title: Identification of direction in gene networks from expression and methylation.
abstract: BACKGROUND
  Reverse-engineering gene regulatory networks from expression data is difficult, especially without temporal measurements or interventional experiments. In particular, the causal direction of an edge is generally not statistically identifiable, i.e., cannot be inferred as a statistical parameter, even from an unlimited amount of non-time series observational mRNA expression data. Some additional evidence is required and high-throughput methylation data can viewed as a natural multifactorial gene perturbation experiment.
  
  
  RESULTS
  We introduce IDEM (Identifying Direction from Expression and Methylation), a method for identifying the causal direction of edges by combining DNA methylation and mRNA transcription data. We describe the circumstances under which edge directions become identifiable and experiments with both real and synthetic data demonstrate that the accuracy of IDEM for inferring both edge placement and edge direction in gene regulatory networks is significantly improved relative to other methods.
  
  
  CONCLUSIONS
  Reverse-engineering directed gene regulatory networks from static observational data becomes feasible by exploiting the context provided by high-throughput DNA methylation data.An implementation of the algorithm described is available at http://code.google.com/p/idem/.
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. dsimcha@gmail.com.
authors.forename: David M
authors.initials: DM
authors.lastname: Simcha
authors.forename: Laurent
authors.initials: L
authors.lastname: Younes
authors.forename: Martin J
authors.initials: MJ
authors.lastname: Aryee
authors.forename: Donald
authors.initials: D
authors.lastname: Geman
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D019175:DNA Methylation; D020869:Gene Expression Profiling; D055785:Gene Knockdown Techniques; D053263:Gene Regulatory Networks; D016013:Likelihood Functions; D008390:Markov Chains; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D015203:Reproducibility of Results
chemical_list: D012333:RNA, Messenger