bch441-work-abc-units/BIN-FUNC-Domain_annotation.R

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R

# tocID <- "BIN-FUNC-Domain_annotation.R"
#
# ---------------------------------------------------------------------------- #
# PATIENCE ... #
# Do not yet work wih this code. Updates in progress. Thank you. #
# boris.steipe@utoronto.ca #
# ---------------------------------------------------------------------------- #
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-FUNC-Domain_annotation unit.
#
# Version: 1.0
#
# Date: 2017 11 13
# Author: Boris Steipe (boris.steipe@utoronto.ca)
#
# Versions:
# 1.0 Live version 2017
# 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 Update your database script 41
#TOC> 1.1 Preparing an annotation file ... 47
#TOC> 1.1.1 If you HAVE NOT done the BIN-ALI-Optimal_sequence_alignment unit 49
#TOC> 1.1.2 If you HAVE done the BIN-ALI-Optimal_sequence_alignment 93
#TOC> 1.2 Execute and Validate 119
#TOC> 2 Plot Annotations 144
#TOC>
#TOC> ==========================================================================
# = 1 Update your database script =========================================
# Since you have recorded domain features at the SMART database, we can store
# the feature annotations in myDB.
# == 1.1 Preparing an annotation file ... ==================================
#
# === 1.1.1 If you HAVE NOT done the BIN-ALI-Optimal_sequence_alignment unit
#
#
# You DON'T already have a file called "<MYSPE>-Annotations.json" in the
# ./data/ directory:
#
# - Make a copy of the file "./data/refAnnotations.json" and put it in your
# project directory.
#
# - Give it a name that is structured like "<MYSPE>-Annotations.json" - e.g.
# if MYSPE is called "Crptycoccus neoformans", your file should be called
# "CRYNE-Annotations.json" (and the "name" of your Mbp1 orthologue is
# "MBP1_CRYNE").
#
# - Open the file in the RStudio editor and delete all blocks for
# the Mbp1 protein annotations except the first one.
#
# - From that block, delete all lines that have annotations you did not
# find in SMART for MBP1_MYSPE.
#
# - Make enough copies of the "Ankyrin fold" and "low complexity" region
# lines to have a line for each feature you found.
#
# - Then delete the comma at the end of the last line.
#
# - Edit the annotations: change MBP1_SACCE to MBP1_<MYSPE> everywhere
# and change the "start" and "end" features to the coordinates you
# recorded in the SMART database.
#
# - Save your file.
#
# - Validate your file online at https://jsonlint.com/
#
# - Update your "makeProteinDB.R" script to load your new
# annotation when you recreate the database. Open the script in the
# RStudio editor, and add the following command at the end:
#
# myDB <- dbAddAnnotation(myDB, fromJSON("<MYSPE>-Annotations.json"))
#
# - save and close the file.
#
# Then SKIP the next section.
#
#
# === 1.1.2 If you HAVE done the BIN-ALI-Optimal_sequence_alignment
#
#
# You DO already have a file called "<MYSPE>-Annotations.json" in the
# ./data/ directory:
#
# - Open the file in the RStudio editor.
#
# - Make as many copies of the "APSES fold" line as you have found
# features in SMART.
#
# - Add a comma after every line except for the last one
#
# - Edit the annotations but include only features that are in the
# myDB$feature table. Check which features are in the databse by executing
#
# myDB$feature$name
#
# - Update the "start" and "end" coordinates for each feature to the
# values you found.
#
# - Save your file.
#
# - Validate your file online at https://jsonlint.com/
#
#
# == 1.2 Execute and Validate ==============================================
#
# - source() your database creation script:
#
# source("makeProteinDB.R")
#
# This should run without errors or warnings. If it doesn't work and you
# can't figure out quickly what's happening, ask on the mailing list for
# help.
#
# - Confirm
# The following commands should retrieve all of the features that have been
# annotated for MBP1_MYSPE
sel <- myDB$protein$name == paste("MBP1_", biCode(MYSPE), sep = "")
(proID <- myDB$protein$ID[sel])
(fanIDs <- myDB$annotation$ID[myDB$annotation$proteinID == proID])
(ftrIDs <- unique(myDB$annotation$featureID[fanIDs]))
myDB$feature$name[ftrIDs] # This should list ALL of your annotated features
# (once). If not, consider what could have gone wrong
# and ask on the list if you have difficulties fixing
# it.
# = 2 Plot Annotations ====================================================
# In this section we will plot domain annotations as colored rectangles on a
# sequence, as an example for using the R plotting system for generic, data
# driven images.
# We need a small utility function that draws the annotation boxes on a
# representation of sequence. It should accept the start and end coordinates,
# the y value where it should be plotted and the color of the box, and plot a
# rectangle using R's rect() function.
drawBox <- function(xStart, xEnd, y, myCol) {
# Draw a box from xStart to xEnd at y, filled with colour myCol
delta <- 0.1
rect(xStart, (y - delta), xEnd, (y + delta),
border = "black", col = myCol)
}
# test this:
plot(c(-1.5, 1.5), c(0, 0), type = "l")
drawBox(-1, 1, 0.0, "peachpuff")
# Next, we define a function to plot annotations for one protein: the name of
# the protein, a horizontal grey line for its length, and all of its features.
plotProtein <- function(DB, name, y) {
# DB: protein database
# name: the name of the protein in the database.
# y: height where to draw the plot
#
# Define colors: we create a vector of color values, one for
# each feature, and we give it names of the feature ID. Then we
# can easily get the color value from the feature name.
# A: make a vector of color values. The syntax may appear unusual -
# colorRampPalette() returns a function, and we simply append
# the parameter (number-of-features) without assigning the function
# to its own variable name.
ftrCol <- colorRampPalette(c("#f2003c", "#F0A200", "#f0ea00",
"#62C923", "#0A9A9B", "#1958C3",
"#8000D3", "#D0007F"),
space="Lab",
interpolate="linear")(nrow(DB$feature))
# B: Features may overlap, so we make the colors transparent by setting
# their "alpha channel" to 1/3 (hex: 55)
ftrCol <- paste0(ftrCol, "55")
# C: we asssign names
names(ftrCol) <- DB$feature$ID
# E.g. color for the third feature: ftrCol[ DB$feature$ID[3] ]
# find the row-index of the protein ID in the protein table of DB
iProtein <- which(DB$protein$name == name)
# write the name of the protein
text(-30, y, adj=1, labels=name, cex=0.75 )
#draw a line from 0 to nchar(sequence-of-the-protein)
lines(c(0, nchar(DB$protein$sequence[iProtein])), c(y, y),
lwd=3, col="#999999")
# get the rows of feature annotations for the protein
iFtr <- which(DB$annotation$proteinID == DB$protein$ID[iProtein])
# draw a colored box for each feature
for (i in iFtr) {
drawBox(DB$annotation$start[i],
DB$annotation$end[i],
y,
ftrCol[ DB$annotation$featureID[i] ])
}
}
# Plot each annotated protein:
# Get the rows of all unique annotated Mbp1 proteins in myDB
iRows <- grep("^MBP1_", myDB$protein$name)
# define the size of the plot-frame to accomodate all proteins
yMax <- length(iRows) * 1.1
xMax <- max(nchar(myDB$protein$sequence[iRows])) * 1.1 # longest sequence
# plot an empty frame
plot(1, 1,
xlim = c(-200, xMax + 100),
ylim = c(0, yMax),
type = "n",
axes = FALSE,
bty = "n",
main = "Mbp1 orthologue domain annotations",
xlab = "sequence position",
ylab="")
axis(1, at = seq(0, xMax, by = 100))
myCol <- colorRampPalette(c("#f2003c", "#F0A200",
"#f0ea00", "#62C923",
"#0A9A9B", "#1958C3",
"#8000D3", "#D0007F"),
space="Lab",
interpolate="linear")(nrow(myDB$feature))
myCol <- paste0(myCol, "55")
legend(xMax - 150, 6,
legend = myDB$feature$name,
cex = 0.7,
fill = myCol)
# Finally, iterate over all proteins and call plotProtein()
for (i in seq_along(iRows)) {
plotProtein(myDB, myDB$protein$name[iRows[i]], i)
}
# The plot shows what is variable and what is constant about the annotations in
# a group of related proteins. Your MBP1_MYSPE annotations should appear at the
# top.
# Task:
# Put a copy of the plot into your journal and interpret it with respect
# to MBP1_MYSPE, i.e. and note what you learn about MBP1_MYSPE from the plot.
# [END]