bch441-work-abc-units/BIN-FUNC-Domain_annotation.R
2020-10-13 22:37:31 +10:00

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R

# tocID <- "BIN-FUNC-Domain_annotation.R"
#
# Purpose: A Bioinformatics Course:
# R code accompanying the BIN-FUNC-Domain_annotation unit.
#
# Version: 1.4
#
# Date: 2017-11 - 2020-10
# Author: Boris Steipe (boris.steipe@utoronto.ca)
#
# Versions:
# 1.4 Add code for shared data import from the Wiki
# 1.3 Add code for database export to JSON and instructions
# for uploading annotations to the Public Student Wiki page
# 1.2 Consistently: data in ./myScripts/ ;
# begin SHARING DATA section
# 1.1 2020 Updates
# 1.0 Live version 2017
# 0.1 First code copied from 2016 material.
#
# TODO:
# Put the domain plot into a function
#
# == 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 50
#TOC> 1.1 Preparing an annotation file ... 57
#TOC> 1.1.1 BEFORE "BIN-ALI-Optimal_sequence_alignment" 60
#TOC> 1.1.2 AFTER "BIN-ALI-Optimal_sequence_alignment" 108
#TOC> 1.2 Execute and Validate 135
#TOC> 2 Plot Annotations 160
#TOC> 3 SHARING DATA 286
#TOC> 3.1 Post MBP1_MYSPE as JSON data 302
#TOC> 3.2 Import shared MBP1_MYSPE from the Wiki 325
#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 BEFORE "BIN-ALI-Optimal_sequence_alignment"
#
# IF YOU HAVE NOT YET COMPLETED THE BIN-ALI-OPTIMAL_SEQUENCE_ALIGNMENT UNIT:
#
# You DON'T already have a file called "<MYSPE>-Annotations.json" in the
# ./myScripts/ directory:
#
# - Make a copy of the file "./data/refAnnotations.json" and put it in your
# myScripts/ 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 in the ./myScripts/ folder.
#
# - Validate your file online at https://jsonlint.com/
#
# - Update your "./myScripts/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,
# jsonlite::fromJSON("./myScripts/<MYSPE>-Annotations.json"))
# ^^^^^^^
# edit this!
#
# - save and close the file.
#
# Then SKIP the next section.
#
#
# === 1.1.2 AFTER "BIN-ALI-Optimal_sequence_alignment"
#
# IF YOU HAVE ALREADY COMPLETED THE BIN-ALI-OPTIMAL_SEQUENCE_ALIGNMENT UNIT:
#
# You SHOULD have a file called "<MYSPE>-Annotations.json" in the
# ./myScripts/ 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$feature table. Check which features are in the database by executing
#
# myDB$feature$name
#
# - Update the "start" and "end" coordinates for each feature to the
# values you found.
#
# - Save your file.
#
# - Validate your file online at https://jsonlint.com/
#
#
# == 1.2 Execute and Validate ==============================================
#
# - source() your database creation script:
#
# source("./myScripts/makeProteinDB.R")
#
# This should run without errors or warnings. If it doesn't work and you
# can't figure out quickly what's happening, ask for help on the
# Discussion Board.
#
# - Confirm
# The following commands should retrieve all of the features that have been
# annotated for MBP1_MYSPE
sel <- myDB$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$name[ftrIDs] # This should list ALL of your annotated features
# (once). If not, consider what could have gone wrong
# and ask on the list if you have difficulties fixing
# it.
# = 2 Plot Annotations ====================================================
# In this section we will plot domain annotations as colored rectangles on a
# sequence, as an example of using the R plotting system for generic, data
# driven images.
# We need a small utility function that draws the annotation boxes on a
# representation of sequence. It should accept the start and end coordinates,
# the y value where it should be plotted and the color of the box, and plot a
# rectangle using R's rect() function.
drawBox <- function(xStart, xEnd, y, myCol, DELTA = 0.2) {
# Draw a box from xStart to xEnd at y, filled with colour myCol
# The height of the box is y +- DELTA
rect(xStart, (y - DELTA), xEnd, (y + DELTA),
border = "black", col = myCol)
}
# test this:
plot(c(-1.5, 1.5), c(0, 0), type = "l")
drawBox(-1, 1, 0.0, "peachpuff")
# Next, we define a function to plot annotations for one protein: the name of
# the protein, a horizontal grey line for its length, and all of its features.
plotProtein <- function(DB, name, y) {
# DB: protein database
# name: the name of the protein in the database.
# y: height where to draw the plot
#
# Define colors: we create a vector of color values, one for
# each feature, and we give it names of the feature ID. Then we
# can easily get the color value from the feature name.
# A: make a vector of color values. The syntax may appear unusual -
# colorRampPalette() returns a function, and we simply append
# the parameter (number-of-features) without assigning the function
# to its own variable name.
ftrCol <- colorRampPalette(c("#f2003c", "#F0A200", "#f0ea00",
"#62C923", "#0A9A9B", "#1958C3",
"#8000D3", "#D0007F"),
space="Lab",
interpolate="linear")(nrow(DB$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
oPar <- par(mar = c(4.2, 0.1, 3, 0.1)) # save the current plot parameters and
# decrease margins
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",
cex.axis = 0.8,
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$feature))
myCol <- paste0(myCol, "55")
legend(xMax - 150, 7,
legend = myDB$feature$name,
cex = 0.7,
fill = myCol,
bty = "n")
# Finally, iterate over all proteins and call plotProtein()
for (i in seq_along(iRows)) {
plotProtein(myDB, myDB$protein$name[iRows[i]], i)
}
par(oPar) # reset the plot parameters
# 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.
# Task:
# It would be better to align the motif borders, at least approximately (not
# all proteins have all motifs). How would you go about doing that?
# = 3 SHARING DATA ========================================================
# It's particularly interesting to compare such annotations across many
# homologous proteins. I have created a page on the Student Wiki () that you can
# edit, and then download the data from the entire class directly to your
# RStudio project.
#
# I have provided a function that extracts all information that refers to a
# single protein from the database, and prints it out as well-formatted JSON,
# suitable to be pasted into our shareable Wiki-page. There is a fair amount of
# bookkeeping involved, but the code is not otherwise very enlightening so I
# will spare you the details - it's in "./scripts/ABC-dbUtilities.R" if you
# would want to have a look.
# == 3.1 Post MBP1_MYSPE as JSON data ======================================
# Task:
# =====
# 1: Run the following code:
cat("{{Vspace}}",
"<!-- ==== BEGIN PROTEIN ==== -->",
"<pre class=\"protein-data\">",
dbProt2JSON(sprintf("MBP1_%s", biCode(MYSPE))),
"</pre>",
"<!-- ===== END PROTEIN ====== -->",
"", sep = "\n"
)
# 2: Copy the entire output from the console.
# 3: Navigate to
# http://steipe.biochemistry.utoronto.ca/abc/students/index.php/Public
# ... edit the page, and paste your output at the top.
# 4: Save your edits.
# == 3.2 Import shared MBP1_MYSPE from the Wiki ============================
# Once we have collected a number of protein annotations, we can access the
# Wiki-page and import the data into our database. The Wiki page is an html
# document with lots of MediaWiki specific stuff - but the contents we are
# interested in is enclosed in <pre class="protein-data"> ... </pre> tags. These
# work like normal HTML <pre> tags, but we have defined a special class for them
# to make it easy to parse out the contents we want. The rvest:: package in
# combination with xml2:: provides us with all the tools we need for such
# "Webscraping" of data....
if (! requireNamespace("rvest", quietly=TRUE)) {
install.packages("rvest")
}
if (! requireNamespace("xml2", quietly=TRUE)) {
install.packages("xml2")
}
# Here's the process:
# The URL is an "open" page on the student Wiki. Users that are not logged in
# can view the contents, but you can only edit if you are logged in.
myURL <- "http://steipe.biochemistry.utoronto.ca/abc/students/index.php/Public"
# First thing is to retrieve the HTML from the url...
x <- xml2::read_html(myURL)
# This retrieves the page source, but that still needs to be parsed into its
# logical elements. HTML is a subset of XML and such documents are structured as
# trees, that have "nodes" which are demarcated with "tags". rvest::html_nodes()
# parses out the document structure and then uses a so-called "xpath" expression
# to select nodes we are interested in. Now, xpath is one of those specialized
# languages of which there are a few more to learn than one would care for. You
# MUST know how to format sprintf() expressions, and you SHOULD be competent
# with regular expressions. But if you want to be really competent in your work,
# basic HTML and CSS is required ... and enough knowledge about xpath to be able
# to search on Stackoverflow for what you need for parsing data out of Web
# documents...
# The expression we use below is:
# - get any node anywhere in the tree ("//*") ...
# - that has a particular attribute("[@ ... ]").
# - The attribute we want is that the class of the node is "protein-data";
# that is the class we have defined for our <pre> tags.
# As a result of this selection, we get a list of pointers to the document tree.
y <- rvest::html_nodes(x, xpath ='//*[@class="protein-data"]')
# Next we fetch the actual payload - the text - from the tree:
# rvest::html_text() gets the text from the list of pointers. The result is a
# normal list of character strings.
z <- rvest::html_text(y)
# Finally we can iterate over the list, and add all proteins we don't already
# have to our database. There may well be items that are rejected because they
# are already present in the database - for example, unless somebody has
# annotated new features, all of the features are already there. Don't worry -
# that is intended; we don't want duplicate entries.
for (thisJSON in z) {
thisData <- jsonlite::fromJSON(thisJSON)
if (! thisData$protein$name %in% myDB$protein$name) {
myDB <- dbAddProtein(myDB, thisData$protein)
myDB <- dbAddTaxonomy(myDB, thisData$taxonomy)
myDB <- dbAddFeature(myDB, thisData$feature)
myDB <- dbAddAnnotation(myDB, thisData$annotation)
}
}
# Finally, we can repeat our domain plot with the results - which now includes the shared proteins:
iRows <- grep("^MBP1_", myDB$protein$name)
yMax <- length(iRows) * 1.1
xMax <- max(nchar(myDB$protein$sequence[iRows])) * 1.1 # longest sequence
# plot an empty frame
oPar <- par(mar = c(4.2, 0.1, 3, 0.1))
plot(1, 1,
xlim = c(-200, xMax + 100),
ylim = c(0, yMax),
type = "n",
axes = FALSE,
bty = "n",
main = "Mbp1 orthologue domain annotations",
xlab = "sequence position",
cex.axis = 0.8,
ylab="")
axis(1, at = seq(0, xMax, by = 100))
myCol <- colorRampPalette(c("#f2003c", "#F0A200",
"#f0ea00", "#62C923",
"#0A9A9B", "#1958C3",
"#8000D3", "#D0007F"),
space="Lab",
interpolate="linear")(nrow(myDB$feature))
myCol <- paste0(myCol, "55")
legend(xMax - 150, 7,
legend = myDB$feature$name,
cex = 0.7,
fill = myCol,
bty = "n")
for (i in seq_along(iRows)) {
plotProtein(myDB, myDB$protein$name[iRows[i]], i)
}
par(oPar) # reset the plot parameters
# ... the more proteins we can compare, the more we learn about the
# architectural principles of this family's domains.
# [END]