PalantiR

PalantiR is a framework for phylogenetic simulation and visualization.

See the documentation at dekoning-lab.github.io/PalantiR

The framework focuses on Mutation-Selection codon models and temporal heterogeneity.

PalantiR is implemented as an R package, with a C++ backend, and interactive js visualizations.

Installation

The package can be installed automatically with devtools:

devtools::install_github("dekoning-lab/PalantiR")

If you are installing from a local clone, use:

devtools::install_local("path/to/palantir/clone")	

Note: Rccparmadillo is a required package. Compiling against it will require gfortran

For MacOS, a copy of gfortran binaries can be downloaded from CRAN tools

Downloading

First, clone the PalantiR repo:

git clone https://github.com/dekoning-lab/PalantiR.git

Dependencies

The dependencies for the R package can be istalled as follows:

install.packages(c("RcppArmadillo", "htmlwidgets"))

Building

If using RStudio, hit "Build & Reload".

Otherwise, navigate to one directory above PalantiR, and run:

R CMD INSTALL --preclean --no-multiarch --with-keep.source PalantiR

In a new R session, run:

library(PalantiR)

Short demonstration

Below we show how to run a simulation with the Mutation-Selection model:

# read phylogeny
p <- Phylogeny("data/mammals.newick")

# read amino acid fitness values
aa_psi <- read.csv("data/amino_acid_fitness_N_1000.csv")

# use first row
psi <- as.numeric(aa_psi[1,])

# make nucleotide substitution model
hky <- HasegawaKishinoYano(equilibrium = c(.25, .25, .25, .25))

# make codon substitution model
ms <- MutationSelection(
	population_size = 1000,
	mutation_rate = 1e-8,
	nucleotide_model = hky,
	fitness = psi)

# sample sequence from model equilibrium
s <- sample_sequence(model = ms, length = 100)

# simulate
sim <- simulate_over_phylogeny(phylogeny = p, model = ms, sequence = s)

We can view the resuling alignment:

# examine alignment
plot(sim$alignment)

# save alignment as fasta
as.fasta(sim$alignment, "PalantiR_ms.fa")

We can also visualize the substitutions that have been simulated:

# examine substitutions
plot(sim, sites = 1:10)

# check specific substitution data
head(sim$substitutions)