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hyginn 2020-09-24 22:15:28 +10:00
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RPR-Genetic_code_optimality.R
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@ -1,20 +1,15 @@
# tocID <- "RPR-Genetic_code_optimality.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-Genetic_code_optimality unit.
#
# Version: 1.2
# Version: 1.3
#
# Date: 2017 10 - 2019 01
# Date: 2017-10 - 2020-09
# Author: Boris Steipe (boris.steipe@utoronto.ca)
#
# Versions:
# 1.3 2020 Maintenance
# 1.2 Change from require() to requireNamespace(),
# use <package>::<function>() idiom throughout,
# use Biocmanager:: not biocLite()
@ -36,20 +31,20 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> --------------------------------------------------------------
#TOC> 1 Designing a computational experiment 57
#TOC> 2 Setting up the tools 73
#TOC> 2.1 Natural and alternative genetic codes 76
#TOC> 2.2 Effect of mutations 134
#TOC> 2.2.1 reverse-translate 145
#TOC> 2.2.2 Randomly mutate 170
#TOC> 2.2.3 Forward- translate 195
#TOC> 1 Designing a computational experiment 58
#TOC> 2 Setting up the tools 74
#TOC> 2.1 Natural and alternative genetic codes 77
#TOC> 2.2 Effect of mutations 135
#TOC> 2.2.1 reverse-translate 146
#TOC> 2.2.2 Randomly mutate 171
#TOC> 2.2.3 Forward- translate 196
#TOC> 2.2.4 measure effect 213
#TOC> 3 Run the experiment 260
#TOC> 4 Task solutions 356
#TOC>
#TOC> 3 Run the experiment 267
#TOC> 4 Task solutions 363
#TOC>
#TOC> ==========================================================================
@ -148,7 +143,7 @@ swappedGC <- function(GC) {
# - we count the number of mutations and evaluate their severity.
# === 2.2.1 reverse-translate
# === 2.2.1 reverse-translate
# To reverse-translate an amino acid vector, we randomly pick one of its
# codons from a genetic code, and assemble all codons to a sequence.
@ -173,7 +168,7 @@ traRev <- function(s, GC) {
}
# === 2.2.2 Randomly mutate
# === 2.2.2 Randomly mutate
# To mutate, we split a codon into it's three nucleotides, then randomly replace
# one of the three with another nucleotide.
@ -198,7 +193,7 @@ randMut <- function(vC) {
# === 2.2.3 Forward- translate
# === 2.2.3 Forward- translate
traFor <- function(vC, GC) {
# Parameters:
@ -212,11 +207,10 @@ traFor <- function(vC, GC) {
vAA[i] <- GC[vC[i]] # translate and store
}
return(vAA)
}
}
# === 2.2.4 measure effect
# === 2.2.4 measure effect
# How do we evaluate the effect of the mutation? We'll take a simple ad hoc
# approach: we divide amino acids into hydrophobic, hydrophilic, and neutral
@ -261,6 +255,13 @@ evalMut <- function(nat, mut) {
# proline is always bad in secondary structure, charged amino acids are terrible
# in the folded core of a protein, replacing a small by a large amino acid in
# the core is very disruptive ... etc.
#
# For our experiment, we should not use a mutation data matrix however:
# empirical mutation probabilities are superbly suited to estimate evolutionary
# relationships. Here however, as we are trying to evaluate effects of random
# mutations on genetic codes, our reasoning would be circular - we would
# discover that the natural genetic code is optimal ... because it is most
# similar to the natural genetic code. That would be Cargo Cult bioinformatics.
# = 3 Run the experiment ==================================================
@ -290,7 +291,7 @@ N <- 200
valSTDC <- numeric(N)
set.seed(112358) # set RNG seed for repeatable randomness
for (i in 1:N) {
for (i in 1:N) { # this takes a few seconds ...
x <- randMut(myDNA) # mutate
x <- traFor(x, stdCode) # translate
valSTDC[i] <- evalMut(myAA, x) # evaluate