Construct phylogenetic tree from distance matrix

Distance matrices are used in phylogeny as non-parametric distance methods and were originally applied to phenetic data using a matrix of pairwise distances.

These distances are then reconciled to produce a tree a phylogramwith informative branch lengths. The distance matrix can come from a number of different sources, including measured distance for example from immunological studies or morphometric analysisvarious pairwise distance formulae such as euclidean distance applied to discrete morphological characters, or genetic distance from sequence, restriction fragmentor allozyme data.

For phylogenetic character data, raw distance values can be calculated by simply counting the number of pairwise differences in character states Hamming distance. 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 multiple sequence alignment as an input.

Distance is often defined as the fraction of mismatches at aligned positions, with gaps either ignored or counted as mismatches. From this is constructed a phylogenetic tree that places closely related sequences under the same interior node 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 multiple sequence alignment.

The main disadvantage of distance-matrix methods is their inability to efficiently use information about local high-variation regions that appear across multiple subtrees.

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Neighbor-joining methods apply general data clustering techniques to sequence analysis using genetic distance as a clustering metric. The simple neighbor-joining method produces unrooted trees, but it does not assume a constant rate of evolution i. The Fitch—Margoliash method uses a weighted least squares method for clustering based on genetic distance.

In practice, the distance correction is only necessary when the evolution rates differ among branches. The distances calculated by this method must be linear ; the linearity criterion for distances requires that the expected values 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 back mutations at individual sites.

This correction is done through the use of a substitution matrix such as that derived from the Jukes—Cantor model of DNA evolution. 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 computational cost.

Finding the optimal least-squares tree with any correction factor is NP-complete[4] so heuristic search methods like those used in maximum-parsimony analysis are applied to the search through tree space. 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 outgroup sequence known to be only distantly related to the sequences of interest in the query set. If the outgroup has been appropriately chosen, it will have a much greater genetic distance 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 too distant adds noise to the analysis.

Horizontal gene transferespecially between otherwise divergent bacteriacan also confound outgroup usage. In general, pairwise distance data are an underestimate of the path-distance between taxa on a phylogram. Pairwise distances effectively "cut corners" in a manner analogous to geographic distance: the distance between two cities may be miles "as the crow flies," but a traveler may actually be obligated to travel miles because of the layout of roads, the terrain, stops along the way, etc.

Between pairs of taxa, some character changes that took place in ancestral lineages will be undetectable, because later changes have erased the evidence often called multiple hits and back mutations in sequence data.

This problem is common to all phylogenetic estimation, but it is particularly acute for distance methods, because only two samples are used for each distance calculation; other methods benefit from evidence of these hidden changes found in other taxa not considered in pairwise comparisons.

For nucleotide and amino acid sequence data, the same stochastic models of nucleotide change used in maximum likelihood analysis can be employed to "correct" distances, rendering the analysis "semi-parametric. Several simple algorithms exist to construct a tree directly from pairwise distances, including UPGMA and neighbor joining NJbut these will not necessarily produce the best tree for the data. To counter potential complications noted above, and to find the best tree for the data, distance analysis can also incorporate a tree-search protocol that seeks to satisfy an explicit optimality criterion.For more complete documentation, see the Phylogenetics chapter of the Biopython Tutorial and the Bio.

Phylo API pages generated from the source code. The Phylo cookbook page has more examples of how to use this module, and the PhyloXML page describes how to attach graphical cues and additional information to a tree. This module is included in Biopython 1. The Phylo module has also been successfully tested on Jython 2. Each function accepts either a file name or an open file handle, so data can be also loaded from compressed files, StringIO objects, and so on.

The second argument to each function is the target format. Currently, the following formats are supported:.

See the PhyloXML page for more examples of using tree objects. Incrementally parse each tree in the given file or handle, returning an iterator of Tree objects i. BaseTree Tree class, depending on the file format. Parse and return exactly one tree from the given file or handle. If the file contains zero or multiple trees, a ValueError is raised. This is useful if you know a file contains just one tree, to load that tree object directly rather than through parse and nextand as a safety check to ensure the input file does in fact contain exactly one phylogenetic tree at the top level.

See examples of this in the unit tests for Phylo in the Biopython source code. Write a sequence of Tree objects to the given file or handle. Passing a single Tree object instead of a list or iterable will also work see, Phylo is friendly.

Given two files or handles and two formats, both supported by Bio. Phyloconvert the first file from the first format to the second format, writing the output to the second file. Within the Phylo module are parsers and writers for specific file formats, conforming to the basic top-level API and sometimes adding additional features. See the PhyloXML page for details.

NewickIO: A port of the parser in Bio. Trees to support the Newick a. NexusIO: Wrappers around Bio. Nexus to support the Nexus tree format.

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Requires RDFlib. The Nexus format actually contains several sub-formats for different kinds of data; to represent trees, Nexus provides a block containing some metadata and one or more Newick trees another kind of Nexus block can represent alignments; this is handled in AlignIO. So to parse a complete Nexus file with all block types handled, use Bio.

Nexus directly, and to extract just the trees, use Bio. The basic objects are defined in Bio. To support additional information stored in specific file formats, sub-modules within Tree offer additional classes that inherit from BaseTree classes. Each sub-class of BaseTree. Tree or Node has a class method to promote an object from the basic type to the format-specific one.

These sub-class objects can generally be treated as instances of the basic type without any explicit conversion. Newick : The Newick module provides minor enhancements to the BaseTree classes, plus several shims for compatibility with the existing Bio. Nexus module. The API for this module is under development and should not be relied on, other than the functionality already provided by BaseTree.

Some additional tools are located in the Utils module under Bio.Computational phylogenetics is the application of computational algorithmsmethods, and programs to phylogenetic analyses.

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The goal is to assemble a phylogenetic tree representing a hypothesis about the evolutionary ancestry of a set of genesspeciesor other taxa. For example, these techniques have been used to explore the family tree of hominid species [1] and the relationships between specific genes shared by many types of organisms.

Many forms of molecular phylogenetics are closely related to and make extensive use of sequence alignment in constructing and refining phylogenetic trees, which are used to classify the evolutionary relationships between homologous genes represented in the genomes of divergent species. The phylogenetic trees constructed by computational methods are unlikely to perfectly reproduce the evolutionary tree 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. Phylogenetic trees generated by computational phylogenetics can be either rooted or unrooted depending on the input data and the algorithm used. A rooted tree is a directed graph that explicitly identifies a most recent common ancestor MRCAusually an imputed sequence that is not represented in the input.

Genetic distance measures can be used to plot a tree with the input sequences as leaf nodes and their distances from the root proportional to their genetic distance 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.

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, such as the assumption of the molecular clock hypothesis. 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 optimization algorithms.

Transforming Distance Matrices into Evolutionary Trees

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. Both rooted and unrooted phylogenetic trees can be further generalized to rooted or unrooted phylogenetic networkswhich allow for the modeling of evolutionary phenomena such as hybridization or horizontal gene transfer.

The basic problem in morphological phylogenetics is the assembly of a matrix 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 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. The inclusion of extinct taxa in morphological analysis is often difficult due to absence of or incomplete fossil records, but has been shown to have a significant effect on the trees produced; in one study only the inclusion of extinct species of apes produced a morphologically derived tree that was consistent with that produced from molecular data.

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.

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.

This results in an easily manipulated data set 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. 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.

The problem of character coding is very different in molecular analyses, as the characters in biological sequence data are immediate and discretely defined - distinct nucleotides in DNA or RNA sequences and distinct amino acids in protein sequences.

construct phylogenetic tree from distance matrix

However, defining homology can be challenging due to the inherent difficulties of multiple sequence alignment. For a given gapped MSA, several rooted phylogenetic trees can be constructed that vary in their interpretations of which changes are " mutations " versus ancestral characters, and which events are insertion mutations or deletion mutations. 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.

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 as mismatches. From this is constructed a phylogenetic tree that places closely related sequences under the same interior node 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 multiple sequence alignments.

The main disadvantage of distance-matrix methods is their inability to efficiently use information about local high-variation regions that appear across multiple subtrees.

Neighbor-joining methods apply general cluster analysis techniques to sequence analysis using genetic distance as a clustering metric. The simple neighbor-joining method produces unrooted trees, but it does not assume a constant rate of evolution i. The Fitch—Margoliash method uses a weighted least squares method for clustering based on genetic distance.Documentation Help Center. Matrix or vector of pairwise distances, such as returned by the seqpdist function.

Vector of structures, each with a Header or Name field. The elements must be unique. The number of elements must comply with the number of samples used to generate the pairwise distances in Dist. Distances is a matrix or vector of pairwise distances, such as returned by the seqpdist function. The available methods are:. Create an array of structures representing a multiple alignment of amino acids:. Build the phylogenetic tree for the multiple sequence alignment from calculated pairwise distances.

Specify the method to compute the distances of the new nodes to all other nodes. Provide leaf names:.

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Behavior changed in Rb. For the Ra or earlier versions, seqlinkage incorrectly doubled the input pairwise distances when building a tree. This bug has been fixed in Rb.

construct phylogenetic tree from distance matrix

If you have been previously selecting a subset of the tree returned by seqlinkage with a distance threshold, consider dividing the threshold by 2. Note that the tree topology has always been computed correctly and not affected by this bug. A modified version of this example exists on your system.

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Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have established my gene clusters and already calculated the distances needed to measure their phylogenetic relationship.

I used an algorithm basically gives a measure of distance between gene clusters and is represented in a dataframe such as Input Example :.

Goal : Would it be possible to construct a tree just based on this type of data? I want to have a. However, I have been able to create network visualizations from this data through Cytoscape but not possibly a tree.

Any further suggestions for this particular example? Following the suggestion in a comment by user hereyou can define how to wrap the distances to a dist object using the lower. However, the provided example will not work, because it does not provide pairwise distances between samples. The solution thus takes your sample names, generates random data and then constructs the tree with the nj function from the ape package.

The newick format of the tree can be saved with ape::write. Learn more. Ask Question. Asked 8 months ago. Active 8 months ago. Viewed times. Thanks once again for your input :. Biohacker Biohacker 95 9 9 bronze badges.

I can also try on python, I just had a preference in this case for R, however when you say wrangle your distances into the correct format? What does this imply? The names are just a flat list of your genenames. Matrix is a lower triangular format distance matrix of all all genes vs all genes.

Pallie is it possible to use as the input for this, the matrix that I have in the example above? Currently my table of interest consists of these three columns. Active Oldest Votes.

Thanks for the reply, your post is quite helpful for orientation and I think I can adapt.

construct phylogenetic tree from distance matrix

In this case a distance matrix is created by calculating the distance between every pair of BGC in the data set, basically a pairwise distance calculation was done for all BGCs. I believe that the example that I provided was not a good one.Let n be a positive integer.

In the literature, a distance matrix of order n is also called a dissimilarity matrix of order n.

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Below, all trees are assumed to be unrooted and edge-weighted. Finally, a distance matrix D is called additive or tree-realizable if and only if there exists a tree which realizes D. See Fig. In the time complexities listed below, the time needed to input all of D is not included. Instead, O 1 is charged to the running time whenever an algorithm requests to know the value of any specified entry of D. Several authors have independently shown how to solve the Phylogenetic Tree from Distance Matrix Problem in O n 2 time.

Distance matrices in phylogeny

See [ 5 ] for a short survey of older algorithms which do not run in O n 2 time. For any given distance matrix, the solution to the Phylogenetic Tree from Distance Matrix Problem is unique. However, if it is known in advance that the input distance matrix is additive, then the time complexity improves as follows.

The same basic technique is used in the O n 2 -time algorithm of Waterman et al.

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A lower bound that implies the optimality of Theorem 3 is given by the next theorem. See [ 12 ] for a counterexample to [ 5 ] and a correct analysis.

On the positive side, the following special case is solvable in linear time by the Culberson-Rudnicki algorithm:. There exists an O n -time algorithm which solves the Phylogenetic Tree from Distance Matrix Problem restricted to additive distance matrices for which the realizing tree contains two leaves only and has all edge weights equal to 1. The main application of the Phylogenetic Tree from Distance Matrix Problem is in the construction of a tree a so-called phylogenetic tree that represents evolutionary relationships among a set of studied objects e.

Here, it is assumed that the objects are indeed related according to a treelike branching pattern caused by an evolutionary process and that their true pairwise evolutionary distances are proportional to the measured pairwise dissimilarities. See, e. Other applications of the Phylogenetic Tree from Distance Matrix Problem can be found in psychology, for example, to describe semantic memory organization [ 1 ], in comparative linguistics to infer the evolutionary history of a set of languages [ 11 ], or in the study of the filiation of manuscripts to trace how manuscript copies of a text whose original version may have been lost have evolved in order to identify discrepancies among them or to reconstruct the original text [ 1313 ].

In general, real data seldom forms additive distance matrices [ 14 ]. A comprehensive description of some of the most popular methods for phylogenetic reconstruction from a non-additive distance matrix such as Neighbor-joining [ 16 ] as well as more background information can be found in, e. See also [ 1 ] and [ 15 ] and the references therein. Skip to main content Skip to table of contents.

This service is more advanced with JavaScript available. Encyclopedia of Algorithms Living Edition. Contents Search. Phylogenetic Tree Construction from a Distance Matrix. Living reference work entry First Online: 24 January Download entry PDF.

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Computational phylogenetics

I would like to produce phylogenetic trees from genetic data. I have found a few tree-drawing packages in R and python that look great, e. But these require data inputs that are already in a tree format e.

I think most people start with vcf files and produce FASTA files, but my starting point is a table of genotypes - I work with a haploid organism so each position is either 0 ref or 1 non-ref. From this I compute pairwise genetic distance using dist in R.

Example data for 5 samples, A-E, with pairwise distance over ten variant positions:. I would like to produce a hierarchical tree output file from pdist e.

I've tried searching but not sure where to start. Output tree:. This is a non-trivial task. To build a tree as in a bifurcating one from a distance matrix, you will need to use phylogenetic algorithms and probably better not do it from a distance matrix note that there might be drawbacks from using Euclidean distance for a binary matrix as well.

However, that said, the task can still be done using the phangorn package. For example, you can create a spectra of splits from the distance matrix i.

Note that in the same package neighborNet is also available but the manual highlights that this function is experimental. I suggest contacting the package author for more information. You can then transform your network in a "phylo" that can be used by ape and probably by ggtree by coercing it:. But again, note that this resulting tree is probably incorrect in a phylogenetic sense i.

Binary data can be efficiently used to construct a phylogenetic tree with MrBayes, as thomas-guillerme mentioned. The input file should include a binary data block and mrbayes commands. The length of the mcmc run will need to be adjusted with respect to the chain convergence. As a start, the code should give a good idea on the relationships the data can infer.

Learn more. How to generate Newick tree output from pairwise distance matrix Ask Question.


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