bch441-work-abc-units/BIN-Data_integration.R

223 lines
8.4 KiB
R

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