Transcription factor binding site prediction with multivariate gene expression data

TL;DR

This paper introduces a flexible joint modeling approach for promoter sequences and multivariate gene expression data to improve the prediction of transcription factor binding sites, capturing combinatorial and spacing effects in multi-sample microarray experiments.

Contribution

It presents a novel adaptive modeling method that captures spacing-dependent regulatory modules and applies it successfully to yeast and Arabidopsis time-course data.

Findings

Successfully identified known cis-acting elements

Predicted novel regulatory elements

Captured spacing effects in transcription factor binding

Abstract

Multi-sample microarray experiments have become a standard experimental method for studying biological systems. A frequent goal in such studies is to unravel the regulatory relationships between genes. During the last few years, regression models have been proposed for the de novo discovery of cis-acting regulatory sequences using gene expression data. However, when applied to multi-sample experiments, existing regression based methods model each individual sample separately. To better capture the dynamic relationships in multi-sample microarray experiments, we propose a flexible method for the joint modeling of promoter sequence and multivariate expression data. In higher order eukaryotic genomes expression regulation usually involves combinatorial interaction between several transcription factors. Experiments have shown that spacing between transcription factor binding sites can significantly affect their strength in activating gene expression. We propose an adaptive model building procedure to capture such spacing dependent cis-acting regulatory modules. We apply our methods to the analysis of microarray time-course experiments in yeast and in Arabidopsis. These experiments exhibit very different dynamic temporal relationships. For both data sets, we have found all of the well-known cis-acting regulatory elements in the related context, as well as being able to predict novel elements.