Genome classification using overlap graph
centralities

Abstract. Genetics is a fast developing field and lot of its development relies on bioinformatics and solving computing problems. The genetic data are huge, for example the human reference genome is about 3 GB and for other species they can be even greater. It is not a trivial task to process them efficiently, recovering useful data for biological and medical sciences.
Researchers have already developed different models and representations of genomes to provide deeper knowledge and explore hidden context in these data. Recent years a lot of publications have been made about how to represent genomes in graphs and examining the graph features of genomes like graph centrality.
The aim of this paper is comparing and examining the graph centrality of viral genomes that could help in the study of these data. We use a number of concepts of genetics and bioinformatics, mostly in meaningful context. Their exact individual definition would place too much burden on the article; the interested readers may turn to the references provided.