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

Antti Honkela, Charles Girardot, E. Hilary Gustafson, Ya Hsin Liu, Eileen E M Furlong, Neil D. Lawrence, Magnus Rattray

    Research output: Contribution to journalArticlepeer-review

    53 Downloads (Pure)


    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 lossof-function mutant expression data for two TFs, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of topranked 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.
    Original languageEnglish
    Pages (from-to)7793-7798
    Number of pages5
    JournalProceedings of the National Academy of Sciences of the United States of America
    Issue number17
    Publication statusPublished - 27 Apr 2010


    • Bayesian inference
    • Gaussian process inference
    • Gene regulation
    • Gene regulatory network
    • Systems biology


    Dive into the research topics of 'Model-based method for transcription factor target identification with limited data'. Together they form a unique fingerprint.

    Cite this