Integer linear programming approaches for non-unique probe selection
Discrete Applied Mathematics
Non-unique probe selection and group testing
Theoretical Computer Science
Note: On the complexity of non-unique probe selection
Theoretical Computer Science
Sequential Forward Selection Approach to the Non-unique Oligonucleotide Probe Selection Problem
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Efficient Algorithms for the Computational Design of Optimal Tiling Arrays
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An Evolutionary Approach to the Non-unique Oligonucleotide Probe Selection Problem
Transactions on Computational Systems Biology X
Bayesian Optimization Algorithm for the Non-unique Oligonucleotide Probe Selection Problem
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
An outlook on design technologies for future integrated systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Two challenges in genomics that can benefit from petascale platforms
Euro-Par'06 Proceedings of the CoreGRID 2006, UNICORE Summit 2006, Petascale Computational Biology and Bioinformatics conference on Parallel processing
Compressed sensing with probabilistic measurements: a group testing solution
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Pattern Recognition Letters
Selecting Oligonucleotide Probes for Whole-Genome Tiling Arrays with a Cross-Hybridization Potential
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Noise-resilient group testing: Limitations and constructions
Discrete Applied Mathematics
Efficient computational design of tiling arrays using a shortest path approach
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
Space pruning monotonic search for the non-unique probe selection problem
International Journal of Bioinformatics Research and Applications
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DNA microarrays are a valuable tool for massivelyparallel DNA-DNA hybridization experiments. Currently,most applications rely on the existence of sequence-specificoligonucleotide probes. In large families of closely relatedtarget sequences, such as different virus subtypes, thehigh degree of similarity often makes it impossible to find aunique probe for every target. Fortunately, this is unnecessary.We propose a microarray design methodology based ona group testing approach. While probes might bind to multipletargets simultaneously, a properly chosen probe set canstill unambiguously distinguish the presence of one targetset from the presence of a different target set. Our methodis the first one that explicitly takes cross-hybridization andexperimental errors into account while accommodating severaltargets.The approach consists of three steps: (1) Pre-selection ofprobe candidates, (2) Generation of a suitable group testingdesign, and (3) Decoding of hybridization results to inferpresence or absence of individual targets.Our results show that this approach is very promising,even for challenging data sets and experimental error ratesof up to 5%. On a data set of 28S rDNA sequences wewere able to identify 660 sequences, a substantial improvementover a prior approach using unique probes which onlyidentified 408 sequences.