What's on in Computing Science?

Date: Tuesday, 11 May, 2010
Time: 11:00
Location: Sir Alwyn Williams Building, 422 Seminar Room
[ Inference Seminar ] Transcription factor target identification with limited data using Gaussian process models
Antti Honkela (Aalto University School of Science and Technology)
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 non-parametric Bayesian procedure. This results in a parsimonious model with relatively few parameters which can be applied to short time series data sets without noticeable over-fitting. 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. I will also present some more recent work on extending the model for joint regulation by several TFs, as well as properly accounting for the experimental structure in typical time series gene expression assays. This is joint work with Neil Lawrence, Magnus Rattray and Michalis Titsias.

Contact: Dr Rónán Daly (rdaly@dcs.gla.ac.uk)

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