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

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

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

`plot(treeUPGMA, main="UPGMA")`

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

## Bootstrap

To run the bootstrap we need to first write a function which computes
a tree from an alignment. So we first need to compute a distance matrix
and afterwards compute the tree. This function we can than give to the
`bootstrap.phyDat`

function.

```
<- function(x) upgma(dist.ml(x))
fun <- bootstrap.phyDat(primates, fun) bs_upgma
```

With the new syntax of R 4.1 this can be written a bit shorter:

`<- bootstrap.phyDat(primates, \(x){dist.ml(x) |> upgma}) bs_upgma `

Finally we can plot the tree with bootstrap values added:

`plotBS(treeUPGMA, bs_upgma, main="UPGMA")`

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.

`<- optim.parsimony(treeUPGMA, primates) treePars `

`## Final p-score 746 after 1 nni operations`

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. The current implementation

- Create a bootstrap data set \(D_b\) from the original data set.
- Take the current best tree and perform tree rearrangements on \(D_b\) and save bootstrap tree as \(T_b\).
- Use \(T_b\) and perform tree rearrangements on the original data set. If this trees has a lower parsimony score than the currently best tree replace it.
- Iterate 1:3 until either a given number of iteration is reached or no improvements have been recorded for a number of iterations.

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

`## [1] 746 746`

Next we assign branch length to the tree. The branch length are proportional to the number of substitutions / site.

`<- acctran(treeRatchet, primates) treeRatchet `

The parsimony ratchet implicitly performs a bootstrap analysis (step 1). We make use of this and store the trees which where visited. This allows us to add bootstrap support values to the tree.

`plotBS(midpoint(treeRatchet), type="phylogram")`

## Branch and bound

For data sets with few species 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. And for more than 20-30 taxa this will take almost forever.

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

`## 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

```
<- pml(treeNJ, data=primates)
fit fit
```

```
##
## model: JC
##
## 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`

.

`<- optim.pml(fit, rearrangement="NNI") fitJC `

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

`logLik(fitJC)`

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

With the default values`pml`

will estimate a Jukes-Cantor
model. The generic function `update`

allows to change
parameters. We will change the model to the GTR + \(\Gamma(4)\) + I model and then optimize all
the parameters.

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

```
##
## model: GTR+G(4)+I
##
## loglikelihood: -2609
##
## unconstrained loglikelihood: -1230
## Proportion of invariant sites: 0.005674
## Discrete gamma model
## Number of rate categories: 4
## Shape parameter: 2.989
##
## Rate matrix:
## a c g t
## a 0.0000 0.613751 35.926802 0.3748
## c 0.6138 0.000000 0.005558 15.0831
## g 35.9268 0.005558 0.000000 1.0000
## t 0.3748 15.083056 1.000000 0.0000
##
## Base frequencies:
## 0.3934 0.3791 0.04024 0.1873
```

With the control parameters the thresholds for the fitting process
can be changed. Here we want just to suppress 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 optimized 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.

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

```
##
## model: GTR+G(4)+I
##
## loglikelihood: -2607
##
## unconstrained loglikelihood: -1230
## Proportion of invariant sites: 0.005565
## Discrete gamma model
## Number of rate categories: 4
## Shape parameter: 2.65
##
## Rate matrix:
## a c g t
## a 0.0000 0.946763 83.505056 0.6435
## c 0.9468 0.000000 0.004268 32.1269
## g 83.5051 0.004268 0.000000 1.0000
## t 0.6435 32.126883 1.000000 0.0000
##
## Base frequencies:
## 0.3964 0.3788 0.04026 0.1846
```

## 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 -2607 35 10 922 <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 -2607 0.0 0.499
## [2,] 2 -3068 461.2 0.000
```

or with the AIC

`AIC(fitJC)`

`## [1] 6187`

`AIC(fitGTR)`

`## [1] 5284`

`AICc(fitGTR)`

`## [1] 5297`

`BIC(fitGTR)`

`## [1] 5405`

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 (D. Posada and Crandall 1998), (David Posada 2008), (Abascal, Zardoya, and Posada 2005). By default
available nucleotide or amino acid models available are compared.

`<- modelTest(primates) mt `

Here we can select only some common models:

`<- modelTest(primates, model=c("JC", "F81", "K80", "HKY", "SYM", "GTR")) mt `

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 the function `as.pml`

evaluates this call to
get a `pml`

object back to use for further optimization or
analysis.

`<- as.pml(mt, "HKY+G+I")) (fit `

```
##
## model: HKY+G(4)+I
##
## loglikelihood: -2612
##
## unconstrained loglikelihood: -1230
## Proportion of invariant sites: 0.002734
## Discrete gamma model
## Number of rate categories: 4
## Shape parameter: 2.127
##
## Rate matrix:
## a c g t
## a 0.00 1.00 56.03 1.00
## c 1.00 0.00 1.00 56.03
## g 56.03 1.00 0.00 1.00
## t 1.00 56.03 1.00 0.00
##
## Base frequencies:
## 0.4205 0.3623 0.04394 0.1733
```

`<- as.pml(mt, "BIC")) (fit `

```
##
## model: HKY+G(4)
##
## loglikelihood: -2613
##
## unconstrained loglikelihood: -1230
## Discrete gamma model
## Number of rate categories: 4
## Shape parameter: 2.145
##
## Rate matrix:
## a c g t
## a 0.00 1.00 51.44 1.00
## c 1.00 0.00 1.00 51.44
## g 51.44 1.00 0.00 1.00
## t 1.00 51.44 1.00 0.00
##
## Base frequencies:
## 0.4193 0.3629 0.04389 0.1739
```

## Bootstrap

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

```
<- bootstrap.pml(fitJC, bs=100, optNni=TRUE,
bs 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")`

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

## Molecular dating with a strict clock

We implemented a strict clock as described in (Felsenstein 2004), p. ???.

So far the starting tree needs to be ultrametric, so we use the UPGMA
tree. When we optimize the tree with `optim.pml`

we have to
make sure set we set `optRooted = TRUE`

!

```
<- pml(treeUPGMA, data=primates, k=4, bf=baseFreq(primates))
fit_strict <- optim.pml(fit_strict, model="GTR", optRooted = TRUE,
fit_strict rearrangement = "NNI", optGamma = TRUE, optInv = TRUE,
control = pml.control(trace = 0))
```

`plot(fit_strict)`

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)
<- "myfile"
file <- read.phyDat(file)
dat <- dist.ml(dat, "F81")
dm <- NJ(dm)
tree # as alternative for a starting tree:
<- pratchet(dat) # parsimony tree
tree <- nnls.phylo(tree, dm) # need edge weights
tree
# 1. alternative: quick and dirty: GTR + G
<- pml(tree, dat, k=4)
fitStart <- optim.pml(fitStart, model="GTR", optGamma=TRUE, rearrangement="stochastic")
fit
# 2. alternative: prepare with modelTest
<- modelTest(dat, tree=tree, multicore=TRUE)
mt order(mt$AICc),]
mt[# choose best model from the table according to AICc
<- as.pml(mt, "AICc")
fitStart
# equivalent to: fitStart = eval(get("GTR+G+I", env), env)
<- optim.pml(fitStart, rearrangement = "stochastic",
fit optGamma=TRUE, optInv=TRUE, model="GTR")
<- bootstrap.pml(fit, bs=100, optNni=TRUE, multicore=TRUE) bs
```

## 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)
<- "myfile"
file <- read.phyDat(file, type = "AA")
dat <- dist.ml(dat, model="JTT")
dm <- NJ(dm)
tree
# parallel will only work safely from command line
# and not at all windows
<- modelTest(dat, model=c("JTT", "LG", "WAG"),
(mt multicore=TRUE))
# run all available amino acid models
<- modelTest(dat, model="all", multicore=TRUE))
(mt
<- as.pml(mt)
fitStart
<- pml(tree, dat, model="JTT", k=4, inv=.2)
fitNJ <- optim.pml(fitNJ, rearrangement = "stochastic",
fit optInv=TRUE, optGamma=TRUE)
fit
<- bootstrap.pml(fit, bs=100, optNni=TRUE, multicore=TRUE) bs
```

# Session info

```
## R version 4.2.0 (2022-04-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04 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/libopenblasp-r0.3.20.so
##
## 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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] knitr_1.39 phangorn_2.9.0 ape_5.6-2
##
## loaded via a namespace (and not attached):
## [1] igraph_1.3.1 Rcpp_1.0.8.3 rstudioapi_0.13 magrittr_2.0.3
## [5] lattice_0.20-45 R6_2.5.1 quadprog_1.5-8 rlang_1.0.2
## [9] fastmatch_1.1-3 fastmap_1.1.0 highr_0.9 stringr_1.4.0
## [13] tools_4.2.0 parallel_4.2.0 grid_4.2.0 nlme_3.1-157
## [17] xfun_0.31 cli_3.3.0 jquerylib_0.1.4 htmltools_0.5.2
## [21] yaml_2.3.5 digest_0.6.29 Matrix_1.4-1 codetools_0.2-18
## [25] sass_0.4.1 prettydoc_0.4.1 evaluate_0.15 rmarkdown_2.14
## [29] stringi_1.7.6 compiler_4.2.0 bslib_0.3.1 generics_0.1.2
## [33] jsonlite_1.8.0 pkgconfig_2.0.3
```

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