222 lines
7.1 KiB
R
222 lines
7.1 KiB
R
# RPR-Biostrings.R
|
|
#
|
|
# Purpose: A Bioinformatics Course:
|
|
# R code accompanying the RPR-Biostrings unit.
|
|
#
|
|
# Version: 1.0
|
|
#
|
|
# Date: 2017 10 20
|
|
# Author: Boris Steipe (boris.steipe@utoronto.ca)
|
|
#
|
|
# Versions:
|
|
# 1.0 2017 Revisions
|
|
# 0.1 First code copied from 2016 material.
|
|
#
|
|
#
|
|
# 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 The Biostrings Package 52
|
|
#TOC> 2 Getting Data into Biostrings Objects 85
|
|
#TOC> 3 Working with Biostrings Objects 106
|
|
#TOC> 3.1 Properties 109
|
|
#TOC> 3.2 Subsetting 146
|
|
#TOC> 3.3 Operators 158
|
|
#TOC> 3.4 Transformations 165
|
|
#TOC> 4 Getting Data out of Biostrings Objects 172
|
|
#TOC> 5 More 181
|
|
#TOC> 5.1 Views 183
|
|
#TOC> 5.2 Iranges 195
|
|
#TOC> 5.3 StringSets 201
|
|
#TOC>
|
|
#TOC> ==========================================================================
|
|
|
|
|
|
# This is a very brief introduction to the biostrings package, other units will
|
|
# be using more of the biostrings functions.
|
|
|
|
|
|
# = 1 The Biostrings Package ==============================================
|
|
|
|
|
|
# First, we install and load the Biostrings package from bioconductor
|
|
|
|
if (! require(Biostrings, quietly=TRUE)) {
|
|
if (! exists("biocLite")) {
|
|
source("https://bioconductor.org/biocLite.R")
|
|
}
|
|
biocLite("Biostrings")
|
|
library(Biostrings)
|
|
}
|
|
|
|
# Examine the package information:
|
|
library(help = Biostrings) # basic information
|
|
browseVignettes("Biostrings") # available vignettes
|
|
data(package = "Biostrings") # available datasets
|
|
|
|
|
|
# At its core, Biostrings objects are "classes" of type XString (you can think
|
|
# of a "class" in R as a special kind of list), that can take on particular
|
|
# flavours for RNA, DNA or amino acid sequence information.
|
|
|
|
class(RNAString("AUG"))
|
|
class(DNAString("ATG"))
|
|
class(AAString("M"))
|
|
|
|
# An essential property of Biostrings objects is that they only allow letters
|
|
# from the applicable IUPAC alphabet:
|
|
RNAString("AUG")
|
|
DNAString("AUG") # Error! No "U" in IUPAC DNA codes
|
|
|
|
|
|
# = 2 Getting Data into Biostrings Objects ================================
|
|
|
|
|
|
# Example: read FASTA. Extract sequence. Convert to DNAString object.
|
|
x <- readLines("./data/S288C_YDL056W_MBP1_coding.fsa")
|
|
x <- dbSanitizeSequence(x)
|
|
myDNAseq <- DNAString(x) # takes the nucleotide sequence and converts into a
|
|
# object of class DNAstring
|
|
|
|
# Multi FASTA files can be read directly as a "XStringSet) ...
|
|
(myDNASet <- readDNAStringSet("./data/S288C_YDL056W_MBP1_coding.fsa"))
|
|
|
|
# ... and if you subset one sequence from the set, you get an XString object
|
|
# back again.
|
|
(Xseq <- myDNASet[[1]])
|
|
|
|
myDNAseq == Xseq # the comparison evaluates to TRUE ...
|
|
identical(myDNAseq, Xseq) # ... and indeed the objects are deemed identical.
|
|
|
|
|
|
|
|
# = 3 Working with Biostrings Objects =====================================
|
|
|
|
|
|
# == 3.1 Properties ========================================================
|
|
str(myDNAseq)
|
|
length(myDNAseq) # This gives you the _number of nucleotides_!
|
|
# By comparison ...
|
|
length(x) # ... is 1: one string only. To get the number of
|
|
# characters in a string, you need nchar().
|
|
nchar(x) # However ...
|
|
nchar(myDNAseq) # ... also works.
|
|
|
|
uniqueLetters(myDNAseq)
|
|
|
|
# Count frequencies - with strings, you would strsplit() into a character
|
|
# vector and then use table(). biost
|
|
alphabetFrequency(myDNAseq)
|
|
|
|
# letterFrequency() works with a defined alphabet - such as what uniqueLetters()
|
|
# returns.
|
|
letterFrequency(myDNAseq, uniqueLetters(myDNAseq))
|
|
|
|
sum(letterFrequency(myDNAseq, c("G", "C"))) / length(myDNAseq) # GC contents
|
|
|
|
dinucleotideFrequency(myDNAseq)
|
|
barplot(sort(dinucleotideFrequency(myDNAseq)), cex.names = 0.5)
|
|
|
|
(triNuc <- trinucleotideFrequency(myDNAseq))
|
|
barplot(sort(triNuc), col="#4499EE33")
|
|
triNuc[triNuc == max(triNuc)]
|
|
triNuc[triNuc == min(triNuc)]
|
|
max(triNuc) / min(triNuc) # AAA is more than 13 times as frequent as CGT
|
|
|
|
# compare to a shuffled sequence:
|
|
(triNuc <- trinucleotideFrequency(sample(myDNAseq)))
|
|
barplot(sort(triNuc), col="#EEEE4433", add = TRUE)
|
|
|
|
# Interpret this plot.
|
|
|
|
|
|
# == 3.2 Subsetting ========================================================
|
|
|
|
# Subsetting any XString object works as expected:
|
|
myDNAseq[4:15]
|
|
|
|
# ... well - maybe not expected, because x[4:15] would not work.
|
|
|
|
# Alternatively to the "[" operator, use the subseq() function - especially for
|
|
# long sequences. This is far more efficient.
|
|
subseq(myDNAseq, start = 1, end = 30)
|
|
|
|
|
|
# == 3.3 Operators =========================================================
|
|
|
|
# RNAstring() and DNAstring() objects compare U and T as equals!
|
|
RNAString("AUGUCUAACCAAAUAUACUCAGCGAGAUAU") ==
|
|
DNAString("ATGTCTAACCAAATATACTCAGCGAGATAT")
|
|
|
|
|
|
# == 3.4 Transformations ===================================================
|
|
|
|
myDNAseq[4:15]
|
|
reverseComplement(myDNAseq[4:15])
|
|
translate(myDNAseq[4:15])
|
|
|
|
|
|
# = 4 Getting Data out of Biostrings Objects ==============================
|
|
|
|
# If you need a character object, use toString():
|
|
|
|
toString(myDNAseq[4:15])
|
|
|
|
# save() and load() works like on all other R objects.
|
|
|
|
|
|
# = 5 More ================================================================
|
|
|
|
# == 5.1 Views =============================================================
|
|
|
|
# Biostring "Views" are objects that store multiple substrings of one
|
|
# Biostring object.
|
|
|
|
(myView <- Views(myDNAseq, start = c(1, 19, 37), end = c(15, 30, 45)))
|
|
|
|
# Views are convenient to store feature annotations
|
|
names(myView) <- c("Feature-A", "Feature-B", "Feature-C")
|
|
cat(sprintf("\n%s\t(%d)\t%s", names(myView), width(myView), myView ))
|
|
|
|
|
|
# == 5.2 Iranges ===========================================================
|
|
|
|
# Biostrings Iranges are like Views with a common start point. These can be
|
|
# useful for feature annotations. Instead of start/end you store start/width.
|
|
|
|
|
|
# == 5.3 StringSets ========================================================
|
|
|
|
# Biostring "StringSets" store multiple sequences.
|
|
#
|
|
ompA <- AAString("MKKTAIAIAVALAGFATVAQA")
|
|
sample(ompA) # sample can work directly on a Biostring object to shuffle it
|
|
|
|
x[1] <- toString(ompA)
|
|
for (i in 2:10) {
|
|
x[i] <- toString(sample(ompA))
|
|
}
|
|
shuffledPeptideSet <- AAStringSet(x)
|
|
names(shuffledPeptideSet) <- c("ompA", paste("shuffle.", 1:9, sep=""))
|
|
shuffledPeptideSet
|
|
|
|
length(shuffledPeptideSet)
|
|
width(shuffledPeptideSet)
|
|
alphabetFrequency(shuffledPeptideSet)
|
|
|
|
|
|
# [END]
|