diff --git a/BIN-FUNC-Domain_annotation.R b/BIN-FUNC-Domain_annotation.R index 6c1a4d9..c45e833 100644 --- a/BIN-FUNC-Domain_annotation.R +++ b/BIN-FUNC-Domain_annotation.R @@ -3,12 +3,13 @@ # Purpose: A Bioinformatics Course: # R code accompanying the BIN-FUNC-Domain_annotation unit. # -# Version: 1.3 +# 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/ ; @@ -18,7 +19,7 @@ # 0.1 First code copied from 2016 material. # # TODO: -# Complete SHARING DATA section ... +# Put the domain plot into a function # # == DO NOT SIMPLY source() THIS FILE! ======================================= # @@ -33,14 +34,15 @@ #TOC> #TOC> Section Title Line #TOC> --------------------------------------------------------------------- -#TOC> 1 Update your database script 48 -#TOC> 1.1 Preparing an annotation file ... 55 -#TOC> 1.1.1 BEFORE "BIN-ALI-Optimal_sequence_alignment" 58 -#TOC> 1.1.2 AFTER "BIN-ALI-Optimal_sequence_alignment" 106 -#TOC> 1.2 Execute and Validate 133 -#TOC> 2 Plot Annotations 158 -#TOC> 3 SHARING DATA 283 -#TOC> 3.1 Post MBP1_MYSPE as JSON data 298 +#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> ========================================================================== @@ -256,10 +258,11 @@ myCol <- colorRampPalette(c("#f2003c", "#F0A200", space="Lab", interpolate="linear")(nrow(myDB$feature)) myCol <- paste0(myCol, "55") -legend(xMax - 150, 6, +legend(xMax - 150, 7, legend = myDB$feature$name, cex = 0.7, - fill = myCol) + fill = myCol, + bty = "n") # Finally, iterate over all proteins and call plotProtein() for (i in seq_along(iRows)) { @@ -295,6 +298,7 @@ par(oPar) # reset the plot parameters # 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: @@ -303,24 +307,128 @@ par(oPar) # reset the plot parameters cat("{{Vspace}}", "", - "
",
+    "
",
     dbProt2JSON(sprintf("MBP1_%s", biCode(MYSPE))),
     "
",
     "",
     "", sep = "\n"
 )
 
-# 2: Copy the entire output,
+# 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.
 
-# Next, once we have collected a number of protein annotations, we can access
-# the page and import the data into our database.
-#
-# Code to come soon ...
 
 
+# ==   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 
 ... 
 tags. These
+# work like normal HTML 
 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 
 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]