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hyginn 2017-10-29 22:19:19 -04:00
parent 553b042f20
commit 373da689df
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RPR-Genetic_code_optimality.R
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@ -3,13 +3,14 @@
# Purpose: A Bioinformatics Course:
# R code accompanying the RPR-Genetic_code_optimality unit.
#
# Version: 1.0
# Version: 1.0.1
#
# Date: 2017 10 16
# Author: Boris Steipe (boris.steipe@utoronto.ca)
#
# Versions:
# 1.0 New material.
# 1.0.1 Fixed two bugs discovered by Suan Chin Yeo.
# 1.0 New material.
#
#
# TODO:
@ -25,7 +26,7 @@
#TOC> ==========================================================================
#TOC>
#TOC>
#TOC> Section Title Line
#TOC> --------------------------------------------------------
#TOC> 1 Designing a computational experiment 57
@ -38,7 +39,7 @@
#TOC> 2.2.4 measure effect 214
#TOC> 3 Run the experiment 261
#TOC> 4 Task solutions 348
#TOC>
#TOC>
#TOC> ==========================================================================
@ -118,10 +119,10 @@ swappedGC <- function(GC) {
# Value: named chr same vector with random amino acid assignments where the
# amino acids have been swapped.
aaOrig <- unique(GC) # the amino acids in the input code
aaSwap <- sample(aa, length(aa)) # shuffled
names(aaSwap) <- aaOrig # name them after the original
GC[1:64] <- aaSwap[GC] # replace original with shuffled
aaOrig <- unique(GC) # the amino acids in the input code
aaSwap <- sample(aaOrig, length(aaOrig)) # shuffled
names(aaSwap) <- aaOrig # name them after the original
GC[1:64] <- aaSwap[GC] # replace original with shuffled
return(GC)
}
@ -138,7 +139,7 @@ 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.
@ -163,7 +164,7 @@ 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.
@ -188,7 +189,7 @@ randMut <- function(vC) {
# === 2.2.3 Forward- translate
# === 2.2.3 Forward- translate
traFor <- function(vC, GC) {
# Parameters:
@ -206,7 +207,7 @@ traFor <- function(vC, GC) {
}
# === 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
@ -291,7 +292,7 @@ hist(valUGC,
# This looks like a normal distribution. Let's assume the effect of mutations
# under the universal genetic code is the mean of this distribution:
effectUGC <- mean(val) # 178.1
effectUGC <- mean(valUGC) # 178.1
# Now we can look at the effects of alternate genetic codes: