Identification of condition-specific regulatory modules by multi-level motif and mRNA expression analysis

Li Chen, Jianhua Xuan, Rebecca B. Riggins, Yue Wang, Eric P. Hoffman, Robert Clarke

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
Pages23-28
Number of pages6
StatePublished - 2008
Externally publishedYes
Event2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008 - Las Vegas, NV, United States
Duration: Jul 14 2008Jul 17 2008

Publication series

NameProceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008

Other

Other2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
CountryUnited States
CityLas Vegas, NV
Period7/14/087/17/08

Keywords

  • Motif enrichment analysis
  • Multi-level regulator identification
  • Statistical significance analysis
  • Support vector regression
  • Transcription regulatory modules

Fingerprint Dive into the research topics of 'Identification of condition-specific regulatory modules by multi-level motif and mRNA expression analysis'. Together they form a unique fingerprint.

  • Cite this

    Chen, L., Xuan, J., Riggins, R. B., Wang, Y., Hoffman, E. P., & Clarke, R. (2008). Identification of condition-specific regulatory modules by multi-level motif and mRNA expression analysis. In Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008 (pp. 23-28). (Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008).