International Mammalian Genome Society

The 15th International Mouse Genome Conference (2001)

Table of Contents * Complex Genetics * Developmental Genetics * Gene Annotation * Gene Discovery * Genome Sequencing * Functional Genomics * Mutagenesis * Presentations * Verne Chapman Memorial Lecture

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POSTER 99 - CREATION AND MINING OF A GENERAL PURPOSE MOUSE GENE EXPRESSION DATABASE

Dr Bruce Aronow
Children's Hospit Research Foundation
Univ Cincin
CHRF 2048
3333 Burnet Avenue
Cincinnati 45242 USA

Co-Authors:  Williams S, Ebert C and a consortium of UC/CHMC investigators 
Institutions:   Divisions of Molecular Developmental Biology, Pediatric Informatics, and University of Cincinnati Genome Informatics Core, University of Cincinnati and Children’s Hospital Medical Center

We have analyzed mRNA expression profiles of 81 normal, developing and disease mouse tissues using Incyte MouseGEM1 microarrays and a single common reference mRNA.  Tissues within organ series included adult and developing lung, cardiac, CNS, GI, urogenital, immunologic, and endocrine tissues. Duplicate Cy3/Cy5 hybridizations with Agilent Bioanalyzer-graded mRNAs and day 1 whole mouse mRNA reference demonstrated excellent reproducibility (even with respect to genes expressed at very low level in the reference mRNA), equivalency to dye reversal, and agreement with direct sample comparisons.  Use of multiple normalization strategies greatly improved quality assurance and expression pattern characterizations based on tissue and organ specificity and optimal replicate correlations. Excluding the most over-expressed genes reduced ability to classify tissue specificity, but less so organ origin.  Tissues from CNS, immunologic, and GI systems exhibited impressive expression diversity and repertoire specificity.  Probing for correlated genes with known biologic relationships within multiple gene ontologies and other known biologic relationships demonstrated the potential of the database to implicate functional associations and potential pathway relationships for unknown genes. These results support the hypothesis that systematic database mining by cross-comparative analysis of diverse biologic systems will greatly augment gene discovery, annotation, and pathway knowledge. (supported by multiple NIH grants of consortium members and the Howard Hughes Medical Institute).