Constructing Sequence Alignments from a Markov Decision Model with Estimated Parameter Values
Fern Y. Hunt, Anthony J. Kearsley, Agnes A. O'Gallagher
Current methods for aligning biological sequences are based on dynamic programming algorithms. If large numbers of sequences or a number of long ones are to be aligned the required computations are expensive in memory and CPU time. In an attempt to bring the tools of large scale linear programming (LP) methods to bear on this problem, we formulate the alignment process as a controlled Markov chain and construct a suggested alignment based on policies that minimize the expected total cost of the alignment.
linear programming, Markov decision process, multiple sequence alignment