Abstract: Understanding transcriptional regulation of gene expression is one of the greatest challenges of modern molecular biology. A central role in this mechanism is played by transcription factors (TF) which typically bind to specific, short DNA sequence motifs which are usually located in the upstream region of the regulated genes. We discuss here a simple and powerful approach for the identification of these cis-regulatory motifs based on human-mouse genomic comparison. By using the catalogue of conserved upstream sequences collected in the CORG database [1] we construct sets of genes sharing the same overrepresented motif in their upstream regions both in human and in mouse. We perform this construction for all possible words from 5 to 8 nucleotides in length and then filter the resulting sets looking for two types of evidence for coregulation: first, we analyse the Gene Ontology annotation of the genes in the set looking for statistically significant common annotation; second, we analyse the expression profiles of the genes in the set as measured by microarray experiments, looking for evidence of coexpression. The sets which pass one or both these filters are conjectured to contain a significant fraction of coregulated genes, and the upstream motifs characterizing the sets are thus good candidates to be the binding sites of the TF's involved in such regulation. In this way we find various known motifs (which we use to validate our approach) and also some new candidate binding sites.

Abstract: The recent advent of high-throughput technology and the exponential increase in computer power have moved biology into a revolutionary mode, shifting the focus of molecular biologists from single genes to whole genomes The possibility of exploring gene function is extremely attractive in such a context of high-throughput data generation and computational inference based on similarities in gene expression has been proved to be a valuable tool for functional characterization. The modern theory of networks offers a new conceptual framework for the analysis of gene expression both at the transcriptomic and proteomic levels: genome-scale data sets can infact be conveniently visualized as networks of gene/protein co-occurrences where genes/proteins are represented by nodes and the relationships between them are represented by connections. This paper reports just an “in silico” approach to gene expression analysis based on a gene co-expression network.

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