New approaches for unravelling reassortment pathways

摘要: Every year the human population encounters epidemic outbreaks of influenza, and history reveals recurring pandemics that have had devastating consequences. The current work focuses on development a robust algorithm for detecting influenza strains composite genomic architecture. These subtypes can be generated through reassortment process, whereby virus inherit gene segments from two different types particles during replication. Reassortant are often not immediately recognised by adaptive immune system hosts hence may source pandemic outbreaks. Owing to their importance in public health infectious ability, it is essential identify reassortant order understand evolution this describe pathways biased towards particular viral segments. Phylogenetic methods been used traditionally viruses. In many studies up now, assumption has if phylogenetic trees differ, because caused them different. While incongruence real differences evolutionary history, also result error. Therefore, we wish develop method distinguishing between topological inconsistency due confounding effects reassortment. describes implementation approaches robustly identifying events. algorithms rest idea significance difference or tree sets, subtree pruning regrafting operations, which mimic effect topologies. first based maximum likelihood (ML) framework (MLreassort) second implements Bayesian approach (Breassort) detection. We focus events found both methods. test simulated dataset small collection data isolated Hong Kong 1999. nature segmented genomes present challenges with respect disease. developed here effectively datasets applied only but other computational demands comparing topologies, further area necessary allow application larger datasets.