Tuesday, April 27, 2010

Model-based method for transcription factor target identification with limited data

Model-based method for transcription factor target identification with limited data

1. Antti Honkelaa,1,
2. Charles Girardotb,
3. E. Hilary Gustafsonb,
4. Ya-Hsin Liub,
5. Eileen E. M. Furlongb,
6. Neil D. Lawrencec,1, and
7. Magnus Rattrayc,1

+ Author Affiliations

aDepartment of Information and Computer Science, Aalto University School of Science and Technology, Helsinki, Finland;
bGenome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; and
cSchool of Computer Science, University of Manchester, Manchester, United Kingdom


Edited by David Baker, University of Washington, Seattle, WA, and approved March 3, 2010 (received for review December 10, 2009)


We present a computational method for identifying potential targets of a transcription factor (TF) using wild-type gene expression time series data. For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets. The expression profile of the TF is modeled as a sample from a Gaussian process prior distribution that is integrated out using a nonparametric Bayesian procedure. This results in a parsimonious model with relatively few parameters that can be applied to short time series datasets without noticeable overfitting. We assess our method using genome-wide chromatin immunoprecipitation (ChIP-chip) and loss-of-function mutant expression data for two TFs, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of top-ranked genes identified by our method are significantly enriched for genes close to bound regions identified in the ChIP-chip data and for genes that are differentially expressed in loss-of-function mutants. Targets of Twist display diverse expression profiles, and in this case a model-based approach performs significantly better than scoring based on correlation with TF expression. Our approach is found to be comparable or superior to ranking based on mutant differential expression scores. Also, we show how integrating complementary wild-type spatial expression data can further improve target ranking performance.

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