cGRNB: a web server for building combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets

BackgroundWe are witnessing rapid progress in the development of methodologies for building the combinatorial gene regulatory networks involving both TFs (Transcription Factors) and miRNAs (microRNAs). There are a few tools available to do these jobs but most of them are not easy to use and not accessible online. A web server is especially needed in order to allow users to upload experimental expression datasets and build combinatorial regulatory networks corresponding to their particular contexts.MethodsIn this work, we compiled putative TF-gene, miRNA-gene and TF-miRNA regulatory relationships from forward-engineering pipelines and curated them as built-in data libraries. We streamlined the R codes of our two separate forward-and-reverse engineering algorithms for combinatorial gene regulatory network construction and formalized them as two major functional modules. As a result, we released the cGRNB (combinatorial Gene Regulatory Networks Builder): a web server for constructing combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets. The cGRNB enables two major network-building modules, one for MPGE (miRNA-perturbed gene expression) datasets and the other for parallel miRNA/mRNA expression datasets. A miRNA-centered two-layer combinatorial regulatory cascade is the output of the first module and a comprehensive genome-wide network involving all three types of combinatorial regulations (TF-gene, TF-miRNA, and miRNA-gene) are the output of the second module.ConclusionsIn this article we propose cGRNB, a web server for building combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets. Since parallel miRNA/mRNA expression datasets are rapidly accumulated by the advance of next-generation sequencing techniques, cGRNB will be very useful tool for researchers to build combinatorial gene regulatory networks based on expression datasets. The cGRNB web-server is free and available online at http://www.scbit.org/cgrnb.

[1]  David J. Arenillas,et al.  MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model , 2011, BMC Bioinformatics.

[2]  Yufei Huang,et al.  Uncover cooperative gene regulations by microRNAs and transcription factors in glioblastoma using a nonnegative hybrid factor model , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Albertha J. M. Walhout,et al.  The interplay between transcription factors and microRNAs in genome‐scale regulatory networks , 2009, BioEssays : news and reviews in molecular, cellular and developmental biology.

[4]  Chaochun Wei,et al.  cGRNexp: a web platform for building combinatorial gene regulation networks based on user-uploaded gene expression datasets , 2012, 2012 IEEE 6th International Conference on Systems Biology (ISB).

[5]  Hui Yu,et al.  Combinatorial network of transcriptional regulation and microRNA regulation in human cancer , 2012, BMC Systems Biology.

[6]  Varda Rotter,et al.  Coupling transcriptional and post-transcriptional miRNA regulation in the control of cell fate , 2009, Aging.

[7]  Yitzhak Pilpel,et al.  Global and Local Architecture of the Mammalian microRNA–Transcription Factor Regulatory Network , 2007, PLoS Comput. Biol..

[8]  R. Küffner,et al.  MIRTFnet: Analysis of miRNA Regulated Transcription Factors , 2011, PloS one.

[9]  An-Yuan Guo,et al.  A Novel microRNA and transcription factor mediated regulatory network in schizophrenia , 2010, BMC Systems Biology.

[10]  Lei Liu,et al.  Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms , 2009, Nucleic acids research.

[11]  Hui Zhou,et al.  starBase: a database for exploring microRNA–mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data , 2010, Nucleic Acids Res..

[12]  Federica Toffalini,et al.  Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data , 2010, Nucleic acids research.

[13]  S. Cohen,et al.  MicroRNAs and gene regulatory networks: managing the impact of noise in biological systems. , 2010, Genes & development.

[14]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..