The identification of functional motifs in temporal gene expression analysis

Abstract

The identification of transcription factor binding sites is essential to the understanding of the regulation of gene expression and the reconstruction of genetic regulatory networks. The in silico identification of cis-regulatory motifs is challenging due to sequence variability and lack of sufficient data to generate consensus motifs that are of quantitative or even qualitative predictive value. To determine functional motifs in gene expression, we propose a strategy to adopt false discovery rate (FDR) and estimate motif effects to evaluate combinatorial analysis of motif candidates and temporal gene expression data. The method decreases the number of predicted motifs, which can then be confirmed by genetic analysis. To assess the method we used simulated motif/expression data to evaluate parameters. We applied this approach to experimental data for a group of iron responsive genes in Salmonella typhimurium 14028S. The method identified known and potentially new ferric-uptake regulator (Fur) binding sites. In addition, we identified uncharacterized functional motif candidates that correlated with specific patterns of expression. A SAS code for the simulation and analysis gene expression data is available from the first author upon request.

A Genome-Wide Analysis of Array-Based Comparative Genomic Hybridization (CGH) Data to Detect Intra-Species Variations and Evolutionary Relationships

“…Wavelet decomposition has been used for analyzing genome and gene expression profiles [18] , [19] , [20] , [21] , however, its application as a distance measure to find evolutionary relationships is novel. We validate the classification accuracy of our method using simulated data sets.…”