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Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions

Javier Carrera12, Guillermo Rodrigo1, Alfonso Jaramillo34 and Santiago F Elena15*

Author Affiliations

1 Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas-UPV, Ingeniero Fausto Elio s/n, 46022 València, Spain

2 ITACA, Universidad Politécnica de Valencia, Ingeniero Fausto Elio s/n, 46022 València, Spain

3 Laboratoire de Biochimie, École-Polytechnique-CNRS UMR7654, Route de Saclay, 91128 Palaiseau, France

4 Epigenomics Project, Genopole-Université d'Évry Val d'Essonne-CNRS UPS3201, 523 Terrasses de l'Agora, 91034 Évry, France

5 The Santa Fe Institute, Hyde Park Road, Santa Fe, NM 87501, USA

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Genome Biology 2009, 10:R96  doi:10.1186/gb-2009-10-9-r96

Published: 15 September 2009

Additional files

Additional data file 1:

Figure S1: z-score distribution from the mutual information calculation between all gene-TF pairs. Figure S2: number of regulations in the model depending on the cutoff threshold selection. Figure S3: efficiency (precision, sensitivity and F-score) of the transcriptional model with respect to the reference set. The vertical dashed line indicates the optimum value for the z-score threshold (= 5) according to the F value. Figure S4: gene distribution in the pathways (clusters) found in the transcriptional network. Figure S5: stress distribution of the transcriptional network. Figure S6: absolute and relative gene expression errors versus the regression coefficient between the experimental and predicted gene expressions for all conditions from the training set. Figure S7: regression coefficient between the experimental and predicted gene expressions for all conditions versus the number of TFs regulating that gene. Figure S8: predictive power for gene expression of the effective model (including the transcriptional and non-transcriptional layers). We show the regression coefficient (R2) between the model and experimental profiles across the 1,436 conditions for the best (top) and worst (bottom) predicted genes.

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Additional data file 2:

Table S1: fit of the distributions of outgoing and incoming connectivities for the transcriptional and non-transcriptional models to different statistical distributions. Table S2: three-gene motifs for the transcriptional model showing abundance and statistical significance. Table S3: four-gene motifs for the transcriptional model showing abundance and statistical significance. Table S4: three-gene motifs for the non-transcriptional model showing abundance and statistical significance. Table S5: four-gene motifs for the non-transcriptional model showing abundance and statistical significance.

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Additional data file 3:

The effective (text file) and transcriptional models (SBML file).

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