Gene regulatory network reconstruction using bayesian networks, the dantzig selector, the lasso and their meta-analysis
2011
Vignes , Matthieu(auteur de correspondance) (INRA , Auzeville (France). UR 0875 Unité de recherche Biométrie et Intelligence Artificielle) | vandel , Jimmy (INRA , Auzeville (France). UR 0875 Unité de recherche Biométrie et Intelligence Artificielle) | Allouche , David (INRA , Auzeville (France). UR 0875 Unité de recherche Biométrie et Intelligence Artificielle) | Ramadan-Alban , Nidal (INRA , Auzeville (France). UR 0875 Unité de recherche Biométrie et Intelligence Artificielle) | Cierco-Ayrolles , Christine (INRA , Auzeville (France). UR 0875 Unité de recherche Biométrie et Intelligence Artificielle) | Schiex , Thomas (INRA , Auzeville (France). UR 0875 Unité de recherche Biométrie et Intelligence Artificielle) | Mangin , Brigitte (INRA , Auzeville (France). UR 0875 Unité de recherche Biométrie et Intelligence Artificielle) | De Givry , Simon (INRA , Auzeville (France). UR 0875 Unité de recherche Biométrie et Intelligence Artificielle)
Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designedto infer gene regulatory networks. The fifth ‘‘Dialogue for Reverse Engineering Assessments and Methods’ (DREAM5) challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on ‘‘Systems Genetics’’ proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the 16 teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics.
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