Grid Methodology for Identifying Co-Regulated Genes and Transcription Factor Binding Sites

Abstract

The identification of the genes that are coordinately regulated is an important and challenging task of bioinformatics and represents a first step in the elucidation of the topology of transcriptional networks. We first compare the performances, in a grid setting, of the Markov clustering algorithm with respect to the k-means using microarray test data sets. The gene expression information of the clustered genes can be used to annotate transcription binding sites upstream co-regulated genes. The methodology uses a regression model that relates gene expression levels to the matching scores of nucleotide patterns allowing us to identify DNA-binding sites from a collection of noncoding DNA sequences from co-regulated genes. Here we discuss extending the approach to multiple species exploiting the grid framework.

Modeling considerations for using expression data from multiple species

“…Researchers have used these multi-species data to study the evolution of gene expression [2][3][4][5][6][7][8] and the evolution and conservation of gene networks [9][10][11] across species. Some have also used microarray expression data across multiple species to predict gene function [12][13][14][15]. Stuart et al, for example, compared gene expression from the widely diverged organisms of humans, flies, worms, and yeast across multiple experimental conditions and reported the prediction and experimental confirmation of genes previously unknown to be involved in important biological processes, such as cell cycle, secretion, and cell signaling.…”