Absolute convergence: true trees from short sequences
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Solving Large Scale Phylogenetic Problems using DCM2
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Reconstructing optimal phylogenetic trees: a challenge in experimental algorithmics
Experimental algorithmics
The role of diverse populations in phylogenetic analysis
Proceedings of the 8th annual conference on Genetic and evolutionary computation
PRec-I-DCM3: a parallel framework for fast and accurate large-scale phylogeny reconstruction
International Journal of Bioinformatics Research and Applications
An open source phylogenetic search and alignment package
International Journal of Bioinformatics Research and Applications
Reconstruction of large phylogenetic trees: A parallel approach
Computational Biology and Chemistry
A new algorithm for reconstruction of phylogenetic tree
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Phylospaces: reconstructing evolutionary trees in tuple space
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Quartets MaxCut: A Divide and Conquer Quartets Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Parallel divide-and-conquer phylogeny reconstruction by maximum likelihood
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
Using treemaps to visualize phylogenetic trees
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Maximal accurate forests from distance matrices
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
A Linear Time Approximation Scheme for Maximum Quartet Consistency on Sparse Sampled Inputs
SIAM Journal on Discrete Mathematics
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Phylogenetic trees are commonly reconstructed based on hard optimization problems such as maximum parsimony (MP) and maximum likelihood (ML). Conventional MP heuristics for producing phylogenetic trees produce good solutions within reasonable time on small datasets (up to a few thousand sequences), while ML heuristics are limited to smaller datasets (up to a few hundred sequences). However, since MP (and presumably ML) is NP-hard, such approaches do not scale when applied to large datasets. In this paper, we present a new technique called Recursive-Iterative-DCM3 (Rec-I-DCM3), which belongs to our family of Disk-Covering Methods (DCMs). We tested this new technique on ten large biological datasets ranging from 1,322 to 13,921 sequences and obtained dramatic speedups as well as significant improvements in accuracy (better than 99.99%) in comparison to existing approaches. Thus, high-quality reconstructions can be obtained for datasets at least ten times larger than was previously possible.