Models, Algorithms, and High Performance Computing for Phylogenetic Inference: Towards Simultaneous Alignment and Tree Building with Maximum Likelihood

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The Maximum Likelihood (ML) criterion for phylogenetic inference [17] has repeatedly been shown to be one of the most accurate models for phylogeny reconstruction. Recent advances in search algorithms and high-performance computing have lead to a new generation of programs for ML–based phylogenetic inference that scale well up to several thousand taxa. Thus, new challenges can now be tackled: Current phylogenetic analyses are based on a fixed input alignment of molecular sequences using conventional alignment tools. However, the impact of alignment variations on phylogenetic analyses grows more significant as the size of the problem increases. Thus, novel programs are required that are capable of optimizing the tree topology and the alignment simultaneously. A handful of existing programs allow for simultaneous tree building and alignment, but they can be used only on very small problems. Thus, one main goal of the proposed project will be to devise, implement, and parallelize models and algorithms for large-scale simultaneous tree building and alignment under ML. Two alternative approaches will be used to tackle this challenge: a direct implementation in RAxML (the author’s ML inference software, currently the fastest for this purpose), and an iterative alignment-improvement/tree-building approach. The second major goal is to devise algorithms for faster computation of support values and ML tree searches, and to develop improved models for multi–gene alignments. A secondary line of research deals with a large number of collaborative projects, including challenging real–data analyses, methodological studies in phylogenetics, and application–driven research on emerging parallel architectures.