223 lines
8.4 KiB
R
223 lines
8.4 KiB
R
# tocID <- "BIN-Data_integration.R"
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#
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# ---------------------------------------------------------------------------- #
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# PATIENCE ... #
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# Do not yet work wih this code. Updates in progress. Thank you. #
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# boris.steipe@utoronto.ca #
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# ---------------------------------------------------------------------------- #
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-Data_integration unit.
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#
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# Version: 1.1
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#
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# Date: 2018 10 - 2019 01
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 1.1 Change from require() to requireNamespace(),
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# use <package>::<function>() idiom throughout
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# 1.0.1 Bugfix: UniProt ID Mapping service API change
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# 1.0 First live version
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#
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#
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# TODO:
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# Develop a fungi-specific BioMart example.
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# (cf.
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# https://cran.r-project.org/web/packages/biomartr/vignettes/Functional_Annotation.html )
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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 Identifier mapping 42
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#TOC> 2 Cross-referencing tables 165
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#TOC>
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#TOC> ==========================================================================
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# = 1 Identifier mapping ==================================================
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# UniProt provides a well-designed ID mapping tool that can be accessed
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# online at http://www.uniprot.org/mapping/
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#
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# Here we will use the UniProt Web API for this tool to map identifiers. The
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# UniProt ID mapping service supports a "RESTful API": responses can be obtained
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# simply via a Web- browsers request. Such requests are commonly sent via the
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# GET or POST verbs that a Webserver responds to, when a client asks for data.
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# GET requests are visible in the URL of the request; POST requests are not
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# directly visible, they are commonly used to send the contents of forms, or
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# when transmitting larger, complex data items. The UniProt ID mapping sevice
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# can accept long lists of IDs, thus using the POST mechanism makes sense. GET()
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# and POST() functions are part of the httr package.
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# To begin, we load httr, which supports sending and receiving data via the
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# http protocol, just like a Web browser.
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if (! requireNamespace("httpr", quietly=TRUE)) {
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install.packages("httpr")
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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 will walk through the process with the refSeqID
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# of yeast Mbp1 and Swi4, and we will also enter a dummy ID to check what
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# happens if the ID can't be mapped:
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myQueryIDs <- "NP_010227 NP_00000 NP_011036"
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# The UniProt ID mapping service API is very straightforward to use: just define
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# the URL of the server and send a list of items labelled as "query" in the body
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# of the request. GET() and POST() are functions from httr.
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URL <- "https://www.uniprot.org/mapping/"
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response <- httr::POST(URL,
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body = list(from = "P_REFSEQ_AC", # Refseq Protein
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to = "ACC", # UniProt ID
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format = "tab",
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query = myQueryIDs))
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cat(httr::content(response))
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# We need to check the status code - if it is not 200, an error ocurred and we
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# can't process the result:
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httr::status_code(response)
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# If the query is successful, tabbed text is returned. We can assign that to a
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# data frame. Note that we use textConnection() to read data directly from a char object, which can go in the spot where read.delim() expects a file-name argument.
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myMappedIDs <- read.delim(file = textConnection(httr::content(response)),
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sep = "\t",
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stringsAsFactors = FALSE)
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myMappedIDs
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# We actually only need columns 1 and 3, and we can also change the names
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# to "From" and "To":
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myMappedIDs <- myMappedIDs[ , c(1,3)]
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colnames(myMappedIDs) <- c("From", "To")
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myMappedIDs
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# If this works as expected, you should see:
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# From To
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# 1 NP_010227 P39678
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# 2 NP_011036 P25302
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#
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# ... and note that there are only two entries, because nothing was returned
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# for the dummy "RefSeq ID" NP_00000
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# If the query can't be fulfilled because of a problem with the server, a
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# WebPage is returned. But the server status is also returned and we can check
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# the status code. I have lately gotten many "503" status codes: Server Not
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# Available...
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# We wrap this into a function:
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myIDmap <- function (s, mapFrom = "P_REFSEQ_AC", mapTo = "ACC") {
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# Use UniProt ID mapping service to map one or more IDs
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# Parameters:
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# s char A string of separated IDs
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# mapFrom char the database in which the IDs in s are valid. Default
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# is RefSeq protein
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# mapTo char the database in which the target IDs are valid. Default
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# is UniProtKB
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# Value
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# a data frame of mapped IDs, with column names From and To, or an
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# empty data frame if the mapping was unsuccessful. No rows are returned
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# for IDs that are not mapped.
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URL <- "https://www.uniprot.org/uploadlists/"
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response <- httr::POST(URL,
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body = list(from = mapFrom,
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to = mapTo,
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format = "tab",
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query = s))
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if (httr::status_code(response) == 200) { # 200: oK
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myMap <- read.delim(file = textConnection(httr::content(response)),
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sep = "\t",
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stringsAsFactors = FALSE)
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myMap <- myMap[ , c(1,3)]
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colnames(myMap) <- c("From", "To")
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} else {
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myMap <- data.frame()
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warning(paste("No uniProt ID mapping returned:",
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"server sent status",
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httr::status_code(response)))
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}
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return(myMap)
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}
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# Try it out ...
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myIDmap("NP_010227 NP_011036 NP_012881 NP_013729 NP_012165")
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# A function UniProtIDmap() is in the ABC-dbUtilities.R script and it is loaded
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# into your workspace on startup.
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# = 2 Cross-referencing tables ============================================
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# Sometimes we get the IDs we need to map in a large table, e.g. from a list of
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# genes in a model organism database such as SGD, or from the Human Genen
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# Nomenclature commission. How do we map one set of identifiers to another one?
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# The function to use is match().
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# Here is a tiny set of identifiers taken from a much larger table to
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# illustrate the principle:
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#
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myIDs <- data.frame(uID = c("P38903", "P31383", "P47177", "P47096", "Q07747",
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"Q08641", "P47129", "P52910", "P00330", "P81450"),
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name = c("2A5D", "2AAA", "2NDP", "3HAO", "AAD4",
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"AB140", "ACF4", "ACS2", "ADH1", "ATP18"),
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refID = c("NP_014657", "NP_009386",
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"NP_012683", "NP_012559",
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"NP_010038", "NP_014882",
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"NP_012616", "NP_013254",
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"NP_014555", "NP_013629"),
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stringsAsFactors = FALSE)
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myIDs
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# Say we want to map "NP_010038", "NP_012559", and "NP_013629", in that order to
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# their gene names.
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myQuery <- c("NP_010038", "NP_999999", "NP_013629")
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# %in% will only tell us if these IDs are present in the table:
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myQuery %in% myIDs$refID
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# ... but not where they are located. But match() does what we need here:
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match(myQuery, myIDs$refID)
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# ... and we can use the result to subset the column that we want to map to:
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myIDs$name[match(myQuery, myIDs$refID)]
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# Note that the output preserves the NA - i.e. the length of the mapped
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# values is exactly the same as the length of the query.
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# task: map the three genes to their UniProt Identifier.
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#
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# Note: if you want to do very many queries in large tables, use the
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# fmatch() function in the "fastmatch" package for a considerable
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# speedup.
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# [END]
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