Inferring angiosperm phylogeny from EST data with widespread gene duplication.

Area of Science:

• Phylogenomics
• Computational Biology
• Plant Science

Background:

• Phylogenetic studies traditionally use single-copy genes, limiting nuclear sequence use in plants due to high gene duplication.
• Expressed sequence tag (EST) libraries offer abundant nuclear data but are underexploited for plant phylogenetics.
• Gene tree parsimony can infer species trees from gene families with duplication and loss, but its phylogenomic application in plants is unevaluated.

Purpose of the Study:

• To evaluate the performance of gene tree parsimony for inferring plant species phylogenies using expressed sequence tag (EST) data.
• To assess the method's efficacy in the context of high gene duplication rates common in plant genomes.

Main Methods:

• A gene tree parsimony analysis was conducted using EST data from six angiosperm species and Pinus as an outgroup.
• Homologous gene clusters were identified from EST data, with 557 clusters deemed phylogenetically informative.
• Maximum likelihood and maximum parsimony were used to infer individual gene trees from these clusters.

Main Results:

• Gene tree parsimony successfully inferred the accepted species tree with strong statistical support.
• Despite challenges with EST data complexity and a significant fraction of unusable clusters, the method demonstrated robustness.
• A closely related species tree topology was recovered when maximum parsimony inferred the gene trees.

Conclusions:

• Gene tree parsimony is a viable and effective method for inferring plant species phylogenies from complex EST data.
• The approach performs well even with high rates of gene duplication, overcoming limitations of traditional phylogenetic methods.
• This study validates the utility of gene tree parsimony for large-scale phylogenomic analyses in plants using EST resources.