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Syst Biol. 2008 Jun;57(3):347-66. doi: 10.1080/10635150802044037.

Optimizing automated AFLP scoring parameters to improve phylogenetic resolution.

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Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand. B>


The amplified fragment length polymorphism (AFLP) technique is an increasingly popular component of the phylogenetic toolbox, particularly for plant species. Technological advances in capillary electrophoresis now allow very precise estimates of DNA fragment mobility and amplitude, and current AFLP software allows greater control of data scoring and the production of the binary character matrix. However, for AFLP to become a useful modern tool for large data sets, improvements to automated scoring are required. We design a procedure that can be used to optimize AFLP scoring parameters to improve phylogenetic resolution and demonstrate it for two AFLP scoring programs (GeneMapper and GeneMarker). In general, we found that there was a trade-off between getting more characters of lower quality and fewer characters of high quality. Conservative settings that gave the least error did not give the best phylogenetic resolution, as too many useful characters were discarded. For example, in GeneMapper, we found that bin width was a crucial parameter, and that although reducing bin width from 1.0 to 0.5 base pairs increased the error rate, it nevertheless improved resolution due to the increased number of informative characters. For our 30-taxon data sets, moving from default to optimized parameter settings gave between 3 and 11 extra internal edges with >50% bootstrap support, in the best case increasing the number of resolved edges from 14 to 25 out of a possible 27. Nevertheless, improvements to current AFLP software packages are needed to (1) make use of replicate profiles to calibrate the data and perform error calculations and (2) perform tests to optimize scoring parameters in a rigorous and automated way. This is true not only when AFLP data are used for phylogenetics, but also for other applications, including linkage mapping and population genetics.

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