148 lines
5.2 KiB
R
148 lines
5.2 KiB
R
# BIN-FUNC-Domain_annotation.R
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-FUNC-Domain_annotation unit.
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#
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# Version: 0.1
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#
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# Date: 2017 08 28
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 0.1 First code copied from 2016 material.
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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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# 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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# = 1 SMART Domain annotations
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# Plot domain annotations as colored rectangles on a sequence.
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# Step one: enter your domain annotations as features into the database.
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#
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# == Update myDB
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# If the reference database has changed, we need to merge it in with myDB.
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load("myDB.03.RData") # load the previous version of myDB
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# the new version of refDB was loaded when you
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# pulled it from GitHub, and then typed init()
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myDB <- dbMerge(myDB) # merge the two databases and update myDB with the result
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save(myDB, file = "myDB.04.RData") # save the new version
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# == Update myDB
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# Every annotated feature requires its own entry in the database. You have added
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# the feature for the "APSES fold" before, so you can copy and edit that code
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# from your myCode.R script. Here is again the table of feature IDs:
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myDB$feature[ , c("ID", "name", "description")]
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# Add every SMART annotated feaure for MBP1_YFO to the database. If you make
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# mistakes, just reload the latest version (probably "myDB.04.RData"), then run
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# your corrected annotation script again. Execute ...
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myDB$proteinAnnotation
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# ... to confirm.
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#
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# Once you are sure your annotations are correct, save the database again.
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save(myDB, file = "myDB.05.RData") # save the new version
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#
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# Now let's plot the annotations.
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#
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# We need a small utility function that draws the annotation boxes on a
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# representation of sequence. It will accept the left and right boundaries, the
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# height and the color of the box and plot it using R's rect() function.
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drawBox <- function(xLeft, xRight, y, colour) {
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# Draw a box from xLeft to xRight at y, filled with colour
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rect(xLeft, (y - 0.1), xRight, (y + 0.1),
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border = "black", col = colour)
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}
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# test this:
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plot(c(-1.5, 1.5), c(0, 0), type = "l")
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drawBox(-1, 1, 0.0, "peachpuff")
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# Next, we define a function to plot annotations for one protein: the name of
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# the protein, a horizontal grey line for its length, and all of its features.
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plotProtein <- function(DB, ID, y) {
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# DB: protein database, probably you want myDB
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# ID: the ID of the protein to plot.
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# y: where to draw the plot
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#
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# Define colors: we create a vector of color values, one for
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# each feature, and we give it names of the feature ID. Then we
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# can easily get the color value from the feature name.
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# A: make a vector of color values. The syntax may appear unusual -
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# colorRampPalette() returns a function, and we simply append
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# the parameter (number-of-features) without assigning the function
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# to its own variable name.
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ftrCol <- colorRampPalette(c("#f2003c", "#F0A200", "#f0ea00",
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"#62C923", "#0A9A9B", "#1958C3",
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"#8000D3", "#D0007F"),
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space="Lab",
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interpolate="linear")(nrow(DB$feature))
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# B: Features may overlap, so we make the colors transparent by setting
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# their "alpha channel" to 1/2 (hex: 7F)
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ftrCol <- paste(ftrCol, "7F", sep = "")
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# C: we asssign names
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names(ftrCol) <- DB$feature$ID
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# E.g. color for the third feature: ftrCol[ DB$feature$ID[3] ]
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# find the row-index of the protein ID in the protein table of DB
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iProtein <- which(DB$protein$ID == ID)
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# write the name of the protein
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text(-30, y, adj=1, labels=DB$protein$name[iProtein], cex=0.75 )
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#draw a line from 0 to nchar(sequence-of-the-protein)
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lines(c(0, nchar(DB$protein$sequence[iProtein])), c(y, y),
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lwd=3, col="#999999")
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# get the rows of feature annotations for the protein
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iFtr <- which(DB$proteinAnnotation$protein.ID == ID)
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# draw a colored box for each feature
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for (i in iFtr) {
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drawBox(DB$proteinAnnotation$start[i],
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DB$proteinAnnotation$end[i],
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y,
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ftrCol[ DB$proteinAnnotation$feature.ID[i] ])
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}
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}
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# Plot each annotated protein:
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# Get the rows of all unique annotated protein IDs in the protein table
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iRows <- which(myDB$protein$ID %in% unique(myDB$proteinAnnotation$protein.ID))
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# define the size of the plot-frame to accomodate all proteins
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yMax <- length(iRows) * 1.1
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xMax <- max(nchar(myDB$protein$sequence[iRows])) * 1.1 # longest sequence
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# plot an empty frame
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plot(1,1, xlim=c(-200, xMax), ylim=c(0, yMax),
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type="n", axes=FALSE, bty="n", xlab="sequence position", ylab="")
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axis(1, at = seq(0, xMax, by = 100))
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# Finally, iterate over all proteins and call plotProtein()
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for (i in 1:length(iRows)) {
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plotProtein(myDB, myDB$protein$ID[iRows[i]], i)
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}
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# The plot shows clearly what is variable and what is constant about the
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# annotations in a group of related proteins. Print the plot and bring it to
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# class for the next quiz.
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#
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# = 1 Tasks
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# [END]
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