Finding a most likely clone ordering from oligonucleotide hybridization data

Using an extension of a statistical model given by E. Lander and M. Waterman, we define the a posteriori probability of a clone ordering based upon oligonucleotide hybridization data. We give algorithms for computing the likelihood of a clone ordering and for finding a clone ordering of maximum likelihood. The dynamic programming algorithm for computing likelihoods runs in time O(mnc), where m is the number of oligonucleotide probes, n is the number of clones, and c is the coverage of the clone library. We use the Expectation-Maximization technique to maximize likelihoods.