ML or NJ-MCL? A comparison between two robust phylogenetic methods (original) (raw)
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Prospects for inferring very large phylogenies by using the neighbor-joining method
Proceedings of The National Academy of Sciences, 2004
Current efforts to reconstruct the tree of life and histories of multigene families demand the inference of phylogenies consisting of thousands of gene sequences. However, for such large data sets even a moderate exploration of the tree space needed to identify the optimal tree is virtually impossible. For these cases the neighborjoining (NJ) method is frequently used because of its demonstrated accuracy for smaller data sets and its computational speed. As data sets grow, however, the fraction of the tree space examined by the NJ algorithm becomes minuscule. Here, we report the results of our computer simulation for examining the accuracy of NJ trees for inferring very large phylogenies. First we present a likelihood method for the simultaneous estimation of all pairwise distances by using biologically realistic models of nucleotide substitution. Use of this method corrects up to 60% of NJ tree errors. Our simulation results show that the accuracy of NJ trees decline only by Ϸ5% when the number of sequences used increases from 32 to 4,096 (128 times) even in the presence of extensive variation in the evolutionary rate among lineages or significant biases in the nucleotide composition and transition͞transversion ratio. Our results encourage the use of complex models of nucleotide substitution for estimating evolutionary distances and hint at bright prospects for the application of the NJ and related methods in inferring large phylogenies.
The neighbor-joining (NJ) method is widely used in reconstructing large phylogenies because of its computational speed and the high accuracy in phylogenetic inference as revealed in computer simulation studies. However, most computer simulation studies have quantified the overall performance of the NJ method in terms of the percentage of branches inferred correctly or the percentage of replications in which the correct tree is recovered. We have examined other aspects of its performance, such as the relative efficiency in correctly reconstructing shallow (close to the external branches of the tree) and deep branches in large phylogenies; the contribution of zero-length branches to topological errors in the inferred trees; and the influence of increasing the tree size (number of sequences), evolutionary rate, and sequence length on the efficiency of the NJ method. Results show that the correct reconstruction of deep branches is no more difficult than that of shallower branches. The presence of zero-length branches in realized trees contributes significantly to the overall error observed in the NJ tree, especially in large phylogenies or slowly evolving genes. Furthermore, the tree size does not influence the efficiency of NJ in reconstructing shallow and deep branches in our simulation study, in which the evolutionary process is assumed to be homogeneous in all lineages.
Generalized neighbor-joining: more reliable phylogenetic tree reconstruction
Molecular Biology and Evolution, 1999
We have developed a phylogenetic tree reconstruction method that detects and reports multiple topologically distant low-cost solutions. Our method is a generalization of the neighbor-joining method of Saitou and Nei and affords a more thorough sampling of the solution space by keeping track of multiple partial solutions during its execution. The scope of the solution space sampling is controlled by a pair of user-specified parameters-the total number of alternate solutions and the number of alternate solutions that are randomly selected-effecting a smooth trade-off between run time and solution quality and diversity. This method can discover topologically distinct low-cost solutions. In tests on biological and synthetic data sets using either the least-squares distance or minimum-evolution criterion, the method consistently performed as well as, or better than, both the neighbor-joining heuristic and the PHYLIP implementation of the Fitch-Margoliash distance measure. In addition, the method identified alternative tree topologies with costs within 1% or 2% of the best, but with topological distances of 9 or more partitions from the best solution (16 taxa); with 32 taxa, topologies were obtained 17 (least-squares) and 22 (minimum-evolution) partitions from the best topology when 200 partial solutions were retained. Thus, the method can find lower-cost tree topologies and near-best tree topologies that are significantly different from the best topology.
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
Systematic Biology, 2003
The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distance-based and parsimony algorithms. This new method is implemented in the PHYML program, which
2006
Phylogeny reconstruction is the process of inferring evolutionary relationships from molecular sequences, and methods that are expected to accurately reconstruct trees from sequences of reasonable length are highly desirable. To formalize this concept, the property of fast-convergence has been introduced to describe phylogeny reconstruction methods that, with high probability, recover the true tree from sequences that grow polynomially in the number of taxa n. While provably fast-converging methods have been developed, the neighbor-joining (NJ) algorithm of Saitou and Nei remains one of the most popular methods used in practice. This algorithm is known to converge for sequences that are exponential in n, but no lower bound for its convergence rate has been established. To address this theoretical question, we analyze the performance of the NJ algorithm on a type of phylogeny known as a 'caterpillar tree'. We find that, for sequences of polynomial length in the number of taxa n, the variability of the NJ criterion is sufficiently high that the algorithm is likely to fail even in the first step of the phylogeny reconstruction process, regardless of the degree of polynomial considered. This result demonstrates that, for general n-taxa trees, the exponential bound cannot be improved.
2005
Phylogeny reconstruction is the process of inferring evolutionary relationships from molecular sequences, and methods that are expected to accurately reconstruct trees from sequences of reasonable length are highly desirable. To formalize this concept, the property of fast-convergence has been introduced to describe phylogeny reconstruction methods that, with high probability, recover the true tree from sequences that grow polynomially in the number of taxa n. While provably fast-converging methods have been developed, the neighbor-joining (NJ) algorithm of Saitou and Nei remains one of the most popular methods used in practice. This algorithm is known to converge for sequences that are exponential in n, but no lower bound for its convergence rate has been established. To address this theoretical question, we analyze the performance of the NJ algorithm on a type of phylogeny known as a 'caterpillar tree'. We find that, for sequences of polynomial length in the number of taxa n, the variability of the NJ criterion is sufficiently high that the algorithm is likely to fail even in the first step of the phylogeny reconstruction process, regardless of the degree of polynomial considered. This result demonstrates that, for general n-taxa trees, the exponential bound cannot be improved.
Computing Large Phylogenies with Statistical Methods: Problems & Solutions
The computation of ever larger as well as more accurate phylogenetic trees with the ultimate goal to compute the "tree of life" represents a major challenge in Bioinformatics. Statistical methods for phylogenetic analysis such as maximum likelihood or bayesian inference, have shown to be the most accurate methods for tree reconstruction. Unfortunately, the size of trees which can be computed in reasonable time is limited by the severe computational complexity induced by these statistical methods. However, the field has witnessed great algorithmic advances over the last 3 years which enable inference of large phylogenetic trees containing 500-1000 sequences on a single CPU within a couple of hours using maximum likelihood programs such as RAxML and PHYML. An additional order of magnitude in terms of computable tree sizes can be obtained by parallelizing these new programs. In this paper we briefly present the MPI-based parallel implementation of RAxML (Randomized Axelerated Maximum Likelihood), as a solution to compute large phylogenies. Within this context, we describe how parallel RAxML has been used to compute the -to the best of our knowledge-first maximum likelihood-based phylogenetic tree containing 10.000 taxa on an inexpensive LINUX PC-Cluster. In addition, we address unresolved problems, which arise when computing large phylogenies for real-world sequence data consisting of more than 1.000 organisms with maximum likelihood, based on our experience with RAxML. Finally, we discuss potential algorithmic and technical enhancements of RAxML within the context of future work. Availability: wwwbode.in.tum.de/~stamatak
Computing large phylogenies with statistical methods: Problems and solutions
The computation of ever larger as well as more accurate phylogenetic trees with the ultimate goal to compute the "tree of life" represents a major challenge in Bioinformatics. Statistical methods for phylogenetic analysis such as maximum likelihood or bayesian inference, have shown to be the most accurate methods for tree reconstruction. Unfortunately, the size of trees which can be computed in reasonable time is limited by the severe computational complexity induced by these statistical methods. However, the field has witnessed great algorithmic advances over the last 3 years which enable inference of large phylogenetic trees containing 500-1000 sequences on a single CPU within a couple of hours using maximum likelihood programs such as RAxML and PHYML. An additional order of magnitude in terms of computable tree sizes can be obtained by parallelizing these new programs. In this paper we briefly present the MPI-based parallel implementation of RAxML (Randomized Axelerated Maximum Likelihood), as a solution to compute large phylogenies. Within this context, we describe how parallel RAxML has been used to compute the -to the best of our knowledge-first maximum likelihood-based phylogenetic tree containing 10.000 taxa on an inexpensive LINUX PC-Cluster. In addition, we address unresolved problems, which arise when computing large phylogenies for real-world sequence data consisting of more than 1.000 organisms with maximum likelihood, based on our experience with RAxML. Finally, we discuss potential algorithmic and technical enhancements of RAxML within the context of future work. Availability: wwwbode.in.tum.de/~stamatak
Systematic Biology, 2010
PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication . A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/. [Bootstrap analysis; branch testing; LRT and aLRT; maximum likelihood; NNI; phylogenetic software; SPR; tree search algorithms.]
The Accuracy of Fast Phylogenetic Methods for Large Datasets
Pacific Symposium on Biocomputing, 2002
Whole-genome phylogenetic studies require various sources of phylogenetic signals to produce an accurate picture of the evolutionary history of a group of genomes. In particular, sequence-based reconstruction will play an important role, especially in r esolving more recent events. But using sequences at the level of whole genomes means working with very large amounts of data—large numbers of sequences—as well