Finding temporal gene expression patterns for translational research
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Updated: May 9, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
Published on: March 7, 2018
Guenter Tusch1, Olvi Tolea, Yuka Kutsumi
1Medical and Bioinformatics Graduate Program, Grand Valley State University, Allendale, MI, USA.
Abstract:
Translational research of time-series of gene-expression microarray datasets makes use on gene expression profiles that have been obtained at different points in time. Our web-based multi-user program helps a researcher find temporal patterns like peaks in large pre-selected microarray data sets that include data from different but related studies in publicly available databases. If all studies use the same platform, data can be combined for a meta-analysis type approach. For combination of data from different platforms we allow only Affymetrix GeneChips, for which a method for pooling of information exists. To search for time patterns, the data are transformed into an abstract layer that is independent from the particular selection of time point in the individual studies.
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