Open Access Research article

Regulatory interactions for iron homeostasis in Aspergillus fumigatus inferred by a Systems Biology approach

Jörg Linde14*, Peter Hortschansky2, Eugen Fazius1, Axel A Brakhage24, Reinhard Guthke14 and Hubertus Haas3

Author Affiliations

1 Research Group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology- Hans Knöll Institute, Beutenbergstraße 11a, 07745 Jena, Germany

2 Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology- Hans Knöll Institute, Beutenbergstraße 11a, 07745 Jena, Germany

3 Division of Molecular Biology/Biocenter, Medical University Innsbruck, Fritz-Pregl-Str.3, A-6020 Innsbruck, Austria

4 Friedrich Schiller Univiersity Jena, Jena, Germany

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BMC Systems Biology 2012, 6:6  doi:10.1186/1752-0509-6-6

Published: 19 January 2012

Additional files

Additional file 1:

Table S1 - Imputation. The whole genome expression data (wild-type and mutant) includes 20.4% missing values. Since clustering and network inference need complete observations, we imputed those missing values following a similar approach applied by Albrecht et al. [52]. First, we removed 1253 genes (rows), which had 100% missing values (genes not spotted on the chip). Then, we tested the following imputation methods. From the R package 'impute' [53] K-nearest-neighbour; from the R package 'pcaMethods' [38]: probabilistic Principal Component Analysis (PCA), Bayesian PCA (BPCA), Single-Value-Decomposition impute (SVD impute), PCA by non-linear iterative partial least squares (NIPALS), Neural network based non-linear PCA (NLPCA), and Local Least Squares (LLS) imputation. The concatenation of the wild-type data and the mutant data was used together, since more data improves imputation. For test purpose we found the largest sub-matrix, which consists of full observations (5566 genes with no missing data) and constructed a test-data matrix by randomly introducing artificial missing values in this sub-matrix, keeping the distribution of missing values within the columns the same like in the original matrix. We used the different imputing methods on the test-data and compared the results to the original data in terms of the root mean square error (RMSE). In the first step, we ran each method separately on a range of parameter settings to identify optimal local parameter values. In the second step, we applied the methods using the respective optimal parameter settings on 500 random test matrices. Finally, we compared the methods to each other using the mean RMSE values. The table summarizes results of both steps.

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Additional file 2:

SrbA binding site. Experimental details and results of real-time in vitro binding analysis of SrbA.

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Additional file 3:

Figure S3 - Cluster validity index. Validity indeces for partitions based on two up to twenty clusters are shown. The maximum denotes the best partition.

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Additional file 4:

Table S4 - Cluster annotation. This table shows to which cluster each gene belongs to. Functional annotations and GO annotations are given.

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Additional file 5:

Table S5 - Overrepresented functional categories for each cluster. Overrepresented categories for each cluster. The file consists of 20 sheets. For each cluster, significantly (p < 0.01) overrepresented functional categories for Funcat level 1-4 and Gene Ontology are presented.

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Additional file 6:

Table S6 - Inferred interaction network based on final prior knowledge. The table summarise results of the inferred network based on the final prior knowledge list. It gives the number of resampling during random perturbation of time series data and during the cross-validation of prior knowledge.

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Additional file 7:

Table S7 - SrbA regulon. The table lists A. fumigatus genes having the experimentally validated SrbA binding sites in their regulatory region.

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