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The phylogenetic likelihood library.

Flouri T, Izquierdo-Carrasco F, Darriba D, Aberer AJ, Nguyen LT, Minh BQ, Von Haeseler A, Stamatakis A - Syst. Biol. (2014)

Bottom Line: By example of two likelihood-based phylogenetic codes we show that the PLL improves the sequential performance of current software by a factor of 2-10 while requiring only 1 month of programming time for integration.We show that, when numerical scaling for preventing floating point underflow is enabled, the double precision likelihood calculations in the PLL are up to 1.9 times faster than those in BEAGLE.On an empirical DNA dataset with 2000 taxa the AVX version of PLL is 4 times faster than BEAGLE (scaling enabled and required).

View Article: PubMed Central - PubMed

Affiliation: Heidelberg Institute for Theoretical Studies, Heidelberg Institute, 69118 Heidelberg, Germany; Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, A-1030 Vienna, Austria; Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria; and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Postfach 6980, 76128 Karlsruhe, Germany; Tomas.Flouri@h-its.org.

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Architecture overview of the PLL.
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Figure 1: Architecture overview of the PLL.

Mentions: The PLL functions are organized into two groups that handle different types of computations: 1) parsers for trees, multiple sequence alignments (MSAs), and partitioned model file formats; and 2) functions for tree space exploration, model parameter optimization, likelihood evaluation, and ancestral state reconstruction. The PLL offers an internal data structure for representing unrooted bifurcating trees, which is particularly useful for developing novel application codes from scratch. Optimized likelihood evaluation functions and topological rearrangement operations such as Subtree Pruning and Regrafting (SPR), Tree Bisection and Reconnection (TBR), and Nearest Neighbor Interchange (NNI) for exploring tree space are also available. In addition, PLL also implements numerical optimization functions such as the Newton-Raphson method for branch lengths or Brent's algorithm for other model parameters. Moreover, the PLL accommodates partitioned analyses where distinct sets of likelihood model parameters are optimized for different parts of the MSA. Finally, PLL also includes a highly optimized and vectorized parsimony implementation (Alachiotis and Stamatakis 2011) that can be used to generate parsimony starting trees or to filter promising SPR and TBR moves based on their parsimony scores. An overview over the software architecture of the PLL is provided in Figure 1.


The phylogenetic likelihood library.

Flouri T, Izquierdo-Carrasco F, Darriba D, Aberer AJ, Nguyen LT, Minh BQ, Von Haeseler A, Stamatakis A - Syst. Biol. (2014)

Architecture overview of the PLL.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4380035&req=5

Figure 1: Architecture overview of the PLL.
Mentions: The PLL functions are organized into two groups that handle different types of computations: 1) parsers for trees, multiple sequence alignments (MSAs), and partitioned model file formats; and 2) functions for tree space exploration, model parameter optimization, likelihood evaluation, and ancestral state reconstruction. The PLL offers an internal data structure for representing unrooted bifurcating trees, which is particularly useful for developing novel application codes from scratch. Optimized likelihood evaluation functions and topological rearrangement operations such as Subtree Pruning and Regrafting (SPR), Tree Bisection and Reconnection (TBR), and Nearest Neighbor Interchange (NNI) for exploring tree space are also available. In addition, PLL also implements numerical optimization functions such as the Newton-Raphson method for branch lengths or Brent's algorithm for other model parameters. Moreover, the PLL accommodates partitioned analyses where distinct sets of likelihood model parameters are optimized for different parts of the MSA. Finally, PLL also includes a highly optimized and vectorized parsimony implementation (Alachiotis and Stamatakis 2011) that can be used to generate parsimony starting trees or to filter promising SPR and TBR moves based on their parsimony scores. An overview over the software architecture of the PLL is provided in Figure 1.

Bottom Line: By example of two likelihood-based phylogenetic codes we show that the PLL improves the sequential performance of current software by a factor of 2-10 while requiring only 1 month of programming time for integration.We show that, when numerical scaling for preventing floating point underflow is enabled, the double precision likelihood calculations in the PLL are up to 1.9 times faster than those in BEAGLE.On an empirical DNA dataset with 2000 taxa the AVX version of PLL is 4 times faster than BEAGLE (scaling enabled and required).

View Article: PubMed Central - PubMed

Affiliation: Heidelberg Institute for Theoretical Studies, Heidelberg Institute, 69118 Heidelberg, Germany; Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, A-1030 Vienna, Austria; Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, A-1090 Vienna, Austria; and Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Postfach 6980, 76128 Karlsruhe, Germany; Tomas.Flouri@h-its.org.

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