human cultures have always been 
 fascinated by their origins as a means to define their position in the world , 
 and to justify their hegemony over the rest of the living world . however , 
 scientific ( testable ) predictions about our origins had to wait for darwin   and his intellectual 
 descendents first to classify   and then to reconstruct the 
 natural history of replicating entities , and hereby to kick - start the field of phylogenetics 
 [ 3 , 4 ] . rooted in the comparison of 
 morphological characters , phylogenies have for the past four decades focused on 
 the relationships between molecular sequences ( e.g. , ) , potentially helped by 
 incorporating morphological information , in order to infer 
 ancestor - to - descendent relationships between sequences , populations , or species . today , molecular 
 phylogenies are routinely used to infer gene or genome duplication events , recombination , horizontal gene transfer , variation of selective 
 pressures and adaptive evolution , divergence times between 
 species , the origin of genetic code , elucidate the origin of 
 epidemics , and host - parasite cospeciation 
 events [ 13 , 14 ] . as complementary tools for 
 taxonomy ( dna barcoding : ) in addition to untangling 
 the ancestral relationships relating a group of taxa or of a set of molecular 
 sequences , phylogenies have also been used for some time outside of the realm 
 of biological sciences as for instance in linguistics [ 17 , 18 ] or in forensics [ 19 , 20 ] . most of these 
 applications are beyond the scope of plant genomics , but they all suggest that 
 sophisticated phylogenetic methods are required to address most of today 's 
 biological questions . while parsimony - based methods are both intuitive and 
 extremely informative , for instance to disentangle genome rearrangements , they also have their 
 limitations due to their minimizing the amount of change . a means to 
 overcome , at least partly , some of these difficulties is to adopt a 
 model - based approach , be in a maximum likelihood or in a bayesian framework . these two frameworks are extremely similar in that they both rely on 
 probabilistic models . bayesian approaches offer a variety of benefits when 
 compared to traditional maximum likelihood , such as computing speed ( although 
 this is not necessarily true , especially under complex models ) , sophistication 
 of the model , and an appropriate treatment of uncertainty , in particular the 
 one about nuisance parameters . as a result , bayesian approaches often make it 
 possible to address more sophisticated biological questions , which usually comes at the 
 expense of longer computing times and higher memory requirements than when 
 using simpler models . because it is 
 not possible or even appropriate to discuss all the latest developments in a 
 given field of study , this review will focus on a very limited number of key 
 phylogenetic topics . of notable exceptions , recent developments in phylogenetic hidden markov models   or applications that map 
 ancestral states on phylogenies   are not treated . we focus 
 instead on the very first steps involved in most phylogenetic analysis , ranging from reconstructing a tree to estimating 
 selective pressures or species divergence times . for each of these steps , some 
 of the most recent theoretical developments are discussed , and pointers to 
 relevant software are provided . the first step in reconstructing a 
 phylogenetic tree from molecular data is to obtain a multiple sequence 
 alignment ( msa ) where sequence data are arranged in a matrix that specifies 
 which residues are homologous . a large number of methods 
 and programs exist   and most have been evaluated 
 against alignment databases , so that it is possible to 
 provide some general guidelines . the easiest 
 sequences to align are probably those of protein - coding genes : proteins diverge 
 more slowly than dna sequences and , as a result . 
 the rule - of - thumb is therefore first to translate dna to amino acid sequences , 
 then perform the alignment at the protein level , before back - translating to the 
 dna alignment in a final step . this procedure avoids inserting gaps in the 
 final dna alignment that are not multiple of three and that would disrupt the 
 reading frame . translation to amino acid sequences can be done directly when 
 downloading sequences , for instance from the national center for biotechnology 
 information ( ncbi : www.ncbi.nlm.nih.gov ) . a number of programs also allow users 
 to perform this translation locally on their computers from an appropriate 
 translation table ( e.g. , dambe , mega [ 30 , 31 ] ; see table 1 ) . again , a number of 
 programs exist , but probcons   appears to be the most 
 accurate single method . an alternative for using one 
 single alignment method is to use consensus or meta - methods , that is , to 
 combine several methods . meta - methods such as m - coffee 
 can return better msas almost twice as often as probcons . finally , when the alignment 
 is obtained at the protein level , back - translation to the dna sequences can be 
 performed either by using a program such as dambe , codonalign , or by using a dedicated 
 server such as protal2dna ( http://bioweb . the alignment of 
 rrna genes with the constraint of secondary structure has now been frequently 
 used in practical research in molecular evolution and phylogenetics [ 5660 ] . the procedure is first to obtain reliable 
 secondary structure and then use the secondary structure to guide the sequence 
 alignment . this has not been automated so far , although both clustal [ 61 , 62 ] and dambe have some functions to alleviate the 
 difficulties . what to do with 
 other noncoding genes is still an open question , especially when it comes to 
 aligning a large number ( > 100 ) of long ( > 20,000 residues ) and 
 divergent sequences ( < 25% identity ) . some authors have attempted to 
 provide rough guidelines to choose the most accurate program depending on these 
 parameters . however , accuracy figures 
 are typically estimated over a large number of test alignments and may not 
 reflect the accuracy that is expected for any particular alignment . more crucially , most of the 
 alignment programs were developed and benchmarked on protein data , so that 
 their accuracy is generally unknown for noncoding sequences . a very general 
 recommendation is then to use different methods   and meta - methods . those parts 
 of the alignment that are similar across the different methods are probably 
 reliable . the parts that differ extensively are often simply eliminated from 
 the alignment when no external information can be used to decide which 
 positions are homologous . poorly aligned regions can cause serious problems as , 
 for instance , when analyzing rrna sequences in which conserved domain and 
 variable domains have different nucleotide frequencies . a simple test of the 
 reliability of an alignment consists in reversing the orientation of the 
 original sequences , and performing the alignment again ; because of the symmetry 
 of the problem , reliable msas are expected to be identical whichever direction 
 is used to align the sequences . these authors further show 
 that reliability of msas decreases with sequence divergence , and that the 
 chance of reconstructing different phylogenies increases with sequence 
 divergence . more sophisticated methods also permit the direct measure of the 
 accuracy of an alignments or the estimation of a distance between two 
 alignments . applications of bayesian 
 inference strictly to pairwise   and multiple [ 67 , 68 ] sequence alignment are still 
 in their infancy . whichever method 
 is used to obtain an msa , a final visual inspection is required , and manual 
 editing is often needed . to this end , a number of editors can be used such as jalview 
 . because an msa 
 represents a hypothesis about sitewise homology at all the positions , obtaining 
 an accurate msa presents some circularity ; an accurate msa often necessitates 
 an accurate guide tree , which in turn demands an accurate alignment . the early 
 realization of this  chicken - egg  conundrum led to the idea that both the msa 
 and the phylogeny should be estimated simultaneously . although this initial 
 algorithm was parsimony - based , it was already too complex to analyze more than 
 a half - dozen sequences of 100 sites or more . subsequent parsimony - based 
 algorithms allowed the analysis of larger data sets   but still showed some 
 limitations when sequence divergence increases . more recently , a bayesian 
 procedure was described and implemented in a program , bali - phy , where 
 uncertainties with respect to the alignment , the tree , and the parameters of the 
 substitution model are all taken into account   ( see also ) . uncertain alignments are a 
 potential problem in large - scale genomic studies   or in whole - genome alignments 
 . in these contexts , disregarding alignment uncertainty can lead to systematic biases when 
 estimating gene trees or inferring adaptive evolution [ 73 , 74 ] . however , these complex 
 bayesian models [ 38 , 72 , 73 ] still require some nonnegligible 
 computing time and resource , and to date , their performance in terms of 
 accuracy is still unclear . once a reliable msa is obtained , 
 the next step in comparing molecular sequences is to choose a metric to 
 quantify divergence . the most intuitive measure of divergence is simply to 
 count the proportion of differences between two aligned sequences ( e.g. , ) . this simple measure is 
 known as the p distance . however , because the size of the state space is 
 finite ( four letters for dna , 20 for amino acids , and 61 for sense codons ) , 
 multiple changes at a position in the alignment will not be observable , and the 
 p distance will underestimate evolutionary distances even for moderately 
 divergent sequences . some of the 
 most famous named nucleotide substitution models are the jukes - cantor model or 
 jc , the kimura two - parameter 
 model or k80 , the hasegawa - kishino - yano 
 model or hky85 , the tamura - nei model or tn93 , and the general 
 time - reversible model or gtr   ( also called rev ) . because 
 substitution rates vary along sequences , two components can be added to these 
 substitution models : a + i  component that models invariable sites   and a  +   component that models among - site rate 
 variation either as a continuous   or as a discrete   mean - one distribution , the latter being more 
 computationally efficient . + f  
 component so that replacement rates are proportional to the frequencies of both 
 the replaced and resulting residues . given the 
 variety of substitution models , the first step of any model - based phylogenetic 
 analysis is to select the most appropriate model [ 84 , 85 ] . the rational for doing so is 
 to balance bias and variance : a highly - parameterized model will describe or fit 
 the data much better than a model that contains a smaller number of parameters ; 
 in turn however , each parameter of the highly - parameterized model will be 
 estimated with lower accuracy for a given amount of data ( e.g. , ) . besides , both empirical and 
 simulation studies show that the choice of a wrong substitution model can lead 
 not only to less accurate phylogenetic estimation , but also to inconsistent 
 results . the objective of model 
 selection is therefore not to select the  best - fitting  model , as this one will 
 always be the model with the largest number of parameters , but rather to select 
 the most appropriate model that will achieve the optimal tradeoff between bias and 
 variance . the approach followed by all model selection procedures is therefore 
 to penalize the likelihood of the parameter - rich model for the additional 
 parameters . because most of the nucleotide substitution models are nested ( all 
 can be seen as a special case of gtr + +i ) , 
 the standard approach to model selection is to perform hierarchical likelihood 
 ratio tests or hlrts . note that in all rigor , likelihood 
 ratio tests can also be performed on nonnested models ; however , the asymptotic 
 distribution of the test statistic ( twice the difference in log - likelihoods ) 
 under the null hypothesis ( the two models perform equally well ) is complicated   and quite often impractical . when models are nested , the asymptotic distribution of the test statistic under 
 the null hypothesis is simply a  distribution whose degree of 
 freedom is the number of additional parameters entering the more complex model 
 ( see   or   for applicability conditions ) . 
 with the hlrt , then all models are compared in a pairwise manner , by traversing 
 a choice - tree of possible nested models . a number of popular programs allow 
 users to compare pairs of models manually ( e.g. , paup , paml [ 49 , 50 ] ) . readily written scripts 
 that select the most appropriate model among a list of named models also exist , 
 such as modeltest   ( which requires paup ) , the r 
 package ape , or dambe . free web servers 
 are also available ; they are either directly based on modeltest   or implement similar ideas 
 ( e.g. , findmodel , available at hcv.lanl.gov/content/hcv-db/findmodel/findmodel.html ) . . however , 
 performing systematic hlrts is not the optimal strategy for model selection in 
 phylogenetics . this is because the model 
 that is finally selected can depend on the order in which the pairwise 
 comparisons are performed . the akaike information 
 criterion ( aic ) or its variant developed in the context of regression and 
 time - series analysis in small data sets ( aicc , ) is commonly used in phylogenetics ( e.g. , ) . one advantage of aic is 
 that it allows nonnested models to be compared , and it is easily implemented . however , in large data sets , both the hlrt and the aic tend to favor 
 parameter - rich models . a slightly different 
 approach was proposed to overcome this selection bias , the bayesian information 
 criterion ( bic : ) , which penalizes more 
 strongly parameter - rich models . all these model selection approaches ( aic , aicc , 
 and bic ) are available in modeltest and prottest . although aic , bic , and dt 
 are generally based on sound principles , they can in practice select different 
 substitution models . the reason for doing so is 
 not entirely clear , but it is likely due to the data having low - information 
 content . one prediction is that , when these model selection procedures end up 
 with different conclusions , all the selected models will return phylogenies 
 that are not significantly different . it is also possible that applying these 
 different criteria outside of the theoretical context in which they were 
 developed might lead to unexpected behaviors . for instance , aiccwas derived under gaussian assumptions for linear fixed - effect models , and other bias correction 
 terms exist under different assumptions . all the above 
 test procedures compare ratios of likelihood values penalized for an increase 
 in the dimension of one of the models , without directly accounting for 
 uncertainty in the estimates of model parameters . the bayesian approach to model selection , called 
 the bayes factor , directly incorporates this uncertainty . it is also more 
 intuitive as it directly assesses if the data are more probable under a given 
 model than under a different one ( e.g. , ) . an extension of this 
 approach makes it possible to select the model not only among the set of named 
 models ( jc to gtr ) but among all 203 nucleotide substitution models that are 
 possible . an alternative use or 
 interpretation of this approach is to integrate directly over the uncertainty 
 about the substitution model , so that the estimated phylogeny fully accounts 
 for several kinds of uncertainty : about the substitution models , and the 
 parameters entering each of these models . there is an 
 element of circularity in model selection , just as in sequence alignment . in 
 theory , when the hlrt is used for model selection , the topology used for all 
 the computations should be that of the maximum likelihood tree . in practice , 
 model selection is based on an initial topology obtained by a fast algorithm 
 such as neighbor - joining [ 105 , 106 ] ( default setting in 
 modeltest ) or by weighbor   ( default setting in 
 findmodel ) on jc distances without any correction for among - site rate variation . as mentioned above , it is known that the choice of a wrong model can affect the 
 tree that is estimated , but it is not always clear how the choice of a nonoptimal 
 topology to select the substitution model affects the tree that is finally 
 estimated . again , this issue with model choice disappears with bayesian 
 approaches that integrate over all possible time - reversible models as in . once the substitution model is 
 selected , the classical approach proceeds to reconstruct the phylogeny . this is probably one area 
 where phylogenetics has seen mixed progress over the last five years , due to 
 both the combinatorial and the computational complexities of phylogenetic 
 reconstruction . the combinatorial complexity relates to the extremely large number of 
 tree topologies that are possible with a large number of sequences . for instance , with five 
 sequences , there are 105 rooted topologies , but with ten sequences , this number 
 soars to over 34 million . an exhaustive search for the phylogeny that has the 
 highest probability is therefore not practical even with a moderate number of 
 sequences . besides , while heuristics exist ( e.g. , stepwise addition ; see   for a review ) , almost none of 
 these is guaranteed to converge on the optimum phylogenetic tree . the common 
 practice is then to use one of these heuristics to find a good starting tree , 
 and then modify repeatedly its topology more or less dramatically to explore 
 its neighborhood for better trees until a stopping rule is satisfied . the art here is in designing 
 efficient tree perturbation methods that adaptively strike a balance between 
 large topological modifications ( that almost always lead to a very different 
 tree with a poor score ) and small modifications ( that almost always lead to an 
 extremely similar tree with lower score ) . some of today 's challenges are about 
 choosing between methods that successfully explore large numbers of trees but 
 that can be costly in terms of computing time , and methods that are faster 
 but may miss some interesting trees . several programs such as leaphy , phyml , and garli are among the best - performing 
 software in a maximum likelihood setting . in a bayesian framework , three popular programs are 
 mrbayes , bambe , and beast . among all these programs and 
 approaches , phyml , garli , and beast are probably among the most efficient 
 programs in terms of computational speed , handling of large data sets and 
 thoroughness of the tree search . a first aspect 
 of the computational complexity relates to estimating the support of a 
 reconstructed phylogeny . this is more complicated than estimating a confidence 
 interval for a real - valued parameter such as a branch length , because a tree 
 topology is a graph and not a number . bootstrap proportions p can be perceived as testing the correctness of internal nodes , 
 and failing to do so , or 1p can be interpreted as a conservative 
 probability of falsely supporting monophyly . since bootstrap proportions 
 are either too liberal or too conservative depending on the exact 
 interpretation given to these values , it is difficult to adjust 
 the threshold below which monophyly can be confidently ruled out . alternatively , an intuitive 
 geometric argument was proposed to explain the conservativeness of bootstrap 
 probabilities , but the workaround was never 
 actually used in the community or implemented in any popular software . the 
 introduction of bayesian approaches in the late 1990s [ 36 , 118 ] suggested a novel approach to 
 estimate phylogenetic support with posterior probabilities . clade or 
 bipartition posterior probabilities can be relatively fast to compute , even for 
 large data sets analyzed under complicated substitution models . as in model selection , they 
 have a clear interpretation as they measure the probability that a clade is 
 correct , given the data and the model . early empirical studies found that posterior 
 probabilities of highly supported nodes were much larger than bootstrap 
 probabilities , and subsequent simulation 
 studies supported this observation ( e.g. , [ 121124 ] ) . some of these differences can be attributed to an artifact of the 
 simulation scheme that was employed , but more specific empirical 
 and simulation studies show that prior specifications can dramatically impact 
 posterior probabilities for trees and clades [ 115 , 126 , 127 ] . in the simplest case , the 
 analysis of simulated star trees with four sequences fails to give the expected 
 three unrooted topologies with equal probability ( 1/3 , 1/3 , 1/3 ) but returns 
 large posterior probabilities for an arbitrary topology [ 115 , 126 ] , even when infinitely long 
 sequences are used [ 128 , 129 ] ( ) . this phenomenon , called the 
 star - tree paradox , seems to disappear when 
 polytomies are assigned nonzero prior probabilities and when nonuniform priors 
 force internal branch length towards zero a theoretical 
 study shows that extremely simple markov chain monte carlo ( mcmc ) samplers , the 
 technique used to estimate posterior probabilities , could take an extremely 
 long time to converge . in practice , however , mcmc 
 samplers such as those implemented in mrbayes are much more sophisticated . in 
 particular , they include different types of moves   and use tempering , where some 
 of the chains of a single run are heated , to improve mixing . as a result , it is unclear 
 whether they suffer from extremely long convergence times . it is also expected 
 that current convergence diagnostic tools such as those implemented in mrbayes 
 would reveal convergence problems . finally , it is also argued 
 that these controversies such as exaggerated clade support , inconsistently 
 biased priors , and the impossibility of hypothesis testing disappear altogether 
 when posterior probabilities at internal nodes are abandoned in favor of 
 posterior probabilities for topologies   ( see section 2.4 below ) . the most 
 fundamental aspect of the computational complexity in phylogenetics is due to 
 the structure of the phylogenies : these are trees or binary graphs on which 
 computations are nested and interdependent , which makes these computations 
 intractable or np - hard . as a result , it is difficult 
 to adopt an efficient  divide and conquer  approach , where a large complicated 
 problem would be split into small simpler tasks , and to take advantage of 
 today 's commodity computing by distributing the computation over multicore 
 architectures or heterogeneous computer clusters . current strategies are 
 limited to distributing the computation of the discrete rate categories ( when 
 using a  +   substitution model ) and part of the search 
 algorithm , or simply to distribute different 
 maximum likelihood bootstrap replicates [ 53 , 54 ] or different mcmc samplers to 
 available processors . science proceeds by testing 
 hypotheses , and it is often necessary to compare phylogenies , for instance to 
 test whether a given data set supports the early divergence of gymnosperms with 
 respect to gnetales and angiosperms ( the anthophyte hypothesis ) , or whether the 
 gnetales diverged first ( the gnetales hypothesis ) [ 135 , 136 ] . because of the importance of 
 comparing phylogenies , a number of tests of molecular phylogenies were 
 developed early . however , this test is 
 invalid if one of the trees is the maximum likelihood tree . in this case because the sh test can be 
 very conservative , an approximately unbiased version was developed : the au test . paup and paml only implement 
 the kh and sh tests ; consel   also implements the au test . a bayesian version of these tests also exists , but the computations are 
 more demanding . indeed , the bayesian approach to hypothesis testing relies on computing the probability of 
 the data under a particular model . this quantity is usually not available as a 
 close - form equation , and it must be approximated numerically . the most 
 straightforward approximation is based on the harmonic mean of the likelihood 
 sampled from the posterior distribution . this approximation was 
 described several times in the context of phylogenies [ 141 , 143 ] and is available from most 
 bayesian programs such as mrbayes or beast . however , the approximation is 
 extremely sensitive to the behavior of the mcmc sampler [ 52 , 142 ] : if extremely low - likelihood 
 values happen to be sampled from the posterior distribution , the harmonic mean 
 will be dramatically affected . to date , a couple of more robust approximations 
 have been described and were shown to be preferable to the harmonic mean 
 estimator . the first is based on 
 thermodynamic integration   and is available in 
 phylobayes ( see table 1 ) . the second approximation   is based on a more direct 
 computation , but its availability is 
 currently limited to one specific model of evolution . while model selection is fully 
 justified on the ground of the bias - variance tradeoff , it should not be forgotten 
 that all these models are simplified representations of the actual substitution 
 process and are all therefore wrong . stated differently , if aic selects the gtr 
 + +i to 
 analyze a data set , it should be clear that this conclusion does not imply that 
 the data evolved under this model . one way to estimate adequacy or absolute model fit is to 
 perform a parametric bootstrap test : first , the selected model is compared with a multinomial model by 
 means of a lrt whose test statistic is s ( twice the log - likelihood difference ) ; the following steps determine the 
 distribution of s under the null 
 hypothesis that the selected model was the generating model ; second , the selected model is used to 
 simulate a large number of data sets ; third , 
 the model selection procedure ( lrt ) is repeated on each simulated data set , and 
 the corresponding test statistics s * 
 are recorded ; fourth , the p - value 
 is estimated as the number of times , the simulated s * test statistics are more 
 extreme ( > , for a one - sided test ) than the original value of s. the results of such tests suggest 
 that the selected substitution model is generally not an adequate 
 representation of the actual substitution process . of course , we do not need a 
 model that incorporates all the minute biological features of evolutionary 
 processes . as argued repeatedly ( e.g. , ) , we need useful models that capture enough of 
 reality of substitution processes to make accurate predictions and avoid 
 systematic biases such as long - branch attraction . more realistic models are obtained by accommodating heterogeneities in the evolutionary 
 process at the level of both sites ( space ) and lineages ( time ) . the simplest 
 site - heterogeneous model is one , where the aligned data are partitioned , 
 usually based on some prior information . for instance , first and second codon 
 positions are known to evolve slower than third codon positions in 
 protein - coding genes , or exposed residues might evolve faster than buried amino 
 acids in globular proteins . a number of models were suggested to analyze such 
 partitioned data sets ( e.g. , ) ; these models are 
 implemented in most general - purpose software ( e.g. , paml , paup , mrbayes ) and 
 can be combined with a a different approach consists 
 in considering that sites can be binned in a number of rate categories ; the use 
 of a dirichlet prior process then makes it possible both to determine the 
 appropriate number of categories and to assign sites to these categories ; the 
 application of this method to protein - coding genes was able to recover the 
 underlying codon structure of these genes . however , several studies 
 suggest that evolutionary patterns can be as heterogeneous within a priori partitions as among 
 partitions [ 37 , 151 ] . in one such approach , different models of evolution are assigned to 
 the different branches of the tree , which can make these models 
 extremely parameter - rich . such a large number of parameters can potentially 
 affect the accuracy of the phylogenetic inference ( see the  bias - variance 
 tradeoff  above ) and present computational issues ( long running times , large 
 memory requirements , and convergence issues ) . but now these branches must 
 be assigned a priori , and both 
 the determination of the number of sets and their placement on the tree can be 
 difficult ( but see section 4 below for a solution to a similar question ) . at 
 the other end of the spectrum of heterotachous models lies the simplest model 
 known as the covarion model , where sites can either be 
 variable along a branch , or not , and can switch between these two categories 
 across time ( e.g. , , also described in a bayesian 
 framework ) . between these two extremes are mixture models , which extend the covarion model by allowing 
 more categories of sites . a number of formulations exist , where each site is 
 assumed to have been generated by either several sets of branch lengths [ 157 , 158 ] or by several rate matrices [ 37 , 96 , 151 ] . one particularity of these 
 models is that they give a semiparametric perspective to the phylogenetic 
 estimation : if a single simple model can not approximate a complex substitution 
 process , the hope is that mixing several simple substitution models makes our 
 models more realistic . in some applications , mixture models can also be used to 
 avoid underestimating uncertainty , first when choosing a single model of 
 evolution and then ignoring this uncertainty when estimating the phylogeny . the 
 mixing therefore involves fitting at each site several sets of branch lengths , 
 or several substitution models to the data , and combining these models using a 
 certain weighting scheme . the difference between the numerous mixture models 
 that have been described lies in the choice of the weight factors , and how 
 these are obtained . in one approach , known as model averaging , a first 
 possibility is to assume that all the models are equally probable , which does 
 not work with an infinite number of models ( individual weights are zero in this 
 case ) . more critically in phylogenetics , this assumption is not coherent for 
 nested models since larger models should be more likely than each submodel . a 
 second possibility is to weight the models with respect to their probability of 
 being the generating model given the data . for practical purposes , the difficulty here is that model 
 averaging requires analyzing the data even for models that , a posteriori , turn out to have 
 extremely small probabilities or weights . this may be seen as a waste of 
 resources ( computing time and storage space ) . mixture models can work within the 
 framework of maximum likelihood , but the treatment of the weight factors is 
 complicated . a 
 prior distribution is set on the weight factors , and a special form of mcmc 
 sampler whose markov chain moves across models with different numbers of 
 parameters , a reversible - jump mcmc sampler ( rj - mcmc ) , is constructed . the 
 advantage of rj - mcmc samplers is that they allow estimating the phylogeny while 
 integrating over the uncertainty pertaining to the parameters of the 
 substitution model and even integrating over the model itself . another bayesian mixture model , named cat for categories , was developed to 
 analyze amino acid alignments . the cat model recently proved successful in a 
 number of empirical [ 159 , 160 ] and simulation   studies in avoiding the 
 artifact known as long - branch attraction . this model is freely 
 available in the phylobayes software ( see table 1 ) . models that 
 describe the evolution of doublets in rna genes , triplets in codon models [ 163 , 164 ] , or other models with local or 
 context dependencies [ 165167 ] exist , but complete dependence 
 models are still in their infancy and , so far , have only been implemented in a 
 bayesian framework [ 168 , 169 ] . one particularly interesting 
 feature of this approach is that complete dependence models incorporate 
 information about the three - dimensional ( 3d ) structure of proteins and 
 therefore permit the explicit modeling of structural constraints or of any 
 other site - interdependence pattern . the incorporation of 3d 
 structures also allows the establishment of a direct relationship between 
 evolution at the dna level and at the phenotypic level . this link between 
 genotype and phenotype is established via a proxy that plays the role of a 
 fitness function which , in retrospect , can be used to predict amino - acid 
 sequences compatible with a given target structure , that is , to help in protein 
 design . fitness functions are however 
 difficult to determine at the molecular level . in addition , while examples of 
 adaptive evolution at the morphological level abound , from darwin 's finches in 
 the galapagos   to cichlid fishes in the east 
 african lakes , the role of natural 
 selection in shaping the evolution of genomes is much more controversial [ 147 , 174 ] . first , the neutral theory of 
 molecular evolution asserts that much of the variation at the dna level is due 
 to the random fixation of mutations with no selective advantage . second , a compelling body of 
 evidence suggests that most of the genomic complexities have emerged by 
 nonadaptive processes . a number of statistical 
 approaches exist either to test neutrality at the population level or to detect 
 positive darwinian evolution at the species level . a shortcoming of neutrality 
 tests is their dependence on a demographic model   and their sensitivity to 
 processes of molecular evolution such as among - site rate variation . they also do not model 
 alternative hypotheses that would permit distinguishing negative selection from 
 adaptive evolution . the development of demographic models based on poisson 
 random fields   and composite likelihoods   makes it possible both to 
 estimate the strength of selection and to assess the impact of a variety of 
 scenarios on allele frequency spectra . but demographic 
 singularities such as bottlenecks can still generate spurious signatures of 
 positive selection [ 180 , 181 ] . when effective population sizes are no longer a concern , for instance in studies at or above 
 the species level , the detection of positive selection in protein - coding genes 
 usually relies on codon models [ 163 , 164 ] ( see   for a review including 
 methods based on amino - acid models ) . codon models permit distinguishing between 
 synonymous substitutions , which are likely to be neutral , and nonsynonymous 
 substitutions , which are directly exposed to the action of selection . if 
 synonymous and nonsynonymous substitutions accumulate at the same rate , then 
 the protein - coding gene is likely to evolve neutrally . alternatively , if 
 nonsynonymous substitutions accumulate slower than synonymous substitutions , it 
 must be because nonsynonymous substitutions are deleterious and this suggests 
 the action of purifying selection . conversely , the accumulation of 
 nonsynonymous substitutions faster than synonymous substitutions suggests the 
 action of positive selection . the nonsynonymous to synonymous rate ratio , 
 denoted  = dn / ds , 
 is therefore interpreted as a measure of selection at the protein level , with  = 1 , < 1 and 
 > 1 indicating neutral evolution , negative or positive selection , 
 respectively . this ratio is also denoted ka / ks , in particular in studies 
 that rely on counts of nonsynonymous and synonymous sites ( e.g. , ) . 
 an extension exists to detect selection in noncoding regions , and a promising phylogenetic 
 hidden markov or phylo - hmm model permits detection of selection in overlapping 
 genes . these rate 
 ratios can be estimated by a number of methods implemented in mega , dambe , 
 hyphy , and paml . the most intuitive 
 methods , called counting methods , work in three steps : ( i ) count synonymous and 
 nonsynonymous sites , ( ii ) count the observed differences at these sites , and 
 ( iii ) apply corrections for multiple substitutions . counting methods are however 
 not optimal in the sense that most work on pairs of sequences and therefore , 
 just like neighbor - joining , fail to account for all the information contained 
 in an alignment . in addition , simulations suggest that counting methods can be 
 sensitive to a variety of biases such as unequal transition and transversion 
 rates , or uneven base , or codon frequencies . counting methods that 
 incorporate these biases perform generally better than those that do not , but 
 the maximum likelihood method still appears more robust to sever biases . in addition , the maximum 
 likelihood method that accounts for all the information in a data set has good 
 power and good accuracy to detect positive selection [ 188 , 189 ] . however , the 
 first studies using these methods found little evidence for adaptive evolution 
 essentially because they were averaging    rate ratios over both lineages and sites . branch models were then developed [ 190 , 191 ] quickly followed by site 
 models [ 192196 ] and by branch - site models [ 189 , 197 ] . all these approaches , as 
 implemented in paml , rely on likelihood ratio tests to detect adaptive 
 evolution : a model where adaptive evolution is permitted is compared with a 
 null model where  can not be greater 
 than one . simulations show that some of these tests are conservative , so that detection of 
 adaptive evolution should be safe as long as convergence of the analyses is 
 carefully checked , including in large - scale 
 analyses . if the model allowing adaptive 
 evolution explains the data significantly better than the null model , then an 
 empirical bayes approach can be used to identify which sites are likely to 
 evolve adaptively . the empirical bayes approach 
 relies on estimates of the model parameters , which can have large sampling 
 errors in small data sets . because these sampling errors can cause the 
 empirical bayes site identification to be unreliable , a bayes empirical bayes 
 approach was proposed and was shown to have good power and low - false positive 
 rates . yet , simulations showed that 
 they did not improve further on bayes empirical bayes estimates , so that the computational 
 overhead incurred by full bayes methods may not be necessary in this case . one 
 particular case , where a bayesian approach is however required , is to tell the 
 signature of adaptive evolution from that of recombination , as these two 
 processes can leave similar signals in dna sequences . indeed , simulations show 
 that recombination can lead to false positive rates as large as 90% when trying 
 to detect adaptive evolution . the codon model with 
 recombination implemented in omegamap can then be used to tease 
 apart these two processes ( e.g. , see ) . the estimation of the dates when 
 species diverged is often perceived to be as important as estimating the 
 phylogeny itself . this explains why so - called  dating methods  were first 
 wished for when molecular phylogenies were first reconstructed . in spite of over four 
 decades of history , one of the reasons for this slow progress is that , unlike the other parts of 
 phylogenetic analysis , divergence times are parameters that can not be estimated 
 directly . only sitewise likelihood values and distances between pairs of 
 sequences are identifiable , that is , directly estimable . distances are 
 expressed as a number of substitutions per site ( sub / site ) and 
 can be decomposed as the product of two quantities : a rate of evolution 
 ( sub / site / unit of time ) and a time duration ( unit of time ) . as a result , time 
 durations and , likewise , divergence times can not be estimated without making an 
 additional assumption on the rates of evolution . the simplest assumption is to 
 posit that rates are constant in time , which is known as the molecular clock 
 hypothesis . this hypothesis can be 
 tested , for instance , with paup or paml , by means of a likelihood ratio test that 
 compares a constrained model ( clock ) with an unconstrained model ( no clock ) . these two models are nested , so that twice the log - likelihood difference 
 asymptotically follows a  distribution . if n sequences are analyzed , the 
 constrained model estimates n  1 divergence 
 times , while the unconstrained model estimates 2n  3 branch lengths . the degree of freedom of this test is then ( 2n  3 )  ( n  1 ) = n  2 . the systematic test of the 
 molecular clock assumption on recent data shows that this hypothesis is too 
 often untenable . the most recent work has then focused on relaxing this assumption , and three different 
 directions have emerged . a first possibility is to 
 relax the clock globally on the 
 phylogeny , but to assume that the hypothesis still holds locally for closely related species [ 210212 ] . recent developments of these 
 local clock models now allow the use of multiple calibration points and of 
 multiple genes , the automatic placement of 
 the clocks on the tree   and the estimation of the 
 number of local clocks . the second possibility to 
 relax the global clock assumption is to assume that rates of evolution evolve 
 in an autocorrelated manner along lineages and to minimize the amount of rate 
 change over the entire phylogeny . the most popular approach in the plant 
 community is sanderson 's penalized likelihood , implemented in r8s . this approach performs well 
 on data sets for which the actual fossil dates are known   but still tends to 
 underestimate the actual amount of rate change . developed a 
 bayesian framework where rates of evolution change in an autocorrelated manner 
 across lineages [ 4547 ] : the rate of evolution of a 
 branch depends on the rate of evolution of its parental branch ; the branches 
 emanating from the root require a special treatment . these bayesian models work 
 by modeling how rates of evolution change in time ( rate prior ) , and how the 
 speciation / population process shapes the distribution of divergence times 
 ( speciation prior ) . these prior distributions can actually be interpreted as 
 penalty functions [ 45 , 209 ] , and they can have simple or 
 more complicated forms . the multidivtime program [ 4547 ] is extremely quick to analyze 
 data thanks to the use of a multivariate normal approximation of the likelihood 
 surface . further work suggested that it might not always be 
 the best performing rate prior [ 218220 ] , but these latter studies had 
 two potential shortcomings : ( i ) they were based on a speciation prior that was 
 so strong that it biased divergence times towards the age of the 
 fossil root [ 219 , 221 ] , and ( ii ) they used a 
 statistical procedure , the posterior bayes factor , that is potentially 
 inconsistent . one potential limitation of the bayesian approach described so 
 far is its dependence on one single tree topology , which must be either known 
 ahead of time or estimated by other means . found a 
 way to relax this requirement by positing that rates of evolution are 
 uncorrelated across lineages , while all the branches of the tree are 
 constrained to follow exactly the same rate prior . as a result , their approach 
 is able to estimate the most probable tree ( given the data and the substitution 
 model ) , the divergence times and the position of the root even without any 
 outgroup or without resorting to a nonreversible model of substitution . further 
 argue that the use of explicit models of rate variation over time might 
 contribute to improved phylogenetic inference . in addition , when the focus 
 is on estimating divergence times , a recent analysis suggests that this 
 uncorrelated model of rate change could outperform the methods described above 
 to accommodate rapid rate change among lineages . implemented in beast , this 
 approach offers a variety of substitution models and prior distributions and 
 presents a graphic user interface that will appeal to numerous researchers . with the advent of high - throughput 
 sequencing technologies such as the whole - genome shotgun approach by pyrosequencing , fast , cheap , and accurate 
 genomic information is becoming available for a growing number of species . if low coverage limits the 
 complete assembly of many genome projects , it still allows the quick access to 
 draft genomes for a growing number of species . as a result , phylogenetic 
 inference can now incorporate large numbers of expressed sequence tags ( ests ) , 
 genes , and occasionally complete 
 genomes . the motivation for 
 developing these so - called phylogenomic approaches is their presumed ability to 
 return fully resolved and well - supported trees by decreasing both sampling 
 errors   and misleading signals due 
 for instance to horizontal gene transfer   or to hidden paralogy . in practice , these 
 large - scale studies can give the impression that incongruence is resolved , but they also can fail to 
 address systematic errors due to the use of too simple models . if the genes incorporated in 
 phylogenomic studies are often concatenated to limit the number of parameters 
 entering the model , it remains important to allow sitewise heterogeneities . if partition models can 
 reduce systematic biases , bayesian mixture models such 
 as cat   appear to be robust to 
 long - branch attraction , a rampant issue in phylogenomics . all together , the 
 accumulation of genomic data and these latest methodological developments seem 
 to make the reconstruction of the tree of life finally within reach . in 
 comparison , dating the tree of life is still in its infancy , even if a number 
 of initiatives such as the timetree server are being developed . these resources are limited 
 to some vertebrates but will hopefully soon be extended to include other large 
 taxonomic groups such as plants . to achieve this goal , however , phylogenetic 
 studies should systematically incorporate divergence times , as is now routine 
 in some research communities ( e.g. , ) . this joint estimation of 
 time and trees is today facilitated by the availability of user - friendly 
 programs such as beast . the near future will probably see the development of 
 mixture models for molecular dating and more sophisticated models that 
 integrate most of the topics discussed here from sequence alignment to 
 detection of sites under selection into one single but yet user - friendly   toolbox .
the reconstruction of phylogenies is becoming an increasingly simple activity . 
 this is mainly due to two reasons : the democratization of computing power and the increased availability of sophisticated yet user - friendly software . 
 this review describes some of the latest additions to the phylogenetic toolbox , along with some of their theoretical and practical limitations . 
 it is shown that bayesian methods are under heavy development , as they offer the possibility to solve a number of long - standing issues and to integrate several steps of the phylogenetic analyses into a single framework . 
 specific topics include not only phylogenetic reconstruction , but also the comparison of phylogenies , the detection of adaptive evolution , and the estimation of divergence times between species .