The simplified partial digest problem: Approximation and a graph-theoretic model

The goal of the Simplified Partial Digest Problem (SPDP) is motivated by the reconstruction of the linear structure of a DNA chain with respect to a given nucleotide pattern, based on the multiset of distances between the adjacent patterns (interpoint distances) and the multiset of distances between each pattern and the two unlabeled endpoints of the DNA chain (end distances). We consider optimization versions of the problem, called SPDP-Min and SPDPMax. The aim of SPDP-Min (SPDP-Max) is to find a DNA linear structure with the same multiset of end distances and the minimum (maximum) number of incorrect (correct) interpoint distances. Results are presented on the worst-case efficiency of approximation algorithms for these problems. We suggest a graph-theoretic model for SPDP-Min and SPDP-Max, which can be used to reduce the search space for an optimal solution in either of these problems. We also present heuristic polynomial time algorithms based on this model. In computational experiments with randomly generated and real-life input data, our best algorithm delivered an optimal solution in 100% of the instances for a number of restriction sites not greater than 50. This work was supported by the Marie Curie BIOPTRAIN fellowship of Alexandr Kovalev, EPSRC grant GR/S64530/01 and the Ministry of Science of Poland grant N N519 314635. ∗Corresponding author Email addresses: [email protected] (Jacek Blazewicz), [email protected] (Edmund K. Burke), [email protected] (Marta Kasprzak), [email protected] (Alexandr Kovalev), [email protected] (Mikhail Y. Kovalyov) Preprint submitted to Elsevier July 27, 2010