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

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• 作者：Huayong Xu (18)
  Hui Yu (19) (20)
  Kang Tu (19)
  Qianqian Shi (20)
  Chaochun Wei (18) (19)
  Yuan-Yuan Li (19)
  Yi-Xue Li (18) (19) (20)
  
• 刊名：BMC Systems Biology
• 出版年：2013
• 出版时间：October 2013
• 年：2013
• 卷：7
• 期：2-supp
• 全文大小：
• 作者单位：Huayong Xu (18)
  Hui Yu (19) (20)
  Kang Tu (19)
  Qianqian Shi (20)
  Chaochun Wei (18) (19)
  Yuan-Yuan Li (19)
  Yi-Xue Li (18) (19) (20)
  
  18. School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 100 Dongchuan Road, Shanhgai, 200240, P.R.China
  19. Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 201203, P.R.China
  20. Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, P.R.China
  
• ISSN：1752-0509

文摘

Background We 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. Methods In 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. Conclusions In 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.