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Living cells are the product of gene expression programs that involve the regulated transcription of thousands of genes. The elucidation of transcriptional regulatory networks in thus needed to understand the cell's working mechanism, and can for example be useful for the discovery of novel therapeutic targets. Although several methods have been proposed to infer gene regulatory networks from gene expression data, a recent comparison on a large-scale benchmark experiment revealed that most current methods only predict a limited number of known regulations at a reasonable precision level. We propose SIRENE, a new method for the inference of gene regulatory networks from a compendium of expression data. The method decomposes the problem of gene regulatory network inference into a large number of local binary classification problems, that focus on separating target genes from non-targets for each TF. SIRENE is thus conceptually simple and computationally efficient. We tested it on a benchmark experiment aimed at predicting regulations in E. coli, and showed that it retrieves of the order of 5 times more known regulations than other state-of-the-art inference methods.
SIRENE has been implemented in Matlab (version 7.5.0) but for the SVM part of the code, it is possible for the user to choose between simpleSVM and libsvm. The documentation of simpleSVM is available here and since the codes are written in Matlab, they are included in the src directory of SIRENE. As far as libsvm is concerned, many different interfaces are possible and we advise the user to get the latest Matlab version on the libsvm website, where help and documentation are also provided. For testing SIRENE on the benchmark dataset, we suggest to use libsvm since it happened to be faster for that problem.
We used in our experiments the expression and regulation data made publicly available by Faith et al, 2007 for E.coli, and downloaded from this website . The expression data consist of a compendium of 445 E. coli Affymetrix Antisense2 microarray expression profiles for 4345 genes. The microarrays were collected under different experimental conditions such as PH changes, growth phases, antibiotics, heat shock, different media, varying oxygen concentrations and numerous genetic perturbations. In our experiment, the expression data for each gene were normalized to zero mean and unit standard deviation. The regulation data consist of 3293 experimentally confirmed regulations between 1211 genes, amongst which 154 transcription factors, extracted from the RegulonDB database .
F. Mordelet and J.-P. Vert. SIRENE: supervised inference of regulatory networks. Bioinformatics, 24(16):i76-i82, 2008.
Adress them to: Fantine Mordelet