A challenging task for biologists and bioinformatic scientists is the analysis of gene expression mediated by cis-regulatory elements (CREs). Regulatory DNA sequences harbor essential information to control specific gene expression and integrate information derived from signaling cascades. By the means of microarrays, the abundance of several thousand transcripts can be monitored simultaneously. This data can be used to model the flow of information from incoming signals via CREs that orchestrate gene expression, which may converge on downstream CREs. In the last few years, many large, directly comparable microarray datasets have been performed in the AtGenExpress project with the plant model organism Arabidopsis thaliana. Each of these datasets constitutes an invaluable information resource for the study of plant developmental processes, physiological responses or interaction with its environment. Moreover, there is an increasing number of multidimensional expression datasets, for which suitable analysis programs that can keep track of all dimensions are still missing. Additional limitations of microarray analysis include overcoming a "smoothing effect" on the relative gene expression when a large number of expression profile datasets are combined for comparison. Ultimately, the investigation of CREs possibly involved in regulating transcription is best aided by using specific gene clusters and determining linkage between gene expression, CRE position, orientation and number.
[pdf]
@incollection{2008_79,
author = {Wanke, Dierk and Kilian, Joachim and Supper, Jochen and Berendzen,
Kenneth W. and Zell, Andreas and Harter, Klaus},
title = {The analysis of gene expression and \emph{cis}-regulatory elements
in large microarray expression datasets},
booktitle = {{Quantum Bio-Informatics: From Quantum Information to Bio-Informatics}},
publisher = {World Scientific Pub Co Inc.},
year = {2008},
volume = {21},
number = {XXI},
pages = {294--314},
month = mar,
abstract = {A challenging task for biologists and bioinformatic scientists is
the analysis of gene expression mediated by \emph{cis}-regulatory
elements (CREs). Regulatory DNA sequences harbor essential information
to control specific gene expression and integrate information derived
from signaling cascades. By the means of microarrays, the abundance
of several thousand transcripts can be monitored simultaneously.
This data can be used to model the flow of information from incoming
signals via CREs that orchestrate gene expression, which may converge
on downstream CREs. In the last few years, many large, directly comparable
microarray datasets have been performed in the AtGenExpress project
with the plant model organism \emph{Arabidopsis thaliana}. Each of
these datasets constitutes an invaluable information resource for
the study of plant developmental processes, physiological responses
or interaction with its environment. Moreover, there is an increasing
number of multidimensional expression datasets, for which suitable
analysis programs that can keep track of all dimensions are still
missing. Additional limitations of microarray analysis include overcoming
a ``smoothing effect" on the relative gene expression when a large
number of expression profile datasets are combined for comparison.
Ultimately, the investigation of CREs possibly involved in regulating
transcription is best aided by using specific gene clusters and determining
linkage between gene expression, CRE position, orientation and number.},
isbn = {981279316X},
journal = {Quantum Bio-Informatics: From Quantum Information to Bio-Informatics},
keywords = {Multidimensional gene expression datasets, microarray expression profiles,
cis-regulatory elements, transcriptional regulation, Arabidopsis
thaliana, AtGenExpress},
url = {http://www.worldscibooks.com/physics/6756.html}
}