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Bioinformatics 2006 22(14):e323-e331; doi:10.1093/bioinformatics/btl228
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Quantification of transcription factor expression from Arabidopsis images

Daniel L. Mace 1,2, Ji-Young Lee 3, Richard W. Twigg 3,4, Juliette Colinas 3,4, Philip N. Benfey 1,3 and Uwe Ohler 1,*

1 Institute for Genome Science and Policy, Duke University Durham NC, 27708
2 Computational Biology and Bioinformatics Program, Duke University Durham NC, 27708
3 Department of Biology, Duke University Durham NC, 27708
4 University Program in Genetics and Genomics, Duke University Durham NC, 27708

*To whom correspondence should be addressed.

Motivation: Confocal microscopy has long provided qualitative information for a variety of applications in molecular biology. Recent advances have led to extensive image datasets, which can now serve as new data sources to obtain quantitative gene expression information. In contrast to microarrays, which usually provide data for many genes at one time point, these image data provide us with expression information for only one gene, but with the advantage of high spatial and/or temporal resolution, which is often lostin microarray samples.

Results: We have developed a prototype for the automatic analysis of Arabidopsis confocal images, which show the expression of a single transcription factor by means of GFP reporter constructs. Using techniques from image registration, we are able to address inherent problems of non-rigid transformation and partial mapping, and obtain relative expression values for 13 different tissues in Arabidopsis roots. This provides quantitative information with high spatial resolution, which accurately represents the underlying expression values within the organism. We validate our approach on a data set of 122 images depicting expression patterns of 30 transcription factors, both in terms of registration accuracy, as well as correlation with cell-sorted microarray data. Approaches like this will be useful to lay the groundwork to reconstruct regulatory networks on the level of tissues or even individual cells.

Contact: uwe.ohler{at}duke.edu

Availability: Upon request from the authors.

Supplementary Data: http://tools.genome.duke.edu/generegulation/


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