382 lines
13 KiB
R
382 lines
13 KiB
R
# BIN-Sequence.R
|
|
#
|
|
# Purpose: A Bioinformatics Course:
|
|
# R code accompanying the BIN-Sequence unit.
|
|
#
|
|
# Version: 1.1
|
|
#
|
|
# Date: 2017 09 28
|
|
# Author: Boris Steipe (boris.steipe@utoronto.ca)
|
|
#
|
|
# Versions:
|
|
# 1.1 Add chartr()
|
|
# 1.0 First live version 2017.
|
|
#
|
|
# TODO:
|
|
#
|
|
#
|
|
# == DO NOT SIMPLY source() THIS FILE! =======================================
|
|
#
|
|
# If there are portions you don't understand, use R's help system, Google for an
|
|
# answer, or ask your instructor. Don't continue if you don't understand what's
|
|
# going on. That's not how it works ...
|
|
#
|
|
# ==============================================================================
|
|
|
|
|
|
#TOC> ==========================================================================
|
|
#TOC>
|
|
#TOC> Section Title Line
|
|
#TOC> ----------------------------------------------
|
|
#TOC> 1 Prepare 56
|
|
#TOC> 2 Storing Sequence 74
|
|
#TOC> 3 String properties 103
|
|
#TOC> 4 Substrings 110
|
|
#TOC> 5 Creating strings: sprintf() 116
|
|
#TOC> 6 Changing strings 147
|
|
#TOC> 6.1 stringi and stringr 199
|
|
#TOC> 6.2 dbSanitizeSequence() 209
|
|
#TOC> 7 Permuting and sampling 221
|
|
#TOC> 7.1 Permutations 228
|
|
#TOC> 7.2 Sampling 271
|
|
#TOC> 7.2.1 Equiprobable characters 273
|
|
#TOC> 7.2.2 Defined probability vector 313
|
|
#TOC> 8 Tasks 341
|
|
#TOC>
|
|
#TOC> ==========================================================================
|
|
|
|
|
|
#
|
|
#
|
|
#
|
|
#
|
|
|
|
|
|
# = 1 Prepare =============================================================
|
|
|
|
# Much basic sequence handling is supported by the Bioconductor package
|
|
# Biostrings.
|
|
|
|
if (! require(Biostrings, quietly=TRUE)) {
|
|
if (! exists("biocLite")) {
|
|
source("https://bioconductor.org/biocLite.R")
|
|
}
|
|
biocLite("Biostrings")
|
|
library(Biostrings)
|
|
}
|
|
# Package information:
|
|
# library(help = Biostrings) # basic information
|
|
# browseVignettes("Biostrings") # available vignettes
|
|
# data(package = "Biostrings") # available datasets
|
|
|
|
|
|
# = 2 Storing Sequence ====================================================
|
|
|
|
|
|
# Sequences can be represented and stored as vectors of single characters ...
|
|
(v <- c("D", "I", "V", "M", "T", "Q"))
|
|
|
|
# ... as strings ...
|
|
(s <- "DIVMTQ")
|
|
|
|
# ... or as more complex objects with rich metadata e.g. as a Biostrings
|
|
# DNAstring, RNAstring, AAString, etc.
|
|
(a <- AAString("DIVMTQ"))
|
|
|
|
# ... and all of these representations can be interconverted:
|
|
|
|
# string to vector ...
|
|
unlist(strsplit(s, ""))
|
|
|
|
# vector to string ...
|
|
paste(v, sep = "", collapse = "")
|
|
|
|
# ... and AAstring to plain string.
|
|
as.character(a)
|
|
|
|
# Since operations with character vectors trivially follow all other vector
|
|
# conventions and syntax, and we will look at Biostrings methods in more
|
|
# detail in a later unit, we will focus on basic strings in the following.
|
|
|
|
|
|
# = 3 String properties ===================================================
|
|
|
|
|
|
length(s) # why ???
|
|
nchar(s) # aha
|
|
|
|
|
|
# = 4 Substrings ==========================================================
|
|
|
|
# Use the substr() function
|
|
substr(s, 2, 4)
|
|
|
|
|
|
# = 5 Creating strings: sprintf() =========================================
|
|
|
|
|
|
# Sprintf is a very smart, very powerful function and has cognates in all
|
|
# other programming languages. It has a small learning curve, but it's
|
|
# totally worth it:
|
|
# the function takes a format string, and a list of other arguments. It returns
|
|
# a formatted string. Here are some examples - watch carefully for sprintf()
|
|
# calls in other code.
|
|
|
|
sprintf("Just a string.")
|
|
sprintf("A string and the number %d.", 5)
|
|
sprintf("More numbers: %d ate %d.", 7, 9) # Sorry
|
|
sprintf("Pi is ~ %1.2f ...", pi)
|
|
sprintf("or more accurately ~ %1.11f.", pi)
|
|
x <- "bottles of beer"
|
|
n <- 99
|
|
sprintf("%d %s on the wall, %d %s - \ntake %s: %d %s on the wall.",
|
|
n, x, n, x, "one down, and pass it around", n-1, x)
|
|
|
|
# Note that in the last example, the value of the string was displayed with
|
|
# R's usual print-formatting function and therefore the line-break "\n" did
|
|
# not actually break the line. To have line breaks, tabs etc, you need to use
|
|
# cat() to display the string:
|
|
|
|
for (i in 99:95) {
|
|
cat(sprintf("%d %s on the wall, %d %s - \ntake %s: %d %s on the wall.\n\n",
|
|
i, x, i, x, "one down, and pass it around", i-1, x))
|
|
}
|
|
|
|
|
|
# = 6 Changing strings ====================================================
|
|
|
|
# Changing case
|
|
tolower(s)
|
|
toupper(tolower(s))
|
|
|
|
|
|
#reverse
|
|
reverse(s)
|
|
|
|
|
|
# chartr(old, new, x) maps all characters in x that appear in "old" to the
|
|
# correpsonding character in "new."
|
|
|
|
chartr("aeio", "uuuu", "We hold these truths to be self-evident ...")
|
|
|
|
# One could implement toupper() and tolower() with this - remember that R has
|
|
# character vectors of uppercase and lowercase letters as language constants.
|
|
chartr(paste0(letters, collapse = ""),
|
|
paste0(LETTERS, collapse = ""),
|
|
"Twinkle, twinkle little star, how I wonder what you are.")
|
|
|
|
# One amusing way to use the function is for a reversible substitution
|
|
# cypher.
|
|
set.seed(112358)
|
|
myCypher <- paste0(sample(letters), collapse = "")
|
|
lett <- paste0(letters, collapse = "")
|
|
(x <- chartr(lett, myCypher, "... seven for a secret, never to be told."))
|
|
chartr(myCypher, lett, x)
|
|
# (Nb. substitution cyphers are easy to crack!)
|
|
|
|
|
|
# substituing characters
|
|
(s <- gsub("IV", "i-v", s)) # gsub can change length, first argument is
|
|
# a "regular expression"!
|
|
|
|
# I use it often to delete characters I don't want ...
|
|
# ... select something, and substitute the empty string for it.
|
|
(s <- gsub("-", "", s))
|
|
|
|
# For example: clean up a sequence
|
|
# copy/paste from UniProt
|
|
(s <- " 10 20 30 40 50
|
|
MSNQIYSARY SGVDVYEFIH STGSIMKRKK DDWVNATHIL KAANFAKAKR ")
|
|
|
|
|
|
# remove numbers
|
|
(s <- gsub("[0-9]", "", s))
|
|
|
|
# remove "whitespace" (spaces, tabs, line breaks)...
|
|
(s <- gsub("\\s", "", s))
|
|
|
|
# == 6.1 stringi and stringr ===============================================
|
|
|
|
# But there are also specialized functions eg. to remove leading/trailing
|
|
# whitespace which may be important to sanitize user input etc. Have a look at
|
|
# the function descriptions for the stringr and the stringi package. stringr is
|
|
# part of the tidyverse, and for the most part a wrapper for stringi functions.
|
|
# https://github.com/tidyverse/stringr
|
|
|
|
|
|
|
|
# == 6.2 dbSanitizeSequence() ==============================================
|
|
|
|
# In our learning units, we use a function dbSanitizeSequence() to clean up
|
|
# sequences that may be copy/pasted from Web-sources
|
|
|
|
s <- ">FASTA header will be removed
|
|
10 20 30 40 50
|
|
MSNQIYSARY SGVDVYEFIH STGSIMKRKK DDWVNATHIL KAANFAKAKR "
|
|
|
|
dbSanitizeSequence(s)
|
|
|
|
|
|
# = 7 Permuting and sampling ==============================================
|
|
|
|
|
|
# An important aspect of working with strings is generating random strings
|
|
# with given statistical properties: reference items to evaluate significance.
|
|
|
|
|
|
# == 7.1 Permutations ======================================================
|
|
|
|
|
|
# One way to produce such reference items is to permute a string. A permuted
|
|
# string has the same composition as the original, but all positional
|
|
# information is lost. The sample() function can be used to permute:
|
|
|
|
# This is the sequence of the ompA secretion signal
|
|
(s <- unlist(strsplit("MKKTAIAVALAGFATVAQA", "")))
|
|
|
|
(x <- sample(s, length(s))) # permuted
|
|
|
|
# Here's a small example how such permuted strings may be useful. As you look
|
|
# at the ompA sequence, you suspect that the two lysines near the +-charged
|
|
# N-terminus may not be accidental, but selected for a positively charged
|
|
# N-terminus. What is the chance that such a sequence has two lysines close to
|
|
# the N-terminus simply by chance? Or put differently: what is the average
|
|
# distance of two lysines in such a sequence to the N-terminus. First, we
|
|
# need an expression that measures the distance. A simple use of the which()
|
|
# function will do just fine.
|
|
|
|
which(s == "K") # shows they are in position 2 and 3, so ...
|
|
mean(which(s == "K")) # ... gives us the average, and ...
|
|
mean(which(x == "K")) # ... gives us the average of the permuted sequence.
|
|
|
|
# So what does the distribution look like? Lets do 10,000 trials.
|
|
|
|
(s <- unlist(strsplit("MKKTAIAVALAGFATVAQA", "")))
|
|
N <- 10000
|
|
d <- numeric(N)
|
|
set.seed(112358)
|
|
for (i in 1:N) {
|
|
d[i] <- mean(which(sample(s, length(s)) == "K"))
|
|
}
|
|
hist(d, breaks = 20)
|
|
abline(v = 2.5, lwd = 2, col = "firebrick")
|
|
sum(d <= 2.5) # 276. 276 of our 10000 samples are just as bunched near the
|
|
# N-terminus or more. That's just below the signifcance
|
|
# threshold of 5 %. It's a trend, but to be sure we are looking
|
|
# at a biological effect we would need to see more
|
|
# sequences.
|
|
|
|
|
|
# == 7.2 Sampling ==========================================================
|
|
|
|
# === 7.2.1 Equiprobable characters
|
|
|
|
# Assume you need a large random-nucleotide string for some statistical model.
|
|
# How to create such a string? sample() can easily create it:
|
|
|
|
nuc <- c("A", "C", "G", "T")
|
|
N <- 100
|
|
set.seed(16818)
|
|
v <- sample(nuc, N, replace = TRUE)
|
|
(mySeq <- paste(v, collapse = ""))
|
|
|
|
# What's the GC content?
|
|
table(v)
|
|
sum(table(v)[c("G", "C")]) # 51 is close to expected
|
|
|
|
# What's the number of CpG motifs? Easy to check with the stringi
|
|
# stri_match_all() function
|
|
|
|
if (! require(stringi, quietly=TRUE)) {
|
|
install.packages("stringi")
|
|
library(stringi)
|
|
}
|
|
# Package information:
|
|
# library(help = stringi) # basic information
|
|
# browseVignettes("stringi") # available vignettes
|
|
# data(package = "stringi") # available datasets
|
|
|
|
|
|
(x <- stri_match_all(mySeq, regex = "CG"))
|
|
length(unlist(x))
|
|
|
|
# Now you could compare that number with yeast DNA sequences, and determine
|
|
# whether there are more or less CpG motifs than expected by chance.
|
|
# (cf. https://en.wikipedia.org/wiki/CpG_site)
|
|
# But hold on: is that a fair comparison? sample() gives us all four nucleotides
|
|
# with the same probability. But the yeast genomic DNA GC content is only
|
|
# 38%. So you would expect fewer CpG motifs based on the statistical properties
|
|
# of the smaller number of Cs and Gs - before biology even comes into play. How
|
|
# do we account for that?
|
|
|
|
# === 7.2.2 Defined probability vector
|
|
|
|
# This is where we need to know how to create samples with specific probability
|
|
# distributions. A crude hack would be to create a sampling source vector with
|
|
# 19 C, 19 G, 31 A and 31 T
|
|
c(rep("C", 19), rep("G", 19), rep(c("A"), 31), rep(c("T"), 31))
|
|
# ... but that doesn't scale if the numeric accuracy needs to be higher.
|
|
#
|
|
# However sample() has an argument that takes care of that: you can explicitly
|
|
# specify the probabilities with which each element of the the sampling vector
|
|
# should be chosen:
|
|
|
|
nuc <- c("A", "C", "G", "T")
|
|
N <- 100
|
|
set.seed(16818)
|
|
myProb <- c(0.31, 0.19, 0.19, 0.31) # sampling probabilities
|
|
v <- sample(nuc, N, prob = myProb, replace = TRUE)
|
|
(mySeq <- paste(v, collapse = ""))
|
|
|
|
# What's the GC content?
|
|
table(v)
|
|
sum(table(v)[c("G", "C")]) # Close to expected
|
|
|
|
# What's the number of CpG motifs?
|
|
(x <- stri_match_all(mySeq, regex = "CG"))
|
|
# ... not a single one in this case.
|
|
|
|
|
|
# = 8 Tasks ===============================================================
|
|
|
|
# Task: Phone numbers that are entered into Web forms can come in many
|
|
# different formats. Write a function sanitizePhone() that accepts
|
|
# a single object as input and returns a single string of only numbers.
|
|
|
|
if (! require(testthat)) {
|
|
install.packages("testthat")
|
|
library(testthat)
|
|
}
|
|
|
|
sanitizePhone <- function(s) {
|
|
# ... your function code here
|
|
}
|
|
|
|
# All tests must pass!
|
|
s <- "1-858 651-5050"
|
|
expect_equal(sanitizePhone(s), "18586515050")
|
|
s <- "1 858 651 5050"
|
|
expect_equal(sanitizePhone(s), "18586515050")
|
|
s <- "+1 (858) 651-5050"
|
|
expect_equal(sanitizePhone(s), "18586515050")
|
|
s <- "18586515050"
|
|
expect_equal(sanitizePhone(s), "18586515050")
|
|
s <- "1 858 6515050"
|
|
expect_equal(sanitizePhone(s), "18586515050")
|
|
s <- "1.858.651.5050"
|
|
expect_equal(sanitizePhone(s), "18586515050")
|
|
s <- "1\t8 5 8\t6 5 1-5 0 5 0"
|
|
expect_equal(sanitizePhone(s), "18586515050")
|
|
s <- "1n8e5v8e6r5 1g5o0n5n0a"
|
|
expect_equal(sanitizePhone(s), "18586515050")
|
|
s <- "IDK"
|
|
expect_equal(sanitizePhone(s), "")
|
|
s <- ""
|
|
expect_equal(sanitizePhone(s), "")
|
|
s <- pi
|
|
expect_equal(sanitizePhone(s), "314159265358979")
|
|
|
|
|
|
|
|
# [END]
|