SUNPLIN: simulation with uncertainty for phylogenetic investigations

Area of Science:

• Computational Biology
• Evolutionary Biology
• Phylogenetics

Background:

• Phylogenetic comparative analyses typically use a single consensus tree, which can be problematic due to incomplete species sampling.
• Existing methods for integrating non-molecular data into molecular phylogenies often involve expanding incomplete trees.
• The computational demands of simulating phylogenetic uncertainty limit the applicability of current approaches.

Purpose of the Study:

• To develop efficient algorithms and implementations for expanding and processing phylogenetic trees.
• To enable computationally feasible simulations for comparative phylogenetic analysis incorporating uncertainty.
• To improve the integration of phylogenetic uncertainty into evolutionary and ecological studies.

Main Methods:

• Developed efficient algorithms for random expansion of phylogenetic trees.
• Implemented algorithms for calculating pairwise phylogenetic distance matrices from expanded trees.
• Provided source code in C++ for standalone use or integration with the R system, and as a web service.

Main Results:

• Achieved significant performance gains compared to existing solutions.
• Demonstrated the feasibility of conducting phylogenetic uncertainty simulations in a reasonable time.
• The developed software effectively captures topological information through distance matrices.

Conclusions:

• The implemented methods offer substantial performance improvements for phylogenetic tree processing.
• These advancements facilitate the incorporation of phylogenetic uncertainty into evolutionary and ecological analyses.
• The approach is applicable to large datasets, enhancing the scope of comparative studies.