Date: 2021-01-11

# Estimating phylogenetic trees with phangorn

#### Klaus Schliep

#### 2021-01-11

# Introduction

These notes should enable the user to estimate phylogenetic trees from alignment data with different methods using the phangorn package (Schliep 2011) . Several functions of package are also described in more detail in (Paradis 2012). For more theoretical background on all the methods see e.g. (Felsenstein 2004)(Yang 2006). This document illustrates some of the `package` features to estimate phylogenetic trees using different reconstruction methods. Small adaptations to the scripts in section @ref(appendix) should enable the user to perform phylogenetic analyses.

# Getting started

The first thing we have to do is to read in an alignment. Unfortunately there exists many different file formats that alignments can be stored in. The function `read.phyDat` is used to read in an alignment. There are several functions to read in alignments depending on the format of the data set (nexus, phylip, fasta) and the kind of data (amino acid or nucleotides) in the ape package (Paradis and Schliep 2019) and phangorn. The function `read.phyDat` calls these other functions and transform them into a `phyDat` object. For the specific parameter settings available look in the help files of the function `read.dna` (for phylip, fasta, clustal format), `read.nexus.data` for nexus files. For amino acid data additional `read.aa` is called.

We start our analysis loading the phangorn package and then reading in an alignment.

    library(ape)
    library(phangorn)
    fdir <- system.file("extdata/trees", package = "phangorn")
    primates <- read.phyDat(file.path(fdir, "primates.dna"),
                            format = "interleaved")

# Distance based methods

After reading in the alignment we can build a first tree with distance based methods. The function dist.dna from the ape package computes distances for many DNA substitution models. To use the function dist.dna we have to transform the data to class DNAbin. For amino acids the function `dist.ml` offers common substitution models (for example “WAG”, “JTT”, “LG”, “Dayhoff”, “cpREV”, “mtmam”, “mtArt”, “MtZoa” or “mtREV24”).

After constructing a distance matrix we reconstruct a rooted tree with UPGMA and alternatively an unrooted tree using Neighbor Joining (Saitou and Nei 1987)(Studier and Keppler 1988). More distance methods like `fastme` are available in the ape package.

    dm  <- dist.ml(primates)
    treeUPGMA  <- upgma(dm)
    treeNJ  <- NJ(dm)

We can plot the trees treeUPGMA and treeNJ with the commands:

    plot(treeUPGMA, main="UPGMA")

Rooted UPGMA tree.

    plot(treeNJ, "unrooted", main="NJ")

Distance based methods are very fast and we will use the UPGMA and NJ tree as starting trees for the maximum parsimony and maximum likelihood analyses.

# Parsimony

The function parsimony returns the parsimony score, that is the number of changes which are at least necessary to describe the data for a given tree. We can compare the parsimony score or the two trees we computed so far:

    parsimony(treeUPGMA, primates)

    ## [1] 751

    parsimony(treeNJ, primates)

    ## [1] 746

The function `optim.parsimony` performs tree rearrangements to find trees with a lower parsimony score. The tree rearrangement implemented are nearest-neighbor interchanges (NNI) and subtree pruning and regrafting (SPR). The later one only works so far with the fitch algorithm. However is also a version of the parsimony ratchet (Nixon 1999) implemented, which is likely to find better trees than just doing NNI / SPR rearrangements.

    treePars  <- optim.parsimony(treeUPGMA, primates)

    ## Final p-score 746 after  1 nni operations

    treeRatchet  <- pratchet(primates, trace = 0)
    parsimony(c(treePars, treeRatchet), primates)

    ## [1] 746 746

For small data sets it is also possible to find all most parsimonious trees using a branch and bound algorithm (Hendy and Penny 1982). For data sets with more than 10 taxa this can take a long time and depends strongly on how tree like the data are.

    (trees <- bab(subset(primates,1:10)))

    ## [1] "lower bound: 368"
    ## [1] "upper bound: 654"

    ## 1 phylogenetic tree

# Maximum likelihood

The last method we will describe in this vignette is Maximum Likelihood (ML) as introduced by Felsenstein (Felsenstein 1981). We can easily compute the likelihood for a tree given the data

    fit = pml(treeNJ, data=primates)
    fit

    ##
    ##  loglikelihood: -3075
    ##
    ## unconstrained loglikelihood: -1230
    ##
    ## Rate matrix:
    ##   a c g t
    ## a 0 1 1 1
    ## c 1 0 1 1
    ## g 1 1 0 1
    ## t 1 1 1 0
    ##
    ## Base frequencies:
    ## 0.25 0.25 0.25 0.25

The function `pml` returns an object of class `pml`. This object contains the data, the tree and many different parameters of the model like the likelihood. There are many generic functions for the class `pml` available, which allow the handling of these objects.

    methods(class="pml")

    ## [1] AICc   BIC    anova  logLik plot   print  simSeq update vcov
    ## see '?methods' for accessing help and source code

The object fit just estimated the likelihood for the tree it got supplied, but the branch length are not optimized for the Jukes-Cantor model yet, which can be done with the function `optim.pml`.

    fitJC  <- optim.pml(fit, TRUE)

    ## optimize edge weights:  -3075 --> -3068
    ## optimize edge weights:  -3068 --> -3068
    ## optimize topology:  -3068 --> -3068
    ## optimize topology:  -3068 --> -3068
    ## 1
    ## optimize edge weights:  -3068 --> -3068
    ## optimize topology:  -3068 --> -3068
    ## 0
    ## optimize edge weights:  -3068 --> -3068

    logLik(fitJC)

    ## 'log Lik.' -3068 (df=25)

With the default values`pml` will estimate a Jukes-Cantor model. The function`update.pml` allows to change parameters. We will change the model to the GTR + \(\Gamma(4)\) + I model and then optimize all the parameters.

    fitGTR <- update(fit, k=4, inv=0.2)
    fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,
        rearrangement = "NNI", control = pml.control(trace = 0))
    fitGTR

    ##
    ##  loglikelihood: -2614
    ##
    ## unconstrained loglikelihood: -1230
    ## Proportion of invariant sites: 0.007784
    ## Discrete gamma model
    ## Number of rate categories: 4
    ## Shape parameter: 3.943
    ##
    ## Rate matrix:
    ##         a        c         g     t
    ## a  0.0000 0.502421 18.551205 0.310
    ## c  0.5024 0.000000  0.009312 8.122
    ## g 18.5512 0.009312  0.000000 1.000
    ## t  0.3100 8.122421  1.000000 0.000
    ##
    ## Base frequencies:
    ## 0.3844 0.3822 0.04015 0.1932

With the control parameters the thresholds for the fitting process can be changed. Here we want just to supress output during the fitting process. For larger trees the NNI rearrangements often get stuck in a local maximum. We added two stochastic algorithms to improve topology search. The first (set `rearrangement="stochastic"`) performs stochastic rearrangements similar as in (Nguyen et al. 2015), which makes random NNI permutation to the tree, which than gets optimised to escape local optima. The second option (rearrangement=“ratchet”) perform the likelihood ratchet (Vos 2003).

While these algorithms may find better trees they will also take more time.

    fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,
        rearrangement = "stochastic", control = pml.control(trace = 0))
    fitGTR

    ##
    ##  loglikelihood: -2608
    ##
    ## unconstrained loglikelihood: -1230
    ## Proportion of invariant sites: 0.005688
    ## Discrete gamma model
    ## Number of rate categories: 4
    ## Shape parameter: 3.049
    ##
    ## Rate matrix:
    ##         a         c         g       t
    ## a  0.0000  0.513255 31.449312  0.3723
    ## c  0.5133  0.000000  0.007069 13.3042
    ## g 31.4493  0.007069  0.000000  1.0000
    ## t  0.3723 13.304171  1.000000  0.0000
    ##
    ## Base frequencies:
    ## 0.3934 0.3792 0.04028 0.1872

## Model selection

We can compare nested models for the JC and GTR + \(\Gamma(4)\) + I model using likelihood ratio statistic

    anova(fitJC, fitGTR)

    ## Likelihood Ratio Test Table
    ##   Log lik. Df Df change Diff log lik. Pr(>|Chi|)
    ## 1    -3068 25
    ## 2    -2608 35        10           920     <2e-16 ***
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

with the Shimodaira-Hasegawa test

    SH.test(fitGTR, fitJC)

    ##      Trees  ln L Diff ln L p-value
    ## [1,]     1 -2608       0.0  0.5021
    ## [2,]     2 -3068     459.9  0.0000

or with the AIC

    AIC(fitJC)

    ## [1] 6187

    AIC(fitGTR)

    ## [1] 5287

    AICc(fitGTR)

    ## [1] 5300

    BIC(fitGTR)

    ## [1] 5407

An alternative is to use the function `modelTest` to compare different nucleotide or protein models the AIC, AICc or BIC, similar to popular programs ModelTest and ProtTest (Posada and Crandall 1998), (Posada 2008), (Abascal, Zardoya, and Posada 2005).

    mt = modelTest(primates)

The results of `modelTest` is illustrated in following table:

Model | df | logLik | AIC | AICw | AICc | AICcw | BIC
--- | --- | --- | --- | --- | --- | --- | ---
JC | 25 | -3068 | 6187 | 0.00 | 6193 | 0.00 | 6273
JC+I | 26 | -3063 | 6177 | 0.00 | 6184 | 0.00 | 6267
JC+G | 26 | -3067 | 6186 | 0.00 | 6193 | 0.00 | 6275
JC+G+I | 27 | -3063 | 6179 | 0.00 | 6187 | 0.00 | 6272
F81 | 28 | -2918 | 5892 | 0.00 | 5900 | 0.00 | 5989
F81+I | 29 | -2909 | 5876 | 0.00 | 5885 | 0.00 | 5976
F81+G | 29 | -2913 | 5883 | 0.00 | 5892 | 0.00 | 5983
F81+G+I | 30 | -2909 | 5877 | 0.00 | 5886 | 0.00 | 5980
K80 | 26 | -2953 | 5958 | 0.00 | 5965 | 0.00 | 6048
K80+I | 27 | -2945 | 5943 | 0.00 | 5950 | 0.00 | 6036
K80+G | 27 | -2945 | 5944 | 0.00 | 5951 | 0.00 | 6037
K80+G+I | 28 | -2942 | 5941 | 0.00 | 5949 | 0.00 | 6037
HKY | 29 | -2625 | 5308 | 0.00 | 5317 | 0.00 | 5408
HKY+I | 30 | -2621 | 5303 | 0.00 | 5312 | 0.00 | 5406
HKY+G | 30 | -2613 | 5285 | 0.18 | 5295 | 0.45 | 5389
HKY+G+I | 31 | -2612 | 5287 | 0.08 | 5297 | 0.14 | 5394
SYM | 30 | -2814 | 5688 | 0.00 | 5697 | 0.00 | 5791
SYM+I | 31 | -2812 | 5685 | 0.00 | 5695 | 0.00 | 5792
SYM+G | 31 | -2805 | 5671 | 0.00 | 5681 | 0.00 | 5778
SYM+G+I | 32 | -2805 | 5673 | 0.00 | 5684 | 0.00 | 5784
GTR | 33 | -2619 | 5303 | 0.00 | 5315 | 0.00 | 5417
GTR+I | 34 | -2614 | 5295 | 0.00 | 5307 | 0.00 | 5412
GTR+G | 34 | -2608 | 5283 | 0.47 | 5295 | 0.29 | 5401
GTR+G+I | 35 | -2607 | 5284 | 0.27 | 5297 | 0.11 | 5405

The thresholds for the optimization in `modelTest` are not as strict as for `optim.pml` and no tree rearrangements are performed. As `modelTest` computes and optimizes a lot of models it would be a waste of computer time not to save these results. The results are saved as call together with the optimized trees in an environment and this call can be evaluated to get a `pml` object back to use for further optimization or analysis.

    env <- attr(mt, "env")
    ls(envir=env)

    ##  [1] "F81"          "F81+G"        "F81+G+I"      "F81+I"        "GTR"
    ##  [6] "GTR+G"        "GTR+G+I"      "GTR+I"        "HKY"          "HKY+G"
    ## [11] "HKY+G+I"      "HKY+I"        "JC"           "JC+G"         "JC+G+I"
    ## [16] "JC+I"         "K80"          "K80+G"        "K80+G+I"      "K80+I"
    ## [21] "SYM"          "SYM+G"        "SYM+G+I"      "SYM+I"        "data"
    ## [26] "tree_F81"     "tree_F81+G"   "tree_F81+G+I" "tree_F81+I"   "tree_GTR"
    ## [31] "tree_GTR+G"   "tree_GTR+G+I" "tree_GTR+I"   "tree_HKY"     "tree_HKY+G"
    ## [36] "tree_HKY+G+I" "tree_HKY+I"   "tree_JC"      "tree_JC+G"    "tree_JC+G+I"
    ## [41] "tree_JC+I"    "tree_K80"     "tree_K80+G"   "tree_K80+G+I" "tree_K80+I"
    ## [46] "tree_SYM"     "tree_SYM+G"   "tree_SYM+G+I" "tree_SYM+I"

    (fit <- eval(get("HKY+G+I", env), env))

    ##
    ##  loglikelihood: -2612
    ##
    ## unconstrained loglikelihood: -1230
    ## Proportion of invariant sites: 0.002694
    ## Discrete gamma model
    ## Number of rate categories: 4
    ## Shape parameter: 2.124
    ##
    ## Rate matrix:
    ##       a     c     g     t
    ## a  0.00  1.00 56.02  1.00
    ## c  1.00  0.00  1.00 56.02
    ## g 56.02  1.00  0.00  1.00
    ## t  1.00 56.02  1.00  0.00
    ##
    ## Base frequencies:
    ## 0.4205 0.3622 0.0439 0.1734

At last we may want to apply bootstrap to test how well the edges of the tree are supported:

    bs = bootstrap.pml(fitJC, bs=100, optNni=TRUE,
        control = pml.control(trace = 0))

Now we can plot the tree with the bootstrap support values on the edges and also look at `consensusNet` to identify potential conflict.

    plotBS(midpoint(fitJC$tree), bs, p = 50, type="p")

Tree with bootstrap support. Unrooted tree (midpoint rooted) with bootstrap support values.

    cnet <- consensusNet(bs, p=0.2)
    plot(cnet, show.edge.label=TRUE)

ConsensusNet from the bootstrap sample.

Several analyses, e.g.`bootstrap` and `modelTest`, can be computationally demanding, but as nowadays most computers have several cores one can distribute the computations using the parallel package. However it is only possible to use this approach if R is running from command line (“X11”), but not using a GUI (for example “Aqua” on Macs) and unfortunately the parallel package does not work at all under Windows.

# Appendix

## Standard scripts for nucleotide analysis

Here we provide two standard scripts which can be adapted for the most common tasks. Most likely the arguments for `read.phyDat` have to be adapted to accommodate your file format. Both scripts assume that the parallel package works on your platform, see comments above.

    library(phangorn)
    file <- "myfile"
    dat <- read.phyDat(file)
    dm <- dist.ml(dat, "F81")
    tree <- NJ(dm)
    # as alternative for a starting tree:
    tree <- pratchet(dat)          # parsimony tree
    tree <- nnls.phylo(tree, dm)   # need edge weights

    # 1. alternative: quick and dirty: GTR + G
    fitStart <- pml(tree, dat, k=4)
    fit <- optim.pml(fitStart, model="GTR", optGamma=TRUE, rearrangement="stochastic")

    # 2. alternative: preper with modelTest
    mt <- modelTest(dat, tree=tree, multicore=TRUE)
    mt[order(mt$AICc),]
    # choose best model from the table according to AICc
    bestmodel <- mt$Model[which.min(mt$AICc)]

    env <- attr(mt, "env")
    fitStart <- eval(get("GTR+G+I", env), env)

    # or let R search the table
    fitStart <- eval(get(bestmodel, env), env)
    # equivalent to:   fitStart = eval(get("GTR+G+I", env), env)
    fit <- optim.pml(fitStart, rearrangement = "stochastic",
        optGamma=TRUE, optInv=TRUE, model="GTR")
    bs <- bootstrap.pml(fit, bs=100, optNni=TRUE, multicore=TRUE)

## Standard scripts for amino acid analysis

You can specify different several models build in which you can specify, e.g. “WAG”, “JTT”, “Dayhoff”, “LG”. Optimizing the rate matrix for amino acids is possible, but would take a long, a very long time and you will need to have a large alignment to estimate all the parameters. So make sure to set `optBf=FALSE` and `optQ=FALSE` in the function `optim.pml`, which is also the default.

    library(phangorn)
    file <- "myfile"
    dat <- read.phyDat(file, type = "AA")
    dm <- dist.ml(dat, model="JTT")
    tree <- NJ(dm)

    # parallel will only work safely from command line
    # and not at all windows
    (mt <- modelTest(dat, model=c("JTT", "LG", "WAG"),
        multicore=TRUE))
    # run all available amino acid models
    (mt <- modelTest(dat, model="all", multicore=TRUE))

    fitStart <- eval(get(mt$Model[which.min(mt$BIC)], env), env)

    fitNJ <- pml(tree, dat, model="JTT", k=4, inv=.2)
    fit <- optim.pml(fitNJ, rearrangement = "stochastic",
        optInv=TRUE, optGamma=TRUE)
    fit

    bs <- bootstrap.pml(fit, bs=100, optNni=TRUE, multicore=TRUE)

# Session info

    ## R version 4.0.3 (2020-10-10)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 20.04.1 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
    ##
    ## locale:
    ##  [1] LC_CTYPE=de_AT.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=de_AT.UTF-8        LC_COLLATE=C
    ##  [5] LC_MONETARY=de_AT.UTF-8    LC_MESSAGES=de_AT.UTF-8
    ##  [7] LC_PAPER=de_AT.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=de_AT.UTF-8 LC_IDENTIFICATION=C
    ##
    ## attached base packages:
    ## [1] grid      stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ## [1] knitr_1.30     magrittr_2.0.1 seqLogo_1.54.3 phangorn_2.6.2 ape_5.4-1
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] igraph_1.2.6     Rcpp_1.0.5       lattice_0.20-41  rlang_0.4.10
    ##  [5] quadprog_1.5-8   fastmatch_1.1-0  highr_0.8        stringr_1.4.0
    ##  [9] tools_4.0.3      parallel_4.0.3   nlme_3.1-151     xfun_0.19
    ## [13] htmltools_0.5.0  yaml_2.2.1       digest_0.6.27    Matrix_1.3-0
    ## [17] prettydoc_0.4.0  codetools_0.2-18 evaluate_0.14    rmarkdown_2.6
    ## [21] stringi_1.5.3    compiler_4.0.3   stats4_4.0.3     pkgconfig_2.0.3

# References

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Felsenstein, Joseph. 1981. “Evolutionary Trees from Dna Sequences: A Maxumum Likelihood Approach.” Journal of Molecular Evolution 17: 368–76.

———. 2004. Inferring Phylogenies. Sunderland: Sinauer Associates.

Hendy, M. D., and D. Penny. 1982. “Branch and Bound Algorithms to Determine Minimal Evolutionary Trees.” Math. Biosc. 59: 277–90.

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Paradis, Emmanuel. 2012. Analysis of Phylogenetics and Evolution with R. Second. New York: Springer.

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Posada, D., and K. A. Crandall. 1998. “MODELTEST: Testing the Model of DNA Substitution.” Bioinformatics 14 (9): 817–18.

Saitou, N., and M. Nei. 1987. “The Neighbor-Joining Method - a New Method for Reconstructing Phylogenetic Trees.” Molecular Biology and Evolution 4 (4): 406–25.

Schliep, Klaus Peter. 2011. “Phangorn: Phylogenetic Analysis in R.” Bioinformatics 27 (4): 592–93. https://doi.org/10.1093/bioinformatics/btq706.

Studier, J. A., and K. J. Keppler. 1988. “A Note on the Neighbor-Joining Algorithm of Saitou and Nei.” Molecular Biology and Evolution 5 (6): 729–31.

Vos, R. A. 2003. “Accelerated Likelihood Surface Exploration: The Likelihood Ratchet.” Systematic Biology 52 (3): 368–73. https://doi.org/10.1080/10635150390196993.

Yang, Ziheng. 2006. Computational Molecular Evolution. Oxford: Oxford University Press.