342 lines
11 KiB
R
342 lines
11 KiB
R
# BIN-Sequence.R
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-Sequence unit.
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#
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# Version: 1.2
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#
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# Date: 2017 09 - 2017 10
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 1.2 Removed irrelevant task. How did that even get in there? smh
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# 1.1 Add chartr()
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# 1.0 First live version 2017.
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#
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# TODO:
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#
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#
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# == DO NOT SIMPLY source() THIS FILE! =======================================
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#
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# If there are portions you don't understand, use R's help system, Google for an
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# answer, or ask your instructor. Don't continue if you don't understand what's
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# going on. That's not how it works ...
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#
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# ==============================================================================
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#TOC> ==========================================================================
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#TOC>
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#TOC> Section Title Line
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#TOC> ----------------------------------------------
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#TOC> 1 Prepare 55
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#TOC> 2 Storing Sequence 73
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#TOC> 3 String properties 102
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#TOC> 4 Substrings 109
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#TOC> 5 Creating strings: sprintf() 115
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#TOC> 6 Changing strings 146
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#TOC> 6.1 stringi and stringr 198
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#TOC> 6.2 dbSanitizeSequence() 208
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#TOC> 7 Permuting and sampling 220
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#TOC> 7.1 Permutations 227
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#TOC> 7.2 Sampling 270
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#TOC> 7.2.1 Equiprobable characters 272
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#TOC> 7.2.2 Defined probability vector 312
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#TOC>
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#TOC> ==========================================================================
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#
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#
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#
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#
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# = 1 Prepare =============================================================
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# Much basic sequence handling is supported by the Bioconductor package
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# Biostrings.
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if (! require(Biostrings, quietly=TRUE)) {
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if (! exists("biocLite")) {
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source("https://bioconductor.org/biocLite.R")
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}
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biocLite("Biostrings")
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library(Biostrings)
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}
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# Package information:
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# library(help = Biostrings) # basic information
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# browseVignettes("Biostrings") # available vignettes
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# data(package = "Biostrings") # available datasets
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# = 2 Storing Sequence ====================================================
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# Sequences can be represented and stored as vectors of single characters ...
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(v <- c("D", "I", "V", "M", "T", "Q"))
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# ... as strings ...
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(s <- "DIVMTQ")
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# ... or as more complex objects with rich metadata e.g. as a Biostrings
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# DNAstring, RNAstring, AAString, etc.
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(a <- AAString("DIVMTQ"))
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# ... and all of these representations can be interconverted:
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# string to vector ...
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unlist(strsplit(s, ""))
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# vector to string ...
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paste(v, sep = "", collapse = "")
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# ... and AAstring to plain string.
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as.character(a)
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# Since operations with character vectors trivially follow all other vector
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# conventions and syntax, and we will look at Biostrings methods in more
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# detail in a later unit, we will focus on basic strings in the following.
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# = 3 String properties ===================================================
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length(s) # why ???
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nchar(s) # aha
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# = 4 Substrings ==========================================================
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# Use the substr() function
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substr(s, 2, 4)
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# = 5 Creating strings: sprintf() =========================================
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# Sprintf is a very smart, very powerful function and has cognates in all
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# other programming languages. It has a small learning curve, but it's
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# totally worth it:
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# the function takes a format string, and a list of other arguments. It returns
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# a formatted string. Here are some examples - watch carefully for sprintf()
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# calls in other code.
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sprintf("Just a string.")
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sprintf("A string and the number %d.", 5)
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sprintf("More numbers: %d ate %d.", 7, 9) # Sorry
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sprintf("Pi is ~ %1.2f ...", pi)
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sprintf("or more accurately ~ %1.11f.", pi)
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x <- "bottles of beer"
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n <- 99
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sprintf("%d %s on the wall, %d %s - \ntake %s: %d %s on the wall.",
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n, x, n, x, "one down, and pass it around", n-1, x)
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# Note that in the last example, the value of the string was displayed with
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# R's usual print-formatting function and therefore the line-break "\n" did
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# not actually break the line. To have line breaks, tabs etc, you need to use
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# cat() to display the string:
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for (i in 99:95) {
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cat(sprintf("%d %s on the wall, %d %s - \ntake %s: %d %s on the wall.\n\n",
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i, x, i, x, "one down, and pass it around", i-1, x))
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}
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# = 6 Changing strings ====================================================
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# Changing case
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tolower(s)
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toupper(tolower(s))
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#reverse
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reverse(s)
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# chartr(old, new, x) maps all characters in x that appear in "old" to the
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# correpsonding character in "new."
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chartr("aeio", "uuuu", "We hold these truths to be self-evident ...")
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# One could implement toupper() and tolower() with this - remember that R has
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# character vectors of uppercase and lowercase letters as language constants.
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chartr(paste0(letters, collapse = ""),
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paste0(LETTERS, collapse = ""),
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"Twinkle, twinkle little star, how I wonder what you are.")
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# One amusing way to use the function is for a reversible substitution
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# cypher.
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set.seed(112358)
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myCypher <- paste0(sample(letters), collapse = "")
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lett <- paste0(letters, collapse = "")
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(x <- chartr(lett, myCypher, "... seven for a secret, never to be told."))
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chartr(myCypher, lett, x)
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# (Nb. substitution cyphers are easy to crack!)
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# substituing characters
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(s <- gsub("IV", "i-v", s)) # gsub can change length, first argument is
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# a "regular expression"!
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# I use it often to delete characters I don't want ...
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# ... select something, and substitute the empty string for it.
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(s <- gsub("-", "", s))
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# For example: clean up a sequence
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# copy/paste from UniProt
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(s <- " 10 20 30 40 50
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MSNQIYSARY SGVDVYEFIH STGSIMKRKK DDWVNATHIL KAANFAKAKR ")
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# remove numbers
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(s <- gsub("[0-9]", "", s))
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# remove "whitespace" (spaces, tabs, line breaks)...
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(s <- gsub("\\s", "", s))
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# == 6.1 stringi and stringr ===============================================
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# But there are also specialized functions eg. to remove leading/trailing
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# whitespace which may be important to sanitize user input etc. Have a look at
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# the function descriptions for the stringr and the stringi package. stringr is
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# part of the tidyverse, and for the most part a wrapper for stringi functions.
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# https://github.com/tidyverse/stringr
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# == 6.2 dbSanitizeSequence() ==============================================
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# In our learning units, we use a function dbSanitizeSequence() to clean up
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# sequences that may be copy/pasted from Web-sources
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s <- ">FASTA header will be removed
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10 20 30 40 50
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MSNQIYSARY SGVDVYEFIH STGSIMKRKK DDWVNATHIL KAANFAKAKR "
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dbSanitizeSequence(s)
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# = 7 Permuting and sampling ==============================================
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# An important aspect of working with strings is generating random strings
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# with given statistical properties: reference items to evaluate significance.
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# == 7.1 Permutations ======================================================
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# One way to produce such reference items is to permute a string. A permuted
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# string has the same composition as the original, but all positional
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# information is lost. The sample() function can be used to permute:
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# This is the sequence of the ompA secretion signal
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(s <- unlist(strsplit("MKKTAIAVALAGFATVAQA", "")))
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(x <- sample(s, length(s))) # permuted
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# Here's a small example how such permuted strings may be useful. As you look
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# at the ompA sequence, you suspect that the two lysines near the +-charged
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# N-terminus may not be accidental, but selected for a positively charged
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# N-terminus. What is the chance that such a sequence has two lysines close to
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# the N-terminus simply by chance? Or put differently: what is the average
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# distance of two lysines in such a sequence to the N-terminus. First, we
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# need an expression that measures the distance. A simple use of the which()
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# function will do just fine.
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which(s == "K") # shows they are in position 2 and 3, so ...
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mean(which(s == "K")) # ... gives us the average, and ...
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mean(which(x == "K")) # ... gives us the average of the permuted sequence.
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# So what does the distribution look like? Lets do 10,000 trials.
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(s <- unlist(strsplit("MKKTAIAVALAGFATVAQA", "")))
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N <- 10000
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d <- numeric(N)
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set.seed(112358)
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for (i in 1:N) {
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d[i] <- mean(which(sample(s, length(s)) == "K"))
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}
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hist(d, breaks = 20)
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abline(v = 2.5, lwd = 2, col = "firebrick")
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sum(d <= 2.5) # 276. 276 of our 10000 samples are just as bunched near the
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# N-terminus or more. That's just below the signifcance
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# threshold of 5 %. It's a trend, but to be sure we are looking
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# at a biological effect we would need to see more
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# sequences.
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# == 7.2 Sampling ==========================================================
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# === 7.2.1 Equiprobable characters
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# Assume you need a large random-nucleotide string for some statistical model.
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# How to create such a string? sample() can easily create it:
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nuc <- c("A", "C", "G", "T")
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N <- 100
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set.seed(16818)
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v <- sample(nuc, N, replace = TRUE)
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(mySeq <- paste(v, collapse = ""))
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# What's the GC content?
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table(v)
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sum(table(v)[c("G", "C")]) # 51 is close to expected
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# What's the number of CpG motifs? Easy to check with the stringi
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# stri_match_all() function
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if (! require(stringi, quietly=TRUE)) {
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install.packages("stringi")
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library(stringi)
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}
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# Package information:
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# library(help = stringi) # basic information
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# browseVignettes("stringi") # available vignettes
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# data(package = "stringi") # available datasets
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(x <- stri_match_all(mySeq, regex = "CG"))
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length(unlist(x))
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# Now you could compare that number with yeast DNA sequences, and determine
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# whether there are more or less CpG motifs than expected by chance.
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# (cf. https://en.wikipedia.org/wiki/CpG_site)
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# But hold on: is that a fair comparison? sample() gives us all four nucleotides
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# with the same probability. But the yeast genomic DNA GC content is only
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# 38%. So you would expect fewer CpG motifs based on the statistical properties
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# of the smaller number of Cs and Gs - before biology even comes into play. How
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# do we account for that?
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# === 7.2.2 Defined probability vector
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# This is where we need to know how to create samples with specific probability
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# distributions. A crude hack would be to create a sampling source vector with
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# 19 C, 19 G, 31 A and 31 T
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c(rep("C", 19), rep("G", 19), rep(c("A"), 31), rep(c("T"), 31))
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# ... but that doesn't scale if the numeric accuracy needs to be higher.
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#
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# However sample() has an argument that takes care of that: you can explicitly
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# specify the probabilities with which each element of the the sampling vector
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# should be chosen:
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nuc <- c("A", "C", "G", "T")
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N <- 100
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set.seed(16818)
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myProb <- c(0.31, 0.19, 0.19, 0.31) # sampling probabilities
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v <- sample(nuc, N, prob = myProb, replace = TRUE)
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(mySeq <- paste(v, collapse = ""))
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# What's the GC content?
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table(v)
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sum(table(v)[c("G", "C")]) # Close to expected
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# What's the number of CpG motifs?
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(x <- stri_match_all(mySeq, regex = "CG"))
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# ... not a single one in this case.
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# [END]
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