# FND-Genetic_code.R # # Purpose: A Bioinformatics Course: # R code accompanying the FND-Genetic_code unit. # # Version: 1.0.1 # # Date: 2017 10 12 # Author: Boris Steipe (boris.steipe@utoronto.ca) # # Versions: # 1.0.1 Comment on "incomplete final line" warning in FASTA # 1.0 First live version # # 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 Storing the genetic code 47 #TOC> 1.1 Genetic code in Biostrings 65 #TOC> 2 Working with the genetic code 97 #TOC> 2.1 Translate a sequence. 126 #TOC> 3 An alternative representation: 3D array 208 #TOC> 3.1 Print a Genetic code table 241 #TOC> 4 Tasks 267 #TOC> #TOC> ========================================================================== # = 1 Storing the genetic code ============================================ # The genetic code maps trinucleotide codons to amino acids. To store it, we # need some mechanism to associate these two informattion items. The most # convenient way to do that is a "named vector" which holds the maino acid # code and assigns the codons as names to its elements. x <- c("M", "*") names(x) <- c("ATG", "TAA") x # Then we can access the vector by the codon as name, and retrieve the # amino acid. x["ATG"] x["TAA"] # == 1.1 Genetic code in Biostrings ======================================== # Coveniently, the standard genetic code as well as its alternatives are # available in the Bioconductor "Biostrings" package: 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 # The standard genetic code vector GENETIC_CODE # The table of genetic codes. This information corresponds to this page # at the NCBI: # https://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes GENETIC_CODE_TABLE # Most of the alternative codes are mitochondrial codes. The id of the # Alternative Yeast Nuclear code is "12" getGeneticCode("12") # Alternative Yeast Nuclear # = 2 Working with the genetic code ======================================= # GENETIC_CODE is a "named vector" str(GENETIC_CODE) # ... which also stores the alternative initiation codons TTG and CTG in # an attribute of the vector. (Alternative initiation codons sometimes are # used instead of ATG to intiate translation, if if not ATG they are translated # with fMet.) attr(GENETIC_CODE, "alt_init_codons") # But the key to use this vector is in the "names" which we use for subsetting # the list of amino acids in whatever way we need. names(GENETIC_CODE) # The translation of "TGG" ... GENETIC_CODE["TGG"] # All stop codons names(GENETIC_CODE)[GENETIC_CODE == "*"] # All start codons names(GENETIC_CODE)[GENETIC_CODE == "M"] # ... or c(names(GENETIC_CODE)[GENETIC_CODE == "M"], attr(GENETIC_CODE, "alt_init_codons")) # == 2.1 Translate a sequence. ============================================= # I have provided a gene sequence in the data directory: # S288C_YDL056W_MBP1_coding.fsa is the yeast Mbp1 FASTA sequence. # read it mbp1 <- readLines("./data/S288C_YDL056W_MBP1_coding.fsa") # You will notice that this generates a Warning message: # Warning message: # In readLines("./data/S288C_YDL056W_MBP1_coding.fsa") : # incomplete final line found on './data/S288C_YDL056W_MBP1_coding.fsa' # The reason for this is that the last character of the file is the letter "A" # and not a "\n" line break. This file is exactly how it was sent from the # server; I think good, defensive programming practice would have been to # include some kind of an end-marker in the file, like a final "\n". This helps # us recognize an incomplete transmission. Let's parse the actual sequence from # the file, and then check for completeness. head(mbp1) # drop the first line (header) mbp1 <- mbp1[-1] head(mbp1) # concatenate it all to a single string mbp1 <- paste(mbp1, sep = "", collapse = "") # how long is it? nchar(mbp1) # how many codons? nchar(mbp1)/3 # That looks correct for the 833 aa sequence plus 1 stop codon. This gives us a # first verification that the file we read is complete, the nucleotides of a # complete ORF should be divisible by 3. # Extract the codons. There are many ways to split a long string into chunks # of three characters. Here we use the Biostrings codons() function. codons() # requires an object of type DNAstring - a special kind of string with # attributes that are useful for Biostrings. Thus we convert the sequence first # with DNAstring(), then split it up, then convert it into a plain # character vector. mbp1Codons <- as.character(codons(DNAString(mbp1))) head(mbp1Codons) # now translate each codon mbp1AA <- character(834) for (i in seq_along(mbp1Codons)) { mbp1AA[i] <- GENETIC_CODE[mbp1Codons[i]] } head(mbp1Codons) head(mbp1AA) tail(mbp1Codons) tail(mbp1AA) # Note the stop! # The TAA "ochre" stop codon is our second verification that the nucleotide # sequence is complete: a stop codon can't appear internally in an ORF. # We can work with the mbp1AA vector, for example to tabulate the # amino acid frequencies: table(mbp1AA) sort(table(mbp1AA), decreasing = TRUE) # Or we can paste all elements together into a single string. But let's remove # the stop, it's not actually a part of the sequence. To remove the last element # of a vector, re-assign it with a vector minus the index of the last element: mbp1AA <- mbp1AA[-(length(mbp1AA))] tail(mbp1AA) # Note the stop is gone! # paste it together, collapsing the elements without separation-character (Mbp1 <- paste(mbp1AA, sep = "", collapse = "")) # = 3 An alternative representation: 3D array ============================= # We don't use 3D arrays often - usually just 2D tables and data frames, so # here is a good opportunity to review the syntax with a genetic code cube: # Initialize, using A C G T as the names of the elements in each dimension cCube <- array(data = character(64), dim = c(4, 4, 4), dimnames = list(c("A", "C", "G", "T"), c("A", "C", "G", "T"), c("A", "C", "G", "T"))) # fill it with amino acid codes using three nested loops for (i in 1:4) { for (j in 1:4) { for (k in 1:4) { myCodon <- paste(dimnames(cCube)[[1]][i], dimnames(cCube)[[2]][j], dimnames(cCube)[[3]][k], sep = "", collapse = "") cCube[i, j, k] <- GENETIC_CODE[myCodon] } } } # confirm cCube["A", "T", "G"] # methionine cCube["T", "T", "T"] # phenylalanine cCube["T", "A", "G"] # stop (amber) # == 3.1 Print a Genetic code table ======================================== # The data structure of our cCube is well suited to print a table. In the # "standard" way to print the genetic code, we write codons with the same # second nucleotide in columns, and arrange rows in blocks of same # first nucleotide, varying the third nucleotide fastest. This maximizes the # similarity of adjacent amino acids in the table if we print the # nucleotides in the order T C A G. It's immidiately obvious that the code # is not random: the universal genetic code is exceptionally error tolerant in # the sense that mutations (or single-nucleotide translation errors) are likely # to result in an amino acid with similar biophysical properties as the # original. nuc <- c("T", "C", "A", "G") for (i in nuc) { for (k in nuc) { for (j in nuc) { cat(sprintf("%s%s%s: %s ", i, j, k, cCube[i, j, k])) } cat("\n") } } # = 4 Tasks =============================================================== # Task: What do you need to change to print the table with U instead # of T? Try it. # Task: Point mutations are more often transitions (purine -> purine; # pyrimidine -> pyrimidine) than transversions (purine -> pyrimidine; # pyrimidine -> purine), even though twice as many transversions # are possible in the code. This is most likely due a deamination / # tautomerization process that favours C -> T changes. If the code # indeed minimizes the effect of mutations, you would expect that # codons that differ by a transition code for more similar amino acids # than codons that differ by a transversion. Is that true? List the set # of all amino acid pairs that are encoded by codons with a C -> T # transition. Then list the set of amino acid pairs with a C -> A # transversion. Which set of pairs is more similar? # Task: How many stop codons do the two mbp1-gene derived amino acid sequences # have if you translate them in the 2. or the 3. frame? # Task: How does the amino acid composition change if you translate the mbp1 # gene with the Alternative Yeast Nuclear code that is used by the # "GTC clade" of fungi? # (cf. https://en.wikipedia.org/wiki/Alternative_yeast_nuclear_code ) # Solution: # Fetch the code GENETIC_CODE_TABLE GENETIC_CODE_TABLE$name[GENETIC_CODE_TABLE$id == "12"] altYcode <- getGeneticCode("12") # what's the difference? (delta <- which(GENETIC_CODE != altYcode)) GENETIC_CODE[delta] altYcode[delta] # translate altYAA <- character(834) for (i in seq_along(mbp1Codons)) { altYAA[i] <- altYcode[mbp1Codons[i]] } table(mbp1AA) table(altYAA) # Task: The genetic code has significant redundacy, i.e. there are up to six # codons that code for the same amino acid. Write code that lists how # many amino acids are present how often i.e. it should tell you that # two amino acids are encoded only with a single codon, three amino # acids have six codons, etc. Solution below, but don't peek. There # are many possible ways to do this. # # # Solution: table(table(GENETIC_CODE)) # [END]