International Mammalian Genome Society

The 15th International Mouse Genome Conference (2001)


POSTER 138 - MOUSEEXPRESS - ANALYSIS OF MUTANT EXPRESSION PROFILES WITH ME

Mr. Michael T. Mader
PhD student
MIPS, Institute for Bioinformatics
GSF National Research Center-Environment & Health,
Ingolstaedter Landstraße 1
Neuherberg
D-85764
Germany

Co-Authors: 2)Alexei Drobyshev, 2)Matthias Seltmann, 2)Yali Chen, 2)Christine Machka, 3)Tamara Korica, 3)Rainer Koenig, 3)Marcus Frohme, 3)Joerg Hoheisel, 4)Martin Vingron, 2)Martin Hrabe de Angelis, 2)Johannes Beckers, 1)H. Werner Mewes
Institutions: 1) MIPS, Institute for Bioinformatics, GSF National Research Center for Environment & Health, 2) IEG, Institue for Experimental Genetics, GSF  National Research Center for Environment and Health, 3) DKFZ, Division of Functional Genome Analysis, 4) MPI für Molekulare Genetik, Computational Molecular Biology

As part of the MouseExpress project of the German Human Genome Project (DHGP) we have set up a microarray database combined with an interactive analysis framework called ME.

ME's standard-conforming object-relational database handles all recent formats of high-density microarray systems (Oligo, Dendrimer, various cDNA/EST-array software formats) and is interlinked with various other data sources like PEDANT and MIPS FunCat (http://mips.gsf.de).  ME's analysis framework handles data submitted to its database as well as directly uploaded data. It is a modular system with a broad variety of interfaces and is accessable at http://mips.gsf.de/proj/mouseExpress/. Direct and interactive statistics feedback for submitted data, especially genome-wide vertebrate expression profiles, is essential to ensure the quality of expression databases. Therefore, ME's online microarray data analysis framework offers a variety of easy-to-use online tools for this purpose which additionally allow researches to track the sources of variability.

ME allows for interactive functional analysis of various kinds of expression data with focus on dichotomic datasets (e.g. mutant-wildtype comparison).

For example, ME can be used to extract specific pathways or functional groups. This technique reduces signal variability due to features out of interest and puts results directly into the functional context.


Abstracts * Officers * Bylaws * Application Form * Meeting Calendar * Contact Information * Home * Resources * News and Views * Membership

Base url http://imgs.org
Last modified: Saturday, November 3, 2012