169 lines
6.3 KiB
R
169 lines
6.3 KiB
R
# tocID <- "RPR-PROSITE_POST.R"
|
|
#
|
|
# Purpose: A Bioinformatics Course:
|
|
# R code accompanying the RPR-Scripting_data_downloads unit.
|
|
#
|
|
# Version: 1.2
|
|
#
|
|
# Date: 2017-10 - 2020-09
|
|
# Author: Boris Steipe (boris.steipe@utoronto.ca)
|
|
#
|
|
# Versions:
|
|
# 1.2 2020 Maintenance
|
|
# 1.1 Change from require() to requireNamespace(),
|
|
# use <package>::<function>() idiom throughout,
|
|
# 1.0.1 Updates for slightly changed interfaces
|
|
# 1.0 First ABC units version
|
|
# 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 Constructing a POST command from a Web query 43
|
|
#TOC> 1.1 Task - fetchPrositeFeatures() function 148
|
|
#TOC> 2 Task solutions 156
|
|
#TOC>
|
|
#TOC> ==========================================================================
|
|
|
|
|
|
# = 1 Constructing a POST command from a Web query ========================
|
|
|
|
|
|
if (! requireNamespace("httr", quietly = TRUE)) {
|
|
install.packages("httr")
|
|
}
|
|
# Package information:
|
|
# library(help = httr) # basic information
|
|
# browseVignettes("httr") # available vignettes
|
|
# data(package = "httr") # available datasets
|
|
|
|
|
|
|
|
|
|
# We have reverse engineered the Web form for a ScanProsite request, and can
|
|
# construct a valid POST request from knowing the required field names. The POST
|
|
# command is similar to GET(), but we need an explicit request body that
|
|
# contains a list of key/value pairs
|
|
|
|
UniProtID <- "P39678"
|
|
|
|
URL <- "https://prosite.expasy.org/cgi-bin/prosite/PSScan.cgi"
|
|
|
|
response <- httr::POST(URL,
|
|
body = list(meta = "opt1",
|
|
meta1_protein = "opt1",
|
|
seq = UniProtID,
|
|
skip = "on",
|
|
output = "tabular"))
|
|
|
|
# Send off this request, and you should have a response in a few
|
|
# seconds. Let's check the status first:
|
|
|
|
httr::status_code(response) # If this is not 200, something went wrong and it
|
|
# makes no sense to continue. If this persists, ask
|
|
# on the Discussion Board what to do.
|
|
|
|
|
|
# The text contents of the response is available with the
|
|
# content() function:
|
|
httr::content(response, "text")
|
|
|
|
# ... should show you the same as the page contents that you have seen in the
|
|
# browser. Now we need to extract the data from the page. For this simple
|
|
# example we can get away with using regular expressions, but in general we need
|
|
# a real XML parser to parse HTML. We'll cover that in a later unit. Here, we
|
|
# strsplit() the response into individual lines, since each of our data elements
|
|
# is on its own line, and then capture the contents. The way Prosite has
|
|
# formatted their HTML we can simply split on the "\\n" newline character - but
|
|
# they could write the same valid HTML without any newline-characters at all.
|
|
# Understand that we are working with a bit of a "hack" here: exploting
|
|
# empirical assumptions rather than a formal specification. But sometimes quick
|
|
# and dirty is fine, because quick.
|
|
|
|
lines <- unlist(strsplit(httr::content(response, "text"), "\\n"))
|
|
head(lines)
|
|
|
|
# Now we define a query pattern for the lines we want:
|
|
# we can use the uID, bracketed by two "|" pipe
|
|
# characters:
|
|
|
|
patt <- sprintf("\\|%s\\|", UniProtID)
|
|
|
|
# ... and select only the lines that match this
|
|
# pattern:
|
|
|
|
( lines <- lines[grep(patt, lines)] )
|
|
|
|
# ... captures the three lines of output.
|
|
|
|
# Now we break the lines apart into tokens: this is another application of
|
|
# strsplit(), but this time we split either on "pipe" characters, "|" OR on tabs
|
|
# "\t". Look at the regex "\\t|\\|" in the strsplit() call:
|
|
|
|
unlist(strsplit(lines[1], "\\t|\\|"))
|
|
|
|
# Its parts are (\\t)=tab (|)=or (\\|)=pipe. Both "t" and "|" need to be escaped
|
|
# with a backslash. "t" has to be escaped because we want to match a tab (\t),
|
|
# not the literal character "t". And "|" has to be escaped because we mean the
|
|
# literal pipe character, not its metacharacter meaning OR. Thus sometimes the
|
|
# backslash turns a special meaning off, and sometimes it turns a special
|
|
# meaning on. Unfortunately there's no easy way to tell - you just need to
|
|
# remember the characters - or have a reference handy. The metacharacters are
|
|
# (){}[]^$?*+.|&- ... and some of them have different meanings depending on
|
|
# where in the regex they are.
|
|
|
|
# Let's put the tokens into named slots of a data frame
|
|
|
|
features <- data.frame()
|
|
for (line in lines) {
|
|
tokens <- unlist(strsplit(line, "\\t|\\|"))
|
|
features <- rbind(features,
|
|
data.frame(uID = tokens[2],
|
|
start = as.numeric(tokens[4]),
|
|
end = as.numeric(tokens[5]),
|
|
psID = tokens[6],
|
|
psName = tokens[7],
|
|
psSeq = tokens[11]))
|
|
}
|
|
features
|
|
|
|
# This forms the base of a function that collects the features automatically
|
|
# from a PrositeScan result. You can write this!
|
|
|
|
|
|
# == 1.1 Task - fetchPrositeFeatures() function ============================
|
|
|
|
|
|
# Task: write a function that takes as input a UniProt ID, fetches the
|
|
# features it contains from ScanProsite and returns a data frame as given above, or
|
|
# an empty data frame if there is an error.
|
|
|
|
|
|
# = 2 Task solutions ======================================================
|
|
|
|
|
|
# I have placed such a function into the ABC-dbUtilities.R script: look it up by
|
|
# clicking on dbFetchPrositeFeatures() in the Environment pane.
|
|
|
|
# Test:
|
|
dbFetchPrositeFeatures("Q5KMQ9")
|
|
|
|
|
|
|
|
|
|
# [END]
|