Approximation algorithms
SIAM Journal on Computing
Optimization problems in multiple-interval graphs
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Removing Noise and Ambiguities from Comparative Maps in Rearrangement Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On the Tractability of Maximal Strip Recovery
TAMC '09 Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation
Inapproximability of Maximal Strip Recovery
ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
Maximal Strip Recovery Problem with Gaps: Hardness and Approximation Algorithms
ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
Inapproximability of maximal strip recovery: II
FAW'10 Proceedings of the 4th international conference on Frontiers in algorithmics
Algorithms for the extraction of synteny blocks from comparative maps
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
Parameterized Complexity
Tractability and approximability of maximal strip recovery
CPM'11 Proceedings of the 22nd annual conference on Combinatorial pattern matching
Tractability and approximability of maximal strip recovery
Theoretical Computer Science
Maximal strip recovery problem with gaps: Hardness and approximation algorithms
Journal of Discrete Algorithms
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Given two genomic maps G and H represented by a sequence of n gene markers, a strip (syntenic block) is a sequence of distinct markers of length at least two which appear as subsequences in the input maps, either directly or in reversed and negated form. The problem Maximal Strip Recovery (MSR) is to find two subsequences G′ and H′ of G and H, respectively, such that the total length of disjoint strips in G′ and H′ is maximized (i.e., conversely, the complement of the problem CMSR is to minimize the number of markers deleted to have a feasible solution). Recently, both MSR and its complement are shown to be NP-complete. A factor-4 approximation is known for the MSR problem and an FPT algorithm is known for the CMSR problem which runs in O(23.61kn+n2) time (where k is the minimum number of markers deleted). We show in this paper that there is a factor-3 asymptotic approximation for CMSR and there is an FPT algorithm which runs in O(3kn+n2) time for CMSR, significantly improving the previous bound.