Network-based comparison of temporal gene expression patterns.
BACKGROUND
In the pursuits of mechanistic understanding of cell differentiation, it is often necessary to compare multiple differentiation processes triggered by different external stimuli and internal perturbations. Available methods for comparing temporal gene expression patterns are limited to a gene-by-gene approach, which ignores co-expression information and thus is sensitive to measurement noise.


METHODS
We present a method for co-expression network based comparison of temporal expression patterns (NACEP). NACEP compares the temporal patterns of a gene between two experimental conditions, taking into consideration all of the possible co-expression modules that this gene may participate in. The NACEP program is available at http://biocomp.bioen.uiuc.edu/nacep.


RESULTS
We applied NACEP to analyze retinoid acid (RA)-induced differentiation of embryonic stem (ES) cells. The analysis suggests that RA may facilitate neural differentiation by inducing the shh and insulin receptor pathways. NACEP was also applied to compare the temporal responses of seven RNA inhibition (RNAi) experiments. As proof of concept, we demonstrate that the difference in the temporal responses to RNAi treatments can be used to derive interaction relationships of transcription factors (TFs), and therefore infer regulatory modules within a transcription network. In particular, the analysis suggested a novel regulatory relationship between two pluripotency regulators, Esrrb and Tbx3, which was supported by in vivo binding of Esrrb to the promoter of Tbx3.


BACKGROUND
The NACEP program and the supplementary documents are available at http://biocomp.bioen.uiuc.edu/nacep.


BACKGROUND
szhong@illinois.edu


BACKGROUND
Supplementary data are available at Bioinformatics online.
Huang
Wei
W
Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Cao
Xiaoyi
X
Zhong
Sheng
S
D002454:Cell Differentiation / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D034622:RNA Interference; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
D014157:Transcription Factors