Comparing chromosomal gene order in related species is an important approach to studying the forces that guide genome organization and evolution. Linked clusters of orthlogous genes found in related genomes are often used to support arguments of evolutionary relatedness or functional selection. Similarly, gene clusters found in distinct regions on the same genome are often presented as evidence of large scale duplication. Computational approaches to indentifying gene clusters face several difficulties. First, identification of orthologous genes is confounded by the presence of gene families and multi-domain proteins. Second, as the gene order and complement of orthologous chromosomal regions diverge progressively due to insertions, deletions and rearrangements, it becomes increasingly difficult to distinguish remnants of common ancestral gene order from coincidental similarities in genomic organization. In this talk, I present computational approaches to identifying and validating gene clusters and discuss how these approaches may be applied to a specific problem: evolution of mammalian insulin signalling.
