title: Data structures for parsimony correlation and biosequence co-evolution.
abstract: We give an algorithm for discovering co-evolution in biosequences from a dataset consisting of aligned data and a phylogeny. The method correlates vectors of parsimony scores on the edges of a graph, averaged over all optimally parsimonious reconstructions of the data. We describe an efficient data structure, and a preprocessing step that allows for rapid, interactive computation of many correlation scores, at the expense of storage space.
authors: Department of Computer Science, East Carolina University , Greenville, North Carolina.
authors: Robert
authors: R
authors: Hochberg
authors: Treena Larrew
authors: TL
authors: Milam
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D003198:Computer Simulation; D019143:Evolution, Molecular; D008957:Models, Genetic; D010802:Phylogeny; D017422:Sequence Analysis, DNA; D012984:Software