Approximation algorithms for multiple sequence alignment
Theoretical Computer Science
A branch-and-cut algorithm for multiple sequence alignment
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Class prediction and discovery using gene expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
A Polynomial-Time Approximation Scheme for Minimum Routing Cost Spanning Trees
SIAM Journal on Computing
A linear space algorithm for computing maximal common subsequences
Communications of the ACM
The complexity of multiple sequence alignment with SP-score that is a metric
Theoretical Computer Science
Probabilistic approximation of metric spaces and its algorithmic applications
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Fast and Accurate Probe Selection Algorithm for Large Genomes
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Improving the layout of oligonucleotide microarrays: pivot partitioning
WABI'06 Proceedings of the 6th international conference on Algorithms in Bioinformatics
Computer-Aided Optimization of DNA Array Design and Manufacturing
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Hardness and approximation of the asynchronous border minimization problem
TAMC'12 Proceedings of the 9th Annual international conference on Theory and Applications of Models of Computation
| Hi-index | 0.00 |
We study the border minimization problem (BMP), which arises in microarray synthesis to place and embed probes in the array. The synthesis is based on a light-directed chemical process in which unintended illumination may contaminate the quality of the experiments. Border length is a measure of the amount of unintended illumination and the objective of BMP is to find a placement and embedding of probes such that the border length is minimized. The problem is believed to be NP-hard. In this paper we show that BMP admits an O(√n log2 n)-approximation, where n is the number of probes to be synthesized. In the case where the placement is given in advance, we show that the problem is O(log2 n)-approximable. We also study a related problem called agreement maximization problem (AMP). In contrast to BMP, we show that AMP admits a constant approximation even when placement is not given in advance.