196 lines
6.8 KiB
R
196 lines
6.8 KiB
R
# tocID <- "BIN-ALI-Dotplot.R"
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#
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#
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# ==============================================================================
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-ALI-Dotplot unit.
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#
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# Version: 0.2
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#
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# Date: 2019 01 07
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 0.2 Change from require() to requireNamespace(),
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# use <package>::<function>() idiom throughout
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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 ___Section___ 42
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#TOC> 2 Tasks 190
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#TOC>
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#TOC> ==========================================================================
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# = 1 ___Section___ =======================================================
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if (!requireNamespace("BiocManager", quietly=TRUE)) {
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install.packages("BiocManager")
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}
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if (!requireNamespace("Biostrings", quietly=TRUE)) {
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BiocManager::install("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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if (!requireNamespace("seqinr", quietly=TRUE)) {
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install.packages("seqinr")
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}
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# Let's load BLOSUM62
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data(BLOSUM62, package = "Biostrings")
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# Now let's craft code for a dotplot. That's surprisingly simple. We build a
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# matrix that has as many rows as one sequence, as many columns as another. Then
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# we go through every cell of the matrix and enter the pairscore we encounter
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# for the amino acid pair whose position corresponds to the row and column
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# index. Finally we visualize the matrix in a plot.
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#
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# First we fetch our sequences and split them into single characters.
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sel <- myDB$protein$name == "MBP1_SACCE"
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MBP1_SACCE <- seqinr::s2c(myDB$protein$sequence[sel])
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sel <- myDB$protein$name == paste("MBP1_", biCode(MYSPE), sep = "")
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MBP1_MYSPE <- seqinr::s2c(myDB$protein$sequence[sel])
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# Check that we have two character vectors of the expected length.
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str(MBP1_SACCE)
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str(MBP1_MYSPE)
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# How do we get the pairscore values? Consider: a single pair of amino acids can
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# be obtained from sequence SACCE and MYSPE eg. from position 13 and 21 ...
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MBP1_SACCE[13]
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MBP1_MYSPE[21]
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# ... using these as subsetting expressions, we can pull the pairscore
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# from the MDM
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BLOSUM62[MBP1_SACCE[13], MBP1_MYSPE[21]]
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# First we build an empty matrix that will hold all pairscores ...
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dotMat <- matrix(numeric(length(MBP1_SACCE) * length(MBP1_MYSPE)),
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nrow = length(MBP1_SACCE), ncol = length(MBP1_MYSPE))
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# ... then we loop over the sequences and store the scores in the matrix.
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#
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for (i in 1:length(MBP1_SACCE)) {
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for (j in 1:length(MBP1_MYSPE)) {
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dotMat[i, j] <- BLOSUM62[MBP1_SACCE[i], MBP1_MYSPE[j]]
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}
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}
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# Even though this is a large matrix, this does not take much time ...
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# Let's have a look at a small block of the values:
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dotMat[1:10, 1:10]
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# Rows in this matrix correspond to an amino acid from MBP1_SACCE, columns in
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# the matrix correspond to an amino acid from MBP1_MYSPE.
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# To plot this, we use the image() function. Here, with default parameters.
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image(dotMat)
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# Be patient, this takes a few moments to render: more than 500,000 values.
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# Nice.
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# What do you expect?
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# What would similar sequences look like?
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# What do you see?
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#You migh notice a thin line of yellow along the diagonal, moving approximately
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# from bottom left to top right, fading in and out of existence. This is the
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# signature of extended sequence similarity.
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# Let's magnify this a bit by looking at only the first 200 amino acids ...
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image(dotMat[1:200, 1:200])
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# ... and, according to our normal writing convention, we would like the
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# diagonal to run from top-left to bottom-right since we write from left to
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# right and from top to bottom...
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image(dotMat[1:200, 1:200], ylim = 1.0:0.0)
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# ... and we would like the range of the x- and y- axis to correspond to the
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# sequence position ...
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image(x = 1:200, y = 1:200, dotMat[1:200, 1:200], ylim=c(200,1))
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# ... and labels! Axis labels would be nice ...
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image(x = 1:200, y = 1:200, dotMat[1:200, 1:200], ylim=c(200,1),
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xlab = "MBP1_MYSPE", ylab = "MBP1_SACCE" )
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# ... and why don't we have axis-numbers on all four sides? Go, make that right
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# too ...
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len <- 200
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image(x = 1:len, y = 1:len, dotMat[1:len, 1:len], ylim=c(len,1),
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xlab = "MBP1_MYSPE", ylab = "MBP1_SACCE", axes = FALSE)
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box()
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axis(1, at = c(1, seq(10, len, by=10)))
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axis(2, at = c(1, seq(10, len, by=10)))
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axis(3, at = c(1, seq(10, len, by=10)))
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axis(4, at = c(1, seq(10, len, by=10)))
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# ... you get the idea, we can infinitely customize our plot. However a good way
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# to do this is to develop a particular view for, say, a report or publication
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# in a script and then put it into a function. I have put a function into the
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# utilities file and called it dotPlot2(). Why not dotPlot() ... that's because
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# there already is a dotplot function in the seqinr package:
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seqinr::dotPlot(MBP1_SACCE, MBP1_MYSPE) # seqinr
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dotPlot2(MBP1_SACCE, MBP1_MYSPE, xlab = "SACCE", ylab = "MYSPE") # Our's
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# Which one do you prefer? You can probably see the block patterns that arise
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# from segments of repetitive, low complexity sequence. But you probably have to
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# look very closely to discern the faint diagonals that correspond to similar
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# sequence.
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# Let's see if we can enhance the contrast between distributed noise and the
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# actual alignment of conserved residues. We can filter the dot matrix with a
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# pattern that enhances diagonally repeated values. Every value in the matrix
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# will be replaced by a weighted average of its neighborhood. Here is a
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# diagonal-filter:
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myFilter <- matrix(numeric(25), nrow = 5)
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myFilter[1, ] <- c( 1, 0, 0, 0, 0)
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myFilter[2, ] <- c( 0, 1, 0, 0, 0)
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myFilter[3, ] <- c( 0, 0, 1, 0, 0)
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myFilter[4, ] <- c( 0, 0, 0, 1, 0)
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myFilter[5, ] <- c( 0, 0, 0, 0, 1)
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# I have added the option to read such filters (or others that you could define on your own) as a parameter of the function.
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dotPlot2(MBP1_SACCE, MBP1_MYSPE, xlab = "SACCE", ylab = "MYSPE", f = myFilter)
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# I think the result shows quite nicely how the two sequences are globally
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# related and where the regions of sequence similarity are. Play with this a bit
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# ... Can you come up with a better filter? If so, eMail us.
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# = 2 Tasks ===============================================================
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# [END]
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