%0 Journal Article %A Alina Frolova %A Bartek Wilczynski %T Distributed Bayesian Networks Reconstruction on the Whole Genome Scale %D 2015 %R 10.1101/016683 %J bioRxiv %P 016683 %X Background Bayesian networks are directed acyclic graphical models widely used to represent the probabilistic relationships between random variables. Recently, they have been applied in various biological contexts, including gene regulatory networks and protein-protein interactions inference. Generally, learning Bayesian networks from experimental data is NP-hard, leading to widespread use of heuristic search methods giving suboptimal results. However, in cases when the acyclicity of the graph can be ensured, it is possible to find the optimal network in polynomial time. While our previously developed tool BNFinder implements polynomial time algorithm, reconstructing networks with the large amount of experimental data still leads to numerous days of the computations given single CPU.Results In the present paper we propose parallelized algorithm designed for multi-core and distributed systems and its implementation in the improved version of BNFinder - our tool for learning optimal Bayesian networks. The new algorithm has been tested on simulated datasets as well as different experimental data showing that it has much better efficiency of parallelization than the previous version. When tested on the DREAM datasets in comparison with other methods, BNFinder gives consistently the best results in terms of the area under the ROC curve as well as in the number of positive predictions at the top of the prediction ranking. The latter is especially important for the purposes of the future experimental validation of the predictions.Conclusions We show that the new method can be used to reconstruct networks in the size range of thousands of genes making it practically applicable to whole genome datasets of prokaryotic systems and large components of eukaryotic genomes. Our benchmarking results on realistic datasets indicate that the tool should be useful to wide audience of researchers interested in discovering dependencies in their large-scale transcriptomic datasets. %U https://www.biorxiv.org/content/biorxiv/early/2015/03/17/016683.full.pdf