Evaluating Estimates of Markov Models of Sequence Evolution through Simulation.
Current methods of evaluating techniques that estimate parameters of Markov models of sequence evolution require sequence data from populations with well-known phylogenetic relationships. Such data is not always available, since phylogenetic relationships can never be known with certainty. We propose generating sequence data for estimation technique evaluation by simulating sequence evolution in a controlled setting. Our elementary simulator assumes a Markov model and uses a binary branching process to dynamically build a phylogenetic tree from an initial seed sequence. We then observe how well estimation techniques recover the Markov model parameters specified in the simulation using the sequences at the leaves of the tree. We demonstrate our evaluation method on Arvestad and Bruno's estimation technique, and show how our approach can reveal performance variations.
Xu
Keyuan
K
Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA.
Verghese
George
G
Doerschuk
Peter
P