270 lines
9.7 KiB
R
270 lines
9.7 KiB
R
# tocID <- "BIN-FUNC-Domain_annotation.R"
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#
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# ---------------------------------------------------------------------------- #
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# PATIENCE ... #
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# Do not yet work wih this code. Updates in progress. Thank you. #
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# boris.steipe@utoronto.ca #
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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-FUNC-Domain_annotation unit.
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#
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# Version: 1.0
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#
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# Date: 2017 11 13
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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 Live version 2017
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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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#
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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 Update your database script 41
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#TOC> 1.1 Preparing an annotation file ... 47
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#TOC> 1.1.1 If you HAVE NOT done the BIN-ALI-Optimal_sequence_alignment unit 49
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#TOC> 1.1.2 If you HAVE done the BIN-ALI-Optimal_sequence_alignment 93
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#TOC> 1.2 Execute and Validate 119
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#TOC> 2 Plot Annotations 144
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#TOC>
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#TOC> ==========================================================================
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# = 1 Update your database script =========================================
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# Since you have recorded domain features at the SMART database, we can store
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# the feature annotations in myDB.
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# == 1.1 Preparing an annotation file ... ==================================
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#
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# === 1.1.1 If you HAVE NOT done the BIN-ALI-Optimal_sequence_alignment unit
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#
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#
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# You DON'T already have a file called "<MYSPE>-Annotations.json" in the
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# ./data/ directory:
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#
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# - Make a copy of the file "./data/refAnnotations.json" and put it in your
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# project directory.
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#
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# - Give it a name that is structured like "<MYSPE>-Annotations.json" - e.g.
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# if MYSPE is called "Crptycoccus neoformans", your file should be called
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# "CRYNE-Annotations.json" (and the "name" of your Mbp1 orthologue is
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# "MBP1_CRYNE").
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#
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# - Open the file in the RStudio editor and delete all blocks for
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# the Mbp1 protein annotations except the first one.
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#
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# - From that block, delete all lines that have annotations you did not
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# find in SMART for MBP1_MYSPE.
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#
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# - Make enough copies of the "Ankyrin fold" and "low complexity" region
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# lines to have a line for each feature you found.
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#
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# - Then delete the comma at the end of the last line.
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#
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# - Edit the annotations: change MBP1_SACCE to MBP1_<MYSPE> everywhere
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# and change the "start" and "end" features to the coordinates you
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# recorded in the SMART database.
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#
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# - Save your file.
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#
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# - Validate your file online at https://jsonlint.com/
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#
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# - Update your "makeProteinDB.R" script to load your new
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# annotation when you recreate the database. Open the script in the
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# RStudio editor, and add the following command at the end:
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#
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# myDB <- dbAddAnnotation(myDB, fromJSON("<MYSPE>-Annotations.json"))
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#
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# - save and close the file.
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#
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# Then SKIP the next section.
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#
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#
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# === 1.1.2 If you HAVE done the BIN-ALI-Optimal_sequence_alignment
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#
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#
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# You DO already have a file called "<MYSPE>-Annotations.json" in the
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# ./data/ directory:
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#
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# - Open the file in the RStudio editor.
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#
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# - Make as many copies of the "APSES fold" line as you have found
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# features in SMART.
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#
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# - Add a comma after every line except for the last one
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#
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# - Edit the annotations but include only features that are in the
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# myDB$feature table. Check which features are in the databse by executing
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#
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# myDB$feature$name
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#
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# - Update the "start" and "end" coordinates for each feature to the
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# values you found.
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#
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# - Save your file.
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#
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# - Validate your file online at https://jsonlint.com/
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#
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#
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# == 1.2 Execute and Validate ==============================================
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#
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# - source() your database creation script:
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#
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# source("makeProteinDB.R")
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#
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# This should run without errors or warnings. If it doesn't work and you
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# can't figure out quickly what's happening, ask on the mailing list for
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# help.
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#
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# - Confirm
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# The following commands should retrieve all of the features that have been
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# annotated for MBP1_MYSPE
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sel <- myDB$protein$name == paste("MBP1_", biCode(MYSPE), sep = "")
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(proID <- myDB$protein$ID[sel])
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(fanIDs <- myDB$annotation$ID[myDB$annotation$proteinID == proID])
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(ftrIDs <- unique(myDB$annotation$featureID[fanIDs]))
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myDB$feature$name[ftrIDs] # This should list ALL of your annotated features
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# (once). If not, consider what could have gone wrong
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# and ask on the list if you have difficulties fixing
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# it.
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# = 2 Plot Annotations ====================================================
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# In this section we will plot domain annotations as colored rectangles on a
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# sequence, as an example for using the R plotting system for generic, data
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# driven images.
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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 should accept the start and end coordinates,
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# the y value where it should be plotted and the color of the box, and plot a
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# rectangle using R's rect() function.
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drawBox <- function(xStart, xEnd, y, myCol) {
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# Draw a box from xStart to xEnd at y, filled with colour myCol
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delta <- 0.1
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rect(xStart, (y - delta), xEnd, (y + delta),
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border = "black", col = myCol)
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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, name, y) {
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# DB: protein database
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# name: the name of the protein in the database.
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# y: height 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 1/3 (hex: 55)
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ftrCol <- paste0(ftrCol, "55")
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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$protein$name == name)
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# write the name of the protein
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text(-30, y, adj=1, labels=name, 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$annotation$proteinID == DB$protein$ID[iProtein])
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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$annotation$start[i],
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DB$annotation$end[i],
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y,
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ftrCol[ DB$annotation$featureID[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 Mbp1 proteins in myDB
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iRows <- grep("^MBP1_", myDB$protein$name)
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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,
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xlim = c(-200, xMax + 100),
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ylim = c(0, yMax),
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type = "n",
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axes = FALSE,
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bty = "n",
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main = "Mbp1 orthologue domain annotations",
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xlab = "sequence position",
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ylab="")
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axis(1, at = seq(0, xMax, by = 100))
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myCol <- colorRampPalette(c("#f2003c", "#F0A200",
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"#f0ea00", "#62C923",
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"#0A9A9B", "#1958C3",
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"#8000D3", "#D0007F"),
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space="Lab",
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interpolate="linear")(nrow(myDB$feature))
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myCol <- paste0(myCol, "55")
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legend(xMax - 150, 6,
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legend = myDB$feature$name,
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cex = 0.7,
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fill = myCol)
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# Finally, iterate over all proteins and call plotProtein()
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for (i in seq_along(iRows)) {
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plotProtein(myDB, myDB$protein$name[iRows[i]], i)
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}
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# The plot shows what is variable and what is constant about the annotations in
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# a group of related proteins. Your MBP1_MYSPE annotations should appear at the
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# top.
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# Task:
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# Put a copy of the plot into your journal and interpret it with respect
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# to MBP1_MYSPE, i.e. and note what you learn about MBP1_MYSPE from the plot.
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# [END]
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