Reconstruction of gene networks using prior knowledge

BackgroundReconstructing gene regulatory networks (GRNs) from expression data is a challenging task that has become essential to the understanding of complex regulatory mechanisms in cells. The major issues are the usually very high ratio of number of genes to sample size, and the noise in the available data. Integrating biological prior knowledge to the learning process is a natural and promising way to partially compensate for the lack of reliable expression data and to increase the accuracy of network reconstruction algorithms.ResultsIn this manuscript, we present PriorPC, a new algorithm based on the PC algorithm. PC algorithm is one of the most popular methods for Bayesian network reconstruction. The result of PC is known to depend on the order in which conditional independence tests are processed, especially for large networks. PriorPC uses prior knowledge to exclude unlikely edges from network estimation and introduces a particular ordering for the conditional independence tests. We show on synthetic data that the structural accuracy of networks obtained with PriorPC is greatly improved compared to PC.ConclusionPriorPC improves structural accuracy of inferred gene networks by using soft priors which assign to edges a probability of existence. It is robust to false prior which is not avoidable in the context of biological data. PriorPC is also fast and scales well for large networks which is important for its applicability to real data.

[1]  Guy Karlebach,et al.  Modelling and analysis of gene regulatory networks , 2008, Nature Reviews Molecular Cell Biology.

[2]  D. Husmeier,et al.  Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge , 2007, Statistical applications in genetics and molecular biology.

[3]  Satoru Miyano,et al.  Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.

[4]  Jörg Stülke,et al.  Connecting parts with processes: SubtiWiki and SubtiPathways integrate gene and pathway annotation for Bacillus subtilis. , 2010, Microbiology.

[5]  Sach Mukherjee,et al.  Network inference using informative priors , 2008, Proceedings of the National Academy of Sciences.

[6]  Haiyan Huang,et al.  Review on statistical methods for gene network reconstruction using expression data. , 2014, Journal of theoretical biology.

[7]  Dario Floreano,et al.  Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods , 2009, J. Comput. Biol..

[8]  N. D. Clarke,et al.  Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges , 2010, PloS one.

[9]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[10]  Peter Bühlmann,et al.  Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm , 2007, J. Mach. Learn. Res..

[11]  Robert D. Leclerc.  Survival of the sparsest: robust gene networks are parsimonious , 2008, Molecular systems biology.

[12]  Tom Burr,et al.  Causation, Prediction, and Search , 2003, Technometrics.

[13]  N. D. Clarke,et al.  Correction: Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges , 2010, PLoS ONE.

[14]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[15]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[16]  Galina V. Glazko,et al.  Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data , 2012, Front. Gene..

[17]  Constantin F. Aliferis,et al.  The max-min hill-climbing Bayesian network structure learning algorithm , 2006, Machine Learning.

[18]  B. Schwikowski,et al.  Condition-Dependent Transcriptome Reveals High-Level Regulatory Architecture in Bacillus subtilis , 2012, Science.

[19]  Jean-Philippe Vert,et al.  TIGRESS: Trustful Inference of Gene REgulation using Stability Selection , 2012, BMC Systems Biology.

[20]  Jörg Stülke,et al.  A community-curated consensual annotation that is continuously updated: the Bacillus subtilis centred wiki SubtiWiki , 2009, Database J. Biol. Databases Curation.

[21]  Jean-Philippe Vert,et al.  SIRENE: supervised inference of regulatory networks , 2008, ECCB.

[22]  Rainer Spang,et al.  Inferring cellular networks – a review , 2007, BMC Bioinformatics.

[23]  J. Collins,et al.  Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.

[24]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[25]  Julio Collado-Vides,et al.  RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units) , 2010, Nucleic Acids Res..

[26]  Diogo M. Camacho,et al.  Wisdom of crowds for robust gene network inference , 2012, Nature Methods.

[27]  Richard Bonneau,et al.  Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks , 2013, Bioinform..