreting
intrinsic patterns and relationships in large-scale gene expression data.
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