# 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]