Cross-Species Alignment of Coexpression Networks

Model organisms are commonly used to study human diseases and to develop suitable interventions. There are, however, many examples of discrepancies between the results from model experiments and clinical trials in human. To continue improving treatments, it is important to elucidate genetic similarities and differences between model organisms and human. In this work we focus on mice. Rather than comparing sequence similarities alone, we consider coexpression networks, in which simultaneous expression of genes is captured. We perform cross-species analysis by means of network alignment, which has proven a powerful tool for detecting clusters of genes that are conserved across species. In this work we extend an existing network alignment algorithm based on a Lagrangian relaxation approach. We implement a method that identifies modules of conserved coexpression. In addition, we introduce two new score models, which are both capable of detecting these modules. For biological validation of the modules we use a Gene Ontology similarity measure. We illustrate the power of our method by presenting an example module with functionally related genes. Summarized, we present and test a method that can be used to assess the transferability from model experiments to human.