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Citation: Steffen, R.; Ogoniak, L.;
Grundmann, N.; Pawluchin, A.;
Soehnlein, O.; Schmitz, J. paPAML:
An Improved Computational Tool to
Explore Selection Pressure on
Protein-Coding Sequences. Genes
2022,13, 1090. https://doi.org/
10.3390/genes13061090
Academic Editor: Zoltan Ivics
Received: 13 May 2022
Accepted: 14 June 2022
Published: 18 June 2022
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genes
G C A T
T A C G
G C A T
Article
paPAML: An Improved Computational Tool to Explore
Selection Pressure on Protein-Coding Sequences
Raphael Steffen 1,* , Lynn Ogoniak 1,2, Norbert Grundmann 2, Anna Pawluchin 1, Oliver Soehnlein 1,3
and Jürgen Schmitz 1, *
1Institute of Experimental Pathology, ZMBE, University of Münster, 48149 Münster, Germany;
l_ogon01@uni-muenster.de (L.O.); anna.pawluchin@uni-muenster.de (A.P.); soehnlein@uni-muenster.de (O.S.)
2Institute of Bioinformatics, Faculty of Medicine, University of Münster, 48149 Münster, Germany;
ngrundma@uni-muenster.de
3Department of Physiology and Pharmacology (FyFa), Karolinska Institutet, 17177 Stockholm, Sweden
*Correspondence: r_stef04@uni-muenster.de (R.S.); jueschm@uni-muenster.de (J.S.)
Abstract:
Evolution is change over time. Although neutral changes promoted by drift effects are
most reliable for phylogenetic reconstructions, selection-relevant changes are of only limited use to
reconstruct phylogenies. On the other hand, comparative analyses of neutral and selected changes
of protein-coding DNA sequences (CDS) retrospectively tell us about episodic constrained, relaxed,
and adaptive incidences. The ratio of sites with nonsynonymous (amino acid altering) versus
synonymous (not altering) mutations directly measures selection pressure and can be analysed by
using the Phylogenetic Analysis by Maximum Likelihood (PAML) software package. We developed a
CDS extractor for compiling protein-coding sequences (CDS-extractor) and parallel PAML (paPAML)
to simplify, amplify, and accelerate selection analyses via parallel processing, including detection
of negatively selected sites. paPAML compiles results of site, branch-site, and branch models and
detects site-specific negative selection with the output of a codon list labelling significance values.
The tool simplifies selection analyses for casual and inexperienced users and accelerates computing
speeds up to the number of allocated computer threads. We then applied paPAML to examine the
evolutionary impact on a new GINS Complex Subunit 3 exon, and neutrophil-associated as well as
lysin and apolipoprotein genes. Compared with codeml (PAML version 4.9j) and HyPhy (HyPhy FEL
version 2.5.26), all paPAML test runs performed with 10 computing threads led to identical selection
pressure results, whereas the total selection analysis via paPAML, including all model comparisons,
was about 3 to 5 times faster than the longest running codeml model and about 7 to 15 times faster
than the entire processing time of these codeml runs.
Keywords:
codeml; paPAML; CDS-extractor; positive selection; purifying selection; PAML; HyPhy;
retrotransposon exonization; neutrophil associated gene; abalone sperm lysin; apolipoprotein
1. Introduction
Germline mutations leave behind heritable changes, and natural selection acts on
such variations within populations. The protein-coding DNA sequences (CDS) of genes,
representing ~1.5% of the human genome [
1
], are especially exposed to natural selection.
CDS are highly variable, mainly due to the alternative splicing of exons. Interspersed by
introns, the exons of a gene are concatenated during splicing of the precursor messenger
RNA and are usually flanked by their untranslated sequence regions (UTR). CDS are
structured in triplets, called codons, which encode the translation to amino acids. (Figure 1).
It is necessary to analyse changes in all codon positions to determine the evolutionary
impact of mutations. Totals of 96%, 100%, and 33% of possible substitutions at the first,
second, or third triplet positions, respectively, lead to an exchange of amino acids (non-
synonymous changes, dN) [
2
,
3
]. Because several codons (see codon wheel in Figure 1)
Genes 2022,13, 1090. https://doi.org/10.3390/genes13061090 https://www.mdpi.com/journal/genes
Genes 2022,13, 1090 2 of 15
represent the same amino acid, described as redundancy of the genetic code, the remaining
silent or synonymous changes (dS, ~4% of the first and ~67% of the third codon position)
leave proteins unchanged. They are fixed in a population via drift effects in the absence of
selective forces.
Genes 2022, 13, 1090 2 of 16
Figure 1. Gene structure, conservation, and DNA to amino acid translation. Exons (including
terminal UTR) and introns are assembled to form genes (regulatory regions are not shown). After
the splicing of introns, mRNA triplets forming the CDS are translated into amino acids according to
the amino acid code (right; codon wheel from 123RF.com, 24366216, accessed on 10 May 2022). The
impact of a point mutation in a codon depends on its position. Totals of 96%, 100%, and 33% of
changes at the first, second, and third codon positions, respectively, result in amino acid changes
(nonsynonymous changes dN) as opposed to synonymous changes (dS), which do not affect the
amino acid sequences/protein translation. A dN/dS ratio (omega ⍵) of 1 indicates the neutral
evolution of the protein. Values lower than 1 suggest negative purifying selection and above 1
positive or adaptive selection.
It is necessary to analyse changes in all codon positions to determine the evolutionary
impact of mutations. Totals of 96%, 100%, and 33% of possible substitutions at the first,
second, or third triplet positions, respectively, lead to an exchange of amino acids
(nonsynonymous changes, dN) [2,3]. Because several codons (see codon wheel in Figure
1) represent the same amino acid, described as redundancy of the genetic code, the
remaining silent or synonymous changes (dS, ~4% of the first and ~67% of the third codon
position) leave proteins unchanged. They are fixed in a population via drift effects in the
absence of selective forces.
Nonsynonymous codon substitutions do affect the corresponding amino acid
sequence. Depending on functional constraints, they are usually under selection pressure,
whether positive (adaptive selection) or negative (purifying selection). This suggests that
the dN to dS substitution ratio, or omega (ω), is an indication of the respective selection
pressures on sequences: If dN substitutions are prevalent (ω > 1), positive selection can be
inferred. In contrast, ω < 1 indicates purifying selection. ω = 1 means that substitutions of
both types are more or less equally distributed [4,5], an indicator of neutral evolution. It
should be mentioned that positive selection is almost always restricted to functionally
critical codons at specific evolutionary periods and is possible throughout all branches of
a phylogenetic tree.
A commonly used tool to determine dN/dS ratios and therefore selection pressure on
multiple orthologous protein-coding sequence regions (evolving from the same ancestors)
during phylogenetic comparisons is the Phylogenetic Analysis by Maximum Likelihood
(PAML) suite (version 4.9j [6]). The integrated codeml analysis evaluates various
Figure 1.
Gene structure, conservation, and DNA to amino acid translation. Exons (including
terminal UTR) and introns are assembled to form genes (regulatory regions are not shown). After
the splicing of introns, mRNA triplets forming the CDS are translated into amino acids according
to the amino acid code (right; codon wheel from 123RF.com, 24366216, accessed on 10 June 2022).
The impact of a point mutation in a codon depends on its position. Totals of 96%, 100%, and 33%
of changes at the first, second, and third codon positions, respectively, result in amino acid changes
(nonsynonymous changes dN) as opposed to synonymous changes (dS), which do not affect the amino
acid sequences/protein translation. A dN/dS ratio (omega
ω
) of 1 indicates the neutral evolution
of the protein. Values lower than 1 suggest negative purifying selection and above 1 positive or
adaptive selection.
Nonsynonymous codon substitutions do affect the corresponding amino acid sequence.
Depending on functional constraints, they are usually under selection pressure, whether
positive (adaptive selection) or negative (purifying selection). This suggests that the dN to
dS substitution ratio, or omega (
ω
), is an indication of the respective selection pressures
on sequences: If dN substitutions are prevalent (
ω
> 1), positive selection can be inferred.
In contrast,
ω
< 1 indicates purifying selection.
ω
= 1 means that substitutions of both
types are more or less equally distributed [
4
,
5
], an indicator of neutral evolution. It
should be mentioned that positive selection is almost always restricted to functionally
critical codons at specific evolutionary periods and is possible throughout all branches of a
phylogenetic tree.
A commonly used tool to determine dN/dS ratios and therefore selection pressure on
multiple orthologous protein-coding sequence regions (evolving from the same ancestors)
during phylogenetic comparisons is the Phylogenetic Analysis by Maximum Likelihood
(PAML) suite (version 4.9j [
6
]). The integrated codeml analysis evaluates various hypothe-
Genes 2022,13, 1090 3 of 15
ses by comparing nested statistical models via likelihood ratio tests (LRTs). Of specific
interest for selection analysis are (1) the site models for detecting sites under positive
selection, (2) branch-site models for detecting branches and sites under positive selection,
and (3) branch models for detecting branches under positive selection as embedded in
PAML. For the phylogenetic scope and additional details of the tests, see Figure 2. None of
the three can detect negative selection.
Genes 2022, 13, 1090 3 of 16
hypotheses by comparing nested statistical models via likelihood ratio tests (LRTs). Of
specific interest for selection analysis are (1) the site models for detecting sites under
positive selection, (2) branch-site models for detecting branches and sites under positive
selection, and (3) branch models for detecting branches under positive selection as
embedded in PAML. For the phylogenetic scope and additional details of the tests, see
Figure 2. None of the three can detect negative selection.
Figure 2. Phylogenetic scope of different tests. The site models and HyPhy FEL calculate significant
(*) positively and negatively selected sites, respectively, averaged over all branches (labelled blue).
The branch-site models combine site-specific and branch-specific positive selection analyses for all
possible foreground branches (two are labelled orange). The branch models use all possible
foreground branches (two are labelled green) to calculate the average ω over all codons (labelled 1
2 3 codon positions). The default settings for the codeml ctl files are given below the corresponding
models.
Site models: The site models allow the ratio of ω to vary among sites (e.g., codons in
DNA sequences or amino acids in proteins). The models include pairs of individual,
informative comparisons for applying subsequent LRT of positive selection. One model
pair consists of the so-called M1a and M2a models. M1a assumes nearly neutral selection
and fixes the upper bounds of ω values at 1 (ω ≤ 1), whereas M2a allows for positive
selection (ω ≤ 1 and ω > 1) [4,7] (see also PAML User Guide Version 4.9j, their Table 2.
Parameters in the site models). The second model pair consists of M7 and M8. M7 assumes
that ω is beta-distributed among sites (interval 0,1) and correspondingly does not allow
for positively selected sites, whereas M8 adds an extra class of sites that allows positive
selection (ω > 1) [5,7]. The M1a/M2a comparison is described as less powerful than the
M7/M8 comparison (see PAML User Guide Version 4.9j, page 29). A site is inferred to be
positively selected if M2a or M8 are significantly more likely than M1a or M7. Codeml
also calculates posterior probabilities of site classes based on the Bayes empirical Bayes
(BEB) test [7], which can identify specific sites under positive selection if the LRT of the
M1a/M2a or M7/M8 comparison is significant.
Branch-site models: The branch-site models can identify positively selected sites
along particular branches of a given phylogeny by allowing the ω ratios to vary among
these branches and sites. Like the branch models, the LRT compares the branch site [7,8]
Figure 2.
Phylogenetic scope of different tests. The site models and HyPhy FEL calculate significant (*)
positively and negatively selected sites, respectively, averaged over all branches (labelled blue). The
branch-site models combine site-specific and branch-specific positive selection analyses for all possible
foreground branches (two are labelled orange). The branch models use all possible foreground
branches (two are labelled green) to calculate the average
ω
over all codons (labelled 1 2 3 codon
positions). The default settings for the codeml ctl files are given below the corresponding models.
Site models:
The site models allow the ratio of
ω
to vary among sites (e.g., codons
in DNA sequences or amino acids in proteins). The models include pairs of individual,
informative comparisons for applying subsequent LRT of positive selection. One model
pair consists of the so-called M1a and M2a models. M1a assumes nearly neutral selection
and fixes the upper bounds of
ω
values at 1 (
ω≤
1), whereas M2a allows for positive
selection (
ω≤
1 and
ω
> 1) [
4
,
7
] (see also PAML User Guide Version 4.9j, their Table 2,
Parameters in the site models). The second model pair consists of M7 and M8. M7 assumes
that
ω
is beta-distributed among sites (interval 0,1) and correspondingly does not allow
for positively selected sites, whereas M8 adds an extra class of sites that allows positive
selection (
ω
> 1) [
5
,
7
]. The M1a/M2a comparison is described as less powerful than the
M7/M8 comparison (see PAML User Guide Version 4.9j, page 29). A site is inferred to be
positively selected if M2a or M8 are significantly more likely than M1a or M7. Codeml also
calculates posterior probabilities of site classes based on the Bayes empirical Bayes (BEB)
test [
7
], which can identify specific sites under positive selection if the LRT of the M1a/M2a
or M7/M8 comparison is significant.
Branch-site models:
The branch-site models can identify positively selected sites
along particular branches of a given phylogeny by allowing the
ω
ratios to vary among
these branches and sites. Like the branch models, the LRT compares the branch site [
7
,
8
]
Genes 2022,13, 1090 4 of 15
with a corresponding null model with fixed omega (
ω
= 1). As with the site models, a BEB
test calculates posterior probabilities of specific sites under positive selection. Similar to the
branch models, the branch-site models require a set of identical trees with all foreground
branches labelled.
Branch models:
The branch models allow for variation of
ω
values among phylo-
genetic branches, enabling the inference of positive selection acting on specific ancestral
or terminal lineages using several
ω
ratios (model = 2 [
9
,
10
]. A null model that fixes the
omega value at 1 (
ω
= 1), which implies neutral selection across the phylogeny, is compared
via LRT with a model allowing omega to vary (
ω
> 1). Any branch or node in which the
null model is significantly less likely than its counterpart is inferred to be under positive
selection pressure.
PAML and its integrated codeml analysis has become one of the most used tools for
testing evolutionary selection hypotheses. All three tests are combined to find evidence
for adaptive selection across a sequence and its phylogeny. However, along with other
limitations, codeml is restricted to running on a single processor core and is therefore
very time consuming, especially when comparing considerable numbers of species or
longer sequences. The tool also does not automatically calculate p-values for the model
likelihood comparisons. Its output can be confusing, especially for inexperienced users,
as it does not highlight the essential results or present them comprehensively. Moreover,
due to the model’s restrictions, PAML cannot detect site-specific negative selection, which
is a frequent problem in selection analysis programs that are mostly focused on positive
selection analyses and adaptive evolution. Furthermore, it requires the preparation of
separate configuration files for each alignment and test, making high-throughput analyses
of multiple sequences even more arduous. There are some alternatives to codeml, such as
HyPhy [11].
HyPhy FEL:
The HYpothesis testing suite using PHYlogenies (HyPhy [
11
]) embeds the
Fixed Effects Likelihood (FEL) algorithm, which also uses ML to infer dN/dS substitution
rates on a per-site basis, and is not restricted to positive selection [
12
]. However, HyPhy
also assumes that selection pressure is constant along the entire phylogeny and still needs
to be run for one alignment and test at a time.
Both codeml and HyPhy require alignment files containing distinct orthologous se-
quences (for example, CDS excluding the stop codon) of multiple species or populations
presenting the outgroup first and their complete phylogeny as a tree file (Newick for-
mat). To easily accomplish this, we first developed the CDS-extractor by using Biopython
to automatically retrieve NCBI-deposited orthologous sequences and genes for various
species. To make selection analyses of protein-coding alignments fast and easy, especially
for large datasets, and their results more comprehensive, we further designed parallel
PAML (paPAML), a Perl-based application to run codeml and HyPhy FEL calculations in
parallel. We selected the Perl programming language to provide a capable and long-lasting
tool that does not interfere with new versions of that language.
To demonstrate the evolutionary impact of novel exon cassettes (e.g., those derived
from exonized Alu elements), we then applied the CDS-extractor and paPAML tools to
comparisons of specific mobile retrotransposed Alu elements. Alus are short interspersed
elements (SINEs) that occupy more than 10% of the human genome [
1
]. In addition to their
well-known property as neutral phylogenetic markers in primates (e.g., Hartig et al. [
13
]),
they constitute a vital initiator of evolving CDS by integrating into genes and using the
host’s splicing system to add novel CDS (e.g., Schrader and Schmitz [
14
,
15
]). Neutrophil-
associated genes are well-known for their adaptive capacity [
16
] and served as another
example to test paPAML. We also analyzed the classical abalone sperm lysin sequence
dataset that contains verifiable amino acid sites under positive and purifying selection [
17
].
The last example refers to an apolipoprotein family (APOL) that recently was investigated
for adaptive evolution [18].
Genes 2022,13, 1090 5 of 15
2. Materials and Methods
2.1. CDS-Extractor and Alignment
The SeqIO and Entrez packages of Biopython (python 3.7 [
19
]) were used to design
the CDS-extractor. The program automatically retrieves gene orthologs derived from the
NCBI Eukaryotic Genome Annotation Pipeline (https://www.ncbi.nlm.nih.gov/genome/
annotation_euk/process/database, accessed on 10 June 2022). To start the extraction of
CDS, the gene name, email address, and a set of species must be selected. Optionally, the
exclusion of intact start and stop codons and ORFs can be chosen as well. The CDS-extractor
is accessible at http://retrogenomics.uni-muenster.de/tools/CDS-extractor (accessed on
10 June 2022). After starting the tool, a fasta results file with species names and accession
numbers as headers is immediately downloadable. The aligner MUSCLE (http://www.
drive5.com/muscle/, accessed on 10 June 2022) can be used for the subsequent multiple
sequence alignment (version 5 [
20
]). After codon-wise gap position removal, the alignment
is ready for paPAML.
2.2. paPAML
The basic concept of our Perl command-line tool paPAML is to run different modules
of codeml and HyPhy FEL in parallel processes, which minimizes the administrating time
and to maximize the visibility of phylogenetic branches and codon positions that were
exposed to natural selection. That is accomplished by simultaneously running independent
codeml calculations of all automatically labelled individual tree topologies (foreground
labelled by, e.g., one #1 mark per tree topology) for the branch-site and branch-specific
tests. This enables shorter total runtimes, especially on large multicore computer systems,
without changing the basic algorithms of either codeml or HyPhy FEL. paPAML enables
user-defined settings of the upper limit of parallel runs allowing for optimal adaptation
to the computational environment. Furthermore, paPAML reduces preparation time for
large datasets by automatically specifying ctl configuration files for each test (default or
entered command line parameters) and tree/tre-files and coordinates to run all subsets
of tests. According to codeml requirements, the outgroup species must be placed as the
first sequence in the alignment. Because PAML automatically removes positions with gaps
in alignments and HyPhy treats such positions as ambiguous bases by default instead,
prior manual removal of all such gap positions is necessary to apply codeml and HyPhy
based on the same alignment. Correspondingly, the positions of detected positively or
negatively selected sites relate directly to the modified alignment of the reference species
(first sequence) and require a subsequent realigning to the original genes of individual
species. Furthermore, stop codons need to be removed. The tree must be in Newick
format. Figure 3illustrates the input and output formats, an example command, and the
parallelization concept. All example data are presented in Supplementary Data S1.
paPAML allows a restart and continuation of aborted runs. Finally, paPAML screens
the result files of each test to combine and summarize them in a final output file. This
includes the automated calculation of p-values for the model comparisons via a chi-squared
test using the extracted lnL-values and degrees of freedom from both models. Furthermore,
paPAML creates a tabular sequence overview. Each codon is connected to its extracted,
site-specific result, making assigning selection pressures to sites in the original sequence
easier. The summarized and structured output allows even inexperienced users to comb
through copious amounts of data. The complete list of options for the command line is as
follows (Table 1):
Genes 2022,13, 1090 6 of 15
Table 1. paPAML parameters.
paPAML-VERSION 1.23
paPAML.pl -p threads [-f controlfiles] [-t tests] [-s significance] [-d] {codemlparams}
paPAML.pl -i [provides status information of runs in a new terminal window]
paPAML.pl -c [removes all temporal folders]
CTRL-c [interrupts the run and all subprocesses]
where:
runs The number of parallel runs.
controlfiles A list of control files. It is assumed they are named with the suffix.ctl. If not given, all files
with that suffix are used for calculation.
tests The used tests (1, 2, 3 or h for HyPhy) to run the data. They can be written like “1” or “12”
(order is not important, default: 123 h).
significance The maximum p-value to print sites and branches/trees under selection. Also used for
printing BEB and HyPhy results (default: 0.05).
-d Original result directories (PAML, HyPhy) are stored.
-I Info about current runs.
-c Cleans all temporary folders.
codemlparams If not provided additional parameters for the ctl file, the default parameters will be used
(see below).
Example: -Mgene 9 -rho 34
default codeml-parameters
-CodonFreq 2
-Malpha 0
-Mgene 0
-RateAncestor 0
-Small_Diff 0.5e-6
-aaDist 0
-alpha 0
-cleandata 1
-clock 0
-fix_alpha 1
-fix_blength −1
-fix_rho 1
-getSE 0
-icode 0
-method 0
-ndata 1
-omega 1
-outfile mlc
-rho 0
-runmode 0
-seqtype 1
2.3. Data Requirements (paPAML)
Both codeml and HyPhy require aligned sequence files in the fasta format for each
locus to be analyzed. These sequences need to be structured in codons with a base pair
count divisible by three (for intact ORFs) and no stop codons. Because codeml and HyPhy
deal differently with sequence gaps (codeml deletes all gap regions and HyPhy fills them
with unspecific bases), it is also necessary to consistently remove all gap regions codon-
wise. Additionally, both programs need a phylogenetic tree/tre file in the Newick format
with all species represented in the alignment. Species names in this phylogeny must be
equivalent to the sequence names in the fasta file. A codeml ctl config file with input
Genes 2022,13, 1090 7 of 15
information is needed for paPAML. The ctl file specifies the seqfile and treefile names
(e.g., “
seqfile = ALIGNMENT.fast
” and “Treefile = NEWICK.tre”). All other PAML-specific
parameters can either be specified by the user via the command line, or default settings
are used (see command-line call: paPAML.pl). One must start paPAML in the directory
containing the input files and ctl files.
Genes 2022, 13, 1090 6 of 16
Figure 3. Input format, basic command-line options, and output of paPAML. The standard input of
codeml and HyPhy is a fasta sequence file of aligned CDS excluding the stop codon, a Newick
Figure 3.
Input format, basic command-line options, and output of paPAML. The standard input of
codeml and HyPhy is a fasta sequence file of aligned CDS excluding the stop codon, a Newick tree/tre
Genes 2022,13, 1090 8 of 15
file representing the corresponding phylogenetic tree, and a ctl file prefilled with the seqfile name (e.g.,
seqfile = ALIGNMENT.fasta), and treefile = NEWICK.tre. The command shown here paPAML.pl
-p 10 enables the usage of ten threads in parallel. The option [-d] allows the storage of directories
containing the data from the subprocesses. The original input and output data, as well as the created
ctl file can be accessed here. Test 1 represents the site models M1a/M2a and M7/M8. Test 2 executes
the branch-site models and compares results for
ω
= 1 (fixed) versus
ω
> 1 (unfixed). Test 3 denotes
the corresponding branch model. Test 4 evaluates site-specific incidences of negative and positive
selection by HyPhy. The output file structure is shown at the bottom of the Figure.
2.4. Necessary Resources (paPAML)
For an optimal performance of paPAML, we advise using a multicore computer with
a UNIX-based operating system such as Linux, FreeBSD, or macOS with multiple pro-
cessors. The speed of analyses is multiplied depending on the number of used threads.
paPAML was developed and tested with Perl version 5.32 but theoretically works with
other versions. It requires the following Perl modules: Proc::ProcessTable (version 0.59) and
Statistics::Distribution (version 1.02). It also requires the preinstalled programs PAML (ver-
sion 4.9j, http://abacus.gene.ucl.ac.uk/software/paml.html, accessed on 10 June 2022 [
6
]),
HyPhy (version 2.5.36, https://github.com/veg/hyphy/releases, accessed on 10 June
2022 [
11
]) and their respective prerequisites. paPAML source code is available at github.com
(https://github.com/RetroWWU/paPAML, accessed on 10 June 2022). The program ac-
cepts any length of sequences and number of species. However, processing time increases
the more extensive the input dataset becomes.
3. Results and Discussion
paPAML is especially useful for frequent applicants and inexperienced users of tests
to determine selection pressures on sequences. It offers a simplified entry of data, compiles
the essential tests for selection pressure, provides structured, compiled output files for
the applied site models M1a/M2a and M7/M8, branch-site models, branch models, and
HyPHy FEL with a list of all analyzed codons and labelled significance values for positive
and negative selection. In addition to the result table, paPAML generates a fasta file with
highlighted selected codons (uppercase letters) for the applied site-specific tests to facilitate
a subsequent MUSCLE [
20
] alignment against the original genes from any species of interest.
Essentially parallelized running of the different models and their extensive sub-processes
(individually labelled branches for the branch-site and branch models) results in substantial
time saving, particularly for long sequences and numerous taxa.
We demonstrated this on a variety of example datasets. All the example datasets
were assembled by using the CDS-extractor, and were analyzed both via paPAML and in
individual PAML codeml and HyPhy runs to test paPAML for the correct identification
of sites and branches under selection pressures and to monitor differences in run time.
Marked differences in computational runtimes and manageability of the data, especially
for longer and more species-rich comparisons, are shown in Table 2.
Table 2. Runtime analysis results for example datasets.
Dataset Number of
Sequences Number of Codons Overall Runtime
paPAML (min)
Longest Runtime
Codeml (min)
Overall Runtime
Codeml (min)
GINS3 3 39 <1 <1 <1
CEBPE 30 281 198 1205 3032
Lysin 25 120 115 405 867
APOL1–4 20 312 79 447 1143
All test runs were performed with the default paPAML settings with 10 parallel runs
(paPAML.pl -p 10). Because we embedded the original PAML and HyPhy modules into
paPAML, we could confirm that compared to codeml (PAML version 4.9j) and HyPhy
Genes 2022,13, 1090 9 of 15
(HyPhy FEL version 2.5.26), all test runs led to identical selection pressure results. In
general, the total selection analysis via paPAML using 10 threads, including all model
comparisons, was about 3 to 6 times faster than the longest running codeml model and
about 7 to 15 times faster than the entire processing time of these codeml runs.
3.1. Four Examples of Test Data
(1) A potentially exonized Alu element sequence in the GINS Complex Subunit 3
gene (GINS3). The process of exonization involves the recruitment of novel exons from,
e.g., intronic mobile elements or transposed elements (TE) without an original protein-
coding function (see Schrader and Schmitz [
14
,
15
]). Here, we tested for selective constraints
on the novel TE exon cassette. Such novel exons usually follow a path from neutral to
adaptive and purifying selection. The dataset contained orthologous sequences from three
primate species (Homo sapiens, the olive baboon Papio anubis, and the Angola colobus
Colobus angolensis) each with a length of 117 base pairs. Analysis of the GINS3 exonized
region dataset finished in about 26 s. The overall runtime for all consecutive codeml
model comparisons was 39 s, and the longest run of a single test took 18 s. Due to the
low number of sequences and species involved, a runtime comparison for GINS3 was not
very informative, but paPAML was still faster overall than the codeml analysis overall.
The parallelization our program provides did not have a large effect on runtimes for small
datasets in general but still is advantageous for such datasets by simplifying in- and output
of the analyses.
Multiple sites in the potential GINS3 Alu exonization [
15
] appear to be under signif-
icant positive selection (Table 3). According to the site model, both the M1a/M2a and
M7/M8 comparisons showed that the models allowing for positive selection fit the data
better than their respective null models (p-value M1a/M2a = 0.035, p-value M7/M8 = 0.026).
Moreover, eight sites were found to be significantly likely to be under positive selection in
both comparisons (p-value < 0.05; codons 2, 8, 24, 25, 26, 28, 29, 37;
Supplementary Table S1
).
The branch-site model indicated that, except for codons 8 and 24, all of these sites have been
under positive selection both in the branch leading to H. sapiens and the branch leading
to the other two species (see Figure 4for significant selection on branches derived from
the branch-site model). There were no significant results in the branch model and HyPhy
FEL test.
(2) The neutrophil-associated CCAAT Enhancer Binding Protein Epsilon (CEBPE)
transcription factor with 30 Placentalia sequences, each 843 base pairs long. The CEBPE gene
significantly influences the development and function of neutrophils, the most abundant
leukocyte subset in human blood, and is expected to be under selective forces [
16
]. paPAML
needed about 3.3 h to finish the more extensive CEBPE dataset. By comparison, codeml’s
most time-consuming model comparison took about 20 h to complete, while the overall
codeml analysis, if executed consecutively, would have taken over 50 h.
Table 3.
Results of the selection analyses. The number of branches under positive selection detected
by the branch-site model did not include branches with significant LRT results but without sites
under positive selection. Overlaps between found sites were counted for all models with which they
were found.
Dataset Number of Codons
in Sequences
Number of Positively
Selected Codons
(Site Models)
Number of Positively
Selected Codons
(Branch-Site Model)
Number of Positively
Selected Codons
(HyPhy FEL)
Number of
Negatively
Selected Codons
(HyPhy FEL)
Number of Branches
under Positive
Selection
GINS3 39 8 0 0 0 2 (branch-site model)
CEBPE 281 0 0 0 68 22 (branch model)
Lysin 120 17 32 11 15 11 (branch model),
9 (branch-site model)
APOL1–4 312 25 3 12 12 5 (branch model)
2 (branch-site model)
Genes 2022,13, 1090 10 of 15
Genes 2022, 13, 1090 10 of 16
in both comparisons (p-value < 0.05; codons 2, 8, 24, 25, 26, 28, 29, 37; Supplementary Table
S1). The branch-site model indicated that, except for codons 8 and 24, all of these sites
have been under positive selection both in the branch leading to H. sapiens and the branch
leading to the other two species (see Figure 4 for significant selection on branches derived
from the branch-site model). There were no significant results in the branch model and
HyPhy FEL test.
Figure 4. Phylogenetic tree of the GINS3 dataset. Branches significantly likely to be under positive
selection according to the branch-site models are marked orange (corresponding to Figure 2) and
labelled with the p-values inferred by their respective model comparisons.
Table 3. Results of the selection analyses. The number of branches under positive selection detected
by the branch-site model did not include branches with significant LRT results but without sites
under positive selection. Overlaps between found sites were counted for all models with which they
were found.
Dataset
Number of
Codons in
Sequences
Number of
Positively
Selected
Codons
(Site
Models)
Number of
Positively
Selected
Codons
(Branch-Site
Model)
Number of
Positively
Selected
Codons
(HyPhy
FEL)
Number of
Negatively
Selected
Codons
(HyPhy
FEL)
Number of
Branches
under
Positive
Selection
GINS3 39 8 0 0 0
2 (branch-
site model)
CEBPE 281 0 0 0 68
22 (branch
model)
Lysin 120 17 32 11 15
11 (branch
model),
9 (branch-
site model)
APOL1-4 312 25 3 12 12
5 (branch
model)
Figure 4.
Phylogenetic tree of the GINS3 dataset. Branches significantly likely to be under positive
selection according to the branch-site models are marked orange (corresponding to Figure 2) and
labelled with the p-values inferred by their respective model comparisons.
Neither the site model nor the branch-site model detected sites under positive selection
in the CEBPE gene. Instead, 68 of the 281 codons were subject to purifying selection
according to the HyPhy FEL algorithm, which did not detect any positively selected sites.
The branch model, on the other hand, found 22 out of 50 branches with signals of positive
selection, including the branch leading to H. sapiens. This may indicate that while the gene
is primarily subject to strong purifying selection and is most likely highly conserved, it
was also regularly the focus of episodic positive selection during its phylogenetic history
(Figure 5). Note that sites under positive selection could be identifiable by expanding the
analyzed species spectrum to other representatives of mammals or vertebrates.
(3) The classical example for positive adaptive selection—the abalone sperm lysin
gene. The dataset consisted of orthologous lysin sequences with a length of 360 base pairs
from 25 species of the sea snail genus Haliotidae (abalones) [21]. The paPAML analysis of
the lysin dataset finished in approximately 115 min. By comparison, the longest-running
codeml test (the branch-site model comparison) took almost 405 min to complete. If the
models were used consecutively, the overall run of all the model comparisons in codeml
would have taken about 14.5 h. Ubiquitous signals of positive selection on multiple
branches as well as negative selection on several sites were found in the lysin example
dataset. All significantly supported positive selection pressures on branches are shown in
Figure 6. The site model showed a remarkably high fit of the positive selection model to
the dataset (p< 0.000001 for both M1a/M2a and M7/M8) as well as 17 of 120 sites under
significant positive selection, although codon 114 was only detected with the M7/M8 model
comparison. The HyPhy FEL algorithm found an overlap with positively selected sites and
that codons 24, 32, 75, 114, and 120 (Supplementary Table S1) were positively selected. On
the other hand, it also found codons 33, 63, 94, 111, 115, and 119 (Supplementary Table S1)
to be under positive selection, which the site model did not confirm. Such differences
between the models are to be expected due to their different methods of calculating selection
pressures. The branch model detected 11 out of 47 branches to be under significant positive
selection with
ω
values averaged over sites. By comparison, the branch-site model detected
specific sites under selection in 9 branches. They were not, however, entirely overlapping,
Genes 2022,13, 1090 11 of 15
with the branch leading to Haliotis varia and the one to H. siboldii found to be positively
selected only by the branch model. Additionally, the branch-site model detected positively
selected sites in the branch leading to H. kamtschatkana and three others that were not
found to be under significant positive selection pressure by the branch model. This was
also expected, as the branch model searches for selection signals by averaging
ω
-values
over sites, while the branch-site model allows sites to have different
ω
-values. About
26 codons were also found to be under positive selection pressure only in specific branches
(for example, codon 15 in the branch leading to H. kamtschatkana). Lastly, the HyPhy
FEL algorithm revealed 15 codons under purifying selection (Supplementary Table S1).
Interestingly, one of them, codon 45, seems to be under positive selection in the branch
leading to H. cyclobates. Because this codon is otherwise highly conserved, averaged over
the entire phylogeny this might indicate a potentially adaptive change unique to the species,
which might have been missed without the added contrast of the detection of negatively
selected sites.
Genes 2022, 13, 1090 11 of 16
2 (branch-
site model)
(2) The neutrophil-associated CCAAT Enhancer Binding Protein Epsilon (CEBPE)
transcription factor with 30 Placentalia sequences, each 843 base pairs long. The CEBPE
gene significantly influences the development and function of neutrophils, the most abun-
dant leukocyte subset in human blood, and is expected to be under selective forces [16].
paPAML needed about 3.3 h to finish the more extensive CEBPE dataset. By comparison,
codeml’s most time-consuming model comparison took about 20 h to complete, while the
overall codeml analysis, if executed consecutively, would have taken over 50 h.
Neither the site model nor the branch-site model detected sites under positive selec-
tion in the CEBPE gene. Instead, 68 of the 281 codons were subject to purifying selection
according to the HyPhy FEL algorithm, which did not detect any positively selected sites.
The branch model, on the other hand, found 22 out of 50 branches with signals of positive
selection, including the branch leading to H. sapiens. This may indicate that while the gene
is primarily subject to strong purifying selection and is most likely highly conserved, it
was also regularly the focus of episodic positive selection during its phylogenetic history
(Figure 5). Note that sites under positive selection could be identifiable by expanding the
analyzed species spectrum to other representatives of mammals or vertebrates.
Figure 5. Phylogenetic tree of the CEBPE dataset. Branches significantly under positive selection
according to the branch models are marked green (corresponding to Figure 2) and labelled with the
p-values inferred by their respective model comparisons.
(3) The classical example for positive adaptive selection—the abalone sperm lysin
gene. The dataset consisted of orthologous lysin sequences with a length of 360 base pairs
from 25 species of the sea snail genus Haliotidae (abalones) [21]. The paPAML analysis of
the lysin dataset finished in approximately 115 min. By comparison, the longest-running
codeml test (the branch-site model comparison) took almost 405 min to complete. If the
models were used consecutively, the overall run of all the model comparisons in codeml
Figure 5.
Phylogenetic tree of the CEBPE dataset. Branches significantly under positive selection
according to the branch models are marked green (corresponding to Figure 2) and labelled with the
p-values inferred by their respective model comparisons.
(4) The apolipoprotein encoding genes APOL1–4, which demonstrably contains many
codon positions under positive selection pressure [
18
]. The paPAML analysis of the APOL
dataset, which contains fewer but longer sequences than our other examples, was completed
in 79 min. The APOL codeml analysis took about 19 h overall, and the most time-intensive
model comparison about 7.5 h. The APOL1–4 gene data showed signs of both positive
and negative selection at many sites (Figure 7). Both site model comparisons indicated
a highly significant fit to the data (p< 0.00001 for both M1a/M2a and M7/M8). A total
of 22 of the 312 codons of the analyzed sequences were identified to be under positive
selection by both models, whereas codons 8, 193, and 263 were found to be under adaptive
selection only by the M7/M8 comparison (Supplementary Table S1). HyPhy FEL also
Genes 2022,13, 1090 12 of 15
showed a large overlap with the site models in identifying positively selected sites; of
the 12 codons found, numbers 165, 209, 247, and 266 were not detected by the codeml
models (Supplementary Table S1). According to the branch-site model, codons 88 and 163
underwent episodic positive selection in the branch leading to the APOL4 of Chlorocebus
sabaeus (green monkey), while the same applies for the codon 36 isoform 4 branch of Gorilla
gorilla (gorilla) (Supplementary Table S1). The branch model was able to find signals of
positive selection in 5 different branches, including the APOL4 C. sabaeus branch, but not
the APOL3 G. gorilla branch. Similar to the results of the abalone sperm lysin dataset, this
difference between the two models is likely caused by the calculating differences inherent
in the branch and the branch-site models. Lastly, HyPhy FEL found 10 sites under purifying
selection without any overlap with codons detected by the other model comparisons. It is of
note that HyPhy FEL is a maximum likelihood-based test like the site model comparisons,
which means that we do not expect to find purifying selection acting on codons that the
site models predict to be positively selected.
Genes 2022, 13, 1090 12 of 16
would have taken about 14.5 h. Ubiquitous signals of positive selection on multiple
branches as well as negative selection on several sites were found in the lysin example
dataset. All significantly supported positive selection pressures on branches are shown in
Figure 6. The site model showed a remarkably high fit of the positive selection model to
the dataset (p < 0.000001 for both M1a/M2a and M7/M8) as well as 17 of 120 sites under
significant positive selection, although codon 114 was only detected with the M7/M8
model comparison. The HyPhy FEL algorithm found an overlap with positively selected
sites and that codons 24, 32, 75, 114, and 120 (Supplementary Table S1) were positively
selected. On the other hand, it also found codons 33, 63, 94, 111, 115, and 119 (Supplemen-
tary Table S1) to be under positive selection, which the site model did not confirm. Such
differences between the models are to be expected due to their different methods of cal-
culating selection pressures. The branch model detected 11 out of 47 branches to be under
significant positive selection with ω values averaged over sites. By comparison, the
branch-site model detected specific sites under selection in 9 branches. They were not,
however, entirely overlapping, with the branch leading to Haliotis varia and the one to H.
siboldii found to be positively selected only by the branch model. Additionally, the branch-
site model detected positively selected sites in the branch leading to H. kamtschatkana and
three others that were not found to be under significant positive selection pressure by the
branch model. This was also expected, as the branch model searches for selection signals
by averaging ω-values over sites, while the branch-site model allows sites to have differ-
ent ω -values. About 26 codons were also found to be under positive selection pressure
only in specific branches (for example, codon 15 in the branch leading to H. kamtschatkana).
Lastly, the HyPhy FEL algorithm revealed 15 codons under purifying selection (Supple-
mentary Table S1). Interestingly, one of them, codon 45, seems to be under positive selec-
tion in the branch leading to H. cyclobates. Because this codon is otherwise highly con-
served, averaged over the entire phylogeny this might indicate a potentially adaptive
change unique to the species, which might have been missed without the added contrast
of the detection of negatively selected sites.
Figure 6. Phylogenetic tree of the lysin dataset. Branches significantly likely to be under positive
selection according to the branch-site and branch models are marked orange and green, respectively
Figure 6.
Phylogenetic tree of the lysin dataset. Branches significantly likely to be under positive
selection according to the branch-site and branch models are marked orange and green, respectively
(corresponding to Figure 2) and labelled with the p-values inferred by their respective model compar-
isons. This includes branches with significant LRT results in the branch-site model but without sites
under positive selection.
Such selection analyses are directly open for subsequent functional studies, for ex-
ample, applied in Müller et al. [
18
]. From the previously detected 27 positive selected
sites using PAML [
18
], we could reconstruct most of them with paPAML (21) plus seven
previously unrecognized cases (Supplementary Data S2). The difference is probably due to
the usage of updated alignments and maximum likelihood-caused variation.
Genes 2022,13, 1090 13 of 15
Genes 2022, 13, 1090 13 of 16
(corresponding to Figure 2) and labelled with the p-values inferred by their respective model com-
parisons. This includes branches with significant LRT results in the branch-site model but without
sites under positive selection.
(4) The apolipoprotein encoding genes APOL1–4, which demonstrably contains
many codon positions under positive selection pressure [18]. The paPAML analysis of the
APOL dataset, which contains fewer but longer sequences than our other examples, was
completed in 79 min. The APOL codeml analysis took about 19 h overall, and the most
time-intensive model comparison about 7.5 h. The APOL1–4 gene data showed signs of
both positive and negative selection at many sites (Figure 7). Both site model comparisons
indicated a highly significant fit to the data (p < 0.00001 for both M1a/M2a and M7/M8). A
total of 22 of the 312 codons of the analyzed sequences were identified to be under positive
selection by both models, whereas codons 8, 193, and 263 were found to be under adaptive
selection only by the M7/M8 comparison (Supplementary Table S1). HyPhy FEL also
showed a large overlap with the site models in identifying positively selected sites; of the
12 codons found, numbers 165, 209, 247, and 266 were not detected by the codeml models
(Supplementary Table S1). According to the branch-site model, codons 88 and 163 under-
went episodic positive selection in the branch leading to the APOL4 of Chlorocebus sabaeus
(green monkey), while the same applies for the codon 36 isoform 4 branch of Gorilla gorilla
(gorilla) (Supplementary Table S1). The branch model was able to find signals of positive
selection in 5 different branches, including the APOL4 C. sabaeus branch, but not the
APOL3 G. gorilla branch. Similar to the results of the abalone sperm lysin dataset, this
difference between the two models is likely caused by the calculating differences inherent
in the branch and the branch-site models. Lastly, HyPhy FEL found 10 sites under purify-
ing selection without any overlap with codons detected by the other model comparisons.
It is of note that HyPhy FEL is a maximum likelihood-based test like the site model com-
parisons, which means that we do not expect to find purifying selection acting on codons
that the site models predict to be positively selected.
Figure 7. Phylogenetic tree of the APOL1–4 dataset. Branches significantly likely to be under posi-
tive selection according to the branch-site models (orange) and branch models (green) (correspond-
ing to Figure 2) are labelled with the p-values inferred by their respective model comparisons. This
includes branches with significant LRT results in the branch-site model but without sites under pos-
itive selection.
Such selection analyses are directly open for subsequent functional studies, for ex-
ample, applied in Müller et al. [18]. From the previously detected 27 positive selected sites
Figure 7.
Phylogenetic tree of the APOL1–4 dataset. Branches significantly likely to be under positive
selection according to the branch-site models (orange) and branch models (green) (corresponding
to Figure 2) are labelled with the p-values inferred by their respective model comparisons. This
includes branches with significant LRT results in the branch-site model but without sites under
positive selection.
3.2. Comparison to Other Selection Analysis Tools
The results of original PAML analyses are difficult to parse for newcomers, not paral-
lelized and therefore very time-inefficient, as well as not easy to include in high-throughput
data analyses. It is necessary to manually calculate LRT results and edit ctl configura-
tion files to change parameters and models. Our program is designed to reduce the time
of a complete selection analysis as much as possible without changing the underlying
assumptions of the base models of either PAML or HyPhy FEL. This is exemplified by
our comparative paPAML and codeml runs, which produced identical results for all four
of our example datasets, whereas paPAML reduced the processing time considerably. It
also simplifies the results and allows both a form of high-throughput analysis and the
continuation of aborted runs if needed. Other programs have been created to alleviate
these problems as well, including but not limited to EasyCodeML [
22
], LMAP [
23
], and
BlastPhyMe [
24
], all of which also automatize PAML LRT calculations, allow for limited
multithreading, and summarize the results. BlastPhyME and EasyCodeML even provide a
graphical user interface (GUI). EasyCodeML also offers drag-and-drop functionality and
visual labelling of tree branches via single clicks for easy use. That, however, severely
limits the ability of these programs to do high-throughput analysis, as each run requires
the use of the GUI, and no command-line version for integration into larger pipelines is
available. All four programs also provide different multithreading methods: LMAP and
BlastPhyMe allow multiple datasets to be run simultaneously by mapping codeml tasks
to different cores. In contrast, EasyCodeML’s multithreading occurs in the site model’s
analysis. paPAML does this by splitting the analysis of each tree topology in the branch
and branch-site models, and then running as many codeml tasks as possible on the number
of cores previously specified by the user. This also includes parallelizing multiple datasets
by starting the first task of a new dataset once a core is no longer working on a task of
the previous one. The parallelization does present limits, specifically that a site-specific
test run is precisely as fast as in base codeml. It is not trivial to compare these different
parallelization methods with one another, though each of them requires large multicore
systems to enable significant reductions in processing time. Because we chose to focus
here on these specific forms of selection analysis, further tests and models of PAML were
Genes 2022,13, 1090 14 of 15
not automatically included in paPAML. This includes the clade model, integrated into
the other three programs. But, in contrast to, for example, BlastPhyMe, it is possible to
change codeml parameters through paPAML’s command line, so alternate tests can still be
executed using the program. Still, they might not be supported by our output summary.
None of the other three programs or PAML itself include a test for negatively selected sites,
limiting them to analyses searching only for positive selection. No other program includes
a tabular overview of the site-specific results mapped to the codons of a sequence. This
allows easy parsing of the results of all site-specific models and should be of specific help for
inexperienced users. Lastly, any restrictions of either the codeml or HyPhy FEL tests apply
to paPAML and the three other programs. paPAML is also directly applicable to detect
selection pressure on prokaryotic genes. However, comparing lineages of prokaryotic genes
with highly diverged or horizontally transferred genes requires a careful interpretation of
the results. One example for the fn3 gene of Bifidobacterium is provided (Supplementary
Data S1; see also Dyachkova et al. [
25
]) and the results of site-specific tests are visualized in
Supplementary Data S3.
The paPAML results compile the actual outcome of all individual tests. It is the task
of the user to interpret such results and compare them. Users should be aware that the
applied selection analyses are based on maximum likelihood estimations, which can cause
slight variations in the results even if the same sequences, trees, and parameters are used.
4. Conclusions
Here we provide a complete solution from extracting orthologous CDS to calculating
and visualizing significance values of positively and negatively selected codons and phy-
logenetic branches applying the CDS-extractor and paPAML. Different site, branch-site,
and branch models provide the most common sources to detect positively selected sites
and branches in phylogenetic trees. With paPAML, this is now expanded by a HyPhy test
for negatively selected sites as well. An automated parallelization of different tests, as
well as an analysis of all possible foreground tree topologies within such tests, provides a
huge timesaver. The necessary comparisons of
ω
ratios and the calculation of significance
values are compiled in a structured outfile. The CDS-extractor is easily applicable by using
a public server at http://retrogenomics.uni-muenster.de/tools/CDS-extractor (accessed
on 10 June 2022). paPAML can be downloaded from https://github.com/RetroWWU/
paPAML (accessed on 10 June 2022) and installed as a stand-alone program on any UNIX
computer, preferably with multiple cores and large memory capacity and with the necessary
environment, including the PAML and HyPhy programs.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/genes13061090/s1, Supplementary Data S1: example data for
APOL1–4, CEBPE, fn3_Bifidobacterium, GINS2, and lysin); Supplementary Data S2: alignment for se-
lected sites of APOL1–4); Supplementary Data S3: alignment for selected sites of fn3_Bifidobacterium);
Supplementary Table S1: positively and negatively selected sites of APOL1–4, CEBPE, GINS2, and
lysin), or deposited at https://github.com/RetroWWU/paPAML (accessed on 10 June 2022).
Author Contributions:
Conceptualization R.S. and J.S., software design paPAML N.G., software de-
sign CDS-extractor A.P. and N.G., data collection and analyses R.S. and L.O., supervision J.S. and O.S.,
primary writing R.S. and J.S., L.O. and O.S. contributed discussion, editing, and writing. Graphics
from L.O. and J.S. All authors have read and agreed to the published version of the manuscript.
Funding:
This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Re-
search Foundation)-RTG2220-project number 281125614 to JS.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: All data are present in the Supplemental Files.
Genes 2022,13, 1090 15 of 15
Acknowledgments:
We thank Delf-Magnus Kummerfeld and Liliya Doronina for their comments on
the manuscript. For editorial assistance, we thank Marsha Bundman. We acknowledge support from
the Open Access Publication fund of the University of Münster.
Conflicts of Interest: The authors declare no conflict of interest.
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