Expansion of Biological Pathways Based on Evolutionary Inference

摘要: The availability of diverse genomes makes it possible to predict gene function based on shared evolutionary history. This approach can be challenging, however, for pathways whose components do not exhibit a history but rather consist distinct "evolutionary modules." We introduce computational algorithm, clustering by inferred models evolution (CLIME), which inputs eukaryotic species tree, homology matrix, and pathway (gene set) interest. CLIME partitions the set into disjoint modules, simultaneously learning number modules tree-based that defines each module. then expands module scanning genome new likely arose under model. Application ∼1,000 annotated human proteomes yeast, red algae, malaria reveals unanticipated modularity coevolving components. is freely available should become increasingly powerful with growing wealth genomes.