Computational phylogenetics

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thereby assuming that the optimal solution cannot occupy that region). Identifying a good bound is the most challenging aspect of the algorithm's application to phylogenetics. A simple way of defining the bound is a maximum number of assumed evolutionary changes allowed per tree. A set of criteria known as Zharkikh's rules severely limit the search space by defining characteristics shared by all candidate "most parsimonious" trees. The two most basic rules require the elimination of all but one redundant sequence (for cases where multiple observations have produced identical data) and the elimination of character sites at which two or more states do not occur in at least two species. Under ideal conditions these rules and their associated algorithm would completely define a tree.

850:" between the model and the input data. However, care must be taken in using these results, since a more complex model with more parameters will always have a higher likelihood than a simplified version of the same model, which can lead to the naive selection of models that are overly complex. For this reason model selection computer programs will choose the simplest model that is not significantly worse than more complex substitution models. A significant disadvantage of the LRT is the necessity of making a series of pairwise comparisons between models; it has been shown that the order in which the models are compared has a major effect on the one that is eventually selected. 866:(BIC), has a similar basic interpretation but penalizes complex models more heavily. Determining the most suitable model for phylogeny reconstruction constitutes a fundamental step in numerous evolutionary studies. However, various criteria for model selection are leading to debate over which criterion is preferable. It has recently been shown that, when topologies and ancestral sequence reconstruction are the desired output, choosing one criterion over another is not crucial. Instead, using the most complex nucleotide substitution model, GTR+I+G, leads to similar results for the inference of tree topology and ancestral sequences. 673:, is often used to reduce the search space by efficiently calculating the likelihood of subtrees. The method calculates the likelihood for each site in a "linear" manner, starting at a node whose only descendants are leaves (that is, the tips of the tree) and working backwards toward the "bottom" node in nested sets. However, the trees produced by the method are only rooted if the substitution model is irreversible, which is not generally true of biological systems. The search for the maximum-likelihood tree also includes a branch length optimization component that is difficult to improve upon algorithmically; general 1031:

result in homoplasies. For morphological data, unfortunately, the only objective way to determine convergence is by the construction of a tree – a somewhat circular method. Even so, weighting homoplasious characters does indeed lead to better-supported trees. Further refinement can be brought by weighting changes in one direction higher than changes in another; for instance, the presence of thoracic wings almost guarantees placement among the pterygote insects because, although wings are often lost secondarily, there is no evidence that they have been gained more than once.

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measurements of average body size, lengths or sizes of particular bones or other physical features, or even behavioral manifestations. Of course, since not every possible phenotypic characteristic could be measured and encoded for analysis, the selection of which features to measure is a major inherent obstacle to the method. The decision of which traits to use as a basis for the matrix necessarily represents a hypothesis about which traits of a species or higher taxon are evolutionarily relevant. Morphological studies can be confounded by examples of

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However, the most appropriate representation of continuously varying phenotypic measurements is a controversial problem without a general solution. A common method is simply to sort the measurements of interest into two or more classes, rendering continuous observed variation as discretely classifiable (e.g., all examples with humerus bones longer than a given cutoff are scored as members of one state, and all members whose humerus bones are shorter than the cutoff are scored as members of a second state). This results in an easily manipulated

1163:, extinct taxa almost invariably have higher proportions of missing data than living ones. However, despite these limitations, the inclusion of fossils is invaluable, as they can provide information in sparse areas of trees, breaking up long branches and constraining intermediate character states; thus, fossil taxa contribute as much to tree resolution as modern taxa. Fossils can also constrain the age of lineages and thus demonstrate how consistent a tree is with the stratigraphic record; 797:. The most general possible time-reversible model, called the GTR model, has six mutation rate parameters. An even more generalized model known as the general 12-parameter model breaks time-reversibility, at the cost of much additional complexity in calculating genetic distances that are consistent among multiple lineages. One possible variation on this theme adjusts the rates so that overall GC content - an important measure of DNA double helix stability - varies over time. 5554: 4255: 5564: 36: 662:; roughly, a tree that requires more mutations at interior nodes to explain the observed phylogeny will be assessed as having a lower probability. This is broadly similar to the maximum-parsimony method, but maximum likelihood allows additional statistical flexibility by permitting varying rates of evolution across both lineages and sites. In fact, the method requires that evolution at different sites and along different lineages must be 3932: 346:. For example, given only a pairwise alignment with a gap region, it is impossible to determine whether one sequence bears an insertion mutation or the other carries a deletion. The problem is magnified in MSAs with unaligned and nonoverlapping gaps. In practice, sizable regions of a calculated alignment may be discounted in phylogenetic tree construction to avoid integrating noisy data into the tree calculation. 4267: 3838: 766:). The longer the amount of time after divergence, the more likely it becomes that two mutations occur at the same nucleotide site. Simple genetic distance calculations will thus undercount the number of mutation events that have occurred in evolutionary history. The extent of this undercount increases with increasing time since divergence, which can lead to the phenomenon of 3290:

The GoLife program builds upon the AToL program by accommodating the complexity of diversification patterns across all of life's history. Our current knowledge of processes such as hybridization, endosymbiosis and lateral gene transfer makes clear that the evolutionary history of life on Earth cannot

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group means for a character are first ordered by size. The pooled within-group standard deviation is calculated ... and differences between adjacent means ... are compared relative to this standard deviation. Any pair of adjacent means is considered different and given different integer scores ... if

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genomes (~16,000 nucleotides, in many animals). However, simulations have shown that it is more important to increase the number of taxa in the matrix than to increase the number of characters, because the more taxa there are, the more accurate and more robust is the resulting phylogenetic tree. This

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can also move genes between otherwise distinct species and sometimes even genera, complicating phylogenetic analysis based on genes. This phenomenon can contribute to "incomplete lineage sorting" and is thought to be a common phenomenon across a number of groups. In species level analysis this can be

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Jackknifing in phylogenetics is a similar procedure, except the columns of the matrix are sampled without replacement. Pseudoreplicates are generated by randomly subsampling the data—for example, a "10% jackknife" would involve randomly sampling 10% of the matrix many times to evaluate nodal support.

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is a data set of the same size (100 points) randomly sampled from the original data, with replacement. That is, each original data point may be represented more than once in the pseudoreplicate, or not at all. Statistical support involves evaluation of whether the original data has similar properties

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calculation in conjunction with a scoring function that penalizes gaps and mismatches, thereby favoring the tree that introduces a minimal number of such events (an alternative view holds that the trees to be favored are those that maximize the amount of sequence similarity that can be interpreted as

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The most naive way of identifying the most parsimonious tree is simple enumeration - considering each possible tree in succession and searching for the tree with the smallest score. However, this is only possible for a relatively small number of sequences or species because the problem of identifying

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Distance-matrix methods of phylogenetic analysis explicitly rely on a measure of "genetic distance" between the sequences being classified, and therefore, they require an MSA as an input. Distance is often defined as the fraction of mismatches at aligned positions, with gaps either ignored or counted

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By contrast, unrooted trees plot the distances and relationships between input sequences without making assumptions regarding their descent. An unrooted tree can always be produced from a rooted tree, but a root cannot usually be placed on an unrooted tree without additional data on divergence rates,

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Maximum Likelihood (also likelihood) optimality criterion is the process of finding the tree topology along with its branch lengths that provides the highest probability observing the sequence data, while parsimony optimality criterion is the fewest number of state-evolutionary changes required for a

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Another important factor that affects the accuracy of tree reconstruction is whether the data analyzed actually contain a useful phylogenetic signal, a term that is used generally to denote whether a character evolves slowly enough to have the same state in closely related taxa as opposed to varying

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As with all statistical analysis, the estimation of phylogenies from character data requires an evaluation of confidence. A number of methods exist to test the amount of support for a phylogenetic tree, either by evaluating the support for each sub-tree in the phylogeny (nodal support) or evaluating

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score, and its companion POY uses an iterative method that couples the optimization of the phylogenetic tree with improvements in the corresponding MSA. However, the use of these methods in constructing evolutionary hypotheses has been criticized as biased due to the deliberate construction of trees

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of the tree are scored and summed over all the nodes in each possible tree. The lowest-scoring tree sum provides both an optimal tree and an optimal MSA given the scoring function. Because the method is highly computationally intensive, an approximate method in which initial guesses for the interior

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Ultimately, there is no way to measure whether a particular phylogenetic hypothesis is accurate or not, unless the true relationships among the taxa being examined are already known (which may happen with bacteria or viruses under laboratory conditions). The best result an empirical phylogeneticist

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between the true model and the model being tested. It can be interpreted as a likelihood estimate with a correction factor to penalize overparameterized models. The AIC is calculated on an individual model rather than a pair, so it is independent of the order in which models are assessed. A related

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All substitution models assign a set of weights to each possible change of state represented in the sequence. The most common model types are implicitly reversible because they assign the same weight to, for example, a G>C nucleotide mutation as to a C>G mutation. The simplest possible model,

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as it subdivides the problem space into smaller regions. As its name implies, it requires as input both a branching rule (in the case of phylogenetics, the addition of the next species or sequence to the tree) and a bound (a rule that excludes certain regions of the search space from consideration,

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and thus a longer branch length than any other sequence, and it will appear near the root of a rooted tree. Choosing an appropriate outgroup requires the selection of a sequence that is moderately related to the sequences of interest; too close a relationship defeats the purpose of the outgroup and

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In phylogenetics, bootstrapping is conducted using the columns of the character matrix. Each pseudoreplicate contains the same number of species (rows) and characters (columns) randomly sampled from the original matrix, with replacement. A phylogeny is reconstructed from each pseudoreplicate, with

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For example, in maximum parsimony analysis, there may be many trees with the same parsimony score. A strict consensus tree would show which nodes are found in all equally parsimonious trees, and which nodes differ. Consensus trees are also used to evaluate support on phylogenies reconstructed with

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The selection of an appropriate model is critical for the production of good phylogenetic analyses, both because underparameterized or overly restrictive models may produce aberrant behavior when their underlying assumptions are violated, and because overly complex or overparameterized models are

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between two related trees. The use of Bayesian methods in phylogenetics has been controversial, largely due to incomplete specification of the choice of move set, acceptance criterion, and prior distribution in published work. Bayesian methods are generally held to be superior to parsimony-based

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Whereas likelihood methods find the tree that maximizes the probability of the data, a Bayesian approach recovers a tree that represents the most likely clades, by drawing on the posterior distribution. However, estimates of the posterior probability of clades (measuring their 'support') can be

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events to explain the observed sequence data. Some ways of scoring trees also include a "cost" associated with particular types of evolutionary events and attempt to locate the tree with the smallest total cost. This is a useful approach in cases where not every possible type of event is equally

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The least-squares criterion applied to these distances is more accurate but less efficient than the neighbor-joining methods. An additional improvement that corrects for correlations between distances that arise from many closely related sequences in the data set can also be applied at increased

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method for clustering based on genetic distance. Closely related sequences are given more weight in the tree construction process to correct for the increased inaccuracy in measuring distances between distantly related sequences. The distances used as input to the algorithm must be normalized to

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representing a mapping from each of the taxa being compared to representative measurements for each of the phenotypic characteristics being used as a classifier. The types of phenotypic data used to construct this matrix depend on the taxa being compared; for individual species, they may involve

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methods can be used to analyze them. For molecular sequences, this problem is exacerbated when the taxa under study have diverged substantially. As time since the divergence of two taxa increase, so does the probability of multiple substitutions on the same site, or back mutations, all of which

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generates a posterior distribution of highly probable trees given the data and evolutionary model, rather than a single "best" tree. The trees in the posterior distribution generally have many different topologies. When the input data is variant allelic frequency data (VAF), the tool EXACT can

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Some phenotypic classifications, particularly those used when analyzing very diverse groups of taxa, are discrete and unambiguous; classifying organisms as possessing or lacking a tail, for example, is straightforward in the majority of cases, as is counting features such as eyes or vertebrae.

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Horizontal gene transfer has complicated the determination of phylogenies of organisms, and inconsistencies in phylogeny have been reported among specific groups of organisms depending on the genes used to construct evolutionary trees. The only way to determine which genes have been acquired

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A comprehensive step-by-step protocol on constructing phylogenetic trees, including DNA/Amino Acid contiguous sequence assembly, multiple sequence alignment, model-test (testing best-fitting substitution models) and phylogeny reconstruction using Maximum Likelihood and Bayesian Inference, is

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The statistical rigor of the bootstrap test has been empirically evaluated using viral populations with known evolutionary histories, finding that 70% bootstrap support corresponds to a 95% probability that the clade exists. However, this was tested under ideal conditions (e.g. no change in

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In general, the more data that are available when constructing a tree, the more accurate and reliable the resulting tree will be. Missing data are no more detrimental than simply having fewer data, although the impact is greatest when most of the missing data are in a small number of taxa.

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algorithms. Although counting the total number of trees for a nontrivial number of input sequences can be complicated by variations in the definition of a tree topology, it is always true that there are more rooted than unrooted trees for a given number of inputs and choice of parameters.

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and whose branch lengths closely reproduce the observed distances between sequences. Distance-matrix methods may produce either rooted or unrooted trees, depending on the algorithm used to calculate them. They are frequently used as the basis for progressive and iterative types of

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Many methods for assessing nodal support involve consideration of multiple phylogenies. The consensus tree summarizes the nodes that are shared among a set of trees. In a *strict consensus,* only nodes found in every tree are shown, and the rest are collapsed into an unresolved

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Because morphological data is extremely labor-intensive to collect, whether from literature sources or from field observations, reuse of previously compiled data matrices is not uncommon, although this may propagate flaws in the original matrix into multiple derivative analyses.

770:, or the misassignment of two distantly related but convergently evolving sequences as closely related. The maximum parsimony method is particularly susceptible to this problem due to its explicit search for a tree representing a minimum number of distinct evolutionary events. 277:

of phenotypes. A major challenge in constructing useful classes is the high likelihood of inter-taxon overlap in the distribution of the phenotype's variation. The inclusion of extinct taxa in morphological analysis is often difficult due to absence of or incomplete

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The statistical support for a node in Bayesian inference is expected to reflect the probability that a clade really exists given the data and evolutionary model. Therefore, the threshold for accepting a node as supported is generally higher than for bootstrapping.

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If more taxa are added to the analysis, the gaps between taxa may become so small that all information is lost. Generalized gap coding works around that problem by comparing individual pairs of taxa rather than considering one set that contains all of the taxa.

138:, and minimum evolution are typical optimality criteria used to assess how well a phylogenetic tree topology describes the sequence data. Nearest Neighbour Interchange (NNI), Subtree Prune and Regraft (SPR), and Tree Bisection and Reconnection (TBR), known as 1131:

Morphological characters that sample a continuum may contain phylogenetic signal, but are hard to code as discrete characters. Several methods have been used, one of which is gap coding, and there are variations on gap coding. In the original form of gap

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as mismatches. Distance methods attempt to construct an all-to-all matrix from the sequence query set describing the distance between each sequence pair. From this is constructed a phylogenetic tree that places closely related sequences under the same

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reflecting minimal evolutionary events. This, in turn, has been countered by the view that such methods should be seen as heuristic approaches to find the trees that maximize the amount of sequence similarity that can be interpreted as homology.

789:, assigns an equal probability to every possible change of state for a given nucleotide base. The rate of change between any two distinct nucleotides will be one-third of the overall substitution rate. More advanced models distinguish between 705:

of the possible trees, which may simply be the probability of any one tree among all the possible trees that could be generated from the data, or may be a more sophisticated estimate derived from the assumption that divergence events such as

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The most common method for assessing tree support is to evaluate the statistical support for each node on the tree. Typically, a node with very low support is not considered valid in further analysis, and visually may be collapsed into a

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Most Bayesian inference methods utilize a Markov-chain Monte Carlo iteration, and the initial steps of this chain are not considered reliable reconstructions of the phylogeny. Trees generated early in the chain are usually discarded as

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of DNA evolution. The distance correction is only necessary in practice when the evolution rates differ among branches. Another modification of the algorithm can be helpful, especially in case of concentrated distances (please refer to

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Qu Y, Zhang R, Quan Q, Song G, Li SH, Lei F (December 2012). "Incomplete lineage sorting or secondary admixture: disentangling historical divergence from recent gene flow in the Vinous-throated parrotbill (Paradoxornis webbianus)".

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The basic assumption underlying the mathematical model of cladistics is a situation where species split neatly in bifurcating fashion. While such an assumption may hold on a larger scale (bar horizontal gene transfer, see above),

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evolutionary rates, symmetric phylogenies). In practice, values above 70% are generally supported and left to the researcher or reader to evaluate confidence. Nodes with support lower than 70% are typically considered unresolved.

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Independent information about the relationship between sequences or groups can be used to help reduce the tree search space and root unrooted trees. Standard usage of distance-matrix methods involves the inclusion of at least one

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of the branch lengths for two individual branches must equal the expected value of the sum of the two branch distances - a property that applies to biological sequences only when they have been corrected for the possibility of

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Ruan Y, House GL, Ekanayake S, Schütte U, Bever JD, Tang H, Fox G (26 May 2014). "Integration of clustering and multidimensional scaling to determine phylogenetic trees as spherical phylograms visualized in 3 dimensions".

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sampling algorithms, although the choice of move set varies; selections used in Bayesian phylogenetics include circularly permuting leaf nodes of a proposed tree at each step and swapping descendant subtrees of a random

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More recent phylogenetic tree/MSA methods use heuristics to isolate high-scoring, but not necessarily optimal, trees. The MALIGN method uses a maximum-parsimony technique to compute a multiple alignment by maximizing a

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Models may also allow for the variation of rates with positions in the input sequence. The most obvious example of such variation follows from the arrangement of nucleotides in protein-coding genes into three-base

1043:. Vertical gene transfer is the passage of genes from parent to offspring, and horizontal (also called lateral) gene transfer occurs when genes jump between unrelated organisms, a common phenomenon especially in 1973:"Parsimony analysis of unaligned sequence data: maximization of homology and minimization of homoplasy, not Minimization of operationally defined total cost or minimization of equally weighted transformations" 188:

that represents the historical relationships between the species being analyzed. The historical species tree may also differ from the historical tree of an individual homologous gene shared by those species.

1105:, it has become feasible to gather large amounts of data (DNA or amino acid sequences) to infer phylogenetic hypotheses. For example, it is not rare to find studies with character matrices based on whole 235:

The set of all possible phylogenetic trees for a given group of input sequences can be conceptualized as a discretely defined multidimensional "tree space" through which search paths can be traced by

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than others; logically, such characters should be given less weight in the reconstruction of a tree. Weights in the form of a model of evolution can be inferred from sets of molecular data, so that

969:. The most common method of evaluating nodal support in a Bayesian phylogenetic analysis is to calculate the percentage of trees in the posterior distribution (post-burn-in) which contain the node. 880:

to visualize the clustering result for the sequences in 3D, and then map the phylogenetic tree onto the clustering result. A better tree usually has a higher correlation with the clustering result.

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problems first applied to phylogenetics in the early 1980s. Branch and bound is particularly well suited to phylogenetic tree construction because it inherently requires dividing a problem into a

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but has been criticized for poor reporting of the basis for the class definitions and for sacrificing information compared to methods that use a continuous weighted distribution of measurements.

142:, are deterministic algorithms to search for optimal or the best phylogenetic tree. The space and the landscape of searching for the optimal phylogenetic tree is known as phylogeny search space. 224:

from the hypothesized MRCA. Identification of a root usually requires the inclusion in the input data of at least one "outgroup" known to be only distantly related to the sequences of interest.

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These measures each have their weaknesses. For example, smaller or larger clades tend to attract larger support values than mid-sized clades, simply as a result of the number of taxa in them.

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to the analysis. Care should also be taken to avoid situations in which the species from which the sequences were taken are distantly related, but the gene encoded by the sequences is highly

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Because many characters involve embryological, or soft-tissue or molecular characters that (at best) hardly ever fossilize, and the interpretation of fossils is more ambiguous than that of

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A non traditional way of evaluating the phylogenetic tree is to compare it with clustering result. One can use a Multidimensional Scaling technique, so called Interpolative Joining to do

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the same methods used to reconstruct the phylogeny from the original data. For each node on the phylogeny, the nodal support is the percentage of pseudoreplicates containing that node.

915:. Less conservative methods, such as the *majority-rule consensus* tree, consider nodes that are supported by a given percentage of trees under consideration (such as at least 50%). 931:

is a method for inferring the variability of data that has an unknown distribution using pseudoreplications of the original data. For example, given a set of 100 data points, a

666:. Maximum likelihood is thus well suited to the analysis of distantly related sequences, but it is believed to be computationally intractable to compute due to its NP-hardness. 614:

The Sankoff-Morel-Cedergren algorithm was among the first published methods to simultaneously produce an MSA and a phylogenetic tree for nucleotide sequences. The method uses a

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Gaubert P, Wozencraft WC, Cordeiro-Estrela P, Veron G (December 2005). "Mosaics of convergences and noise in morphological phylogenies: what's in a viverrid-like carnivoran?".

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as a result of gene exchange between various bacteria leading to multi-drug-resistant bacterial species. There have also been well-documented cases of horizontal gene transfer

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Fitch WM, Markowitz E (October 1970). "An improved method for determining codon variability in a gene and its application to the rate of fixation of mutations in evolution".

370:. The main disadvantage of distance-matrix methods is their inability to efficiently use information about local high-variation regions that appear across multiple subtrees. 817:. A less hypothesis-driven example that does not rely on ORF identification simply assigns to each site a rate randomly drawn from a predetermined distribution, often the 762:

between two sequences increases linearly only for a short time after the two sequences diverge from each other (alternatively, the distance is linear only shortly before

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The problem of character coding is very different in molecular analyses, as the characters in biological sequence data are immediate and discretely defined - distinct

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dealt with by larger sampling or better whole genome analysis. Often the problem is avoided by restricting the analysis to fewer, not closely related specimens.

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quite wide of the mark, especially in clades that aren't overwhelmingly likely. As such, other methods have been put forwards to estimate posterior probability.

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Prevosti FJ, Chemisquy MA (2009). "The impact of missing data on real morphological phylogenies: Influence of the number and distribution of missing entries".

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at various sites along the gene or amino acid sequences being studied. At their simplest, substitution models aim to correct for differences in the rates of

216:(MRCA), usually an inputed sequence that is not represented in the input. Genetic distance measures can be used to plot a tree with the input sequences as 727:

methods; they can be more prone to long-branch attraction than maximum likelihood techniques, although they are better able to accommodate missing data.

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prevent large artifacts in computing relationships between closely related and distantly related groups. The distances calculated by this method must be

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assume that the largest set of genes that have been inherited together have been inherited vertically; this requires analyzing a large number of genes.

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Funk DJ, Omland KE (2003). "Species-level paraphyly and polyphyly: Frequency, causes, and consequences, with insights from animal mitochondrial DNA".

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Some tools that use Bayesian inference to infer phylogenetic trees from variant allelic frequency data (VAFs) include Canopy, EXACT, and PhyloWGS.

2607:"Inferring pattern and process: maximum-likelihood implementation of a nonhomogeneous model of DNA sequence evolution for phylogenetic analysis" 1004:

can hope to attain is a tree with branches that are well supported by the available evidence. Several potential pitfalls have been identified:

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Strait DS, Grine FE (December 2004). "Inferring hominoid and early hominid phylogeny using craniodental characters: the role of fossil taxa".

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Swiderski DL, Zelditch ML, Fink WL (September 1998). "Why morphometrics is not special: coding quantitative data for phylogenetic analysis".

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Some tools that use maximum likelihood to infer phylogenetic trees from variant allelic frequency data (VAFs) include AncesTree and CITUP.

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compute the probabilities of trees exactly, for small, biologically relevant tree sizes, by exhaustively searching the entire tree space.

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Ray S, Jia B, Safavi S, van Opijnen T, Isberg R, Rosch J, Bento J (22 August 2019). "Exact inference under the perfect phylogeny model".

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Bootstrap support can provide high estimates of node support as a result of noise in the data rather than the true existence of a clade.

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can be used to produce phylogenetic trees in a manner closely related to the maximum likelihood methods. Bayesian methods assume a prior

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Blomberg SP, Garland T, Ives AR (April 2003). "Testing for phylogenetic signal in comparative data: behavioral traits are more labile".

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Goloboff PA (1997). "Self-Weighted Optimization: Tree Searches and Character State Reconstructions under Implied Transformation Costs".

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alignments are refined one node at a time. Both the full and the approximate version are in practice calculated by dynamic programming.

334:. For a given gapped MSA, several rooted phylogenetic trees can be constructed that vary in their interpretations of which changes are " 443: 282:

records, but has been shown to have a significant effect on the trees produced; in one study only the inclusion of extinct species of

2437:"Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing" 5539: 4994: 3870: 5595: 4345: 2965:"Frequentist properties of Bayesian posterior probabilities of phylogenetic trees under simple and complex substitution models" 2928:

Hillis DM, Bull JJ (1993). "An Empirical Test of Bootstrapping as a Method for Assessing Confidence in Phylogenetic Analysis".

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sequence known to be only distantly related to the sequences of interest in the query set. This usage can be seen as a type of

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in constructing and refining phylogenetic trees, which are used to classify the evolutionary relationships between homologous

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can allow for higher mutation rates in the third nucleotide of a given codon without affecting the codon's meaning in the

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of divergent species. The phylogenetic trees constructed by computational methods are unlikely to perfectly reproduce the

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the means are separated by a "gap" greater than the within-group standard deviation ... times some arbitrary constant.

809:(ORF) is known, rates of mutation can be adjusted for position of a given site within a codon, since it is known that 4025: 714:. The choice of prior distribution is a point of contention among users of Bayesian-inference phylogenetics methods. 540: 131: 74: 5114: 3305:"Bears in a forest of gene trees: phylogenetic inference is complicated by incomplete lineage sorting and gene flow" 5119: 4717: 4105: 1838:

Sankoff D, Morel C, Cedergren RJ (October 1973). "Evolution of 5S RNA and the non-randomness of base replacement".

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have been developed to locate a highly parsimonious tree, if not the best in the set. Most such methods involve a

481:): that modification, described in, has been shown to improve the efficiency of the algorithm and its robustness. 5364: 5354: 5278: 4553: 4110: 4043: 2149:

Mau B, Newton MA (1997). "Phylogenetic inference for binary data on dendrograms using Markov chain Monte Carlo".

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whether the phylogeny is significantly different from other possible trees (alternative tree hypothesis tests).

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computationally expensive and the parameters may be overfit. The most common method of model selection is the

5557: 5144: 4888: 4627: 127: 5089: 5497: 4999: 854: 548:(MP) is a method of identifying the potential phylogenetic tree that requires the smallest total number of 493:

search methods like those used in maximum-parsimony analysis are applied to the search through tree space.

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Archie JW (1985). "Methods for coding variable morphological features for numerical taxonomic analysis".

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variations in rates, so that the mutation rate of a given site is correlated across sites and lineages.

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algorithm is a general method used to increase the efficiency of searches for near-optimal solutions of

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De Laet J (2005). "Parsimony and the problem of inapplicables in sequence data.". In Albert VA (ed.).

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produced a morphologically derived tree that was consistent with that produced from molecular data.

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properties of representative organisms, while the more recent field of molecular phylogenetics uses

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accurately be depicted - for every branch of the tree - as a single, typological, bifurcating tree.

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homology, a point of view that may lead to different optimal trees ). The imputed sequences at the

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Hendy MD, Penny D (1982). "Branch and bound algorithms to determine minimal evolutionary trees".

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Felsenstein J (July 1985). "Confidence Limits on Phylogenies: An Approach Using the Bootstrap".

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Day WH (1987). "Computational complexity of inferring phylogenies from dissimilarity matrices".

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Concentrating the missing data across a small number of characters produces a more robust tree.

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Wheeler WC, Gladstein DS (1994). "MALIGN: a multiple nucleic acid sequence alignment program".

1111: 767: 404:) methods produce rooted trees and require a constant-rate assumption - that is, it assumes an 5030: 2286:"Misleading results of likelihood-based phylogenetic analyses in the presence of missing data" 1504:

Wiens JJ (2001). "Character analysis in morphological phylogenetics: problems and solutions".

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is often much less orderly. Research since the cladistic method was introduced has shown that

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in nucleotide sequences. The use of substitution models is necessitated by the fact that the

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method produces unrooted trees, but it does not assume a constant rate of evolution (i.e., a

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Both rooted and unrooted phylogenetic trees can be further generalized to rooted or unrooted

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techniques to sequence analysis using genetic distance as a clustering metric. The simple

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to assign probabilities to particular possible phylogenetic trees. The method requires a

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computational cost. Finding the optimal least-squares tree with any correction factor is

465: 461: 343: 4780: 3366: 2747: 2452: 2400: 2242: 2186:"Bayesian phylogenetic inference using DNA sequences: a Markov Chain Monte Carlo Method" 2061:"Reconstruction of clonal trees and tumor composition from multi-sample sequencing data" 1941: 1664: 1474: 5475: 5427: 5420: 5017: 4929: 4790: 4751: 4575: 4519: 4509: 4469: 4383: 4378: 4373: 4291: 4038: 3992: 3908: 3743: 3655: 3602: 3562: 3550: 3432: 3405: 3386: 3329: 3304: 3259: 3170: 3158: 3127: 3096:"Weighting against homoplasy improves phylogenetic analysis of morphological data sets" 2902: 2840: 2764: 2731: 2671: 2582: 2557: 2522: 2495: 2471: 2436: 2386: 2359: 2334: 2315: 2261: 2226: 2166: 2085: 2060: 2000: 1768: 1720: 1695: 1648: 1400: 1259: 1027: 1023: 957: 818: 806: 786: 779: 743: 711: 698: 655: 647: 582: 469: 327: 173: 172:

Many forms of molecular phylogenetics are closely related to and make extensive use of

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Application of computational algorithms, methods and programs to phylogenetic analyses

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is common, making the assumption of a bifurcating pattern unsuitable, leading to

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It may require cleanup to comply with Knowledge's content policies, particularly

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In general, organisms can inherit genes in two ways: vertical gene transfer and

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Proceedings of the National Academy of Sciences of the United States of America

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incorporates age information into data matrices for phylogenetic analyses.

1160: 1080:, once thought rare, is in fact quite common, particularly in plants. Also 814: 794: 755: 516: 2732:"Model selection may not be a mandatory step for phylogeny reconstruction" 2667: 2632: 2494:

Deshwar AG, Vembu S, Yung CK, Jang GH, Stein L, Morris Q (February 2015).

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tree in which the distances from the root to every branch tip are equal.

405: 4974: 825:. Finally, a more conservative estimate of rate variations known as the 5395: 5296: 5218: 5205: 4839: 4639: 4526: 4504: 4457: 4452: 4400: 4368: 4283: 3903: 3642: 3606: 3122: 2906: 2659: 2170: 1756: 1404: 1194: 1073: 1044: 707: 558: 554: 319: 307: 208:

depending on the input data and the algorithm used. A rooted tree is a

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The basic problem in morphological phylogenetics is the assembly of a

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Hybrids, speciation, introgressions and incomplete lineage sorting

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at individual sites. This correction is done through the use of a

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El-Kebir M, Oesper L, Acheson-Field H, Raphael BJ (June 2015).

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counts the number of extra steps needed to contradict a clade.

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Owing to the development of advanced sequencing techniques in

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to indicate that relationships within a clade are unresolved.

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representing optimal evolutionary ancestry between a set of

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Malikic S, McPherson AW, Donmez N, Sahinalp CS (May 2015).

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method uses standard statistical techniques for inferring

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and their distances from the root proportional to their

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can be challenging due to the inherent difficulties of

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generated by computational phylogenetics can be either

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that encodes a hypothesis about the relative rates of

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A major contributor to this article appears to have a

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Evolution; International Journal of Organic Evolution

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Evolution; International Journal of Organic Evolution

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Tests for phylogenetic signal exist. 402:Weighted Pair Group Method with Arithmetic mean 153:data obtained by measuring and quantifying the 2921: 2645: 2639: 1871: 1869: 1327: 1325: 1323: 1321: 1319: 998: 4299: 3864: 3580: 3578: 3576: 2851: 2555: 1579: 1317: 1315: 1313: 1311: 1309: 1307: 1305: 1303: 1301: 1299: 1279: 1277: 1275: 853:An alternative model selection method is the 581:-style minimization mechanism operating on a 3530: 3454: 3138: 3084: 3047:Phillips MJ, Delsuc F, Penny D (July 2004). 1880:. 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The goal is to find a 55:. Please discuss further on the 34: 5365:Extended evolutionary synthesis 4554:Gene-centered view of evolution 4111:Phylogenetic niche conservatism 3613: 3397: 3345: 3309:Molecular Biology and Evolution 3296: 3270: 3235: 3206: 3056:Molecular Biology and Evolution 3040: 2997: 2809: 2780: 2723: 2611:Molecular Biology and Evolution 2605:Galtier N, Gouy M (July 1998). 2487: 2428: 2190:Molecular Biology and Evolution 2184:Yang Z, Rannala B (July 1997). 2101: 2052: 1609:Molecular Biology and Evolution 1596: 1145: 987: 694:Bayesian inference in phylogeny 5493:Hologenome theory of evolution 5360:History of molecular evolution 4586:Evolutionarily stable strategy 4475:Last universal common ancestor 3814:. 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Roberts. p. 442. 1788:Mathematical Biosciences 1041:horizontal gene transfer 1035:Horizontal gene transfer 878:dimensionality reduction 719:Markov chain Monte Carlo 703:probability distribution 525:Horizontal gene transfer 475:concentration of measure 253:horizontal gene transfer 5486:Dual inheritance theory 5325:History of paleontology 4101:Molecular phylogenetics 4051:Distance-matrix methods 3899:Molecular phylogenetics 2942:10.1093/sysbio/42.2.182 2827:10.1109/CCGrid.2014.126 2802:10.1038/protex.2013.065 2689:Pol D (December 2004). 2462:10.1073/pnas.1522203113 1215:Microbial phylogenetics 1082:paraphyletic speciation 884:Evaluating tree support 837:Choosing the best model 823:log-normal distribution 479:curse of dimensionality 444:Fitch–Margoliash method 438:Fitch–Margoliash method 350:Distance-matrix methods 318:sequences and distinct 5174:Punctuated equilibrium 4495:Non-adaptive radiation 4443:Evolutionary arms race 4121:Phylogenetics software 4035:Probabilistic methods 3984:Long branch attraction 2416:Cite journal requires 2333:Larget B (July 2013). 1852:10.1038/newbio245232a0 1332:Felsenstein J (2004). 1139: 768:long branch attraction 264:Morphological analysis 96:phylogenetic inference 18:Phylogenetic inference 5466:Evolutionary medicine 5340:Mendelian inheritance 5048:Biological complexity 5036:Programmed cell death 4728:Phenotypic plasticity 4448:Evolutionary pressure 4438:Evidence of evolution 4336:Timeline of evolution 3914:Evolutionary taxonomy 3321:10.1093/molbev/msu186 3069:10.1093/molbev/msh137 2736:Nature Communications 2351:10.1093/sysbio/syt014 1334:Inferring Phylogenies 1210:Evolutionary dynamics 1200:Computational biology 1134: 1127:Continuous characters 1086:phylogenetic networks 1049:antibiotic resistance 977:Step counting methods 952:Posterior probability 844:likelihood ratio test 679:Newton–Raphson method 245:phylogenetic networks 53:neutral point of view 5440:Teleology in biology 5335:Blending inheritance 4713:Genetic assimilation 4576:Artificial selection 4315:Evolutionary biology 4073:Three-taxon analysis 3979:Phylogenetic network 3845:at Wikimedia Commons 2648:Biochemical Genetics 2030:(Suppl 1): i97–106. 1290:Dalhousie University 1205:Disk-covering method 1014:Convergent evolution 712:stochastic processes 508:experimental control 275:convergent evolution 92:phylogeny inference, 5503:Molecular evolution 5461:Ecological genetics 5330:Transitional fossil 5120:Sexual reproduction 4960:endomembrane system 4889:pollinator-mediated 4845:dolphins and whales 4623:Parental investment 4116:Phylogenetic signal 3761:(6). Archived from 3367:2012MolEc..21.6117Q 3217:, Doyle JJ (eds.). 2748:2019NatCo..10..934A 2453:2016PNAS..113E5528J 2401:2019arXiv190808623R 2284:Simmons MP (2012). 2243:2009PLoSO...4.7891K 1942:2004MolPE..31..874S 1903:Journal of Heredity 1815:Molecular Evolution 1665:1967Sci...155..279F 1475:2004JHumE..47..399S 1240:Population genetics 1155:The role of fossils 1118:Phylogenetic signal 1088:rather than trees. 1020:evolve convergently 927:In statistics, the 811:wobble base pairing 675:global optimization 671:dynamic programming 573:search methods for 466:substitution matrix 434:) across lineages. 340:insertion mutations 180:represented in the 165:sequences encoding 140:tree rearrangements 5476:Cultural evolution 4591:Fisher's principle 4520:Handicap principle 4510:Parallel evolution 4374:Adaptive radiation 4044:Bayesian inference 4039:Maximum likelihood 3679:Systematic Biology 3587:Systematic Zoology 3461:Systematic Biology 3188:Arnold ML (1996). 2969:Systematic Biology 2930:Systematic Biology 2695:Systematic Biology 2660:10.1007/bf00486096 2339:Systematic Biology 1971:De Laet J (2015). 1757:10.1007/BF02458863 1545:Systematic Biology 1539:Jenner RA (2001). 1506:Systematic Biology 1428:Systematic Biology 1393:Systematic Biology 1260:Taxonomy (biology) 1053:between eukaryotes 1024:maximum likelihood 958:Bayesian inference 819:gamma distribution 807:open reading frame 787:Jukes-Cantor model 780:Substitution model 744:substitution model 699:Bayesian inference 688:Bayesian inference 677:tools such as the 656:substitution model 648:maximum likelihood 642:Maximum likelihood 583:tree rearrangement 470:Jukes-Cantor model 344:deletion mutations 302:Molecular analysis 198:Phylogenetic trees 174:sequence alignment 128:Maximum likelihood 5576: 5575: 5192:Uniformitarianism 5145:Sex-determination 4650:Sexual dimorphism 4645:Natural selection 4549:Unit of selection 4515:Signalling theory 4281: 4280: 4026:Maximum parsimony 4019:Inference methods 3967:Phylogenetic tree 3841:Media related to 3821:978-1-139-49287-4 3795:on 11 August 2011 3788:978-0-521-88068-8 3735:978-0-19-850942-4 3375:10.1111/mec.12080 3355:Molecular Ecology 3228:978-0-19-535668-7 3199:978-0-19-509975-1 3023:10.1111/jzs.12024 2871:978-1-936221-16-5 2836:978-1-4799-2784-5 2793:Protocol Exchange 1989:10.1111/cla.12098 1887:978-0-19-856493-5 1824:978-3-662-12530-4 1712:10.4137/EBO.S7048 1377:978-0-87969-712-9 1364:Mount DM (2004). 1343:978-0-87893-177-4 1230:Phylogenetic tree 1103:molecular biology 1078:hybrid speciation 872:Protocol Exchange 862:alternative, the 616:maximum parsimony 546:Maximum parsimony 535:Maximum parsimony 523:across lineages. 515:too distant adds 186:evolutionary tree 112:phylogenetic tree 85: 84: 77: 48:with its subject. 16:(Redirected from 5603: 5566: 5556: 5555: 5355:Modern synthesis 5115:Multicellularity 5110:Mosaic evolution 4995:auditory ossicle 4677:Social selection 4660:Flowering plants 4655:Sexual selection 4308: 4301: 4294: 4285: 4284: 4269: 4268: 4257: 4256: 4056:Neighbor-joining 4010:Ghost population 3940: 3935: 3934: 3873: 3866: 3859: 3850: 3849: 3840: 3825: 3804: 3802: 3800: 3791:. 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2088: 2056: 2050: 2049: 2039: 2015: 2009: 2008: 1968: 1962: 1961: 1925: 1919: 1918: 1898: 1892: 1891: 1873: 1864: 1863: 1835: 1829: 1828: 1810: 1804: 1803: 1783: 1777: 1776: 1740: 1734: 1733: 1723: 1691: 1685: 1684: 1659:(3760): 279–84. 1641: 1635: 1634: 1624: 1600: 1594: 1593: 1577: 1571: 1570: 1560: 1536: 1530: 1529: 1501: 1495: 1494: 1458: 1452: 1451: 1423: 1417: 1416: 1388: 1382: 1381: 1361: 1348: 1347: 1329: 1294: 1293: 1281: 1185:Bayesian network 1165:stratocladistics 760:genetic distance 681:are often used. 595:branch and bound 589:Branch and bound 579:steepest descent 512:genetic distance 446:uses a weighted 428:neighbor-joining 424:cluster analysis 418:Neighbor joining 412:Neighbor-joining 222:genetic distance 80: 73: 69: 66: 60: 46:close connection 38: 37: 30: 21: 5611: 5610: 5606: 5605: 5604: 5602: 5601: 5600: 5581: 5580: 5577: 5572: 5544: 5471:Group selection 5444: 5369: 5273: 5200: 5162:Tempo and modes 5156: 5011: 4915: 4732: 4691: 4567: 4560: 4537:Species complex 4350: 4341:History of life 4317: 4312: 4282: 4277: 4245: 4209: 4183: 4157:Symplesiomorphy 4135: 4077: 4014: 3943: 3936: 3929: 3923: 3887:Relevant fields 3882: 3877: 3833: 3828: 3822: 3798: 3796: 3789: 3765: 3750: 3736: 3713: 3711:Further reading 3708: 3671: 3667: 3618: 3614: 3599:10.2307/2413151 3583: 3574: 3535: 3531: 3494: 3490: 3453: 3449: 3402: 3398: 3361:(24): 6117–33. 3350: 3346: 3301: 3297: 3283: 3281: 3276: 3275: 3271: 3240: 3236: 3229: 3211: 3207: 3200: 3186: 3182: 3143: 3139: 3092: 3085: 3051: 3045: 3041: 3002: 2998: 2961: 2957: 2926: 2922: 2899:10.2307/2408678 2883: 2879: 2872: 2856: 2852: 2837: 2814: 2810: 2787:Bast F (2013). 2785: 2781: 2728: 2724: 2687: 2683: 2644: 2640: 2603: 2599: 2554: 2539: 2492: 2488: 2433: 2429: 2417: 2415: 2406: 2405: 2383: 2376: 2331: 2327: 2282: 2278: 2223: 2219: 2182: 2178: 2163:10.2307/1390728 2147: 2143: 2106: 2102: 2057: 2053: 2016: 2012: 1969: 1965: 1926: 1922: 1899: 1895: 1888: 1874: 1867: 1836: 1832: 1825: 1811: 1807: 1784: 1780: 1741: 1737: 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5367: 5362: 5357: 5352: 5347: 5342: 5337: 5332: 5327: 5322: 5321: 5320: 5311:Charles Darwin 5308: 5307: 5306: 5294: 5289: 5283: 5281: 5275: 5274: 5272: 5271: 5266: 5261: 5256: 5251: 5249:Non-ecological 5246: 5241: 5236: 5231: 5226: 5221: 5216: 5210: 5208: 5202: 5201: 5199: 5198: 5189: 5180: 5166: 5164: 5158: 5157: 5155: 5154: 5149: 5148: 5147: 5142: 5137: 5132: 5127: 5117: 5112: 5107: 5102: 5097: 5092: 5087: 5082: 5077: 5072: 5067: 5066: 5065: 5055: 5050: 5045: 5040: 5039: 5038: 5033: 5022: 5020: 5013: 5012: 5010: 5009: 5008: 5007: 5002: 5000:nervous system 4997: 4992: 4987: 4979: 4978: 4977: 4972: 4967: 4962: 4957: 4952: 4942: 4937: 4932: 4926: 4924: 4917: 4916: 4914: 4913: 4908: 4903: 4898: 4893: 4892: 4891: 4881: 4880: 4879: 4874: 4873: 4872: 4867: 4857: 4852: 4847: 4842: 4837: 4836: 4835: 4830: 4820: 4810: 4805: 4804: 4803: 4793: 4788: 4783: 4778: 4777: 4776: 4766: 4761: 4760: 4759: 4749: 4743: 4741: 4734: 4733: 4731: 4730: 4725: 4720: 4715: 4710: 4705: 4699: 4697: 4693: 4692: 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topics 4079: 4078: 4076: 4075: 4070: 4069: 4068: 4063: 4058: 4048: 4047: 4046: 4041: 4033: 4028: 4022: 4020: 4016: 4015: 4013: 4012: 4007: 4006: 4005: 3995: 3986: 3981: 3976: 3975: 3974: 3964: 3963: 3962: 3951: 3949: 3948:Basic concepts 3945: 3944: 3942: 3941: 3926: 3924: 3922: 3921: 3916: 3911: 3906: 3901: 3896: 3890: 3888: 3884: 3883: 3876: 3875: 3868: 3861: 3853: 3847: 3846: 3832: 3831:External links 3829: 3827: 3826: 3820: 3805: 3787: 3770: 3740: 3734: 3714: 3712: 3709: 3707: 3706: 3665: 3628:(3): 326–339. 3612: 3593:(3): 326–345. 3572: 3529: 3488: 3447: 3396: 3344: 3295: 3269: 3234: 3227: 3205: 3198: 3180: 3153:(3): 225–245. 3137: 3106:(5): 758–773. 3083: 3039: 3016:(4): 260–273. 2996: 2955: 2936:(2): 182–192. 2920: 2893:(4): 783–791. 2877: 2870: 2850: 2835: 2808: 2779: 2722: 2681: 2638: 2597: 2568:(1): 445–466. 2537: 2500:Genome Biology 2486: 2427: 2418:|journal= 2374: 2325: 2296:(2): 208–222. 2276: 2217: 2176: 2157:(1): 122–131. 2141: 2120:(9): 1349–56. 2114:Bioinformatics 2100: 2071:(12): i62-70. 2065:Bioinformatics 2051: 2024:Bioinformatics 2010: 1983:(5): 550–567. 1963: 1920: 1909:(5): 417–418. 1893: 1886: 1865: 1846:(147): 232–4. 1830: 1823: 1805: 1794:(2): 277–290. 1778: 1735: 1686: 1636: 1595: 1572: 1531: 1496: 1469:(6): 399–452. 1453: 1418: 1383: 1376: 1349: 1342: 1295: 1270: 1268: 1265: 1263: 1262: 1257: 1252: 1247: 1242: 1237: 1232: 1227: 1222: 1217: 1212: 1207: 1202: 1197: 1192: 1190:Bioinformatics 1187: 1181: 1180: 1179: 1172: 1169: 1156: 1153: 1147: 1144: 1128: 1125: 1119: 1116: 1098: 1097:Taxon sampling 1095: 1068: 1065: 1061:parsimoniously 1036: 1033: 1012:Main article: 1009: 1006: 1000: 997: 989: 986: 982:Bremer support 978: 975: 953: 950: 924: 921: 907: 906:Consensus tree 904: 894: 891: 885: 882: 838: 835: 831:autocorrelated 829:method allows 778:Main article: 775: 772: 739: 736: 692:Main article: 689: 686: 643: 640: 629: 628:MALIGN and POY 626: 621:interior nodes 611: 608: 603:tree structure 590: 587: 539:Main article: 536: 533: 498: 495: 462:back mutations 439: 436: 416:Main article: 413: 410: 375: 372: 354:Main article: 351: 348: 303: 300: 265: 262: 260: 257: 210:directed graph 194: 191: 83: 82: 42: 40: 33: 26: 9: 6: 4: 3: 2: 5608: 5597: 5594: 5592: 5589: 5588: 5586: 5579: 5569: 5565: 5561: 5559: 5551: 5550: 5547: 5541: 5538: 5536: 5533: 5531: 5528: 5526: 5523: 5519: 5516: 5515: 5514: 5513:Phylogenetics 5511: 5509: 5506: 5504: 5501: 5499: 5496: 5494: 5491: 5487: 5484: 5482: 5479: 5477: 5474: 5473: 5472: 5469: 5467: 5464: 5462: 5459: 5457: 5454: 5453: 5451: 5447: 5441: 5438: 5434: 5431: 5429: 5426: 5422: 5419: 5418: 5417: 5416:Structuralism 5414: 5412: 5409: 5407: 5404: 5402: 5399: 5397: 5394: 5392: 5391:Catastrophism 5389: 5388: 5387: 5384: 5382: 5379: 5378: 5376: 5372: 5366: 5363: 5361: 5358: 5356: 5353: 5351: 5350:Neo-Darwinism 5348: 5346: 5343: 5341: 5338: 5336: 5333: 5331: 5328: 5326: 5323: 5319: 5318: 5314: 5313: 5312: 5309: 5305: 5304: 5300: 5299: 5298: 5295: 5293: 5290: 5288: 5285: 5284: 5282: 5280: 5276: 5270: 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