TY - GEN
T1 - Identification of condition-specific regulatory modules by multi-level motif and mRNA expression analysis
AU - Chen, Li
AU - Xuan, Jianhua
AU - Riggins, Rebecca B.
AU - Wang, Yue
AU - Hoffman, Eric P.
AU - Clarke, Robert
PY - 2008
Y1 - 2008
N2 - Many computational methods have been developed to identify condition-specific transcription regulatory modules through sequence analysis and gene expression profiling. However, both gene expression data and motif binding data are noisy sources for regulatory module identification, which often results in many false positives in practice. In this paper, we propose a multi-level regulatory module identification method to discover significantly and stably enriched motif sets and their regulated gene modules. Specifically, motif binding strengths and gene expression profiles are integrated through support vector regression. Hypothesis testing is followed to discover significant regulatory modules. Finally, a multi-level procedure is designed to facilitate the identification of reliable regulatory modules. The experimental results on a breast cancer time course microarray data set show that the proposed method can successfully identify the significant and reliable regulatory modules at different conditions, which may provide important insights to the pathways related to breast cancer.
AB - Many computational methods have been developed to identify condition-specific transcription regulatory modules through sequence analysis and gene expression profiling. However, both gene expression data and motif binding data are noisy sources for regulatory module identification, which often results in many false positives in practice. In this paper, we propose a multi-level regulatory module identification method to discover significantly and stably enriched motif sets and their regulated gene modules. Specifically, motif binding strengths and gene expression profiles are integrated through support vector regression. Hypothesis testing is followed to discover significant regulatory modules. Finally, a multi-level procedure is designed to facilitate the identification of reliable regulatory modules. The experimental results on a breast cancer time course microarray data set show that the proposed method can successfully identify the significant and reliable regulatory modules at different conditions, which may provide important insights to the pathways related to breast cancer.
KW - Motif enrichment analysis
KW - Multi-level regulator identification
KW - Statistical significance analysis
KW - Support vector regression
KW - Transcription regulatory modules
UR - http://www.scopus.com/inward/record.url?scp=62649137710&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62649137710&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:62649137710
SN - 1601320558
SN - 9781601320551
T3 - Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
SP - 23
EP - 28
BT - Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
T2 - 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
Y2 - 14 July 2008 through 17 July 2008
ER -