162 lines
5.4 KiB
R
162 lines
5.4 KiB
R
# RPR-PROSITE_POST.R
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the RPR-Scripting_data_downloads unit.
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#
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# Version: 1.0.1
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#
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# Date: 2017 10 - 2018 12
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 1.0.1 Updates for slightly changed interfaces
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# 1.0 First ABC units version
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# 0.1 First code copied from 2016 material.
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#
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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 Constructing a POST command from a Web query 44
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#TOC> 1.1 Task - fetchPrositeFeatures() function 145
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#TOC> 2 Task solutions 153
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#TOC>
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#TOC> ==========================================================================
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# = 1 Constructing a POST command from a Web query ========================
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if (! require(httr, quietly=TRUE)) {
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install.packages("httr")
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library(httr)
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}
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# Package information:
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# library(help = httr) # basic information
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# browseVignettes("httr") # available vignettes
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# data(package = "httr") # available datasets
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# We have reverse engineered the Web form for a ScanProsite request, and can now
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# construct a POST request. The command is similar to GET(), but we need an
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# explicit request body: a list of key/value pairs
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UniProtID <- "P39678"
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URL <- "https://prosite.expasy.org/cgi-bin/prosite/PSScan.cgi"
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response <- POST(URL,
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body = list(meta = "opt1",
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meta1_protein = "opt1",
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seq = UniProtID,
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skip = "on",
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output = "tabular"))
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# Send off this request, and you should have a response in a few
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# seconds. Let's check the status first:
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status_code(response) # If this is not 200, something went wrong and it
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# makes no sense to continue. If this persists, ask
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# on the mailing list what to do.
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# The text contents of the response is available with the
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# content() function:
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content(response, "text")
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# ... should show you the same as the page contents that
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# you have seen in the browser. The date we need Now we need to extract
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# the data from the page: we need regular expressions, but
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# only simple ones. First, we strsplit() the response into
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# individual lines, since each of our data elements is on
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# its own line. We simply split on the "\\n" newline character.
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lines <- unlist(strsplit(content(response, "text"), "\\n"))
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head(lines)
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# Now we define a query pattern for the lines we want:
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# we can use the uID, bracketed by two "|" pipe
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# characters:
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patt <- sprintf("\\|%s\\|", UniProtID)
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# ... and select only the lines that match this
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# pattern:
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lines <- lines[grep(patt, lines)]
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lines
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# ... captures the four lines of output.
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# Now we break the lines apart into tokens: this is another application of
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# strsplit(), but this time we split either on "pipe" characters, "|" OR on tabs
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# "\t". Look at the regex "\\t|\\|" in the strsplit() call:
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unlist(strsplit(lines[1], "\\t|\\|"))
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# Its parts are (\\t)=tab (|)=or (\\|)=pipe. Both "t" and "|" need to be escaped
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# with a backslash. "t" has to be escaped because we want to match a tab (\t),
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# not the literal character "t". And "|" has to be escaped because we mean the
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# literal pipe character, not its metacharacter meaning OR. Thus sometimes the
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# backslash turns a special meaning off, and sometimes it turns a special
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# meaning on. Unfortunately there's no easy way to tell - you just need to
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# remember the characters - or have a reference handy. The metacharacters are
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# (){}[]^$?*+.|&- ... and some of them have different meanings depending on
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# where in the regex they are.
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# Let's put the tokens into named slots of a data frame
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features <- data.frame()
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for (line in lines) {
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tokens <- unlist(strsplit(line, "\\t|\\|"))
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features <- rbind(features,
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data.frame(uID = tokens[2],
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start = as.numeric(tokens[4]),
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end = as.numeric(tokens[5]),
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psID = tokens[6],
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psName = tokens[7],
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stringsAsFactors = FALSE))
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}
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features
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# This forms the base of a function that collects the features automatically
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# from a PrositeScan result. You can write this!
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# == 1.1 Task - fetchPrositeFeatures() function ============================
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# Task: write a function that takes as input a UniProt ID, fetches the
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# features it contains from ScanProsite and returns a list as given above, or
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# a list of length 0 if there is an error.
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# = 2 Task solutions ======================================================
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# I have placed such a function into the ABC-dbUtilities.R script: look it up by
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# clicking on dbFetchPrositeFeatures() in the Environment pane.
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# Test:
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dbFetchPrositeFeatures("P39678")
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# [END]
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