The Maximum Likelihood Point for a Phylogenetic Tree is Not Unique

1994 ◽  
Vol 43 (4) ◽  
pp. 560-564 ◽  
Author(s):  
M. Steel
Keyword(s):  
Maximum Likelihood ◽  
Phylogenetic Tree ◽  
Likelihood Point

scholarly journals Evolution of the SRGAP2 Gene Is Linked to Intelligence in Mammals

2016 ◽  
Vol 1 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Basant K. Tiwary
Keyword(s):  
Maximum Likelihood ◽  
Phylogenetic Tree ◽  
Cortical Neurons ◽  
Rho Gtpase ◽  
Significant Negative Correlation ◽  
Rapid Expansion ◽  
Likelihood Method ◽  
Nucleotide Substitutions ◽  
Nonsynonymous Substitutions

Background/Aims: A recent duplication of the gene encoding SLIT-ROBO Rho GTPase-activating protein 2 (SRGAP2) in the primate lineage has been proposed to be associated with the human-specific extraordinary development of intelligence. There is no report regarding the role of the SRGAP2 gene in the expression of neural traits indicating intelligence in mammals. Methods: A phylogenetic tree of the SRGAP2 gene from 11 mammals was reconstructed using MrBayes. The evolution of neural traits along the branches of the phylogenetic tree was modeled in the BayesTraits, and the dN/dS ratio (i.e. the ratio between the number of nonsynonymous substitutions per nonsynonymous site and the number of synonymous substitutions per synonymous site) was estimated using the codon-based maximum likelihood method (CODEML) in PAML (phylogenetic analysis by maximum likelihood). Results: Two neural traits, namely brain mass and the number of cortical neurons, showed statistical dependency on the underlying evolutionary history of the SRGAP2 gene in mammals. A significant positive correlation between the increase in cortical neurons and the rate of nucleotide substitutions in the SRGAP2 gene was observed concomitantly with a significant negative correlation between the increase in cortical neurons and the rate of nonsynonymous substitutions in the gene. The SRGAP2 gene appears to be under intense pressure of purifying selection in all mammalian lineages under stringent functional constraint. Conclusion: This work indicates a key role of the SRGAP2 gene in the rapid expansion of neurons in the brain cortex, thereby facilitating the evolution of remarkable intelligence in mammals.

scholarly journals Distinguishing Felsenstein Zone from Farris Zone Using Neural Networks

2020 ◽  
Vol 37 (12) ◽  
pp. 3632-3641
Author(s):  
Alina F Leuchtenberger ◽  
Stephen M Crotty ◽  
Tamara Drucks ◽  
Heiko A Schmidt ◽  
Sebastian Burgstaller-Muehlbacher ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Maximum Likelihood ◽  
Phylogenetic Tree ◽  
Maximum Parsimony ◽  
Reconstruction Method ◽  
Long Branch Attraction ◽  
Tree Reconstruction ◽  
Phylogenetic Tree Reconstruction ◽  
The Neural Network

Abstract Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently, resulting in unresolved or disputed phylogenies. We show that a neural network can distinguish between four-taxon alignments that were evolved under conditions susceptible to either long-branch attraction or long-branch repulsion. When likelihood and parsimony methods are discordant, the neural network can provide insight as to which tree reconstruction method is best suited to the alignment. When applied to the contentious case of Strepsiptera evolution, our method shows robust support for the current scientific view, that is, it places Strepsiptera with beetles, distant from flies.

scholarly journals Two C++ Libraries for Counting Trees on a Phylogenetic Terrace

2017 ◽  
Author(s):  
R. Biczok ◽  
P. Bozsoky ◽  
P. Eisenmann ◽  
J. Ernst ◽  
T. Ribizel ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Phylogenetic Tree ◽  
Phylogenetic Inference ◽  
Source Codes ◽  
Likelihood Score ◽  
Order Of Magnitude ◽  
Tree Topologies ◽  
Tree Space ◽  
Bayesian Phylogenetic Inference ◽  
Counting Trees

AbstractMotivationThe presence of terraces in phylogenetic tree space, that is, a potentially large number of distinct tree topologies that have exactly the same analytical likelihood score, was first described by Sanderson et al, (2011). However, popular software tools for maximum likelihood and Bayesian phylogenetic inference do not yet routinely report, if inferred phylogenies reside on a terrace, or not. We believe, this is due to the unavailability of an efficient library implementation to (i) determine if a tree resides on a terrace, (ii) calculate how many trees reside on a terrace, and (iii) enumerate all trees on a terrace.ResultsIn our bioinformatics programming practical we developed two efficient and independent C++ implementations of the SUPERB algorithm by Constantinescu and Sankoff (1995) for counting and enumerating the trees on a terrace. Both implementations yield exactly the same results and are more than one order of magnitude faster and require one order of magnitude less memory than a previous 3rd party python implementation.AvailabilityThe source codes are available under GNU GPL at https://github.com/[email protected]

scholarly journals Inferring demographic parameters in bacterial genomic data using Bayesian and hybrid phylogenetic methods

2017 ◽  
Author(s):  
Sebastian Duchene ◽  
David Duchene ◽  
Jemma Geoghegan ◽  
Zoe Anne Dyson ◽  
Jane Hawkey ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Maximum Likelihood ◽  
Phylogenetic Tree ◽  
Hybrid Method ◽  
Hybrid Approach ◽  
Likelihood Method ◽  
Demographic Parameters ◽  
Computational Burden ◽  
Randomisation Test ◽  
Fully Bayesian

Background: Recent developments in sequencing technologies make it possible to obtain genome sequences from a large number of isolates in a very short time. Bayesian phylogenetic approaches can take advantage of these data by simultaneously inferring the phylogenetic tree, evolutionary timescale, and demographic parameters (such as population growth rates), while naturally integrating uncertainty in all parameters. Despite their desirable properties, Bayesian approaches can be computationally intensive, hindering their use for outbreak investigations involving genome data for a large numbers of pathogen isolates. An alternative to using full Bayesian inference is to use a hybrid approach, where the phylogenetic tree and evolutionary timescale are estimated first using maximum likelihood. Under this hybrid approach, demographic parameters are inferred from estimated trees instead of the sequence data, using maximum likelihood, Bayesian inference, or approximate Bayesian computation. This can vastly reduce the computational burden, but has the disadvantage of ignoring the uncertainty in the phylogenetic tree and evolutionary timescale. Results: We compared the performance of a fully Bayesian and a hybrid method by analysing six whole-genome SNP data sets from a range of bacteria and simulations. The estimates from the two methods were very similar, suggesting that the hybrid method is a valid alternative for very large datasets. However, we also found that congruence between these methods is contingent on the presence of strong temporal structure in the data (i.e. clocklike behaviour), which is typically verified using a date-randomisation test in a Bayesian framework. To reduce the computational burden of this Bayesian test we implemented a date-randomisation test using a rapid maximum likelihood method, which has similar performance to its Bayesian counterpart. Conclusions: Hybrid approaches can produce reliable inferences of evolutionary timescales and phylodynamic parameters in a fraction of the time required for fully Bayesian analyses. As such, they are a valuable alternative in outbreak studies involving a large number of isolates.

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