title: A combinatorial approach to determine the context-dependent role in transcriptional and posttranscriptional regulation in Arabidopsis thaliana.
abstract: BACKGROUND
  While progresses have been made in mapping transcriptional regulatory networks, posttranscriptional regulatory roles just begin to be uncovered, which has arrested much attention due to the discovery of miRNAs. Here we demonstrated a combinatorial approach to incorporate transcriptional and posttranscriptional regulatory sequences with gene expression profiles to determine their probabilistic dependencies.
  
  
  RESULTS
  We applied the proposed method to microarray time course gene expression profiles and could correctly predict expression patterns for more than 50% of 1,132 genes, based on the sequence motifs adopted in the network models, which was statistically significant. Our study suggested that the contribution of miRNA regulation towards gene expression in plants may be more restricted than that of transcription factors; however, miRNAs might confer additional layers of robustness on gene regulation networks. The programs written in C++ and PERL implementing methods in this work are available for download from our supplemental data web page.
  
  
  CONCLUSIONS
  In this study we demonstrated a combinatorial approach to incorporate miRNA target motifs (miRNA-mediated posttranscriptional regulatory sites) and TFBSs (transcription factor binding sites) with gene expression profiles to reconstruct the regulatory networks. The proposed approach may facilitate the incorporation of diverse sources with limited prior knowledge.
authors.affiliation: Division of Structural and Computational Biology, School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore. lule@mail.tsinghua.edu.cn
authors.forename: Le
authors.initials: L
authors.lastname: Lu
authors.forename: Jinming
authors.initials: J
authors.lastname: Li
mesh_terms: D017360:Arabidopsis / Q000235:genetics* / Q000378:metabolism; D029681:Arabidopsis Proteins / Q000378:metabolism; D001499:Bayes Theorem; D016000:Cluster Analysis; D020869:Gene Expression Profiling; D018506:Gene Expression Regulation, Plant; D017343:Genes, Plant; D035683:MicroRNAs / Q000235:genetics / Q000378:metabolism; D009154:Mutation; D020411:Oligonucleotide Array Sequence Analysis; D010940:Plant Proteins / Q000378:metabolism; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D018749:RNA, Plant / Q000235:genetics / Q000378:metabolism; D012984:Software; D014157:Transcription Factors / Q000378:metabolism; D014158:Transcription, Genetic; D044767:Ubiquitin-Protein Ligases / Q000378:metabolism
chemical_list: D029681:Arabidopsis Proteins; D035683:MicroRNAs; D010940:Plant Proteins; D012333:RNA, Messenger; D018749:RNA, Plant; D014157:Transcription Factors; C471983:AT2G32950 protein, Arabidopsis; D044767:Ubiquitin-Protein Ligases