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Inferring transcriptional regulatory networks from high-throughput data
Wang, Rui-Sheng; Wang, Yong; Zhang, Xiang-Sun; Chen, Luonan
2007-11-15
Source PublicationBIOINFORMATICS
ISSN1367-4803
Volume23Issue:22Pages:3056-3064
AbstractMotivation: Inferring the relationships between transcription factors (TFs) and their targets has utmost importance for understanding the complex regulatory mechanisms in cellular systems. However, the transcription factor activities (TFAs) cannot be measured directly by standard microarray experiment owing to various post-translational modifications. In particular, cooperative mechanism and combinatorial control are common in gene regulation, e.g. TFs usually recruit other proteins cooperatively to facilitate transcriptional reaction processes. Results: In this article, we propose a novel method for inferring transcriptional regulatory networks (TRN) from gene expression data based on protein transcription complexes and mass action law. With gene expression data and TFAs estimated from transcription complex information, the inference of TRN is formulated as a linear programming (LP) problem which has a globally optimal solution in terms of L-1 norm error. The proposed method not only can easily incorporate ChIP-Chip data as prior knowledge, but also can integrate multiple gene expression datasets from different experiments simultaneously. A unique feature of our method is to take into account protein cooperation in transcription process. We tested our method by using both synthetic data and several experimental datasets in yeast. The extensive results illustrate the effectiveness of the proposed method for predicting transcription regulatory relationships between TFs with co-regulators and target genes. Availability: The software TRNinfer is available from http://intelligent.eic.osaka-sandai.ac.jp/chenen/TRNinfer.htm Contact: chen@eic.osaka-sandai.ac.jp and zxs@amt.ac.cn Supplementry information: Supplementary data are available at Bioinformatics online.
DOI10.1093/bioinformatics/btm465
Language英语
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000251197800013
PublisherOXFORD UNIV PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/4409
Collection应用数学研究所
Corresponding AuthorZhang, Xiang-Sun
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China
2.Renmin Univ China, Sch Informat, Beijing 100872, Peoples R China
3.Shanghai Univ, Inst Syst Biol, Shanghai 200444, Peoples R China
4.Osaka Sangyo Univ, Osaka 5748530, Japan
5.JST, ERATO Aihara Complex Modelling Project, Tokyo 1538530, Japan
6.Univ Tokyo, Inst Ind Sci, Tokyo 1538530, Japan
Recommended Citation
GB/T 7714
Wang, Rui-Sheng,Wang, Yong,Zhang, Xiang-Sun,et al. Inferring transcriptional regulatory networks from high-throughput data[J]. BIOINFORMATICS,2007,23(22):3056-3064.
APA Wang, Rui-Sheng,Wang, Yong,Zhang, Xiang-Sun,&Chen, Luonan.(2007).Inferring transcriptional regulatory networks from high-throughput data.BIOINFORMATICS,23(22),3056-3064.
MLA Wang, Rui-Sheng,et al."Inferring transcriptional regulatory networks from high-throughput data".BIOINFORMATICS 23.22(2007):3056-3064.
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