Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana

doi:10.1186/1471-2105-8-454

BMC Bioinformatics
Volume 8

Viewing options:

Abstract
Full text
PDF (725KB)
Additional files

Associated material:

Related literature:

Articles citing this article
on BioMed Central
on Google Scholar
on ISI Web of Science
on PubMed Central
Other articles by authors
on Google Scholar

Redestig H
Weicht D
Selbig J
Hannah MA

on PubMed

Redestig H
Weicht D
Selbig J
Hannah MA
Related articles/pages
on Google

on Google Scholar

on PubMed

Tools:

Post to:

Methodology article

Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana

Henning Redestig¹ , Daniel Weicht¹ , Joachim Selbig¹^,2 and Matthew A Hannah¹

¹Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany

²University of Potsdam, Am Neuen Palais, D-14469, Potsdam, Germany

author email corresponding author email

BMC Bioinformatics 2007, 8:454doi:10.1186/1471-2105-8-454


Published:	18 November 2007

Additional files

Additional file 1:

Literature study of transcription factors and their targets considered in this study. We focused on TFs that have previously been implicated in stress responses, but as not all respond under the conditions used to generate the AtGenExpress dataset, some were therefore excluded from our test set. Wherever multiple genes were knocked out or over-expressed, or where functional redundancy has been implicated, the average of those genes was used.

Format: CSV Size: 4KB Download file

Additional file 2:

The numbers of over-represented hexamers and annotations (MapMan bins) in the experimentally defined regulons. With randomly chosen genes we would not expect any over-representation. A clear majority of the larger regulons have several over-represented hexamers annotations. 'OX' indicates that the targets were found using over-expression, 'KO' using knock-out and 'ChIP' using ChIP-chip experiment.

Format: CSV Size: 1KB Download file