Abstract
BackgroundIn phylogenetic analysis we face the problem that several subclade topologies are known or easily inferred and well supported by bootstrap analysis, but basal branching patterns cannot be unambiguously estimated by the usual methods (maximum parsimony (MP), neighbor-joining (NJ), or maximum likelihood (ML)), nor are they well supported. We represent each subclade by a sequence profile and estimate evolutionary distances between profiles to obtain a matrix of distances between subclades.ResultsOur estimator of profile distances generalizes the maximum likelihood estimator of sequence distances. The basal branching pattern can be estimated by any distance-based method, such as neighbor-joining. Our method (profile neighbor-joining, PNJ) then inherits the accuracy and robustness of profiles and the time efficiency of neighbor-joining.ConclusionsPhylogenetic analysis of Chlorophyceae with traditional methods (MP, NJ, ML and MrBayes) reveals seven well supported subclades, but the methods disagree on the basal branching pattern. The tree reconstructed by our method is better supported and can be confirmed by known morphological characters. Moreover the accuracy is significantly improved as shown by parametric bootstrap.
Highlights
In phylogenetic analysis we face the problem that several subclade topologies are known or inferred and well supported by bootstrap analysis, but basal branching patterns cannot be unambiguously estimated by the usual methods (maximum parsimony (MP), neighborjoining (NJ), or maximum likelihood (ML)), nor are they well supported
Our analysis is based on a general time reversible (GTR) substitution model with a gamma rate distribution estimated from the data set
Starting from random trees, eight Markov chains are run in parallel to sample trees using the Markov Chain Monte Carlo (MCMC) principle
Summary
In phylogenetic analysis we face the problem that several subclade topologies are known or inferred and well supported by bootstrap analysis, but basal branching patterns cannot be unambiguously estimated by the usual methods (maximum parsimony (MP), neighborjoining (NJ), or maximum likelihood (ML)), nor are they well supported. Neighbor-joining [1] or other improved distance methods, e.g., WEIGHBOR [2], BIONJ [3], FASTME [4] and a further approach considering maximum-likelihood estimated triplets of sequences [5], are relatively fast (O(n3) for n taxa), but first reduce the information contained in the characters to a matrix of distances. All of the above methods aim to estimate a fully resolved tree from scratch. This is more information than one needs, or than the data support. We are only interested in the basal branching pattern (page number not for citation purposes)
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