biRte: Bayesian inference of context-specific regulator activities and transcriptional networks

UNLABELLED In the last years there has been an increasing effort to computationally model and predict the influence of regulators (transcription factors, miRNAs) on gene expression. Here we introduce biRte as a computationally attractive approach combining Bayesian inference of regulator activities with network reverse engineering. biRte integrates target gene predictions with different omics data entities (e.g. miRNA and mRNA data) into a joint probabilistic framework. The utility of our method is tested in extensive simulation studies and demonstrated with applications from prostate cancer and Escherichia coli growth control. The resulting regulatory networks generally show a good agreement with the biological literature. AVAILABILITY AND IMPLEMENTATION biRte is available on Bioconductor (http://bioconductor.org). CONTACT frohlich@bit.uni-bonn.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

[1]  K. Sachs,et al.  Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data , 2005, Science.

[2]  Christie S. Chang,et al.  The BioGRID interaction database: 2013 update , 2012, Nucleic Acids Res..

[3]  K. Inagaki,et al.  Selenite assimilation into formate dehydrogenase H depends on thioredoxin reductase in Escherichia coli. , 2007, Journal of biochemistry.

[4]  Holger Fröhlich,et al.  Reconstructing Consensus Bayesian Network Structures with Application to Learning Molecular Interaction Networks , 2013, GCB.

[5]  Walter Krämer,et al.  Review of Modern applied statistics with S, 4th ed. by W.N. Venables and B.D. Ripley. Springer-Verlag 2002 , 2003 .

[6]  P. Zhang,et al.  Replication and Fine Mapping for Association of the C2orf43, FOXP4, GPRC6A and RFX6 Genes with Prostate Cancer in the Chinese Population , 2012, PloS one.

[7]  Holger Fröhlich,et al.  Joint Bayesian inference of condition-specific miRNA and transcription factor activities from combined gene and microRNA expression data , 2012, Bioinform..

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

[9]  Achim Tresch,et al.  Structure Learning in Nested Effects Models , 2007, Statistical applications in genetics and molecular biology.

[10]  Mathisca C. M. de Gunst,et al.  Identification of context-specific gene regulatory networks with GEMULA - gene expression modeling using LAsso , 2012, Bioinform..

[11]  Kenneth E. Rudd,et al.  EcoGene: a genome sequence database for Escherichia coli K-12 , 2000, Nucleic Acids Res..

[12]  D. Tindall,et al.  p300 acetyltransferase regulates androgen receptor degradation and PTEN-deficient prostate tumorigenesis. , 2014, Cancer research.

[13]  G. Unden,et al.  The oxygen‐responsive transcriptional regulator FNR of Escherichia coli : the search for signals and reactions , 1997, Molecular microbiology.

[14]  Alexander J. Hartemink,et al.  Principled computational methods for the validation discovery of genetic regulatory networks , 2001 .

[15]  Gordon K Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .

[16]  Rainer Spang,et al.  A Least Angle Regression Model for the Prediction of Canonical and Non-Canonical miRNA-mRNA Interactions , 2012, PloS one.

[17]  X. Hao,et al.  TFDP3 was expressed in coordination with E2F1 to inhibit E2F1-mediated apoptosis in prostate cancer. , 2014, Gene.

[18]  Daniel Hernández-Lobato,et al.  Expectation Propagation for microarray data classification , 2010, Pattern Recognit. Lett..

[19]  A. Harris,et al.  Targeting the ATF4 pathway in cancer therapy , 2012, Expert opinion on therapeutic targets.

[20]  A. Belayew,et al.  The Helicase-Like Transcription Factor and its implication in cancer progression , 2008, Cellular and Molecular Life Sciences.

[21]  Markus J. Herrgård,et al.  Integrating high-throughput and computational data elucidates bacterial networks , 2004, Nature.

[22]  Korbinian Strimmer,et al.  From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data , 2007, BMC Systems Biology.

[23]  F. Blattner,et al.  IscR‐dependent gene expression links iron‐sulphur cluster assembly to the control of O2‐regulated genes in Escherichia coli , 2006, Molecular microbiology.

[24]  G. Lynch,et al.  The Control of the False Discovery Rate in Fixed Sequence Multiple Testing , 2016, 1611.03146.

[25]  G. Bennett,et al.  Genetic reconstruction of the aerobic central metabolism in Escherichia coli for the absolute aerobic production of succinate. , 2005, Biotechnology and bioengineering.

[26]  T. Mizuno,et al.  Tuning of the porin expression under anaerobic growth conditions by His‐to‐Asp cross‐phosphorelay through both the EnvZ‐osmosensor and ArcB‐anaerosensor in Escherichia coli , 2000, Genes to cells : devoted to molecular & cellular mechanisms.

[27]  P. Geurts,et al.  Inferring Regulatory Networks from Expression Data Using Tree-Based Methods , 2010, PloS one.

[28]  S. Levy,et al.  Activation of the Escherichia coli nfnB gene by MarA through a highly divergent marbox in a class II promoter , 2002, Molecular microbiology.

[29]  Wei Keat Lim,et al.  Master Regulators Used As Breast Cancer Metastasis Classifier , 2008, Pacific Symposium on Biocomputing.

[30]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[31]  B. Demple,et al.  Redox signaling and gene control in the Escherichia coli soxRS oxidative stress regulon--a review. , 1996, Gene.

[32]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[33]  D. Touati,et al.  Anaerobic activation of arcA transcription in Escherichia coli: roles of Fnr and ArcA , 1994, Molecular microbiology.

[34]  Min Lin,et al.  Genome-wide transcriptome and proteome analysis of Escherichia coli expressing IrrE, a global regulator of Deinococcus radiodurans. , 2011, Molecular bioSystems.

[35]  Holger Fröhlich,et al.  Fast and efficient dynamic nested effects models , 2011, Bioinform..

[36]  J. Gralla,et al.  Osmotic Stress. , 2009, EcoSal Plus.

[37]  Olga G. Troyanskaya,et al.  Nested effects models for high-dimensional phenotyping screens , 2007, ISMB/ECCB.

[38]  Tetsuya Hayashi,et al.  Escherichia coli , 1983, CABI Compendium.

[39]  E. George,et al.  APPROACHES FOR BAYESIAN VARIABLE SELECTION , 1997 .

[40]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[41]  T. Lamark,et al.  The complex bet promoters of Escherichia coli: regulation by oxygen (ArcA), choline (BetI), and osmotic stress , 1996, Journal of bacteriology.

[42]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[43]  Charity W. Law,et al.  voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.

[44]  T. Speed,et al.  Summaries of Affymetrix GeneChip probe level data. , 2003, Nucleic acids research.

[45]  Holger Fröhlich,et al.  Analyzing gene perturbation screens with nested effects models in R and bioconductor , 2008, Bioinform..

[46]  Manu Setty,et al.  Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma , 2012, Molecular systems biology.

[47]  Takeshi Mizuno,et al.  Transcriptome analysis of all two‐component regulatory system mutants of Escherichia coli K‐12 , 2002, Molecular microbiology.

[48]  Sundari Chodavarapu,et al.  Escherichia coli Dps interacts with DnaA protein to impede initiation: a model of adaptive mutation , 2008, Molecular microbiology.

[49]  Achim Tresch,et al.  Nested effects models for learning signaling networks from perturbation data , 2009, Biometrical journal. Biometrische Zeitschrift.

[50]  Robert Castelo,et al.  Reverse Engineering Molecular Regulatory Networks from Microarray Data with qp-Graphs , 2009, J. Comput. Biol..

[51]  P. Kiley,et al.  FNR‐dependent activation of the class II dmsA and narG promoters of Escherichia coli requires FNR‐activating regions 1 and 3 , 2000, Molecular microbiology.

[52]  Rainer Spang,et al.  Non-transcriptional pathway features reconstructed from secondary effects of RNA interference , 2005, Bioinform..