Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Sequence-Length Requirements for Phylogenetic Methods
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
Rec-I-DCM3: A Fast Algorithmic Technique for Reconstructing Large Phylogenetic Trees
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
PRec-I-DCM3: A Parallel Framework for Fast and Accurate Large Scale Phylogeny Reconstruction
ICPADS '05 Proceedings of the 11th International Conference on Parallel and Distributed Systems - Workshops - Volume 02
Maximum likelihood of evolutionary trees is hard
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Phylospaces: reconstructing evolutionary trees in tuple space
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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Phylogenetic trees are important in biology since their applications range from determining protein function to understanding the evolution of species. Maximum Likelihood (ML) is a popular optimization criterion in phylogenetics. However, inference of phylogenies with ML is NP-hard. Recursive-Iterative-DCM3 (Rec-I-DCM3) is a divide-and-conquer framework that divides a dataset into smaller subsets (subproblems), applies an external base method to infer subtrees, merges the subtrees into a comprehensive tree, and then refines the global tree with an external global method. In this study we present a novel parallel implementation of Rec-I-DCM3 for inference of large trees with ML. Parallel-Rec-I-DCM3 uses RAxML as external base and global search method. We evaluate program performance on 6 large real-data alignments containing 500 up to 7.769 sequences. Our experiments show that P-Rec-I-DCM3 reduces inference times and improves final tree quality over sequential Rec-I-DCM3 and stand-alone RAxML.