221 lines
7.6 KiB
R
221 lines
7.6 KiB
R
# ABC_makeYFOlist.R
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#
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# Purpose: Create a list of genome sequenced fungi with protein annotations and
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# Mbp1 homologues.
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#
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# Version: 1.1
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#
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# Date: 2016 09 - 2017 08
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# V 1.1 Update 2017
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# V 1.0 First code 2016
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#
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# TODO:
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# actually rerun for 2017
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# type out workflow
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#
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# ==============================================================================
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# DO NOT source() THIS FILE!
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# This file is code I provide for your deeper understanding of a process and
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# to provide you with useful sample code. It is not actually necessary for
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# you to run this code, but I encourage you to read it carefully and discuss
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# if there are parts you don't understand.
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# Run the commands that interact with the NCBI servers only if you want to
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# experiment with the code and/or parameters. I have commented out those
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# parts. If you simply want to reproduce the process you can simply
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# load() the respective intermediate results.
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# ==============================================================================
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# CREATING A YFO LIST
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# ==============================================================================
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# This script will create a list of "YFO" species and save it in an R object
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# YFOspecies that is stored in the data subdirectory of this project from where
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# it can be loaded.
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# ==== GOLD species ============================================================
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#
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# Fetch and parse genome data from the NCBI genome project database
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# === Initialize
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if (!require(httr)) { # httr provides interfaces to Webservers on the Internet
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install.packages("httr")
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library(httr)
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}
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# The URL where the genome data can be downloaded
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URL <- "ftp://ftp.ncbi.nlm.nih.gov/genomes/GENOME_REPORTS/eukaryotes.txt"
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# Read the data directly from the NCBI ftp server and put it into a dataframe.
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# This will take about a minute.
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# GOLDdata <- read.csv(URL,
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# header = TRUE,
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# sep = "\t",
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# stringsAsFactors = FALSE)
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# save(GOLDdata, file="data/GOLDdata.RData")
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load(file="data/GOLDdata.RData")
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# What columns does the table have, how is it structured?
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str(GOLDdata)
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# What groups of organisms are in the table? How many of each?
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table(GOLDdata$Group)
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# What subgroups of fungi do we have?
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table(GOLDdata$SubGroup[GOLDdata$Group == "Fungi"])
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# How many of the fungi have protein annotations?
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sum(GOLDdata$Proteins[GOLDdata$Group == "Fungi"] != "-")
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# Get a subset of the data, with fungi that have protein annotations
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GOLDfungi <- GOLDdata[GOLDdata$Group == "Fungi" &
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GOLDdata$Proteins != "-" , ]
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# check what we have in the table
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head(GOLDfungi)
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# For our purpose of defining species, we pick only the first two words from the
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# "X.Organism.Name" column ... here is a function to do this:
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#
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makeBinomial <- function(s) {
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# input:
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# s: a string which is expected to contain a binomial
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# species name as the first two words, followed by other text
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# output:
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# the first two words separeted by a single blank
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#
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x <- unlist(strsplit(s, "\\s+")) # split second element on
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# one or more whitespace
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return(paste(x[1:2], collapse=" ")) # return first two elements
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}
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# iterate through GOLDdata and extract species names
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GOLDspecies <- character()
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for (i in 1:nrow(GOLDfungi)) {
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GOLDspecies[i] <- makeBinomial(GOLDfungi$X.Organism.Name[i])
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}
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# Species of great interest may may appear more than once, one for each sequenced strain: e.g. brewer's yeast:
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sum(GOLDspecies == "Saccharomyces cerevisiae")
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# Therefore we use the function unique() to throw out duplicates. Simple:
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GOLDspecies <- unique(GOLDspecies)
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length(GOLDspecies)
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# i.e. we got rid of about half of the species.
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# ==== BLAST species ===========================================================
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#
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# Use BLAST to fetch proteins related to Mbp1 and identifying the species that
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# contain them.
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#
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# Scripting agains NCBI APIs is not exactly enjoyable - there is usually a fair
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# amount of error handling involved that is not supported by the API in a
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# principled way but requires rather ad hoc solutions. The code I threw
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# together to make a BLAST interface for the course is in the file BLAST.R
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# Feel encouraged to study how this works.
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source("BLAST.R") # load the function and its utilites
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# Use BLAST() to find yeast Mbp1 homologues in other fungi in refseq
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# hits <- BLAST("NP_010227", # Yeast Mbp1 RefSeq ID
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# nHits = 1000, # 633 hits in 2016
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# E = 0.01, #
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# limits = "txid4751[ORGN]") # = fungi
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# save(hits, file="data/BLASThits.RData")
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load(file="data/BLASThits.RData")
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# This is a very big list that can't be usefully analyzed manually. Here
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# we are only interested in the species names that it contains.
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# How many hits in the list?
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length(hits$hits)
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# Let's look at the first one
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str(hits$hit[[1]])
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# the species information is in the $def element - the definition line of the
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# sequence record ... but we need a function to retrieve it. This one is a great
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# example, because it is really messy. Have a look:
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hits$hits[[1]]$def
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# We get so many species because the exact same hit has been found by BLAST in a
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# number of RefSeqs sequence records, all of which are different strains of
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# yeast. We only need one of those though, any one, so we shall parse out the
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# first one. For this, We will simply use the immensely versatile strsplit()
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# function, split on square brackets, take the second element of the resulting
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# array and run that through our makeBinomial function.
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# define the function (i.e. execute the lines below)
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parseDeflineSpecies <- function(s) {
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# input:
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# s: a string which is expected to contain a binomial
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# species name in square brackets embedded in other text
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# output:
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# the species name found the first bracketed string
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#
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x <- unlist(strsplit(s, "\\]|\\[")) # split on "]" or "[" characters
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return(makeBinomial(x[2])) # return Binomial name from
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# second element of vector
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}
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#test it
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parseDeflineSpecies(hits$hits[[1]]$def)
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parseDeflineSpecies(hits$hits[[11]]$def)
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parseDeflineSpecies(hits$hits[[111]]$def)
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# now run a simple loop to extract all the species names into a vector
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BLASTspecies <- character()
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for (i in 1:length(hits$hits)) {
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BLASTspecies[i] <- parseDeflineSpecies(hits$hits[[i]]$def)
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}
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# You can confirm in the Values section of the Environment pane that
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# BLASTspecies has the expected size. Again, species may appear more than once,
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# e.g.
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sum(BLASTspecies == "Saccharomyces cerevisiae")
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# Therefore we use the function unique() to throw out duplicates. Simple:
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BLASTspecies <- unique(BLASTspecies)
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length(BLASTspecies)
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# i.e. we got rid of about one third of the species.
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#
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# You should think about this: what does it mean that on average we have three
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# hits by sequence similarity to Mbp1 in other species?
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# ==== Intersecting BLAST and GOLD species lists ===============================
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# Now we can compare the two lists for species that appear in both sources: the
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# simplest way is to use the set operation functions union(), intersection()
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# etc. See here:
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?union
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YFOspecies <- intersect(GOLDspecies, BLASTspecies)
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# Just one final thing: some of the species will be our so-called "reference" species for which I will develop model solutions. I have defined them in the .utilities.R file to make them available for future purposes. separately and remove them from the list.
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#
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REFspecies
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YFOspecies <- sort(setdiff(YFOspecies, REFspecies))
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# save(YFOspecies, file = "data/YFOspecies.RData")
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# [END]
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