Graph theory analysis of genomic problems: community analysis of fragile sites correlations and of pseudogenes alignments

Graph theory offers the ideal framework to model biological systemic properties. Recently these methods were successfully applied in proteomics and in the study of metabolic networks. In this paper we want to show that these same tools are equally powerful also to address genomic problems, like alignment networks or the networks obtained by looking at suitable correlators of chromosomic features. We shall in particular address two examples. In the first example we shall study human common fragile sites (CFS), a class of “hyper-sensitive” segments of DNA. The interest in CFS is motivated by their largely debated role in cancerogenesis. In order to functionally characterize them we developed a novel genome-wide approach based on graph theory and Gene Ontology vocabulary. We obtain a few non-trivial results fitting with largely accepted knowledge and a more recently advanced proposal about the role of CFS in tumor cell biology. The second application is a preliminary work on a potential new type of transcriptional regulatory mechanism. It involves pseudogenes which are non-functional copies of genes. This mechanism should imply similarity between the upstream sequences of genes and pseudogenes. We constructed the upstream similarity network in the budding yeast S. Cerevisiae. Network properties suggest that pseudogenes-mediated regulation could be a common feature in eukaryotic organisms.