Comparison of two alignment techniques within a single complex data set: POY versus Clustal

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Abstract
Sensitivity analyses can be performed with respect to different methodologies, differential analytical parameters or models within a single methodology, or alignment parameters. The latter investigations are particularly relevant when divergence and/or the size of molecular data sets make alignment of sequences difficult. Sensitivity analyses are often performed for analyses incorporating Direct Optimization (via POY), either to select optimal alignment parameters or to investigate the stability of topology across parameter sets. Such investigations are rarely, if ever, performed for Clustal alignments as some manual adjustments are nearly always incorporated in the final alignment. Exploration of the performance of both POY and Clustal for a large insect data set incorporating three genes (18S, 28S, H3) and morphology reveals that the performance of POY, as measured by and ILD metric, is predictable across the landscape topology with minimal incongruence when all parameters are treated equally. In contrast, Clustal alignment followed by parsimony analysis yields a landscape with less overall variance, but less predictable behaviour across the parameter topology.© The Willi Hennig Society 2005.