Optimization

Optimization of SCoT-PCR reaction system in Dactylis glomerata by orthogonal design

B. Zeng, Huang, X., Huang, L. K., Zhang, J., Yan, H. D., Luo, D., Liang, H., and Yuan, Y., “Optimization of SCoT-PCR reaction system in Dactylis glomerata by orthogonal design”, vol. 14, pp. 3052-3061, 2015.

The effects of 5 factors (template DNA, Mg2+, dNTPs, Taq DNA polymerase, and primer) on the polymerase chain reaction (PCR) were investigated to optimize the start codon targeted polymor­phism (SCoT)-PCR system of Dactylis glomerata L., using an orthogo­nal design L16 (45). A suitable SCoT-PCR system for D. glomerata was established; the 20 μL reaction volume contained 3.0 mM Mg2+, 0.2 mM dNTPs, 1.0 U Taq DNA polymerase, 0.2 μM primer, 20 ng tem­plate DNA, and 2 μL 10X buffer.

Optimizing reproducibility evaluation for random amplified polymorphic DNA markers

J. R. Ramos, Telles, M. P. C., Diniz-Filho, J. A. F., Soares, T. N., Melo, D. B., and Oliveira, G., “Optimizing reproducibility evaluation for random amplified polymorphic DNA markers”, vol. 7, pp. 1384-1391, 2008.

The random amplified polymorphic DNA (RAPD) technique is often criticized because it usually shows low levels of repeatability; thus it can generate spurious bands. These problems can be partially overcome by rigid laboratory protocols and by performing repeatability tests. However, because it is expensive and time-consuming to obtain genetic data twice for all individuals, a few randomly chosen individuals are usually selected for a priori repeatability analysis, introducing a potential bias in genetic parameter estimates.

A simple genetic algorithm for multiple sequence alignment

C. Gondro and Kinghorn, B. P., “A simple genetic algorithm for multiple sequence alignment”, vol. 6, pp. 964-982, 2007.

Multiple sequence alignment plays an important role in molecular sequence analysis. An alignment is the arrangement of two (pairwise alignment) or more (multiple alignment) sequences of ‘residues’ (nucleotides or amino acids) that maximizes the similarities between them. Algorithmically, the problem consists of opening and extending gaps in the sequences to maximize an objective function (measurement of similarity). A simple genetic algorithm was developed and implemented in the software MSA-GA.

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