Abstract
A common approach for reverse engineering biological networks from data is to deduce the existence of interactions among nodes from information theoretic measures. Estimating these quantities in a multidimensional space is computationally demanding for large datasets. This hampers the application of elaborate algorithms – which are crucial for discarding spurious interactions and determining causal relationships – to large-scale network inference problems. To alleviate this issue we have developed PREMER, a software tool which can automatically run in parallel and sequential environments, thanks to its implementation of OpenMP directives. It recovers network topology and estimates the strength and causality of interactions using information theoretic criteria, and allowing the incorporation of prior knowledge. A preprocessing module takes care of imputing missing data and correcting outliers if needed. PREMER (https://sites.google.com/site/premertoolbox/) runs on Windows, Linux and OSX, it is implemented in Matlab/Octave and Fortran 90, and it does not require any commercial software.
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References
Bonneau, R., Reiss, D.J., Shannon, P., Facciotti, M., Hood, L., Baliga, N.S., Thorsson, V.: The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 7(5), R36 (2006)
Cellucci, C., Albano, A., Rapp, P.: Statistical validation of mutual information calculations: comparison of alternative numerical algorithms. Phys. Rev. E: Stat. Nonlin. Soft Matter Phys. 71(6), 066208 (2005)
Dagum, L., Menon, R.: OpenMP: an industry standard API for shared-memory programming. IEEE Comput. Sci. Eng. 5(1), 46–55 (1998)
Folch-Fortuny, A., Villaverde, A.F., Ferrer, A., Banga, J.R.: Enabling network inference methods to handle missing data and outliers. BMC Bioinform. 16(1), 283 (2015)
Huynh-Thu, V.A., Irrthum, A., Wehenkel, L., Saeys, Y., Geurts, P.: Inferring regulatory networks from expression data using tree-based methods. PLOS ONE 5(9), e12776 (2010)
Jang, I., Margolin, A., Califano, A.: hARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests. Interface Focus 3(4), 20130011 (2013)
Le Novère, N.: Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet. 16, 146–158 (2015)
Meyer, P., Lafitte, F., Bontempi, G.: minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinform. 9(1), 461 (2008)
Samoilov, M., Arkin, A., Ross, J.: On the deduction of chemical reaction pathways from measurements of time series of concentrations. Chaos 11(1), 108–114 (2001)
Schaffter, T., Marbach, D., Floreano, D.: GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27(16), 2263–2270 (2011)
Schreiber, T.: Measuring information transfer. Phys. Rev. Lett. 85(2), 461 (2000)
Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)
Villaverde, A.F., Ross, J., Morán, F., Banga, J.R.: MIDER: network inference with mutual information distance and entropy reduction. PLOS ONE 9(5), e96732 (2014)
Acknowledgements
AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C fellowship ED481B2014/133-0. KB was supported by the German Federal Ministry of Research and Education (BMBF, OncoPath consortium). JRB acknowledges funding from the Spanish government (MINECO) and the European Regional Development Fund (ERDF) through the project “SYNBIOFACTORY” (grant number DPI2014-55276-C5-2-R). This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 686282 (CanPathPro). We thank David R. Penas and David Henriques for assistance with the implementation.
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Villaverde, A.F., Becker, K., Banga, J.R. (2016). PREMER: Parallel Reverse Engineering of Biological Networks with Information Theory. In: Bartocci, E., Lio, P., Paoletti, N. (eds) Computational Methods in Systems Biology. CMSB 2016. Lecture Notes in Computer Science(), vol 9859. Springer, Cham. https://doi.org/10.1007/978-3-319-45177-0_21
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DOI: https://doi.org/10.1007/978-3-319-45177-0_21
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