Bayesian Inference in Phylogenetic Analysis

The growing peak of Bayesian methods in phylogenetic inference in the last two decades is the result of the implementation of Markov chain Monte Carlo algorithms (MCMC; which include Monte Carlo method) and Metropolis-Hastings algorithm, on the estimation of posterior probability distributions and the exploration of more parameter-rich evolutionary models (Nylander, et, al. 2004). Statistically, the Bayesian inference calculates the probability that a hypothesis be true given the posterior probability based on priors probabilities and the likelihood under each hypothesis. Here the probability is used to represent uncertainty about the phylogeny and in the parameters of the model and not as expected frequency of occurrence like in classical or frequentist statistics (Yang, 2006). Under a philosophical context, the Bayesian and Maximum Likelihood approaches are inference methods, which are based in calculation of probabilities of evolutionary transformations of characters (for example, according to a nucleotide substitution model) instead of evaluation of possible synapomorphies, also any homologous characters (apomorphic and plesiomorphic) can be used for inferring phylogenies and they assume that evolution may be reticulated and not always dichotomous (Lukhtanov, 2010).

At present, is possible to identify six key topics related with Bayesian approach in phylogenetic reconstruction: (1) Integration of complex evolutionary models, (2) heterogeneity across the sites (3) heterogeneity across the data in analysis of combined data (Huelsenbeck et, al. 2001), (4) computational efficiency (Ronquist & Huelsenbeck, 2003), (5) posterior probability for a tree or clade has an easy interpretation(Yang, 2006) and (6) incorporation of priors values. The development of Bayesian MCMC algorithms is the associated cause with the increase in computational efficiency making possible to analyze more complex and realistic evolutionary models. This does not mean that parameters rich models are appropriated for all data set because we may make the mistake of overparameterization and more complex evolutionary models are associated with more topological uncertainty (Nylander, et, al. 2004). However, the interesting is that it open up the possibility of exploring more realistic models and complex (but a real model can be simple) to recognize heterogeneity across the sites and combined data.

In term of computational efficiency, although Maximum Likelihood analysis has gained ground with the improvement and development of new algorithms implemented in software such PhyML (Guindon and Gascuel, 2003) and RAxML (Stamatakis, 2007) and now is possible perform moderately fast and accurate bootstrapping to determine confidence, in Bayesian inference the interpretation of posterior probability is easier, it is the probability that the tree or clade is correct given the data, model and priors (Yang, 2006), whereas the interpretation of bootstrap although tend to be more conservative has been controversial specially with model misspecification and when the signal is only detectable at some sites (Ronquist and Deans, 2009). Despite the above, Bayesian posterior probability according to Yang (2006) can be spuriously high due to lack of convergence, poor mixing, misspecification of the likelihood substitution model and misspecification and sensitivity of the priors. About this last, it allows to incorporate prior knowledge about a particular hypothesis or to use vague or uninformative priors when the little information is available or do not want to build the analysis on any previous (Ronquist et, al. 2008). Since, priors can be subjective, is convenient to assess the influence of the priors on the posterior probability. Pickett and Randle (2005) pointed out that with a uniform prior the posterior probability of a clade depends on the both the size on the clade and the number of species.

Regarding to the lack of convergence and poor mixing in Bayesian approach them leading to spuriously high support for the trees visited in the chain (Yang & Rannala, 2005), multiple long chains from different starting points could be a possible solution when the data set is very large. However, Bayesian inference to face to another problem, this is inconsistency. Under Bayes´ criterion the tree topologies are estimated without estimating branch lengths this is integrating branch lengths for a given tree topology over a distribution of possible values (Goloboff & Pol, 2005), then statistical consistency can be lose and Bayesian inference is biased in favor of topologies that group long branches together, even when the true model and priors distributions of evolutionary parameters over a group of phylogenies are known. According to Kolaczkowski and Thornton (2009), this bias becomes more severe as more data are analyzed and sequences sites evolve heterogeneously and is relatively weak when the true model is simple. So, Bayesian inference is less efficient and less robust to the use of an incorrect evolutionary model than ML. Despite the above, Bayesian MCMC inference is a promising approach to phylogenetic analysis and computational biology that has been in progress with the development of algorithms, methods and software, nevertheless more research is needed on how to incorporate previous knowledge into appropriate informative priors and how to deal with complex models and large data sets without falling into inconsistency.

References

Goloboff P., Pol D. Parsimony and Bayesian phylogenetics. 2005. In: Albert V. (ed). Parsimony, phylogeny, and genomics. Oxford University Press. 210-266.

Guindon S., Gascuel O. 2003. PhyML: " A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Systematic Biology. 2003 52(5):696-704.

Huelsenbeck J. P., Ronquist F., Rasmus N., Bollback J. P. 2001. Bayesian Inference of Phylogeny and Its Impact on Evolutionary Biology. Science. 294 (5550) : 2310-2314

Kolaczkowski B., Thornton J. W. 2009. Long-Branch Attraction Bias and Inconsistency in Bayesian Phylogenetics. PLoS One. 4:e7891. doi:10.1371/journal.pone.0007891

Lukhtanov V. A. 2010. From Haeckel’s Phylogenetics and Hennig’s Cladistics to the Method of Maximum Likelihood: Advantages and Limitations of Modern and Traditional Approaches to Phylogeny Reconstruction. Entomological Review. Vol. 90(3) : 299-310

Nylander J. A. A., Ronquist F., Huelsenbeck J. P., Nieves-Aldrey J. L. 2004. Bayesian Phylogenetic Analysis of Combined Data. Syst Biol. 53(1) : 47-67

Pickett K. M., Randle C. P. 2005. Strange Bayes indeed: Uniform topological priors imply non-uniform clade priors. Mol. Phyl. Evol. 34:203-211

Ronquist F., Deans A. R. 2009.Bayesian Phylogenetics and Its Influence on Insect Systematics. Systematic Entomology 35 (3): 349-378.

Ronquist F., van der Mark P., Huelsenbeck J. P. 2009. Bayesian phylogenetic analysis using MrBayes. In: Lemey P., Salemi M., and Anne-Mieke V. (eds.) The Phylogenetic Handbook: a Practical Approach to Phylogenetic Analysis and Hypothesis Testing. Cambridge University Press. 219-236.

Stamatakis, A., Blagojevic, F., Nikolopoulos, D., Antonopoulos, C. 2007 Exploring New Search Algorithms and Hardware for Phylogenetics: RAxML Meets the IBM Cell. The Journal of VLSI Signal Processing. 48 : 271–286

Yang Z. 2006. Computational Molecular Evolution. Oxford University Press, Oxford, England

Yang, Z., and B. Rannala. 2005. Branch-length prior influences Bayesian posterior probability of phylogeny. Systematic Biology 54: 455-470.

MAXIMUM LIKELIHOOD AND MAXIMUM PARSIMONY UNDER A SIMPLE MODEL.

Jiménez- Silva C. L.
Universidad Industrial de Santander.
Laboratorio de Sistemática y Biogeografía

INTRODUCTION

Stochastic models for nucleotide substitution are becoming increasingly important as a foundation for inferring phylogenetic trees from genetic sequence data. Such models allow for tree reconstruction through either maximum likelihood-based approaches or the _tting of transformed functions of the data to trees (see Swo_ord et al. (Swo_ord et al., 1996) for a recent survey). The models are also useful for analysing the performance of other, more conventional tree reconstruction methods, which are not explicitly based on such models, such as the popular maximum parsimony method (Fitch, 1971). Maximum parsimony (MP) is a popular technique for phylogeny reconstruction. However, MP is often criticized as being a statistically unsound method and one that fails to make explicit an underlying ‘‘model’’ of evolution (Steel and Penny, 2000). Parsimony does not make explicit assumptions about the evolutionary process. Some authors argue that parsimony makes no assumptions at all and that, furthermore, phylogenies should ideally be inferred without invoking any assumptions about the evolutionary process (Wiley 1981). Others point out that it is impossible to make any inference without a model; that a lack of explicit assumptions does not mean that the method is ‘assumption-free’ as the assumptions may be merely implicit; that the requirement for explicit specification of the assumed model is a strength rather than weakness of the model-based approach since then the fit of the model to data can be evaluated and improved (e.g. Felsenstein 1973).

METHODS

In the analysis were simulated sequences of 1000 bp under JC69 model and all branches on the tree are assumed to have the same length, were 10 replicates of each simulation under Seq-Gen v1.3.2 program (Rambaut & Grassly, 1997). For different topologies, 3 taxa, 4 taxa, 6 taxa and 12 taxa. The sequences generated were analyzed in parsimony using TNT (Goloboff et. al, 2008) program and Winclada and NONA (Goloboff, 1999; Nixon, K. C. 1999) program to 3 taxa. Also, the sequences were analyzed in Maximum likelihood using PhyML (Guindon & Gascuel, 2005), to check an equivalence between parsimony and likelihood under a particular model, this is JC69. The nucleotide models evaluated were JC69 in PhyML, For each simulation was performed the same procedure and finally the topologies generated were compared with Tree C program (Arias & Miranda-Esquivel) assuming on equal an exactly equal nodes. It obtains eventually a total of 200 comparisons were made and the process was automated by constructing scripts in bash.

RESULTS

When, I compared the phylogenetic reconstructions, data showed equivalence between parsimony and likelihood under a JC69 model. Equivalence here means that the most parsimonious tree and the ML tree under the said model are identical in every possible data set. But, this result was only present with the data set of a few terminals, ie 3 and 4 taxa.

JC model was assumed for comparison because I refer to it, as the fully symmetric model since it makes no distinction between any of the character states and being with each sequence being 1000 nt long. As a first approximation, there is no selection at any of the sites, and therefore it is more ‘‘parsimonious’’ to assume one common mechanism for all sites rather than 1000 different mechanisms, one for each site.

That parsimony and likelihood trees used for working with the JC model, sometimes Called the Neyman model with four states. It assumes rates of evolution on the branch of the tree each freely Vary from site to site. In this case, we have some underlying constraints on the type of substitution model (ie, Jukes-Cantor type), but no constraints on the edge parameters from site to site. This is even more general than the type of approach considered by Olsen (see Swofford et al. 1996, p. 443) in which the rate at which a site evolves can vary freely from site to site, but the ratios of the edge lengths are equal across the sites. (Steel and Penny, 2000). On the methodology used in this work, a free parameters model was assumed. For this purpose, was assigned the custom model option in Phyml. When the custom model option was selected, also it is possible to Give to the program a user-defined nucleotide frequency distribution at equilibrium, where calculated parameters are given by the data. Based on this, it is proposed that this type of Underlying model Almost Certainly is too flexible, because it allows many new parameters for each edge. It might be regarded as the model one might start with if one knew virtually nothing about any common underlying mechanism

linking the evolution of different characters on a tree. (Steel and Penny, 2000).

For the data set of 6 and 12 taxa the results were different; between both methods they were obtained under three equal nodes. This difference of equal nodes based on the number of terminals, in concordance with Hendy and Penny (1989) showed that with four species and binary characters evolving under the clock, parsimony is always consistent in recovering the tree, although ML and parsimony do not appear to be equivalent under the model. With five or more species evolving under the clock, it is known that parsimony can be inconsistent in estimating the trees (Hendy and Penny 1989; Zharkikh and Li 1993). Thus it is not equivalent to likelihood.

While, you can consider parsimony and likelihood to be equivalent under the JC69 model, those studies often used small trees with three to six taxa. The cases for much larger trees are not known. However, it appears easier to identify cases of inconsistency of parsimony on large trees than on small trees (Kim 1996; Huelsenbeck and Lander 2003), suggesting that likelihood and parsimony are in general not equivalent on large trees.

REFERENCES

Arias J. S., Miranda-Esquivel D. M. 2007. Tree C.

W. M. Fitch. Toward de_ning the course of evolution: minimum change for a speci_c tree topology.Systematic Zoology, 20:406{416, 1971.

D. L. Swo_ord, G. J. Olsen, P. J. Waddell, and D. M. Hillis. Phylogenetic inference. In D. M. Hillis, C. Moritz, and B. K. Marble, editors, Molecular Systematics, chapter 11, pages 407{514. Sinauer Associates, 2nd edition, 1996.

Goloboff, P., 1999. NONA (No Name) ver. 2. Published by the author, Tucuman, Argentina

Felsenstein, J. 1973b. Maximum likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters. Syst. Zool. 22:240–249.

Hendy, M. D. and Penny, D. 1989. A framework for the quantitative study of evolutionary trees. Syst. Zool. 38:297–309.

Huelsenbeck, J. P. and Lander, K. M. 2003. Frequent inconsistency of parsimony under a simple model of cladogenesis. Syst Biol 52:641–648.

Kim, J. 1996. General inconsistency conditions for maximum parsimony: effects of branch lengths and increasing numbers of taxa. Syst. Biol. 45:363–374.

Nixon, K. C. 1999. Winclada (BETA) ver. 0.9.9 PUBLISHED BY THE AUTHOR, ITHACA, NY. I have become weary of Clados generated trees being published without citation. Please cite the program

M. Steel and D. Penny, Parsimony, likelihood and the role of models in molecular phyloge-netics. Molecular Biology and Evolution 17 839{850 (2000).

Wiley, E. O. 1981. Phylogenetics. The Theory and Practice of Phylogenetic Systematics. John Wiley & Sons, New York.

Zharkikh, A. and Li,W. -H. 1993. Inconsistency of the maximum parsimony method: the case of five taxa with a molecular clock. Syst. Biol. 42:113–125.

Evaluation of topological congruence based on GTR model and different approaches to integrate the rate heterogeneity implemented under RAxML and PhyML

Introduction

The variation of substitution rates across nucleotide sites is a well-known process that can vary substantially for different positions in a sequence (Salemi & Vandamme). Accordingly, the rate of each sequence is modeled as a random variable drawn from a specified prior distribution, the most common empirical approach is to use a gamma density function to model the rate of distribution across sites (Mayrose et. al, 2005). However, is possible find opposing views for and against about implementing empirically this distribution especially with GTR model, some objections relate to the computational cost, memory consumption, rates not unimodals, etc. Innovative approaches have been raised to incorporate rate heterogeneity in programs such RAxML (Stamakis, 2006), nevertheless, the likelihood values produced can not be directly compared to likelihood values of other ML programs and little is known about the topological congruence when comparing its results. Given that, the objective of this study is to evaluate the topological congruence based on GTR model and different approaches to incorporates the rate heterogeneity under RAxML and PhyML programs.

Methods

In the analysis, sequences of 2000 bp were simulated under GRT and GTRGAMMA models under Seq-Gen v1.3.2 program (Rambaut & Grassly, 1997) in order to use GTR as control and avoid, a priori, a biased in favor of Γ. The sequences generated were analyzed in parsimony using TNT (Goloboff et. al, 2008) program and a topology was reconstructed. The parsimony´s topology and randomly selected topologies were used as starting trees in RAxML (Stamakis, 2006) and PhyML (Guindon & Gascuel, 2005) programs to determine if the initial topology influences the reconstruction of the phylogenetic relationships in both programs under the different models selected. The nucleotide models evaluated were GTRCAT, GTRMIX, GTRGAMMA and GTRCAT_GAMMA in RAxML versus GTR, GTRGAMMA in PhyML, also for each model was evaluated the number of categories to optimize the per-site individual rates in order to preclude the effect of over or subestimation, the categories defined were: 4, 10, 25, 35, 45, 50. For each simulation was performed the same procedure and finally the topologies generated were compared with Tree C program (Arias & Miranda-Esquivel) based on equal an exactly equal nodes. A total of 264 comparisons were made and the process was automated by constructing scripts in bash.

Results

The findings in this study indicate that RAxML with sequences simulated under GTR or GTRGAMMA recovered the shorter topology when it used the models GTRMIX, GTRGAMMA and GTRCAT_GAMMA from a random initial topology. However, if you provided a fixed starting tree (Maximum Parsimony topology) the topology recovered had an additional node (21 nodes). GTRCAT approximation was the only that recovered in both cases the same topology with 21 nodes coinciding with the GTR and GTRGAMMA models under PhyML. This implies, although GTRCAT approximation allows to computing trees under realistic approximation of nucleotide substitution (Stamakis, 2006), it do not perform the optimal topological inference based on the congruence between the nodes reconstructed. The analysis with distinct rate categories settings did not influence the tree recovered in PhyML, with one equal node and no node exactly equal in all cases, while, in RAxML the results are unstable between categories and did not follow a trend with increasing number of rate categories specified. Despite this, depending of the model selected, the number of equals nodes increases with specific categories, although the findings were variable under sequences simulated under GTR and GTRGAMMA (see table: http://dl.dropbox.com/u/16494208/Tabla.ods).

On the other hand, when the topologies estimated under different models implemented in PhyML and RAxML were compared (see appendix), the number or equal nodes recovered, from random starting tree, was better with the GTRMIX approximation in half the categories (10, 25, 50) and the worth scenario was obtained under GTRCAT with two equal nodes in 50 category. Additionally, topologies recovered from fixed starting tree (parsimony topology), showed a surprisingly high number of equal nodes (until 18 nodes) and exactly equal nodes (until 17 nodes) independently of the category analyzed.

Trees recovered with GTRGAMMA and GTRMIX models implemented in RAxML versus trees recovered with GTR/GTRGAMMA in PhyML, reconstructed the highest number of equal nodes followed by GTRGAMMA_CAT and GTRCAT, although the latter did not showed exactly equal nodes. The results found were exactly equals in the comparisons of the resulting trees under the models implemented in PhyML and the models used in RAxML in each simulation. But differences were detected when the comparision was performed under GTRAGAMMA simulation, because the behavior changed and the number of equal nodes increased in various categories under GRTCAT and GTRCAT_GAMMA followed by GTRGAMMA and GTRMIX versus GTR/GTRGAMMA in PhyML. Possibly, in this specific case, the rate heterogeneity described with gamma quantified from GRTCAT and GTRCAT_GAMMA approaches in RAxML is the closest to the response obtained with PhyML.

Conclusions

This study showed that the topological congruence in analysis using differents approaches to evaluate the rate heterogeneity in GTR is optimally treated with PhyML and RAxML, however PhyML is more stable and conservative also it is not strongly influenced by the number of categories. Contrary, RAxML is more sensible to the number of categories but it was not possible identify some type of trend or behavior to explain the changes. Additional findings, indicated that the topological congruence can be influenced by the starting tree in the analysis and by the model of simulation.

References

Arias J. S., Miranda-Esquivel D. M. 2007. Tree C.

Guindon S., Gascuel O. 2003. PhyML:"A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood."Systematic Biology. 2003 52(5):696-704.

Goloboff, P. A., Farris, J. S. and Nixon, K. C. 2008. TNT, a free program for phylogenetic analysis. Cladistics, 24: 774–786.

Mayrose I, Friedman N, Pupko T. 2005. A Gamma mixture model better accounts for among site rate heterogeneity. Bioinformatics. 21(Suppl. 2):ii151–ii158.

Rambaut, A. and Grassly, N. C. 1997. Seq-Gen: An application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees. Comput. Appl. Biosci. 13: 235-238.

Salemi M., Vandamme A. M. (eds.). 2003. The Phylogenetic Handbook: a Practical Approach to Phylogenetic Analysis and Hypothesis Testing. 2nd Edition, 381-404, Cambridge University Press, Cambridge.

Stamatakis, A. 2006. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22.

Annexes

Script for simulation of molecular sequences

#!/bin/bash

cd /home/andromeda/Seq-Gen.v1.3.2/source

for m in *.tree; do

for a in *.txt; do

echo "./seq-gen -mGTR -l2000 -n1 -z150 -op < $m > gtr$m$l.dat" >>gtr.txt

echo "./seq-gen -mGTR -g4 -l2000 -n1 -z150 -op < $m > gtrg$m$l.dat" >>gtrg.txt

chmod +x $a

./$a

done

done

mkdir gtr

mv gtr*.dat gtr

Script to run on PhyML and RAxML

RAxML:

#!/bin/bash

cd /home/andromeda/RAxML-7.0.4/

for s in *.datdo

for mod in GTRGAMMA GTRCAT GTRMIX GTRCAT_GAMMA;

do

for a in 4 10 25 35 45 50;

do

./raxml -f d -t parsi.tree -s $s -c $a -m $mod -n par$s$a$mod

done

done

done

mkdir parsi

mv RAxML_result.* parsi

PhyML

#!/bin/bash

cd /home/andromeda/PhyML_3.0/

for s in *.dat;

do

# categories

for a in 4 10 25 35 45 50;

do

echo "./phyml -i $s -d nt -q -n 1 -p -m GTR -c $a -a 1 -s BEST -o tlr --r_seed 12345 -- print_site_lnl --run gg$s$a" >> PHYML$s$a.txt

echo "./phyml -i $s -d nt -q -n 1 -p -m GTR -c $a -s BEST -o tlr --r_seed 12345 --print_site_lnl --run g$s$a" >> PHYML$s$a.txt

echo "./phyml -i $s -d nt -q -n 1 -p -m GTR -c $a -a 1 -s BEST -u parsi.tree -o tlr --r_seed 12345 --print_site_lnl --run ggp$s$a" >> PHYML$s$a.txt

echo "./phyml -i $s -d nt -q -n 1 -p -m GTR -c $a -s BEST -u parsi.tree -o tlr --r_seed 12345 --print_site_lnl --run gp$s$a" >> PHYML$s$a.txt

chmod +x PHYML$s$a.txt

./PHYML$s$a.txt

done

done

mkdir parsiphy

#mv RAxML_result.* parsi

Script to add header and change format

#!/bin/bash

for i in pargtrgarbol.tree.*; do

sed -i "1itread 'tree(s) from TNT, for data in e.nex'" $i

echo "proc-;" >>$i

done

Tree C´s script : comparison trees

#!/bin/bash

for i in gpgtrgarbol.tree.dat25.txt; do

for e in pargtrgarbol.tree.*; do

echo "./treec_linux -f $i -l $e -o sal$e$i.dat -t" >> treec.txt

chmod +x treec.txt

./treec.txt

done

done

mkdir comparison

mv *.dat comparison

Does the phylogenetic Species Category exist in virus?

Jiménez- Silva C. L.
Universidad Industrial de Santander.
Laboratorio de Sistematica y Biogeografia

Introduction

Phylogenetic specie is the smallest diagnosable group identified as an ancestor-descendant populations evolving separately from others (Cacraft, 1983). The species can be considered a species level, among all those available in the hierarhy Phylogenetic. Only monophyletic groups can be recognized and formally named taxa. This principle is based on the groups which include all the descendants of a single common ancestor are the only groups with real and natural existence in relation to the evolutionary process (De Luna and Mishler, 1996). The real existence of the species gives genealogical relationships with other group of organisms, their historical behavior, so it is considered that the species are different because they have diverged evolutionarily. Wiley (1978) supports this assertion by suggesting that the species is a single lineage of ancestral-descendant populations, they retain their identity from other lineages and have their own evolutionary tendencies and historical fate.

According to the above and the ontological status of phylogenetic species suggests that instead of viewing species as natural kinds we should think of them as individuals (Ghiselin, 1974 and Hull, 1978). Given the class/individual distinction, Ghiselin and Hull argue that species are individuals, not classes. Their argument assumes that the term ‘species’ is a theoretical term in evolutionary theory, so their argument focuses on the role of ‘species’ in that theory. Here is Hull's version of the argument, which can be dubbed the ‘evolutionary unit argument.’ Since Darwin, species have been considered units of evolution. The "individuals" can you discover connectors or space - time, ie, the status of "individual" at a time and place particle is linked to another state at another time and space site for historical connections (Dupré 2001, Reydon de 2003, y Crane 2004). From this concept and its ontological status is to intend whether the phylogenetic Species exist in virus Category.

The idea that species are individuals has a number of implications. For one, the relationship between an organism and its species is not a member/class relation but a part/whole relation. An organism belongs to a particular species only if it is appropriately causally connected to the other organisms in that species. The organisms of a species must be parts of a single evolving lineage (Hull, 1978). If belonging to a species turns on an organism's insertion in a lineage, then qualitative similarity can be misleading. Two organisms may be very similar morphologically, genetically, and behaviorally, but unless they belong to the same spatiotemporally continuous lineage they cannot belong to the same species.

Virus Species

The international Committee on taxonomy of viruses (ICTV) endorsed the following definition of virus species: a virus species is a polythetic class of viruses that constitutes a lineage of replication and occupies a particular ecological niche (Van Regenmortel & Mahy, 2004). This definition applies for viruses like units called "classes", in a biological classification scheme, an individual organism or a virus can be a member of several abstract classes like a species or a genus. Class membership must be distinguished from the so-called ‘‘part-whole’’ relationship which exists only between two concrete objects, one being a part of the other in the way, for instance, that cells and organs are parts of an organism. It is not possible for a concrete object like a virus to be ‘‘part’’ of an abstract entity like a species (similarly a thought cannot be part of an object). The mixing of logical categories has led to much debate in viral taxonomy (Bos, 2003; Van Regenmortel, 2003).

A class is defined by properties that are constant and immutable. This allows members of such a class to be recognized with absolute certainty since one or more property is necessarily present in every member of the class. Virus families, for instance, are universal classes because they consist of members, all of which share a number of defining properties that are both necessary and sufficient for class membership. Allocating a virus to a family is thus an easy task since a few structural or chemical attributes will suffice to allocate the virus to a particular family. (Van Regenmortel, 2006).

Classifying viruses consists in inventing taxonomic classes like particular families or species and allocating individual viruses to these classes in order to achieve some order whereby similar viral agents are grouped together. The failure to distinguish between real objects such as organisms and viruses and the mental constructions and abstractions needed to build up any classification system has been a fertile source of confusion in taxonomy (Van Regenmortel, 2003) Distinguishing between real, tangible objects like viruses (i.e. concrete individuals) and mental constructs like virus species and genera (i.e. classes) that exist only in the mind is a basic requirement for clear thinking. Although a taxonomic class is defined by properties possessed by concrete objects, it is an abstract, conceptualized collection, i.e. a mental construct.

Returning to the phylogenetic species concept, this raises the real Existence of the species Conceived as discrete units, ie, restricted in space and time. Inconsistent with the vision Referred to by (ICTV), Which Takes the virus species as a class being these, units exist independent and unrestricted that teporales or spatial boundaries. If we encuena that "classes" are abstractions, ie, a mental construct, missing reality. The concept proposed by De Luna and Mishler (1996), on the phylogenetic concept where raise the species as a group with real existence in relation to the natural evolutionary process. The virus species is not wonderful to hang phylogenetic species category and be closer to a nominalist concept (real missing) or phenetic.

References

Bos, L., 2003. Virus nomenclature; continuing topicality. Arch. Virol. 148, 1235–1246.

Crane, J. 2004, “On the Metaphysics of Species”, Philosophy of Science, 71: 156–173

Cracraft, J. 1983. Species concepts and speciation analysis. Current Ornithology, 1, 159–187.

Dupré, J., 2001, “In Defense of Classification”, Studies in the History and Philosophy of Biology and the Biomedical Sciences, 32: 203–219.

Ghiselin, M., 1974, “A Radical Solution to the Species Problem”, Systematic Zoology, 23: 536–544

Hull, D., 1978, “A Matter of Individuality”, Philosophy of Science, 45: 335–360
Reydon, T., 2003, “Species Are Individuals Or Are They?” Philosophy of Science, 70: 49–56.

Van Regenmortel, M.H.V. and Mahy, B.W.J., 2004. Emerging issues in virus taxonomy. Emerg. Infect. Dis. 10, 8–13.

Van Regenmortel, M.H.V., 2003. Viruses are real, virus species are man-made taxonomic constructions. Arch. Virol. 148, 2481–2488.

Van Regenmortel, M.H.V., 2006. Virologists, taxonomy and the demands of logic. Arch. Virol. 151, in press.

Wiley, E. O.,1978. The evolutionary species concept reconsideres. Syst. Zool., 27: 17-26.

Evaluation of genetic recombination on phylogenetics relationships in Dengue virus

A. Susana Ortiz Bae

Laboratorio de Sistematica y Biogeografia

Introduction

Genetic recombination in RNA viruses is an evolutionary process defined as the exchange of genetic information from two or more nucleotide sequences into recombinant progeny virus (Mahy, 2009). This mechanism apparently plays a significant role in the generation of genetic diversity especially important under adverse conditions (Worobey et al. 1999). Despite above, the process is a rare event in natural populations of Dengue virus in which through a copy-choice mechanism, the polymerase switches between parental viral molecules during replication (Lai, 1992). However, the approximate inference of recombination can be based regarding their effect on genealogy as events that can change or not the final topology or branch lengths and therefore can disrupt the pattern of phylogenetic relationships between viral sequences (Wiuf et al. 2000). Given that many recombinant sequences are mosaics comprising regions with quite different phylogenetic histories, this study evaluated the effect of genetic recombination on phylogenetics relationships in Dengue virus type 1 sequence, based on the detection of conflicting phylogenic signals under parsimony criterion.

Methods

Viral recombinants , parental and no recombinants sequences from dengue strains used in this study were provide from the analysis reported by Chen et al. (2008) and Worobey et al. (1999). Prior to the analysis of these sequences was constructed a data set and recombination events were simulated between regions of the serotypes with the aim to detect sensitivity to genetic recombination in the inter-serotype level. The sequences from literature, was partitioned in specific coding regions corresponding to recombination breakpoints and were aligned, then the the phylogenetic relationships were reconstructed under phylogenetic inference criteria such as parsimony implementing heuristic method and finally the pattern of relationships were summarized on a strict consensus. Representative sequences from Denv-2, Denv-3 and Denv4 were used as outgroups to root the tree.

Results and Discussion

Based on phylogenies inferred from the structural and non-structural regions, the results pointed differential levels of resolution and rearrangement of the parental sequences as recombinant sequences were eliminated depending on the analyzed partition. This was clearly seen given the pattern of phylogenetic relationships between Philippines84 with Nauru74 and ARG992 strains which are closely related in the region encoding the capside. On the other hand, from the region encoding the membrane (prM/M) protein the Philippines84 strain diverges to associate with Thailand80 strain while the region coding for envelope (E) protein and at the junction with the membrane protein (preM/E), Phylippines84 is reassociated with both viral sequences Nauru74 and Thailand80 which indicates these sequences as parental of Philippines84 in a higher or lower component depending on the partition analyzed and corroborates Philippines84 as descendant of one ancestor that derived independently.

Moreover, despite the reassociations between Thailand80 strains with Philippines84, this was never dissociated from its parental sequence Jamaica77 which indicates an important contribution of this sequence to the genetic diversity of Thailand80. Concerning the strain BR 90, although suggested by Woorobey et al. (1999) no clearly evidenced the pattern of relationships with its parental sequences Jamaica77 and Singapore90 when new sequences are incorporated into the analysis, however seems that Jamaica77 classified as genotype VI represent a significant component in the C and preM regions while Singapore90 classified as genotype I, contributes most notably to E and junction preM/E regions. Such discordant relationships and different topologies between regions within the same sequence may suggest the existence of intra-serotype mosaic genomes produced by recombination between divergent parent lineages (Worebey and Holmes, 1999).

Similarly for GD23_95 strain, phylogenetic trees based on recombinants and non-recombinants regions showed that the variation in the sequence on the topology generated under Parsimony criterion is nor a strong indication of its recombinant condition, opposite to that found by Chen et al. (2008) by the neighbor-joining method, then such a definition could be an artifact. Additionally, given the elimination of the putative recombinant sequences on the topology, we often observe an improvement in the resolution of phylogenetic relationships, except in the preM region where three sequences were localizated in the same clade. Nonetheless this response was nor evident on topologies where the GD23_95 strain was eliminated, which could promote a false positive. Finally, it is important to note that although not recover a general pattern of phylogenetic relationships, each partition reflects a hypothesis that despite being different between these may be clouded by the presence of recombinant sequences which may contain regions with very different evolutionary histories within individual strains present.

Conclusions

Phylogenetic approaches can provide a method for detecting and characterizing recombination events among conflicting phylogenetic signals in gene sequence data with different patterns of relationship for different sequence regions in Dengue viruses, however, is necessary be careful in the some affirmations about recombinant events given that different regions pointed different patterns of phylogenetic relationships and regions non-recombinants don’t imply resolution on this relationships.

References

Lai, M.M.C., 1992. RNA recombination in animal and plant viruses. Microbiol. Rev. 56, 61–79.
Mahy, B., 2009. The Dictionary of virology. Elsevier, 4ed. 518p.
Wiuf, C. & Hein, J., 2000. Genetics 155, 451–462.
Worobey, M., Rambaut, A., Holmes, E.C., 1999. Widespread intraserotype recombination in natural populations of dengue virus. Proc. Natl. Acad. Sci. U.S.A. 96, 7352–7357.
Worobey, M., Holmes, E.C., 1999. Evolutionary aspects of recombination in RNA viruses. Mol. Journal of General Virology , 80, 2535±2543

Delimitation biological species in genus
Acanthoxyla (Phasmidae) using
mitochondrial and nuclear DNA

Gualdrón-Diaz J. C.

1. Introduction

From the broader perspective of evolutionary theory, delimiting species is im-
portant in the context of understanding many evolutionary mechanism and pro-
cesses(Sites & Marshall, 2003). The species boundary will define the limist
within or across which evolutionary processes operate (Barton & Gale , 1993).
Species are also routinely used as fundamental units of analysis in biogeogra-
phy, ecology, macroevolution and conservation biology (Brown et al., 1996) and
a better understanding of these larger scale processes requires that systema-
tists employ methods to delimit objectively and rigorously what species are in

nature.

There have been many proposals for the conceptual and practical handling of
species and among these, the biological species concept (Mayr, 1942) has been
the most thoroughly promoted and most widely accepted. One of the more
frequently stated deficiencies of the BSC is its inapplicability to uniparental
organisms (Foottit, 1997). The parthenogenesis is a common phenomenon in
the Animal Kingdom. It has been estimated that there are over 1000 obligately
parthenogenetic species situated over a broad range of taxa and over 15000
species which reproduce by cyclic parthenogenesis (White, 1978; Bell, 1982).
In stick insects(phasmids) the hybrid speciation and parthenogenesis both
appear to be unusually common phenomena (Bullini, 1994). In New Zealand
thera are parthenogenetic populations of otherwise sexual species of Clitarchus
and Argosarchus (Salmon, 1991). However, genus Acanthoxyla is entirely parthenogenetic. It comprises eight species without males and no closely related bisexual species (Jewell & Brock, 2002). This eight Acanthoxyla species exhibit a degree of morphological diversity not evident in any other New Zealand stick insects genus (Morgan & Trewick, 2005). The aim of this work is to make a nested clade analysis and phylogeography of haplotypes using nuclear and mitochondrial DNA to delineate species Acanthoxyla (Phasmatidae).

2. Materials and Methods

Five different genes of the genera Acanthoxyla and Clitarchus were used. 21
localities of New Zealand and 171 sequences in total of both genera were ana-
lyzed (fig. 1). Three were sequences nuclear genes, elongation factor 1x (EF1x),
phosphoglucose isomerase(PGI) and the large ribosomal subunit (28S) and two
were mitochondrial, cytochrome oxidase subunits I and II (COI-II) listed in
Buckley et al. (2008). The sequences have been deposited in GenBank under
accession EU492930-EU493090; Two operational criteria to delimit specie were
used, nested clade analysis (NCA)and phylogeographic analysis sensu Avise.
The nucleotide sequences were aligned using multiple alignment with Mus-
cle 3.6 software package. The nested clade analysis conducted using the ANECA software, which implements programs TCSv2.1 and GEODIS for analysis(Panchal, 2007). Haplotype networks were reconstructed in TCS v2.1. Then examined the relationship between haplotype/clades and geography through a statistical analysis of permutations, using the program GEODIS. For the phylogeographic analysis sensu Avise (Avise et al, 1987): ML bootstrap analysis (1000 permutations) were realized using the softwate Phyml 3.0 (Guindon & Gascuel, 2003).


3 Results and Discussion

3.1 Nested clade analysis

The cladistic analysis of haplotypes for mitochondrial genes shows 10 clades for
both COI and COII, in terms of nuclear genes, 28S, PGI and EF1x, there are
9, 11 and 7 respectively clades, but none of them considers the fragmentation
allopatric pattern as inferred from the clades formed. Therefore can not assign
the species status of these genera using this method since only fragmentation
allopatric is the important biological implication of the NCA to infer species
status (Templeton 2001).
Patterns inferred from the clades formed were restricted gene flow with iso-
lation by distance, contiguous range expansion and restricted gene flow with
isolation by distance (restricted dispersal by distance in non-sexual species) for
COII genes, EF1x and PGI, respectively. The 28S and COI gene showed Incon-
clusive outcome.

3.2 Phylogeographic analysis sensu Avise

The phylogeographic analysis, with mitochondrial genes (COI-COII) reveals the
presence of two different groups, which have a high value of confidence limits
(fig. 2-3), according to Avise (1995) assert that to infer species status requires a
confidence limit ( 95%) therefore they deserve the status of species. By contrast
with the results obtained using nuclear DNA cladograms show low resolution
and with diverging results obtained with mitochondrial DNA, in the analysis
with the 28S gene, showed levels very low limits of confidence, the evidence
suggests an clagograma unresolved with this type of gene. Similar results were
obtained through EF1x and PGI genes, however these if you showed higher
values of confidence limit (fig. 4-6).

Given the resutlados obtained, it appears that for the nested clade analsis
neither nuclear nor mitochondrial DNA showed species status. As for the re-
sults obtained with the phylogeographic analysis, differences in the phylogenetic relationships of the genus Acanthoxyla haplotypes generated with mitochondrial DNA with respect to nuclear. The mitochondrial DNA results show that only A. inermis and A. geisovii deserve species status.

4. References

Avise, J. C., Arnold, J., Ball, R. M., Bermingham, E., Lamb, T., Neigel, L. E., Reeb, C. A. & Saunders, N. C., 1987. Intraspecific phylogeography: The mitocondrial DNA bridge between population ge­netics and systematics. Annual Review in Ecology and Systematics, 18: 489–522.

Bell, G. 1982. The Masterpiece of Nature. The Evolution and Genetics of Sexuality, University of California Press, Berkeley and Los Angeles

Buckley T.R., Attanayake D., Park D., Ravidran S., Jewell T.R., Normark B.B. 2008. Investigating hybridization in the parthenogenetic New Zealand stick insects Acanthoxyla (Phasmatodea) using single-copy nuclear loci. Molecular Phylogenetics and Evolution 48: 335-349.

Bullini L. 1994. Origin and evolution of animal hybrid species. Trends Ecol. Evol. 9:422-426.

Guindon S. & Gascuel O. 2003. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Systematic Biology, 52(5):696-704, 2003.

Jewell T., Brock, P.D. 2002. A review of the New Zealand stick insects: new genera synonymy, keys and a catalogue. J. Orthopteran Res. 11, 189-197.

Mayr, E. 1942. Systematics and the Origin of Species from the Viewpoint of a Zoologist. Columbia University Press: New York.

Panchal, M. (2007), 'The automation of Nested Clade Phylogeographic Analysis', bioinformatics 23, 509-510.

Salmon, J.T., 1991. The Stick Insects of New Zealand. Redd, Auckland, New Zealand.

Sites J. W. Jr., Marshall J. C. 2003. Delimiting species: A Renaissance issue in systematic biology. Trends Ecol. Evol. ;18:462-470.

Templeton AR (2001) Using phylogeographic analyses of gene trees to test species status and processes. Molecular Ecology, 10, 779–791.

White, M.J.D. 1978. Modes of Speciation, W.H, Freeman and Company, San Francisco.

5. Figures



Figure 1. Map of New Zealand showing sampling localities


Figura 2. Maximun likelihood gene trees for the mtDNA data (COII). Numbers above branches are bootstrap value.


Figura 3. Maximun likelihood gene trees for the mtDNA data (COI). Numbers above branches are bootstrap value.


Figura 4. Maximun likelihood gene trees for the 28S data. Numbers above branches are bootstrap value


Figura 5. Maximun likelihood gene trees for the PGI data. Numbers above branches are bootstrap value.


Figura 6. Maximun likelihood gene trees for the EF1 data. Numbers above branches are bootstrap value.