Optimization in Sanger sequencing

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http://hdl.handle.net/2183/34528
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Optimization in Sanger sequencingDate
2019-09Citation
Carpente, L., Cerdeira-Pena, A., Lorenzo-Freire, S., Places, Á.S., 2019. Optimization in Sanger sequencing. Computers & Operations Research 109, 250–262. https://doi.org/10.1016/j.cor.2019.05.011
Abstract
[Abstract]: The main objective of this paper is to solve the optimization problem that is associated with the classification of DNA samples in PCR plates for Sanger sequencing. To achieve this goal, we design an integer linear programming model. Given that the real instances involve the classification of thousands of samples and the linear model can only be solved for small instances, the paper includes a heuristic to cope with bigger problems. The heuristic algorithm is based on the simulated annealing technique. This algorithm obtains satisfactory solutions to the problem in a short amount of time. It has been tested with real data and yields improved results compared to some commercial software typically used in (clinical) laboratories. Moreover, the algorithm has already been implemented in the laboratory and is being successfully used.
Keywords
Optimization
Sanger sequencing
Integer linear programming
Simulated annealing
Sanger sequencing
Integer linear programming
Simulated annealing
Description
© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article Carpente, L., Cerdeira-Pena, A., Lorenzo-Freire, S., Places, Á.S., 2019. Optimization in Sanger sequencing. Computers & Operations Research 109, 250–262 has been accepted for publication in Computers & Operations Research. The Version of Record is available online at https://doi.org/10.1016/j.cor.2019.05.011
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Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
1873-765X