Package 'scoup' reference manual

Title:	Simulate Codons with Darwinian Selection Modelled as an OU Process
Description:	An elaborate molecular evolutionary framework that facilitates straightforward simulation of codon genetic sequences subjected to different degrees and/or patterns of Darwinian selection. The model was built upon the fitness landscape paradigm of Sewall Wright, as popularised by the mutation-selection model of Halpern and Bruno. This enabled realistic evolutionary process of living organisms to be reproduced seamlessly. For example, an Ornstein-Uhlenbeck fitness update algorithm is incorporated herein. Consequently, otherwise complex biological processes, such as the effect of the interplay between genetic drift and mutation on the inference of diversifying selection, may now be investigated with minimal effort. Frequency-dependent and deterministic fitness landscape update techniques are also available.
Authors:	Hassan Sadiq [aut, cre, cph]
Maintainer:	Hassan Sadiq <[email protected]>
License:	GPL (>= 2)
Version:	0.99.17
Built:	2024-10-24 03:24:12 UTC
Source:	https://github.com/bioc/scoup

Obtain Gamma Distributed Amino Acid Selection Coefficients

Description

Obtain a vector of values from the Gamma distribution that may be conveniently used as amino acid selection coefficients.

Usage

    aaGamma(vNvS, nsynVar)
aaGamma(vNvS, nsynVar)

Arguments

`vNvS`	Ratio of the variance of the coefficients of the non-synonymous codons relative to the variance of the synonymous selection coefficients. Can be assigned a value equal to zero to eliminate synonymous selection.
`nsynVar`	A non-negative value that corresponds to the variance of the coefficients of the non-synonymous codons. That is, the the between-amino-acid variance.

Details

Twenty random observations from a $\code{Gamma}(1, 1/\sqrt{\code{nsynVar}})$ distribution are sampled as the amino acid selection coefficients. The associated variance of the synonymous coefficients (synVar) is calculated as nsynVar/vNvS. If $\code{nsynVar} < 10^{-12}$ , all the amino acid coefficients will be equal to a single random draw from a $\code{Gamma}(1,1/\code{synVar})$ distribution.

Value

Returns an object of class aminoSC, a vector that at least contains the following components.

coeffs: A vector of 20 numeric elements that represent the sampled amino acid coefficients. The coefficients are ordered in terms of the 1-letter amino acid IUPAC labels. That is, (A, C, . . . , W, Y).
synVar: Variance of the selection coefficients of the synonymous codons.
nsynVar: Variance of the selection coefficients of the non-synonymous codons.

Author(s)

Hassan Sadiq

Examples

    test1 <- aaGamma(0.50, 1e-04)
    coeffs(test1)
    synVar(test1)
    nsynVar(test1)
    
    test2 <- aaGamma(1e-02, 0)
    coeffs(test2)
    synVar(test2)
    nsynVar(test2)
test1 <- aaGamma(0.50, 1e-04)
    coeffs(test1)
    synVar(test1)
    nsynVar(test1)
    
    test2 <- aaGamma(1e-02, 0)
    coeffs(test2)
    synVar(test2)
    nsynVar(test2)

Obtain Gaussian Distributed Amino Acid Selection Coefficients

Description

Obtain a vector of values from a Normal distribution that may be conveniently used as amino acid selection coefficients.

Usage

    aaGauss(vNvS, nsynVar)
aaGauss(vNvS, nsynVar)

Arguments

`vNvS`	Ratio of the variance of the selection coefficients of the non-synonymous codons relative to the variance of the synonymous coefficients. It can be assigned a value equal to zero to eliminate synonymous selection.
`nsynVar`	A non-negative value that corresponds to the variance of the coefficients among the non-synonymous codons. That is, the between-amino-acid variance.

Details

An observation is sampled from a Normal(0,nsynVar) distribution independently for each of the 20 amino acid residues. The variance of the synonymous selection coefficients (synVar) is calculated as nsynVar/vNvS. If nsynVar is less than $10^{-12}$ , all the amino acid coefficients will be equal to a single random draw from a Normal(0,synVar) distribution.

Value

Returns an object of class aminoSC, a vector that at least contains the following component.

coeffs: A vector of 20 numeric elements that represent the sampled amino acid coefficients. The coefficients are ordered in terms of the 1-letter amino acid IUPAC labels. That is, (A, C, . . . , W, Y).
synVar: Variance of the selection coefficients of the synonymous codons.
nsynVar: Variance of the selection coefficients of the non-synonymous codons.

Author(s)

Hassan Sadiq

Examples

    case0 <- aaGauss(0.50, 1e-04)
    nsynVar(case0)
    synVar(case0)
    coeffs(case0)
    
    case1 <- aaGauss(1e-02, 0)
    nsynVar(case1)
    synVar(case1)
    coeffs(case1)
case0 <- aaGauss(0.50, 1e-04)
    nsynVar(case0)
    synVar(case0)
    coeffs(case0)
    
    case1 <- aaGauss(1e-02, 0)
    nsynVar(case1)
    synVar(case1)
    coeffs(case1)

Simulate Codon Sequence Alignment

Description

Obtain an alignment of codon sequences that have been artificially subjected to natural selection, imposed as changes in the fitness landscape along the branches of a symmetric evolutionary tree.

Usage

    alignsim(adaptIn, seqIn, ...)
alignsim(adaptIn, seqIn, ...)

Arguments

`adaptIn`	A list of class `discrete`, `omega` or `ou`.
`seqIn`	A list of class `seqParameters`.
`...`	Arguments to be passed to methods such as '`omega`' and '`ou`'. See `modelIn` and `filename` in details below.

Details

This is the primary function of the package. Codon sequence alignment be simulated in terms of the population genetics paradigm. Fitness landscape may be kept static or set to be renewed along the branches of a balanced phylogeny based on any of the three available methods: Ornstein-Uhlenbeck, frequency-dependent or deterministic. Other possible inputs include, modelIn: a hbParameters object. Only applicable when adaptIn is an ou object. filename: a string that specifies the full path of the file that will contain the simulated alignment in NEXUS format. Say it is given as "seq.nex", a file with that name will be printed in the working directory. When set as NA (default), no file will be saved. When set as NULL, a DNAStringSet object will be returned.

Value

A NEXUS format file is saved in the specified (or working) directory. In addition, a scoup object that contains the following entries is returned.

seqs: A matrix of integers between 1 and 61. The integers are the positions of the simulated codons within an ordered set of nucleotide triplets. The rows are the extant sequences and the columns are alignment sites.
dNdS: A matrix of the corresponding site-wise (or codon-wise) dN/dS value for all the fitness landscapes utilised in the simulation.
aInfo: A string of text that contains details of the parameter values that were used during simulation of the codon sequence alignment.
cseq: A dataframe of the simulated codon sequence alignment.
seqCOL: A DNAStringSet object with colorful sequences. Only applicable when filename=NULL.

Author(s)

Hassan Sadiq

References

Sadiq, H. et al. (in progress) scoup: Simulate Codon Sequences with Darwinian Selection Incorporated as an Ornstein-Uhlenbeck Process.

Pages H, Aboyoun P, Gentleman R, DebRoy S (2024). Biostrings: Efficient manipulation of biological strings. R package version 2.72.1, https://bioconductor.org/packages/Biostrings.

Examples

    alignEntry <- seqDetails(c(ntaxa=8,nsite=10))
    
    dsim <- alignsim(discreteInput(), alignEntry)
    aInfo(dsim)
    cseq(dsim)
    
    wsim <- alignsim(wInput(), alignEntry, filename=NULL)
    seqCOL(wsim)
    dNdS(wsim)
    
    osim <- alignsim(ouInput(), alignEntry, modelIn=hbInput())
    osim
alignEntry <- seqDetails(c(ntaxa=8,nsite=10))
    
    dsim <- alignsim(discreteInput(), alignEntry)
    aInfo(dsim)
    cseq(dsim)
    
    wsim <- alignsim(wInput(), alignEntry, filename=NULL)
    seqCOL(wsim)
    dNdS(wsim)
    
    osim <- alignsim(ouInput(), alignEntry, modelIn=hbInput())
    osim

Amino Acid Selection Coefficients

Description

A numerical vector of values that are associated with the amino acid selection coefficients.

Objects from the Class

Objects of this class (aminoSC) can be created by calls of the form new("aminoSC", coeffs=..., synVar=..., nsynVar=...). The two amino acid sampling functions (that is, aaGamma and aaGauss) that are available in the scoup package return objects of this class.

Slots

coeffs:: numeric vector returned by the coeffs method.
synVar:: numeric value returned by the synVar method.
nsynVar:: numeric value returned by the nsynVar method.

Methods

coeffs: signature(x = "aminoSC"): vector of twenty values that correspond to the amino acid selection coefficients. The entries are ordered in increasing alphabetical order in terms of the one-letter IUPAC naming structure.
nsynVar: signature(x = "aminoSC"): variance of the probability distribution where the returned amino acid selection coefficients were sampled.
show: signature(object = "aminoSC"): summary of the contents of the aminoSC object including a snippet of the sampled coefficients as well as the values of the synVar $(\sigma_s^2)$ and the nsynVar $(\sigma_n^2)$ parameters.
synVar: signature(x = "aminoSC"): variance of the uniform distribution where the synonymous selection coefficients should be sampled.

Author(s)

Hassan Sadiq

Examples

    aasc1 <- aaGamma(1e-10, 1e-04)
    coeffs(aasc1)
    show(aasc1)
aasc1 <- aaGamma(1e-10, 1e-04)
    coeffs(aasc1)
    show(aasc1)

Generate a Balanced Bifurcating Evolutionary Tree

Description

Obtain an evolutionary tree that is such that all its internal nodes have exactly two offspring and all the branches on the tree have equal length.

Usage

    biTree(ntaxa, bLength, terModel=NA)
biTree(ntaxa, bLength, terModel=NA)

Arguments

`ntaxa`	Number of extant taxa. It must be an integer (`t`) that may be expressed as `2^m`, where `m` is itself a positive integer.
`bLength`	Branch length. All the branches of the generated tree will have the same length that is equal to the specified value.
`terModel`	A text that would be added as suffix to the extant taxa names. If set as `NA` (default), no suffix will be added. This is useful for assigning a model to the leaves in branch-specific analyses.

Value

tree: A bifurcating tree in newick format.

Author(s)

Hassan Sadiq

Examples

    biTree(16, 0.01, "{foreground}")
    
    biTree(16, 0.01, " #1")

    biTree(16, 0.01)
biTree(16, 0.01, "{foreground}")
    
    biTree(16, 0.01, " #1")

    biTree(16, 0.01)

Transform Amino Acid to Codon Selection Coefficients

Description

Convert a 20-element vector of amino acid selection coefficients to a 61-element vector of codon selection coefficients.

Usage

    codonCoeffs(s01x22, fixed=NULL)
codonCoeffs(s01x22, fixed=NULL)

Arguments

s01x22

A 22-element vector of class aminoSC.

fixed

A vector of integers between 1 and 20 that indicates which amino acid to assign positive coefficients to, based on the alphabetical order of the 1-letter IUPAC notation. That is, (1=A, 2=C, 3=D, ..., 19=W, 20=Y). All the other amino acids are assigned zero coefficients. This input is only necessary when the specified between-amino-acid variance is less than 10^(-12). The default is NULL.

Details

Consider a vector of amino acid selection coefficients, (s_x: s_A, s_C, s_D, ..., s_W, s_Y) that are subset of s01x22. All the synonymous codons that encode each amino acid are assigned independently sampled values from Uniform(s_x - 3*synVar; s_x + 3*synVar) distribution, where synVar is the synonymous variance and it is also retrieved from s01x22. For amino acids M and W, the corresponding codon coefficient is simply set equal to s_M and s_W, respectively. The output from the function is of the order (s_(AAA), s_(AAC), s_(AAG), ..., s_(TTC), s_(TTG), s_(TTT)), excluding the stop codons.

Value

Returns a codonvalues object that will contain the following.

coeffs: A 61-element vector of codon selection coefficients ordered alphabetically with respect to the IUPAC nucleotide triplets nomenclature.

Author(s)

Hassan Sadiq

Examples

    # Example 1:
    aasc1 <- aaGamma(1e-10, 1e-04)
    ccfs0 <- codonCoeffs(aasc1)
    coeffs(ccfs0)
    
    # Example 2:
    aasc2 <- aaGauss(1e-10, 1e-04)
    ccfs1 <- codonCoeffs(aasc2)
    coeffs(ccfs1)

    # Example 3:
    aasc3 <- aaGauss(1e-03, 0)
    ccfs2 <- codonCoeffs(aasc3, c(2,6))
    coeffs(ccfs2)
# Example 1:
    aasc1 <- aaGamma(1e-10, 1e-04)
    ccfs0 <- codonCoeffs(aasc1)
    coeffs(ccfs0)
    
    # Example 2:
    aasc2 <- aaGauss(1e-10, 1e-04)
    ccfs1 <- codonCoeffs(aasc2)
    coeffs(ccfs1)

    # Example 3:
    aasc3 <- aaGauss(1e-03, 0)
    ccfs2 <- codonCoeffs(aasc3, c(2,6))
    coeffs(ccfs2)

Generate Codon Frequencies From Selection Coefficients

Description

Obtain codon frequencies from specified selection coefficients in a way that accounts for the magnitude of the coefficients in the real number line.

Usage

    codonFreq(sc01x61)
codonFreq(sc01x61)

Arguments

sc01x61

Vector of sense codon selection coefficients that are ordered alphabetically in terms of the IUPAC nucleotide triplets nomenclature.

Details

This conversion to frequencies accommodates the magnitude and signs of the selection coefficients because the frequency for the ith codon is estimated as:

$\pi_{i}^{} = \begin{cases} \log(s_{i}^{}) \big/ \sum_{j=1}^{61} \log(s_{j}^{}) & \text{if } s_{j}^{} > 0 \;\; \forall_{j}, \\[0.5cm] e^{s_{i}^{}}_{} \big/ \sum_{j=1}^{61} e^{s_{j}^{}}_{} & \text{otherwise,} \\ \end{cases}$

where $s_{i}^{} \in \code{sc01x61}$ is the selection coefficient of the ith codon.

Value

Returns a codonvalues object that contains the following.

freqs: A vector of 61 fractional values that sum to one and represent the frequencies of sense codons that are ordered alphabetically in terms of the IUPAC nucleotide triplets nomenclature.

Author(s)

Hassan Sadiq

Examples

    aaEG1 <- aaGamma(1e-03, 0)
    csc01 <- codonCoeffs(aaEG1, 4)
    cFq <- codonFreq(csc01)
    freqs(cFq)
aaEG1 <- aaGamma(1e-03, 0)
    csc01 <- codonCoeffs(aaEG1, 4)
    cFq <- codonFreq(csc01)
    freqs(cFq)

Codon Frequencies and Selection Coefficients

Description

A numerical vector of values that correspond to the selection coefficients of the sense codons.

Objects from the Class

Objects of this class (codonvalues) can be created by calls of the form new("codonvalues", cdnums=...). Two codon-related transformation functions (that is, codonCoeffs and codonFreq) that are available in the scoup package return objects of this class.

Slots

cdnums:: vector of 61 values that could correspond to the selection coefficients or the frequencies of the sense codons depending on the method called.

Methods

coeffs: signature(x = "codonvalues"): vector of 61 values that correspond to the selection coefficients of the sense codons. The entries are ordered in increasing alphabetical order in terms of the IUPAC nucleotide triplets naming structure.
freqs: signature(x = "codonvalues"): vector of 61 values that correspond to the frequencies of the sense codons. The entries are ordered in increasing alphabetical order in terms of the IUPAC nucleotide triplets naming structure.
show: signature(object = "codonvalues"): prints the first six relevant (that is, coefficients or frequencies) codon values.

Author(s)

Hassan Sadiq

Examples

    aasc1 <- aaGamma(1e-10, 1e-04)
    ccfs0 <- codonCoeffs(aasc1)
    cFq <- codonFreq(ccfs0)
    coeffs(ccfs0)
    freqs(cFq)
aasc1 <- aaGamma(1e-10, 1e-04)
    ccfs0 <- codonCoeffs(aasc1)
    cFq <- codonFreq(ccfs0)
    coeffs(ccfs0)
    freqs(cFq)

Deterministic Simulation Model Input

Description

Creates an object suitable for use when interested in generating an alignment of genetic sequences following the deterministic simulation technique available in the scoup package.

Objects from the Class

Objects can be created by calls of the form new("discrete", lscape=..., sampler=..., nodeIndex=..., psize=..., t3mdl=...). Objects can also be created straightforwardly with the discreteInput function.

Slots

lscape:: numeric matrix returned by the lscape method.
sampler:: numeric value that can be set as 1 or 2. It indicates the probability distribution where the amino acid selection coefficients should be sampled.
nodeIndex:: numeric input only relevant for implicit execution of the simulation algorithm. It is of no practical utility to the end-user.
psize:: numeric value returned by the effpop method.
t3mdl:: character input that may be used to specify suffix for the leaves on the returned phylogeny. It is intended to facilitate inference analyses with external software such as PAML or HyPhy.

Methods

aaSCupdate: signature(parameters="discrete"): background function that is not intended for end-use. It updates the amino acid selection coefficients intermittently during the sequence simulation process.
alignsim: signature(adaptIn="discrete", seqIn="seqParameters"): primary simulation function available in the scoup package.
effpop: signature(x="discrete"): effective population size.
lscape: signature(x="discrete"): numerical matrix that contains parameters of the fitness landscape. The first row will contain the ratio of the variance of the non-synonymous to synonymous selection coefficients (vNvS) and the second row will contain the variance of the non-synonymous selection coefficients $\sigma^2_\code{n}$ . The number of columns will be equal to the number of internal (bifurcating) stages. A phylogeny with $2^{\code{m}}$ leaves will have m internal stages.
sampler: signature(x="discrete"): probability distribution where the amino acid selection coefficients should be obtained.
show: signature(object="discrete"): prints characteristics of the corresponding genetic sequence, including the population size and the number of extant taxa.
sitesim: signature(parameters="discrete", nodeLength="numeric"): background function that is not to be used by an end-user. It generates the DNA data at each site independently.

Author(s)

Hassan Sadiq

Examples

    dtest <- discreteInput()
    effpop(dtest)
    lscape(dtest)
    sampler(dtest)
dtest <- discreteInput()
    effpop(dtest)
    lscape(dtest)
    sampler(dtest)

Populate Deterministic Seascape Model Parameters

Description

Create an object that has a discrete class attribute. It is particularly useful for defining one of the possible inputs of the main simulation function alignsim, when interested in simulating codon sequences that evolve with fitness landscapes that change at every internal node.

Usage

    discreteInput(defList=list())
discreteInput(defList=list())

Arguments

defList

A list that may contain up to five named entries. See Details for further information.

Details

If fully specified, defList will be a list with five elements. The preferred list content include (a.) p02xnodes: a 2-row matrix with rows that are properly named as “vNvS” and “nsynVar”. Entries in the “vNvS” row should be the ratio of the variance of the non-synonymous selection coefficients to the variance of the synonymous coefficients. Entries in the “nsynVar” row should be the variance of the non-synonymous selection coefficients. The number of the matrix columns will be used to determine the number of internal nodes to assume for the simulation phylogeny. Each column of the matrix will be used to determine the parameters of the sampling distribution where the coefficient updates will be sampled at every node. Default is a $2 \times 4$ matrix, wherein all the values in the “vNvS” row are equal to 1 and all the entries in the “nsynVar” row are equal to $10^{-5}_{}$ . (b.) technique: a binary integer that could be 1 for Gaussian or 2 for Gamma (default) distribution. It informs about the probability distribution to be used for updating the coefficients. (c.) pSize: (default = 1000) is the effective population size. (d.) nodeIndex: a nuisance input that is best left unspecified. It is updated within the alignsim operation. (e.) leafModel: a text that may be used to suffix the names of the terminal nodes (default = NA). Note that this function was not designed to be used in isolation. Its purpose is to complement the alignsim simulation function.

Value

A discrete object that contains the following.

lscape: Matrix containing landscape parameters.
sampler: Name of the sampling distribution used for selection coefficient updates.
effpop: Effective population size.

Author(s)

Hassan Sadiq

Examples

    dtest <- discreteInput()
    effpop(dtest)
    lscape(dtest)
    sampler(dtest)
dtest <- discreteInput()
    effpop(dtest)
    lscape(dtest)
    sampler(dtest)

Estimate dN/dS Value Analytically

Description

Obtain an analytical estimate for the ratio of non-synonymous to synonymous rate (dN/dS) of codon substitution.

Usage

    dndsCalculator(pi01x61, q61x61)
dndsCalculator(pi01x61, q61x61)

Arguments

`pi01x61`	Vector of sense codon frequencies that are ordered alphabetically with respect to nucleotide triplets according to IUPAC nomenclature.
`q61x61`	A $61 \times 61$ matrix of sense codon instantaneous substitution rates, where the rows and the columns are ordered in terms of IUPAC-lettered nucleotide triplets.

Details

The returned dN/dS estimate is obtained from the ratio of the following expressions.

$dN = \frac{\sum_{j}^{}\sum_{i\neq j}^{} \pi_{i}^{}\,\text{A}_{ij}^{} \,I_{N}^{}}{\sum_{j}^{}\sum_{i\neq j}^{} \pi_{i}^{}\,\mu_{ij}^{} \,I_{N}^{}}, \hspace*{1.00cm} dS = \frac{\sum_{j}^{}\sum_{i\neq j}^{} \pi_{i}^{}\,\text{A}_{ij}^{} \,I_{S}^{}} {\sum_{j}^{}\sum_{i\neq j}^{} \pi_{i}^{}\,\mu_{ij}^{} \,I_{S}^{}},$

where $A$ and $\pi$ are the input codon frequency vector (pi01x61) and the instantaneous substitution rate matrix (q61x61), respectively. The notation $\mu$ denotes codon mutation matrix (embedded as a function of the HKY85 nucleotide model) while $I_{S}^{}$ and $I_{N}^{}$ are Boolean matrices with ones at positions occupied by synonymous and non-synonymous codons, respectively.

Value

dnds: An estimate for the corresponding dN/dS

Author(s)

Hassan Sadiq

References

Spielman, S. J. and Wilke, C. O. (2015). The Relationship between dN/dS and Scaled Selection Coefficients, Molecular Biology and Evolution 32(4): 1097-1108.

Examples

    aasc <- aaGauss(0.5, 1e-03)
    codonsc <- codonCoeffs(aasc)
    piFreq <- codonFreq(codonsc)
    smat <- subsMatrix(codonsc, 1000)
    dndsCalculator(piFreq, smat)
aasc <- aaGauss(0.5, 1e-03)
    codonsc <- codonCoeffs(aasc)
    piFreq <- codonFreq(codonsc)
    smat <- subsMatrix(codonsc, 1000)
    dndsCalculator(piFreq, smat)

Construct Fixation Rate Matrix

Description

Construct a 61 $\times$ 61 matrix that contains the rate at which a mutant (column) codon gets fixed at a position previously occupied by a resident (row) codon for all the pairwise combinations of the 61 sense codons.

Usage

    fixMatrix(sc01x61, effpopsize)
fixMatrix(sc01x61, effpopsize)

Arguments

`sc01x61`	A vector of sense codon selection coefficients that are ordered alphabetically in terms of the IUPAC nucleotide triplets nomenclature.
`effpopsize`	Effective population size.

Details

If the additive fitness of a mutant codon (j) relative to a resident codon (i) is given by $s_{ij}^{} = (s_{j}^{}-s_{i}^{})$ , then the rate at which codon j gets fixed in a codon position occupied by codon i can be expressed as follows.

$f_{ij}^{} = \begin{cases} \frac{1 - e^{-2s_{ij}^{}}_{}}{1 - e^{-2N_{\texttt{e}}^{} s_{ij}^{}}_{}} & \text{if codons $i$ and $j$ differs only by one nucleotide,} \\[1ex] 0 & \text{otherwise,} \end{cases}$

where $s_{i}^{},\,s_{j}^{} \in$ sc01x61 and $N_{e}^{}$ (effpopsize) is the effective population size. If $s_{i}^{}-s_{j}^{} =0$ , then $f_{ij}^{}=1$ .

Value

fixMtx: A $61 \times 61$ matrix of the fixation rates of the sense codons. The rows and columns are ordered alphabetically in terms of the IUPAC nucleotide triplets nomenclature. That is, AAA, AAC, AAG, ..., TTG, TTT.

Author(s)

Hassan Sadiq

References

Bershtein, S. and Serohijos, A. W. R. and Shakhnovich, E. I. (2017), Bridging the Physical Scales in Evolutionary Biology: From Protein Sequence Space to Fitness of Organisms and Populations, Current Opinion in Structural Biology 42: 31-40.

Halpern, A. L. and Bruno, W. J. (1998). Evolutionary Distances for Protein-Coding Sequences: Modelling Site-Specific Residue Frequencies, Molecular Biology and Evolution 15(7): 910-917.

McCandlish, D. M. (2011), Visualizing Fitness Landscapes, Evolution 65(6): 1544-1558.

Examples

    aasc <- aaGauss(0.5, 1e-03)
    codonsc <- codonCoeffs(aasc)
    fixMatrix(codonsc, 1000)
aasc <- aaGauss(0.5, 1e-03)
    codonsc <- codonCoeffs(aasc)
    fixMatrix(codonsc, 1000)

Generate Halpern-Bruno Substitution Model Parameters

Description

Create a hbParameters object that contains values necessary to construct a Halpern-Bruno mutation-selection codon substitution model.

Usage

    hbInput(hbVector=0)
hbInput(hbVector=0)

Arguments

hbVector

A named vector that provides values of the parameters necessary to successfully create a codon substitution model for simulation of genetic sequences. See Details for further information.

Details

When fully specified, hbVector will be a four-element named vector. That is, hbVector=c(Ne, meth, vNvS, nsynVar). Ne is an integer that represents the effective population size (default = 1000). meth is a binary integer that indicates the probability distribution from where the initial selection coefficients must be sampled. It can be 1 for a truncated Gaussian or 2 for a Gamma (default) distribution. vNvS is the ratio of the variance of the non-synonymous to synonymous selection coefficients. Default value is 1. The user can set the variance of the non-synonymous selection coefficients with nsynVar (default is $10^{-5}$ ). This function was not intended for independent use. Rather as a complement to the alignsim simulation function.

Value

A hbParameters object that contains the following.

psize: Effective population size.
vNvS: Ratio of the variance of the non-synonymous to the synonymous selection coefficients.
nsVar: Variance of the non-synonymous selection coefficients.
sampler: Probability distribution for generating the initial selection coefficients.

Author(s)

Hassan Sadiq

Examples

    h0 <- hbInput()
    vNvS(h0)
    h0
    
    h1 <- hbInput(c(Ne=100, meth=2, vNvS=1e-08, nsynVar=1e-08))
    sampler(h1)
h0 <- hbInput()
    vNvS(h0)
    h0
    
    h1 <- hbInput(c(Ne=100, meth=2, vNvS=1e-08, nsynVar=1e-08))
    sampler(h1)

Halpern-Bruno Mutation-Selection Evolutionary Model Input

Description

Creates an object of class (hbParameters) that contains the principal entries necessary to construct a Halpern-Bruno mutation-selection evolutionary model.

Objects from the Class

Objects of this class can be created by calls of the form new("hbParameters", psize=??, vNvS=??, sampler=??, nsynVar=??, words=??). The object is an important input of the alignsim function when interested in simulating sequences with respect to the Ornstein-Uhlenbeck framework.

Slots

psize:: numeric value returned by the effpop method.
vNvS:: numeric value returned by the vNvS method.
sampler:: numeric value that can be set as 1 or 2. It indicates the probability distribution where the amino acid selection coefficients should be sampled.
nsynVar:: numeric value returned by the nsynVar method.
words:: comments about the specified Halpern-Bruno model parameters. It is a character format string that is eventually added to the simulated sequence for reference.

Methods

effpop: signature(x = "hbParameters"): effective population size.
nsynVar: signature(x = "hbParameters"): variance of the non-synonymous selection coefficients $\sigma^2_\code{n}$ .
sampler: signature(x = "hbParameters"): probability distribution where the amino acid selection coefficients should be randomly retrieved.
show: signature(object = "hbParameters"): prints characteristics of the defined model including the population size, the vNvS and the $\sigma^2_\code{n}$ .
vNvS: signature(x = "hbParameters"): ratio of the variance of the non-synonymous to synonymous selection coefficients.

Author(s)

Hassan Sadiq

Examples

    h1 <- hbInput(c(Ne=100, meth=2, vNvS=1e-08, nsynVar=1e-08))
    sampler(h1)
    h1
h1 <- hbInput(c(Ne=100, meth=2, vNvS=1e-08, nsynVar=1e-08))
    sampler(h1)
    h1

Frequency-Dependent Evolutionary Model Specification

Description

Creates an object that contains the inputs that are necessary to define a frequency-dependent evolutionary algorithm and subsequently simulate genetic sequence alignment based on the framework using the alignsim function in the scoup package.

Objects from the Class

Objects of this class can be created by calls of the form new("omega", nsynVar=..., psize=..., sampler=..., aaPlus=..., vNvS=...). The object is an important input of the alignsim function when interested in simulating sequences with respect to the frequency-dependent framework. The wInput function in the scoup package returns this kind of object.

Slots

nsynVar:: numeric value returned by the nsynVar method.
psize:: numeric value returned by the effpop method.
sampler:: numeric value that can be set as 1 or 2. It indicates the probability distribution where the amino acid selection coefficients should be sampled.
aaPlus:: indices of the amino acids (after the corresponding one-letter IUPAC names are arranged in increasing alphabetical order) that should be assigned non-zero vNvS.
vNvS:: numeric value returned by the vNvS method.

Methods

alignsim: signature(adaptIn="omega", seqIn="seqParameters"): primary simulation function availed in the scoup package.
effpop: signature(x="omega"): effective population size.
lscape: signature(x="omega"): IUPAC one-letter notations of the amino acids that were assigned non-zero vNvS values.
nsynVar: signature(x="omega"): variance of the non-synonymous selection coefficients $\sigma^2_\code{n}$ .
sampler: signature(x="omega"): probability distribution where the amino acid selection coefficients should be randomly retrieved.
show: signature(object="omega"): prints the vNvS and the count of amino acids that had positive vNvS values.
sitesim: signature(parameters="omega", nodeLength="numeric"): background function that is not available to end-user. It generates the DNA data at each site independently.
vNvS: signature(x="omega"): ratio of the variance of the non-synonymous to synonymous selection coefficients.

Author(s)

Hassan Sadiq

Examples

    w1 <- wInput(list(aaPlus=c(4,2,11), nsynVar=10))
    lscape(w1)
    w1
w1 <- wInput(list(aaPlus=c(4,2,11), nsynVar=10))
    lscape(w1)
    w1

Ornstein-Uhlenbeck Stochastic Simulation Model Object

Description

Contains the inputs that are necessary to define an Ornstein-Uhlenbeck (OU) evolutionary process.

Objects from the Class

Class ou objects can be created by calls of the form new("ou", var=??, theta=??, mu=??, words=??). This type of object is returned by the ouInput function in the scoup package. It is an important input of the alignsim function when interested in codon sequences that evolved following the OU framework.

Slots

var:: numeric value returned by the asymVar method.
theta:: numeric value returned by the reversion method.
mu:: numeric value returned by the asymMean method.
words:: descriptive text that contains details of the set parameter values. Useful as reference comments to be included in the generated sequence alignment.

Methods

aaSCupdate: signature(parameters = "ou"): background function that is not intended for end-use. It updates the amino acid selection coefficients intermittently during the sequence simulation process.
alignsim: signature(adaptIn="ou", seqIn="seqParameters"): primary simulation function available in the scoup package.
asymMean: signature(x="ou"): asymptotic mean, $\mu$ , of the OU evolutionary algorithm.
asymVar: signature(x="ou"): asymptotic variance, $\Sigma^2$ , of the OU evolutionary framework.
reversion: signature(x="ou"): reversion parameter, $\theta$ , that acts as a selective pull in the OU process.
show: signature(object="ou"): prints the values of $\Sigma^2$ , $\mu$ and $\theta$ .
sitesim: signature(parameters="ou", nodeLength="numeric"): background function that is not to be used by an end-user. It generates the DNA data at each site independently.

Author(s)

Hassan Sadiq

Examples

    o1 <- ouInput( c(eVar=1e-02, Theta=10))
    asymMean(o1)
    asymVar(o1)
o1 <- ouInput( c(eVar=1e-02, Theta=10))
    asymMean(o1)
    asymVar(o1)

Simulate the Trend of an Ornstein-Uhlenbeck Process

Description

Simulate the next state of an Ornstein-Uhlenbeck (OU) process for a given value.

Usage

    ouEvolve(xInit, deltaT, sysTheta, asymptoteVar, asymptoteMew)
ouEvolve(xInit, deltaT, sysTheta, asymptoteVar, asymptoteMew)

Arguments

`xInit`	Starting point of the OU process.
`deltaT`	Jump size.
`sysTheta`	Reversion rate.
`asymptoteVar`	Asymptotic variance.
`asymptoteMew`	Asymptotic mean.

Details

The state at time k (that is, $x_{t_{k}}$ ) of a process that evolves according to an OU algorithm can be expressed as an observation from a Gaussian distribution as follows.

$x_{t_{k}^{}}^{} \sim \textsf{Normal}\bigg(\mu + \left(x_{t_{k-1}^{}}^{}- \mu\right)e^{-\theta \Delta t}_{};\,\frac{\sigma^{2}_{}}{2\theta}\left(1 -e^{-2\theta \Delta t}_{}\right)\bigg)$

Observe that when time interval (deltaT) $\Delta t = t_{k}-t_{k-1}$ approaches infinity, the asymptotic mean (asymptoteMew) and the asymptotic variance (asymptoteVar) of the distribution are $\mu$ and $\Sigma^2=\sigma^2/2\theta$ respectively, where $\theta$ is the reversion rate.

Value

xnew: A scalar that represents the updated state of the OU process.

Author(s)

Hassan Sadiq

References

Uhlenbeck, G. E. and Ornstein, L. S. (1930), On the Theory of the Brownian Motion, Physical Review 36: 823-841.

Examples

    x0 <- 0.015
    xvec <- c()
    xvec[1] <- x0
    for(k in seq(2,100)){
        xstate <- ouEvolve(x0, 0.002, 10, 0.001, 0)
        xvec[k] <- xstate
        x0 <- xstate
    }
    plot(xvec, type="l")
x0 <- 0.015
    xvec <- c()
    xvec[1] <- x0
    for(k in seq(2,100)){
        xstate <- ouEvolve(x0, 0.002, 10, 0.001, 0)
        xvec[k] <- xstate
        x0 <- xstate
    }
    plot(xvec, type="l")

Populate Parameters of the Ornstein-Uhlenbeck Algorithm

Description

Create an ou object that will contain the parameters necessary to simulate a codon sequence alignment that evolves according to an Ornstein-Uhlenbeck (OU) process.

Usage

    ouInput(ouVector=0)
ouInput(ouVector=0)

Arguments

ouVector

A vector that contains carefully named elements. Each element represents a parameter in an OU model. See Details for more information.

Details

In its full form, ouVector is a three-element vector. Its contents each represents part of the parameters required to implement an OU process. The vector contents include, eMean, eVar and Theta. Input eMean is the asymptotic mean ( $\mu$ ) and zero is its default value. eVar denotes the asymptotic variance ( $\Sigma^2$ ). It has a 0.01 default value. Theta (default = 0.01) represents the reversion rate ( $\theta$ ). This function was aimed as a complement to alignsim, not for use in isolation.

Value

An ou object that contains the following.

asymMean: Asymptotic mean of the OU process.
asymVar: Asymptotic variance of the OU process.
reversion: Reversion rate of the OU process.

Author(s)

Hassan Sadiq

References

Uhlenbeck, G. E. and Ornstein, L. S. (1930), On the Theory of the Brownian Motion, Physical Review 36: 823-841.

Examples

    o0 <- ouInput()
    reversion(o0)
    o0
    
    o1 <- ouInput( c(eVar=1e-02, Theta=10))
    asymMean(o1)
    asymVar(o1)
o0 <- ouInput()
    reversion(o0)
    o0
    
    o1 <- ouInput( c(eVar=1e-02, Theta=10))
    asymMean(o1)
    asymVar(o1)

Simulate Codons with Darwinian Selection Added as an OU Process

Description

The primary objective of this package is to facilitate more rigorous understanding of phylogenetic inferences of natural selection from codon sequences. Concepts from the Halpern-Bruno mutation-selection model and the Ornstein-Uhlenbeck stochastic process were creatively fused such that the end-product is a novelty with respect to computational genetic simulation. Users are able to seamlessly adjust the model parameters to mimic complex evolutionary procedures that may have been otherwise infeasible. For example, it is possible to explicitly interrogate the concepts of static and changing fitness landscapes with regards to Darwinian natural selection in the context of DNA sequences. The ratio of the variance in selection coefficients, vN/vS, is presented as a new measure of the net selection effect acting on genetic sequences. This package could be very useful for generating more appropriate test data sets for validation of likelihood-based ( $\omega$ ) codon models of evolution.

Details

Three simulation algorithms are available. (a.) The Ornstein-Uhlenbeck simulation technique. This technique was built around the stochastic Brownian motion evolutionary paradigm. Explicit parameters exist to control the extent of drift, mutation and selection that are acting on the biological system. (b.) The frequency-dependent approach where every substitution event that corresponds to a shift in the fitness landscape. (c.) The deterministic method where the model parameters may be fixed for each internal node of the phylogeny.

Author(s)

Hassan Sadiq

References

Halpern, A. L. and Bruno, W. J. (1998). Evolutionary Distances for Protein-Coding Sequences: Modelling Site-Specific Residue Frequencies, Molecular Biology and Evolution 15(7): 910-917.

Sadiq, H. et al. (in progress) scoup: Simulate Codon Sequences with Darwinian Selection Incorporated as an Ornstein-Uhlenbeck Process.

Uhlenbeck, G. E. and Ornstein, L. S. (1930), On the Theory of the Brownian Motion, Physical Review 36: 823-841.

Output from the `scoup::alignsim` Genetic Sequence Simulator

Description

Stores the results from a successful implementation of any of the simulation algorithms available in the scoup package.

Objects from the Class

Objects can be created by calls of the form new("scoup", seqs=..., DNDS=..., aInfo=..., cseq=..., seqCOL=...).

Slots

seqs:: numerical matrix returned by the seqs method.
DNDS:: numerical matrix returned by the dNdS method.
aInfo:: character phrase returned by the aInfo method.
cseq:: data frame returned by the cseq method.
seqCOL:: DNAStringSet object returned by the seqCOL method.

Methods

aInfo: signature(x="scoup"): details of the parameters used to execute the simulation process. This includes, the branch length of all the nodes of the balanced phylogeny, the name of the probability distribution where the amino acid selection coefficients were sampled as well as the (vNvS & non-synonymous selection) parameter set used at each internal node ("generation") stage.
cseq: signature(x="scoup"): data frame that contains the simulated genetic sequence.
dNdS: signature(x="scoup"): analytical estimates of the magnitude of the imposed selection effect. It is calculated node-wise as the ratio of the non-synonymous to synonymous substitutions.
seqCOL: signature(x="scoup"): a DNAStringSet version of the simulated genetic sequence alignment.
seqs: signature(x="scoup"): expression of the simulated sequence as a matrix of integers, where each entry corresponds to the position of the associated codon in an an alphabetically increasing ordered set of the DNA triplets of the 61 sense codons.
show: signature(object="scoup"): sentence that contains the number of codon sites and the number of extant taxa that make up the simulated genetic sequence alignment.

Author(s)

Hassan Sadiq

Examples

    alignEntry <- seqDetails(c(ntaxa=8,nsite=10))
    dsim <- alignsim(discreteInput(), alignEntry)
    aInfo(dsim)
    cseq(dsim)
alignEntry <- seqDetails(c(ntaxa=8,nsite=10))
    dsim <- alignsim(discreteInput(), alignEntry)
    aInfo(dsim)
    cseq(dsim)

Populate Sequence Alignment Information

Description

Create a seqParameters object that contains features of the sequence that needs to be simulated.

Usage

    seqDetails(seqVector=0)
seqDetails(seqVector=0)

Arguments

seqVector

A named vector that provides characteristics of the intended sequence alignment. See Details for further information.

Details

If fully specified, seqVector should be a four-element named vector. That is, seqVector = c(ntaxa, nsite, blength, terModel). ntaxa should be of the form $2^m$ , where m is an integer. It corresponds to the number of extant taxa, default is 64. nsite, also an integer (default = 250), is the number of codon sites. blength is the length of each branch on the balanced symmetric tree that will be used for the simulation (default = 0.10). terModel is a text that will be added as a suffix to the leaf names on the phylogeny (default = NA). It is meant to facilitate assignment of models to the terminal nodes for branch-wise selection analyses. The purpose of this function is to complement alignsim.

Value

A seqParameters object that contains the following.

sites: Number of alignment sites.
taxa: Number of extant taxa.
nodes: Number of internal (bifurcating) stages on the evolutionary tree. A tree with $2^{\code{m}}$ leaves will have m internal stages.
branchL: Length of the branches on the phylogeny.
phylogeny: Evolutionary tree in newick format.
details: Text that describes the evolutionary tree.

Author(s)

Hassan Sadiq

Examples

    t0 <- seqDetails()
    sites(t0)

    t1 <- seqDetails(c(ntaxa=16, nsite=10, blength=0.20, terModel=" #1"))
    details(t1)
t0 <- seqDetails()
    sites(t0)

    t1 <- seqDetails(c(ntaxa=16, nsite=10, blength=0.20, terModel=" #1"))
    details(t1)

Simulated Codon Sequence Structure

Description

A S4 object that contains information about the structure (that is, size, length, etc) of the simulated genetic sequence.

Objects from the Class

This is the object class of the output from the seqDetails function. It is a core input of the alignsim function. Objects can be created by calls of the form new("seqParameters", sites=??, taxa=??, nodes=??, branchL=??, phylogeny=??, details=??).

Slots

sites:: numeric value returned by the sites method.
taxa:: numeric value returned by the taxa method.
nodes:: numeric value returned by the nodes method.
branchL:: numeric value returned by the branchL method.
phylogeny:: character returned by the phylogeny method.
details:: character returned by the details method.

Methods

alignsim: signature(adaptIn="discrete", seqIn="seqParameters"): an option of the primary simulation function in the scoup package. This setting activates the deterministic framework.
alignsim: signature(adaptIn="omega", seqIn="seqParameters"): an option of the primary simulation function in the scoup package. This setting activates the frequency-dependent framework.
alignsim: signature(adaptIn="ou", seqIn="seqParameters"): an option of the primary simulation function in the scoup package. This setting activates the Ornstein-Uhlenbeck framework.
branchL: signature(xo="seqParameters"): branch length. Only balanced evolutionary trees are permitted. Therefore, all tree nodes have the same length.
details: signature(xo="seqParameters"): note that contain the important parameter settings that generated the corresponding data. It is added as comments to the saved output.
nodes: signature(xo="seqParameters"): number of internal (bifurcating) stages of the balanced phylogeny. An evolutionary tree with $2^\code{m}$ extant taxa will have m nodes.
phylogeny: signature(xo="seqParameters"): newick string of the phylogeny utilised for the codon sequence simulation.
show: signature(object="seqParameters"): summary descriptive details about the corresponding sequence alignment.
sites: signature(xo="seqParameters"): number of codon sites that make up the sequence.
taxa: signature(xo="seqParameters"): number of leaves on the phylogeny.

Author(s)

Hassan Sadiq

Examples

    t0 <- seqDetails()
    sites(t0)
t0 <- seqDetails()
    sites(t0)

Write Numeric Codon Alignment to a NEXUS File

Description

Save numeric codon alignment matrix to a file in NEXUS format. It is particularly useful when data with site partitions is required.

Usage

    seqWriter(alignmentMatrix, treeInfo=NA, addText="", fileTag=NULL)
seqWriter(alignmentMatrix, treeInfo=NA, addText="", fileTag=NULL)

Arguments

`alignmentMatrix`	A numerical matrix of codon sequence alignment that is similar to the `seqs` matrix from the output of `alignsim`. The rows of the matrix should each correspond to an extant taxa and the columns should be the alignments sites. The entries of the matrix should be integers between 1 and 61 and they will be decoded in terms of the ordered IUPAC sense codon triplets. That is, 1=`AAA`, 2=`AAC`, 3=`AAG`, 4=`AAT`, 5=`ACA`, ..., 57=`TGT`, 58=`TTA`, 59=`TTC`, 60=`TTG`, `TTT`.
`treeInfo`	Phylogeny to be printed with the sequence. If unspecified (default = `NA`) a balanced phylogeny with branch length = 0.10 and number of extant taxa set as the number of rows of the input `alignmentMatrix` will be used.
`addText`	A string of comments to be printed with the alignment (default = "").
`fileTag`	Full path to where the output file should be printed. It should be a string (default = `NULL`). If not provided, the NEXUS file returned will be saved as `cranrSeqs.nex` in a temporary directory.

Value

NULL: A NEXUS file with codon alignment printed therein will be saved in a temporary (or specified) directory.

Author(s)

Hassan Sadiq

Examples

    sqAlign <- alignsim(ouInput(), seqDetails(), hbInput(), NA)
    seqWriter(seqs(sqAlign))
sqAlign <- alignsim(ouInput(), seqDetails(), hbInput(), NA)
    seqWriter(seqs(sqAlign))

Build Mutation-Selection Codon Substitution Matrix

Description

Construct an instantaneous codon substitution matrix based on the mutation-selection framework.

Usage

    subsMatrix(sc01x61, effpopsize)
subsMatrix(sc01x61, effpopsize)

Arguments

`sc01x61`	Vector of selection coefficients associated with the 61 sense codons, ordered alphabetically according to the nucleotide triplets and the IUPAC naming structure.
`effpopsize`	Effective population size.

Details

Given an arbitrary scaling constant (k), codon fixation rates ( $f_{ij}$ ) and mutation rates ( $m_{ij}$ ), the instantaneous rate by which codon i is substituted by another codon j may be expressed as follows.

$q_{ij} = \begin{cases} k \times \text{m}_{ij}^{} \times \text{f}_{ij}^{} & \text{if $i$ and $j$ differs by only one nucleotide,} \\[1ex] 0 & \text{if $i$ and $j$ differs by more than one nucleotide,} \end{cases}$

and $q_{ii}^{}=-\sum_{j}q_{ij}^{}$ . The HKY85 nucleotide mutation matrix was embedded into the alignsim function (with transition to transversion rate, $\kappa$ = 4, average rate, $\mu$ = 1 and equal equilibrium frequencies).

Value

mainMatrix: Instantaneous codon substitution matrix such that the rows and the columns are arranged with respect to the IUPAC naming structure of nucleotide triplets in alphabetical order.

Author(s)

Hassan Sadiq

References

Halpern, A. L. and Bruno, W. J. (1998). Evolutionary Distances for Protein-Coding Sequences: Modelling Site-Specific Residue Frequencies, Molecular Biology and Evolution 15(7): 910-917.

Hasegawa, M., Kishino, H. and Yano, T. (1985). Dating of the Human-Ape Splitting by a Molecular Clock of Mitochondria DNA, Journal of Molecular Evolution 22: 160-174.

Examples

    aacoeffs <- aaGauss(1e-03, 0)
    codonsc <- codonCoeffs(aacoeffs)
    subsMatrix(codonsc, 1000)
aacoeffs <- aaGauss(1e-03, 0)
    codonsc <- codonCoeffs(aacoeffs)
    subsMatrix(codonsc, 1000)

Populate Frequency-Dependent Simulation Model Parameters

Description

Create an omega object. The utility is for defining the parameters that are necessary for simulating codon sequences that mimic the evolutionary process described by the frequency-dependent models.

Usage

    wInput(wList=list())
wInput(wList=list())

Arguments

wList

A list that may contain up to five named entries. See Details for further information.

Details

In its full form, wList will contain five named elements. The elements include (a.) pSize: an integer that represents the effective population size (default = 1000). (b.) vNvS: a numerical value that corresponds to the ratio of the variance of the non-synonymous to the synonymous selection coefficients (default = 1). (c.) aaPlus: its default is a vector of integers between 1 and 20, inclusive. It gives the indices, if the one-letter IUPAC amino acid notations were ordered alphabetically, of the residues that should be assigned non-zero coefficient variances. (d.) technique: it informs of the preferred probability distribution where the selection coefficients should be sampled. It could be set as 1 for Gaussian or 2 for Gamma (default) distribution. (e.) nsynVar: variance of the non-synonymous selection coefficients. This is a complementary function to alignsim.

Value

An omega object that contains the following.

nsynVar: Variance of the non-synonymous selection coefficients.
technique: Probability density function for sampling the amino acid selection coefficients.
aaPlus: Indices of the amino acids to be assigned non-zero coefficient variance values.
vNvS: Ratio of the variance of the non-synonymous to the synonymous selection coefficients.
psize: Effective population size.

Author(s)

Hassan Sadiq

References

Goldman, N. and Yang, Z. (1994), A Codon-Based Model of Nucleotide Substitution for Protein-Coding DNA Sequences, Molecular Biology and Evolution 11(5): 725-736.

Muse, S. V. and Gaut, B. S. (1994), A A Likelihood Approach for Comparing Synonymous and Nonsynonymous Nucleotide Substitution Rates, with Application to the Chloroplast Genome, Molecular Biology and Evolution 11(5): 715-724.

Examples

    w0 <- wInput()
    vNvS(w0)
    w0

    w1 <- wInput(list(aaPlus=c(4,2,11), nsynVar=10))
    lscape(w1)
    w1
w0 <- wInput()
    vNvS(w0)
    w0

    w1 <- wInput(list(aaPlus=c(4,2,11), nsynVar=10))
    lscape(w1)
    w1

Package 'scoup'

Help Index

Obtain Gamma Distributed Amino Acid Selection Coefficients

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Obtain Gaussian Distributed Amino Acid Selection Coefficients

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Simulate Codon Sequence Alignment

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Amino Acid Selection Coefficients

Description

Objects from the Class

Slots

Methods

Author(s)

See Also

Examples

Generate a Balanced Bifurcating Evolutionary Tree

Description

Usage

Arguments

Value

Author(s)

Examples

Transform Amino Acid to Codon Selection Coefficients

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Generate Codon Frequencies From Selection Coefficients

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Codon Frequencies and Selection Coefficients

Description

Objects from the Class

Slots

Methods

Author(s)

See Also

Examples

Deterministic Simulation Model Input

Description

Objects from the Class

Slots

Methods

Author(s)

See Also

Examples

Populate Deterministic Seascape Model Parameters