Title: Integrative analysis of time course microarray data and DNA sequence data via log-linear models for identifying dynamic transcriptional regulatory networks

Authors: Hyung-Seok Choi; Youngchul Kim; Kwang-Hyun Cho; Taesung Park

Addresses: Bio & Health Group, Future IT R&D Lab., LG Electronics Inc., 221 Yangjae-Dong, Seocho-Gu, Seoul 137-130, Korea ' Biostatistics and Epidemiology Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA 22908-0717, USA ' Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Korea ' Department of Statistics, Seoul National University, Gwanak-gu, Seoul 151-747, Korea

Abstract: Since eukaryotic transcription is regulated by sets of Transcription Factors (TFs) having various transcriptional time delays, identification of temporal combinations of activated TFs is important to reconstruct Transcriptional Regulatory Networks (TRNs). Our methods combine time course microarray data, information on physical binding between the TFs and their targets and the regulatory sequences of genes using a log-linear model to reconstruct dynamic functional TRNs of the yeast cell cycle and human apoptosis. In conclusion, our results suggest that the proposed dynamic motif search method is more effective in reconstructing TRNs than the static motif search method.

Keywords: dynamic TRN; transcriptional regulatory networks; transcription factor binding motifs; eukaryotic transcription; log-linear model; heterogeneous data integration; microarray data; DNA sequence data; yeast cell cycle; human apoptosis; dynamic motif search; bioinformatics.

DOI: 10.1504/IJDMB.2013.050975

International Journal of Data Mining and Bioinformatics, 2013 Vol.7 No.1, pp.38 - 57

Received: 17 Apr 2010
Accepted: 06 Nov 2010
Published online: 20 Oct 2014 *

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