Microarray databases: standards and ontologies

A single microarray can provide information on the expression of tens of thousands of genes. The amount of information generated by a microarray-based experiment is sufficiently large that no single study can be expected to mine each nugget of scientific information. As a consequence, the scale and complexity of microarray experiments require that computer software programs do much of the data processing, storage, visualization, analysis and transfer. The adoption of common standards and ontologies for the management and sharing of microarray data is essential and will provide immediate benefit to the research community.

[1]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993 .

[2]  Thomas R. Gruber,et al.  A Translation Approach to Portable Ontologies , 1993 .

[3]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[4]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[5]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[6]  Christian A. Rees,et al.  Systematic variation in gene expression patterns in human cancer cell lines , 2000, Nature Genetics.

[7]  P. Waddell,et al.  Cluster inference methods and graphical models evaluated on NCI60 microarray gene expression data. , 2000, Genome informatics. Workshop on Genome Informatics.

[8]  D. Botstein,et al.  A gene expression database for the molecular pharmacology of cancer , 2000, Nature Genetics.

[9]  Jason E. Stewart,et al.  Minimum information about a microarray experiment (MIAME)—toward standards for microarray data , 2001, Nature Genetics.

[10]  David Botstein,et al.  The Stanford Microarray Database , 2001, Nucleic Acids Res..

[11]  Jonathan Crabtree,et al.  A relational schema for both array-based and SAGE gene expression experiments , 2001, Bioinform..

[12]  L. Stein Creating a bioinformatics nation , 2002, Nature.

[13]  T. Hudson,et al.  Control genes and variability: absence of ubiquitous reference transcripts in diverse mammalian expression studies. , 2002, Genome research.

[14]  Jason E. Stewart,et al.  Design and implementation of microarray gene expression markup language (MAGE-ML) , 2002, Genome Biology.