Phylogenomic supertrees: integration of maximal data andassessment of input tree shape.
Year: 2011
Type: dissertation
Abstract: In this thesis, three distinct studies were carried out to investigate various aspects\npertaining to the properties and applicability of phylogenomic data used in a supertree\ncontext. While the availability of genomic scale data is rapidly diminishing as a problem\nin the field of phylogenomics, there is now a greater need for an appropriate means of\nanalysing such data.\nSupertrees have emerged as a useful approach in handling large data sets and have\nbeen shown to work extremely well in a phylogenomic context (e.g. Creevey et al., 2004,\nFitzpatrick et al., 2006, Pisani et al., 2007). While supertree studies do generally sample\nsignificantly more genomic data than their supermatrix counterpart, much of the genome,\nwhich has evolved in the light of gene duplication, is not considered in this method.\nFurther to this, typically, in the supertree approach complete genomic data is exclusively\nused, which can result in a very limited taxon sampling compared to alternative\napproaches that use expressed sequence tag (EST) data. Here, in attempt to address these\nshortcomings, the viability of integrating genes with a history of duplication in the\nsupertree approach, as well as the extent to which a combined data set of complete and\npartial genomes (ESTs) can be used to increase taxon sampling in this context, is\ninvestigated.\nAdditionally, in this thesis, the effect of input tree shape biases is assessed. It has\nbeen shown previously that some commonly used supertree methods are biased with\nrespect to the tree shape they produce (Wilkinson et al., 2005). However, since some\nsupertree methods (e.g. matrix representation with parsimony; MRP) have an inherent\nphylogenetic component, the observed shape predispositions of these supertree methods\nmay be attributed to such methodological elements. As such, here the effective shape bias\nof various phylogenetic methods is assessed using a phylogenomic data set.
Author: Thérèse A. Holton
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W648879837