ModuleNet:An R package on regulatory network building  

作　　者：ZHOU Dao HE Dong LUO QingMing ZHOU YanHong 

机构地区：[1]Hubei Key Laboratory of Bioinformatics and Molecular Imaging, Huazhong University of Science and Technology, Wuhan 430074, China

出　　处：《Chinese Science Bulletin》2010年第30期3430-3435,共6页

基　　金：supported by the National Natural Science Foundation of China (90608020 and 30370354);the National Platform Project of China (2005DKA64001);the Ministry of Education of China (NCET-060651)

摘　　要：Many researchers have used microarray gene expression data to investigate gene regulatory networks in specific life stages. In these analyses,Bayesian network was widely applied to regulatory network building from expression profiles because of its solid mathematical foundation and its robust analysis ability in noisy data. However,the building of Bayesian network is time consuming and the searching space is really large. Considering the biological feature of transcription factors (TFs) and targets (TGs),the regulatory network is possible to be separated into core TFs networks and the interactions from TFs to TGs. We developed an R package named ModuleNet which used Bayesian network model to the inner TFs network building and genetic algorithm on TF-TG interactions prediction. With determined number of transcription factors,the searching space and time requirements of ModuleNet is linear increasing according to the number of targets. After application to yeast cell-cycle expression profile,the results demonstrated the prediction accuracy of ModuleNet. Furthermore,significantly enriched Gene Ontology (GO) terms with similar expression behaviors were detected automatically by ModuleNet from expression profile,and the relationships from TFs to GO terms were figured out. The source code is available by asking for the author.Many researchers have used microarray gene expression data to investigate gene regulatory networks in specific life stages. In these analyses, Bayesian network was widely applied to regulatory network building from expression profiles because of its solid mathematical foundation and its robust analysis ability in noisy data. However, the building of Bayesian network is time consum- ing and the searching space is really large. Considering the biological feature of transcription factors （TFs） and targets （TGs）, the regulatory network is possible to be separated into core TFs networks and the interactions from TFs to TGs. We developed an R package named ModuleNet which used Bayesian network model to the inner TFs network building and genetic algorithm on TF-TG interactions prediction. With determined number of transcription factors, the searching space and time requirements of ModuleNet is linear increasing according to the number of targets. After application to yeast cell-cycle expression profile, the results demonstrated the prediction accuracy of ModuleNet. Furthermore, significantly enriched Gene Ontology （GO） terms with similar expression behaviors were detected automatically by ModuleNet from expression profile, and the relationships from TFs to GO terms were figured out. The source code is available by asking for the author.

关 键 词：网络建设 监管 贝叶斯网络模型 基因表达数据 基因表达谱 转录因子 封装 基因调控网络 

分 类 号：Q-332[生物学] TP393[自动化与计算机技术—计算机应用技术]