REGNET: mining context-specific human transcription networks using composite genomic information

BMC Genomics

Dougu Nam¹  Seon-Young Kim⁴  Yong Sung Kim⁴  Chan Young Park³  Sora Yoon³  Young-Kyu Park⁴  Young-Kyo Seo³  Sang-Mun Chi² 

[1] Division of Mathematical Sciences, UNIST, Ulsan, Republic of Korea;School of Computer Science and Engineering, Kyungsung University, Busan, Republic of Korea;School of Life Sciences, UNIST, Ulsan, Republic of Korea;Medical Genomics Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Republic of Korea

关键词: KEGG;    Gene Ontology;    TFBS;    Transcription network;    Microarray;    Composite gene-set analysis;   

Others  :  1216641
DOI  :  10.1186/1471-2164-15-450

 received in 2013-08-22, accepted in 2014-05-27,  发布年份 2014

【 摘 要 】

Background

Genome-wide expression profiles reflect the transcriptional networks specific to the given cell context. However, most statistical models try to estimate the average connectivity of the networks from a collection of gene expression data, and are unable to characterize the context-specific transcriptional regulations. We propose an approach for mining context-specific transcription networks from a large collection of gene expression fold-change profiles and composite gene-set information.

Results

Using a composite gene-set analysis method, we combine the information of transcription factor binding sites, Gene Ontology or pathway gene sets and gene expression fold-change profiles for a variety of cell conditions. We then collected all the significant patterns and constructed a database of context-specific transcription networks for human (REGNET). As a result, context-specific roles of transcription factors as well as their functional targets are readily explored. To validate the approach, nine predicted targets of E2F1 in HeLa cells were tested using chromatin immunoprecipitation assay. Among them, five (Gadd45b, Dusp6, Mll5, Bmp2 and E2f3) were successfully bound by E2F1. c-JUN and the EMT transcription networks were also validated from literature.

Conclusions

REGNET is a useful tool for exploring the ternary relationships among the transcription factors, their functional targets and the corresponding cell conditions. It is able to provide useful clues for novel cell-specific transcriptional regulations. The REGNET database is available at http://mgrc.kribb.re.kr/regnet webcite.

【 授权许可】

   
2014 Chi et al.; licensee BioMed Central Ltd.

【 图 表 】

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