222 lines
7.0 KiB
R
222 lines
7.0 KiB
R
# RPR-Biostrings.R
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the RPR-Biostrings unit.
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#
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# Version: 1.0
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#
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# Date: 2017 10 20
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 1.0 2017 Revisions
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# 0.1 First code copied from 2016 material.
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#
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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 The Biostrings package 57
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#TOC> 2 Getting Data into Biostrings Objects 91
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#TOC> 3 Working with Biostrings Objects 111
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#TOC> 3.1 Properties 114
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#TOC> 3.2 Subsetting 151
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#TOC> 3.3 Operators 163
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#TOC> 3.4 Transformations 170
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#TOC> 4 Getting Data out of Biostrings Objects 177
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#TOC> 5 More 186
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#TOC> 5.1 Views 188
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#TOC> 5.2 Iranges 200
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#TOC> 5.3 StringSets 206
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#TOC>
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#TOC> ==========================================================================
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# This is a very brief introduction to the biostrings package, other units will
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# be using more of the biostrings functions.
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# = 1 The Biostrings package ==============================================
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# First, we install and load the Biostrings package from bioconductor
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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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# Examine the ackage 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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# At its core, Biostrings objects are "classes" of type XString (you can think
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# of a "class" in R as a special kind of list), that can take on particular
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# flavours for RNA, DNA or amino acid sequence information.
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class(RNAString("AUG"))
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class(DNAString("ATG"))
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class(AAString("M"))
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# An essential property of Biostrings objects is that they only allow letters
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# from the applicable IUPAC alphabet:
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RNAString("AUG")
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DNAString("AUG") # Error! No "U" in IUPAC DNA codes
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# = 2 Getting Data into Biostrings Objects ================================
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# Example: read FASTA. Extract sequence. Convert to DNAString object.
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x <- readLines("./data/S288C_YDL056W_MBP1_coding.fsa")
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x <- dbSanitizeSequence(x)
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myDNAseq <- DNAString(x) # takes the nucleotide sequence and conerts into a
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# object of class DNAstring
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# Multi FASTA files can be read directly ...
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readDNAStringSet("./data/S288C_YDL056W_MBP1_coding.fsa") # Note: XStringSet
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# ... and if you subset one sequence from the set, you get an XString object
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( x <- readDNAStringSet("./data/S288C_YDL056W_MBP1_coding.fsa")[[1]] )
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myDNAseq == x
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identical(myDNAseq, x)
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# = 3 Working with Biostrings Objects =====================================
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# == 3.1 Properties ========================================================
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str(myDNAseq)
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length(nchar(x)) # This gives you the _number of nucleotides_!
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# By comparison ...
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length(x) # ... is 1: one string only. To get the number of
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# characters in a string, you need nchar().
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nchar(x) # However ...
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nchar(myDNAseq) # ... also works.
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uniqueLetters(myDNAseq)
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# Count frequencies - with strings, you would strsplit() into a character
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# vector and then use table(). biost
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alphabetFrequency(myDNAseq)
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# letterFrequency() works with a defined alphabet - such as what uniqueLetters()
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# returns.
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letterFrequency(myDNAseq, uniqueLetters(myDNAseq))
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sum(letterFrequency(myDNAseq, c("G", "C"))) / length(myDNAseq) # GC contents
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dinucleotideFrequency(myDNAseq)
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barplot(sort(dinucleotideFrequency(myDNAseq)), cex.names = 0.5)
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(x <- trinucleotideFrequency(myDNAseq))
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barplot(sort(x), col="#4499EE33")
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x[x == max(x)]
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x[x == min(x)]
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max(x) / min(x) # AAA is more than 13 times as frequent as CGT
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# compare to a shuffled sequence:
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(x <- trinucleotideFrequency(sample(myDNAseq)))
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barplot(sort(x), col="#EEEE4433", add = TRUE)
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# Interpret this plot.
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# == 3.2 Subsetting ========================================================
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# Subsetting any XString object works as expected:
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myDNAseq[4:15]
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# ... well - maybe not expected, because x[4:15] would not work.
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# Alternatively to the "[" operator, use the subseq() function - especially for
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# long sequences. This is far more efficient.
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subseq(myDNAseq, start = 1, end = 30)
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# == 3.3 Operators =========================================================
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# RNAstring() and DNAstring() objects compare U and T as equals!
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RNAString("AUGUCUAACCAAAUAUACUCAGCGAGAUAU") ==
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DNAString("ATGTCTAACCAAATATACTCAGCGAGATAT")
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# == 3.4 Transformations ===================================================
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myDNAseq[4:15]
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reverseComplement(myDNAseq[4:15])
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translate(myDNAseq[4:15])
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# = 4 Getting Data out of Biostrings Objects ==============================
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# If you need a character object, use toString():
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toString(myDNAseq[4:15])
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# save() and load() works like on all other R objects.
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# = 5 More ================================================================
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# == 5.1 Views =============================================================
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# Biostring "Views" are objects that store mutliple substrings of one
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# Biostring object.
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(myView <- Views(myDNAseq, start = c(1, 19, 37), end = c(15, 30, 45)))
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# Views are convenient to store feature annotations
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names(myView) <- c("Feature-A", "Feature-B", "Feature-C")
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cat(sprintf("\n%s\t(%d)\t%s", names(myView), width(myView), myView ))
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# == 5.2 Iranges ===========================================================
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# Biostrings Iranges are like Views with a common start point. These can be
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# useful for feature annotations. Instead of start/end you store start/width.
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# == 5.3 StringSets ========================================================
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# Biostring "StringSets" store multiple sequences.
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#
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ompA <- AAString("MKKTAIAIAVALAGFATVAQA")
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sample(ompA) # sample can work directly on a Biostring object to shuffle it
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x[1] <- toString(ompA)
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for (i in 2:10) {
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x[i] <- toString(sample(ompA))
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}
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shuffledPeptideSet <- AAStringSet(x)
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names(shuffledPeptideSet) <- c("ompA", paste("shuffle.", 1:9, sep=""))
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shuffledPeptideSet
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length(shuffledPeptideSet)
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width(shuffledPeptideSet)
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alphabetFrequency(shuffledPeptideSet)
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# [END]
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