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# The alphabet to use:
# DNA, RNA or Protein
alphabet = DNA
# Input tree to use:
input.tree.file = ../../Data/LSUrooted.dnd
input.tree.format=Newick
# Print a tree with ids as bootstrap values.
# This is helpful when setting up complexe non-homogeneous models.
# Setting this option will cause the program to exit after printing the tree.
//output.tree.path = LSUrooted_wid.dnd
#Info file specifying rate and/or ancestral state for each site:
input.infos = infos.csv
input.infos.rates = Rates //or 'none' to ignore rates
input.infos.states = States //or 'none' to ignore states
# The output file:
output.sequence.file = LSUSim.fasta
# The alignment format:
# Must be one of Mase, Fasta, Phylip
output.sequence.format = Fasta()
# ----------------------------------------------------------------------------------------
# Model specification
# ----------------------------------------------------------------------------------------
# Homogeneous model?
# no => Homogeneous case
# general => Specify the model by hand.
nonhomogeneous = no
# Options for homogeneous and one-per_branch models:
# Available models.
# For proteins, the DCmutt method is used for JTT92 and DSO78.
# You can use the 'empirical' option to specify another model.
# JCnuc, K80, T92, HKY85, F84, TN93, JCprot, DSO78, JTT92 or empirical
# Append +G2001 or +TS98 to the model name to add a covarion model.
model = HKY85(kappa=2.843, theta=0.7, theta1=0.4, theta2=0.6)
# Rate Across Sites variation
rate_distribution = Gamma(n=4, alpha=0.358)
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