Updated MYSPE data and entire workflow. Changed all .RData to .rds
This commit is contained in:
hyginn
@@ -1,14 +1,16 @@
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# ABC_makeMYSPElist.R
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# tocID <- "scripts/ABC-makeMYSPElist.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: 1.2
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# Version: 1.3
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#
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# Date: 2016 09 - 2019 01
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# Date: 2016 09 - 2020 09
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions
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# 1.3 Rewrite to change datasource. NCBI has not been updated
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# since 2012. Use ensembl fungi as initial source.
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# 1.2 Change from require() to requireNamespace()
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# 1.1.2 Moved BLAST.R to ./scripts directory
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# 1.1 Update 2017
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@@ -36,18 +38,17 @@
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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 The strategy 55
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#TOC> 2 GOLD species 67
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#TOC> 2.1 Initialize 72
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#TOC> 2.2 Import 79
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#TOC> 2.3 Unique species 131
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#TOC> 3 BLAST species 173
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#TOC> 3.1 find homologous proteins 180
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#TOC> 3.2 Identify species in "hits" 204
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#TOC> 4 Intersect GOLD and BLAST species 249
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#TOC> 5 Cleanup and finish 267
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#TOC> Section Title Line
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#TOC> --------------------------------------------------------
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#TOC> 1 The strategy 56
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#TOC> 2 PACKAGES AND INITIALIZATIONS 68
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#TOC> 3 ENSEMBL FUNGI 76
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#TOC> 3.1 Import 79
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#TOC> 4 BLAST SEARCH 156
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#TOC> 4.1 find homologous proteins 162
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#TOC> 4.2 Identify species in "hits" 193
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#TOC> 5 MERGE ENSEMBL AND BLAST RESULTS 283
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#TOC> 6 STUDENT NUMBERS 366
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#TOC>
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#TOC> ==========================================================================
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@@ -55,129 +56,110 @@
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# = 1 The strategy ========================================================
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# This script will create a list of "MYSPE" species and save it in an R object
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# MYSPEspecies that is stored in the data subdirectory of this project from where
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# it can be loaded. The strategy is as follows: we download a list of all
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# genome projects and then select species for which protein annotations are
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# available - i.e. these are all genome-sequenced species that have been
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# annotated. Then we search for fungal species that have homologues to MBP1.
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# Then we intersect the two lists to give us genome-sequenced species that
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# also have Mbp1 homologues ...
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# MYSPEspecies that is stored in the data subdirectory of this project from
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# where it can be loaded. The strategy is as follows: we download a list of
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# annotated fungal genomes from ensembl.fungi. All these are genome-sequenced
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# species that have been annotated.
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# Next we perform a BLAST search, to identify fungal species that have
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# genes that are homologous to yeast MBP1.
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#
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# ...
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# = 2 GOLD species ========================================================
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# Fetch and parse the Genomes OnLine Database of the Joint Genome Institute
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# (https://gold.jgi.doe.gov/). Use the data that is hosted at the NCBI.
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# == 2.1 Initialize ========================================================
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# = 2 PACKAGES AND INITIALIZATIONS ========================================
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# httr provides interfaces to Webservers on the Internet
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if (! requireNamespace("httr", quietly = TRUE)) {
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install.packages("httr")
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}
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# == 2.2 Import ============================================================
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# The URL of the genome data directory at the NCBI:
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# is https://ftp.ncbi.nlm.nih.gov/genomes/GENOME_REPORTS
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# Note the relative size of the prokaryotes and the eukaryotes data.
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# What's in this directory?
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URL <- "ftp://ftp.ncbi.nlm.nih.gov/genomes/GENOME_REPORTS/README"
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GOLDreadme <- readLines(URL) # read the file into a vector
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cat(GOLDreadme, sep = "\n") # display the contents
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# Retrieve the file "eukaryotes" via ftp from the NCBI ftp server and put it
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# into a dataframe. This will take a few moments.
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# URL <- "ftp://ftp.ncbi.nlm.nih.gov/genomes/GENOME_REPORTS/eukaryotes.txt"
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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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# or ...
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load(file="data/GOLDdata.RData")
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# = 3 ENSEMBL FUNGI =======================================================
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# What columns does the table have, how is it structured?
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str(GOLDdata)
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# == 3.1 Import ============================================================
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# What groups of organisms are in the table? How many of each?
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table(GOLDdata$Group)
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# Navigate to https://fungi.ensembl.org and click on the link to the full
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# list of all species: https://fungi.ensembl.org/species.html
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# On the page, click on the spreadsheet symbol top right and choose
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# "download whole table". The file will be named "Species.csv", in your
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# usual downloads folder. Move it to the data folder, and read it.
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# What subgroups of fungi do we have?
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table(GOLDdata$SubGroup[GOLDdata$Group == "Fungi"])
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sDat <- read.csv("./data/Species.csv")
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str(sDat)
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# How many of the fungi have protein annotations? The README file told us that
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# the column "Proteins" contains "Number of Proteins annotated in the assembly".
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# Looking at a few ...
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head(GOLDdata$Proteins, 30)
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# ... we see that the number varies, and some have a hyphen, i.e. no
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# annotations. The hyphens make this a char type column (as per: all elements
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# of a vector must have the same type). Therefore we can't read this as numbers
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# and filter by some value > 0. But we can filter for all genomes that don't
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# have the hyphen:
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sum(GOLDdata$Proteins[GOLDdata$Group == "Fungi"] != "-")
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# The most obvious way to partition these is according to Classification ...
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# (poking around a bit in the UniProt taxonomy database shows that the
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# classification used here is the taxonomic rank of "order").
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# how many classifications do we have?
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length(unique(sDat$Classification)) # 66
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# Subset the data, with fungi that have protein annotations
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GOLDfungi <- GOLDdata[GOLDdata$Group == "Fungi" &
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GOLDdata$Proteins != "-" , ]
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# To have a good set for the class, we should have about 100.
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# Let's see for which of these we can find Mbp1 homologues.
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# First, we'll keep only the colums for name, classification, and taxID, and
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# drop the rest ...
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sDat <- sDat[ , c("Name", "Classification", "Taxon.ID")]
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colnames(sDat) <- c("name", "order", "taxID")
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# check what we have in the table
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nrow(GOLDfungi)
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head(GOLDfungi)
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# Next, we make an extra column: genus - the first part of the binomial name.
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# We'll use the gsub() function, and for that we need a "regular expression"
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# that matches to all characters from the first blank to the end of the string:
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myPatt <- "\\s.*$" # one whitespace (\\s) ...
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# followed by any character (.) 0..n times (*) ...
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# until the end of the string
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# using gsub() we substitue all matching characters with the empty string "" -
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# this deletes the matching characters
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# Test this:
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gsub(myPatt, "", "Genus") # one word: unchanged
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gsub(myPatt, "", "gEnus species") # two words: return only first
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gsub(myPatt, "", "geNus species strain 123") # many words: return only first
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# == 2.3 Unique species ====================================================
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# apply this to the "name" column and add the result as a separate column
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# called "genus"
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sDat$genus <- gsub(myPatt, "", sDat$name)
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# what do we get?
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c(head(unique(sDat$genus)),
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tail(unique(sDat$genus))) # inspect the first and last few. Note that there
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# is a problem that we have to keep in mind.
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# (Always inspect your results!)
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# Drop all rows for which the genus contains special chracters -
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# like "[Candida]"
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sDat <- sDat[ ! grepl("[^a-zA-Z]", sDat$genus) , ]
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length(table(sDat$genus)) # how many genus?
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hist(table(sDat$genus), col = "#E9F4FF") # Distribution ...
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# most genus have very few, but
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# some have very many species.
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sort(table(sDat$genus), decreasing = TRUE)[1:10] # Top ten...
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# We should have at least one species from each taxonomic order, but we can
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# add a few genus until we have about 100 validated species.
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# Let's add a column for species, by changing our regular expression a bit,
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# using ^ (start of string), \\S (NOT a whitespace),
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# and + (one or more matches), capturing the match (...), and returning
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# it as the substitution (\\1) ...
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myPatt <- "^(\\S+\\s\\S+)\\s.*$"
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sDat$species <- gsub(myPatt, "\\1", sDat$name)
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# And we reorder the columns, just for aesthetics:
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sDat <- sDat[ , c("name", "species", "genus", "order", "taxID")]
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# Final check:
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any(grepl("[^a-zA-Z -]", sDat$species)) # FALSE means no special characters
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# For our purpose of defining species, we will select only species, not strains
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# from this list. To do this, we pick the first two words i.e. the systematic
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# binomial name from the "X.Organism.Name" column, and then we remove redundant
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# species. Here is a function:
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#
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# Now we check which of these have Mbp1 homologues ...
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getBinom <- function(s) {
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# Fetch the first two words from a string.
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# Parameters:
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# s: char a string which is expected to contain a binomial species name
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# as the first two words, possibly followed by other text.
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# Value: char the first two words separated by a single blank
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#
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x <- unlist(strsplit(s, "\\s+")) # split s on 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] <- getBinom(GOLDfungi$X.Organism.Name[i])
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}
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head(GOLDspecies)
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length(GOLDspecies)
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# N.b. this would be more efficiently (but perhaps less explicitly) coded with
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# one of the apply() functions, instead of a for-loop.
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# GOLDspecies <- unlist(lapply(GOLDfungi$X.Organism.Name, getBinom))
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# Species of great interest may appear more than once, one for each sequenced
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# 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 40% of the species by removing duplicates.
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# = 4 BLAST SEARCH ========================================================
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# = 3 BLAST species =======================================================
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#
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# Next, we filter our list by species that have homologues to the yeast Mbp1
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# gene. To do this we run a BLAST search to find all related proteins in any
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# fungus. We list the species that appear in that list, and then we select those
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# that appear in our GOLD table as well.
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#
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# == 3.1 find homologous proteins ==========================================
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# We run a BLAST search to find all proteins related to yeast Mbp1 in any
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# fungus. With the results, we'll annotate our sDat table.
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# == 4.1 find homologous proteins ==========================================
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#
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# Use BLAST to fetch proteins related to Mbp1 and identify the species that
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# contain them.
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@@ -188,20 +170,27 @@ length(GOLDspecies)
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# to make a BLAST interface (demo-quality, not research-quality) is in the file
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# ./scripts/BLAST.R Feel encouraged to study how this works. It's a pretty
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# standard task of communicating with servers and parsing responses - everyday
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# fare in thebioinformatics lab. Surprisingly, there seems to be no good BLAST
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# fare in the bioinformatics lab. Surprisingly, there seems to be no good BLAST
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# parser in currently available packages.
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# source("./scripts/BLAST.R") # load the function and its utilities
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#
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# DON'T use this for BLAST searches unless you have read the NCBI policy
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# for automated tasks. If you indicriminately pound on the NCBI's BLAST
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# server, they will blacklist your IP-address. See:
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# https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE=DeveloperInfo
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#
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# Use BLAST() to find yeast Mbp1 homologues in other fungi in refseq
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# BLASThits <- BLAST("NP_010227", # Yeast Mbp1 RefSeq ID
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# db = "refseq_protein", # database to search in
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# nHits = 3000, # 720 hits in 2017
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# nHits = 3000, # 945 hits in 2020
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# E = 0.01, #
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# limits = "txid4751[ORGN]") # = fungi
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# save(BLASThits, file="data/BLASThits.RData")
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load(file="data/BLASThits.RData")
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# saveRDS(BLASThits, file="data/BLASThits.rds")
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#
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# NO NEED TO ACTUALLY RUN THIS:you can load the results from the data directory
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#
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BLASThits <- readRDS(file = "data/BLASThits.rds")
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# == 3.2 Identify species in "hits" ========================================
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# == 4.2 Identify species in "hits" ========================================
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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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@@ -224,61 +213,208 @@ str(BLASThits$hit[[277]])
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BLASTspecies <- character()
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for (i in seq_along(BLASThits$hits)) {
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BLASTspecies[i] <-BLASThits$hits[[i]]$species
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BLASTspecies[i] <- BLASThits$hits[[i]]$species
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}
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# You can confirm that BLASTspecies has the expected size.
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length(BLASTspecies)
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# if we delete some of these later on, we still want to remember which hit
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# they came from. Thus we name() the elements with their index, which is the
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# same as the index of the hit in BLASThits
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names(BLASTspecies) <- 1:length(BLASTspecies)
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# let's plot the distribution of E-values
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eVals <- numeric()
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for (i in seq_along(BLASThits$hits)) {
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eVals[i] <- BLASThits$hits[[i]]$E
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}
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range(eVals)
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sum(eVals == 0)
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# let's plot the log of all values > 0 to see how they are distributed
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# plotting only one vectyor of numbers plots their index as x, and
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# their value as y ...
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plot(log(eVals[eVals > 0]), col = "#CC0000")
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# This is very informative: I would suspect that the first ten or so are
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# virtually identical to the yeast protein, then we have about 700 hits with
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# decreasing similarity, and then about 200 more that may actually be false
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# positives. Also - we plotted them by index, that means the table is SORTED:
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# Lower E-values strictly come before higher E-values.
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# Again, some species appear more than once, e.g. ...
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sum(BLASTspecies == "Saccharomyces cerevisiae")
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# ... corresponding to the five homologous gene sequences (paralogues) of yeast.
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# Therefore we use unique() to throw out duplicates:
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BLASTspecies <- unique(BLASTspecies)
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# Therefore we remove duplicates. Removing duplicates will leave the FIRST
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# in a list alone, and only remove the SUBSEQUENT ones. Which means, from each
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# species, we will retain only the protein that has the highest similarity
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# to yeast Mbp1, not any of its more distant paralogues.
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sel <- ! duplicated(BLASTspecies)
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BLASTspecies <- BLASTspecies[sel]
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length(BLASTspecies)
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# i.e. we got rid of about two thirds of the hits.
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tail(BLASTspecies) # see how the names are useful!
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# again - there are some special characters ...
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# what are they?
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BLASTspecies[grep("[^a-zA-Z ]", BLASTspecies)]
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# You should think about this: what is the biological interpretation of the
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# finding that on average we have three sequences that are similar to Mbp1 in
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# other species?
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# remove the brackets ...
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BLASTspecies <- gsub("\\[|\\]", "", BLASTspecies)
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# drop any new duplicates ...
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BLASTspecies <- BLASTspecies[ ! duplicated(BLASTspecies)]
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# check the number again:
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length(BLASTspecies)
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# Think a bit about this: what may be the biological reason to find that
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# on average, in 300 fungi across the entire phylogenetic tree, we have
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# three sequences that are homologous to yeast Mbp1?
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# Let's look at the distribution of E-values in this selection (Subsetting FTW):
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# we plot all values that are TRUE in the vector "sel" that we created above,
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# AND greater than 0
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plot(log(eVals[sel & eVals > 0]), col = "#00CC00")
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# = 4 Intersect GOLD and BLAST species ====================================
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# = 5 MERGE ENSEMBL AND BLAST RESULTS =====================================
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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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MYSPEspecies <- intersect(GOLDspecies, BLASTspecies)
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# Again: interpret this:
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# - what is the number of GOLDspecies?
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# - what is the number of BLAST species?
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# - how many species are present in both lists?
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# - what does it mean if a species is in GOLD but not in the BLAST list?
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# - what does it mean if a species has been found during BLAST, but it
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# is not in GOLD?
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# = 5 Cleanup and finish ==================================================
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# One final thing: some of the species will be our so-called "reference" species
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# which we use for model solutions and examples in the course. They are defined
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# in the .utilities.R file of this project. We remove them from the list so that
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# we don't inadvertently assign them.
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# Next we add the blast result to our sDat dataframe. We'll store the index,
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# the E-value, and the Query-bounds from which we can estimate which domains
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# of Mbp1 are actually covered by the hit. (True orthologues MUST align with
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# Mbp1's N-terminal APSES domain.)
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#
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# First we pull the hits we wanted from the BLASTspecies:
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iHits <- as.numeric(names(BLASTspecies))
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length(iHits) # one index for each TRUE in sel
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REFspecies
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# add columns to sDat
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l <- nrow(sDat)
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sDat$iHit <- numeric(l) # index of the hit in the BLAST results
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sDat$eVal <- numeric(l) # E-value of the hit
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sDat$lAli <- numeric(l) # length of the aligned region
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MYSPEspecies <- sort(setdiff(MYSPEspecies, REFspecies))
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# extract and merge
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for (iHit in iHits) {
|
||||
thisSp <- BLASThits$hits[[iHit]]$species
|
||||
sel <- sDat$species == thisSp
|
||||
|
||||
# save(MYSPEspecies, file = "data/MYSPEspecies.RData")
|
||||
sDat$iHit[sel] <- iHit
|
||||
sDat$eVal[sel] <- BLASThits$hits[[iHit]]$E
|
||||
sDat$lAli[sel] <- BLASThits$hits[[iHit]]$lengthAli
|
||||
}
|
||||
|
||||
# Are all reference species accounted for?
|
||||
selA <- sDat$iHit != 0 # all rows which matched to a BLAST hit
|
||||
REFspecies %in% sDat$species[selA] # yes, all there
|
||||
|
||||
selB <- sDat$species %in% REFspecies # all rows which have one of REF species
|
||||
|
||||
sum(selA & selB) # How many rows?
|
||||
|
||||
# sDat of course includes all duplicates. Some may be multiply sequenced, some
|
||||
# may be different strains. We'll use the same strategy as before and keep
|
||||
# only the best hit: order the rows by E-value, then drop all rows which
|
||||
# are duplicated.
|
||||
|
||||
|
||||
# drop all rows without BLAST hits ...
|
||||
sDat <- sDat[ ! (sDat$iHit == 0) , ]
|
||||
|
||||
# order sDat by E-value ...
|
||||
sDat <- sDat[order(sDat$eVal, decreasing = FALSE) , ]
|
||||
|
||||
# drop all rows with duplicated species ...
|
||||
sDat <- sDat[ ! duplicated(sDat$species) , ]
|
||||
|
||||
# Lets look at the E-values ...
|
||||
plot(log(sDat$eVal[sDat$eVal > 0]), col = "#00CC00")
|
||||
|
||||
# and alignment lengths ...
|
||||
plot(sDat$lAli, col = "#00DDAA")
|
||||
|
||||
# How many ...
|
||||
length(unique(sDat$name))
|
||||
length(unique(sDat$species))
|
||||
length(unique(sDat$genus))
|
||||
length(unique(sDat$order))
|
||||
|
||||
# To get the final dataset, we remove the reference species with their
|
||||
# entire orders ...
|
||||
REForders <- unique(sDat$order[sDat$species %in% REFspecies])
|
||||
sel <- sDat$order %in% REForders
|
||||
REFdat <- sDat[sel , ]
|
||||
sDat <- sDat[ ! sel , ]
|
||||
|
||||
# REFdat should now contain only the REFspecies ...
|
||||
( REFdat <- REFdat[REFdat$species %in% REFspecies , ] )
|
||||
|
||||
# ... but all of them
|
||||
sum(REFspecies %in% REFdat$species)
|
||||
|
||||
# ... and we have enough left in sDat to prune sDat to unique genus ...
|
||||
sDat <- sDat[ ! duplicated(sDat$genus) , ]
|
||||
|
||||
# saveRDS(sDat, file = "data/sDat.rds")
|
||||
# saveRDS(REFdat, file = "data/REFdat.rds")
|
||||
|
||||
|
||||
|
||||
# = 6 STUDENT NUMBERS =====================================================
|
||||
#
|
||||
# An asymmetric function to retrieve a MYSPE species
|
||||
|
||||
students <- read.csv("../BCH441-2020-students.csv")
|
||||
|
||||
sN <- students$Student.Number
|
||||
range(sN)
|
||||
any(duplicated(gsub(".+(.......)$", "\\1", sN)))
|
||||
|
||||
N <- 7
|
||||
x <- numeric(N)
|
||||
for (i in 1:N) {
|
||||
x[i] <- H(substr(gsub(".+(.......)$", "\\1", sN), i, i))
|
||||
}
|
||||
plot(x, col = "#BB0000", type = "b")
|
||||
|
||||
keys <- as.numeric(gsub(".+(....).$", "\\1", sN))
|
||||
any(duplicated(keys))
|
||||
|
||||
# =====
|
||||
set.seed(112358)
|
||||
names(sN) <- sample(1:nrow(sDat), length(sN))
|
||||
|
||||
MYSPEmap <- data.frame(keys = sprintf("%04d", 0:9999),
|
||||
iMYSPE = sample(1:nrow(sDat), 10000, replace = TRUE))
|
||||
rownames(MYSPEmap) <- MYSPEmap$keys
|
||||
|
||||
for (i in 1:length(sN)) {
|
||||
rMap <- gsub(".+(....).$", "\\1", sN[i])
|
||||
MYSPEmap[rMap, "iMYSPE"] <- as.integer(names(sN)[i])
|
||||
}
|
||||
|
||||
# saveRDS(MYSPEmap, "./data/MYSPEmap.rds")
|
||||
|
||||
getMYSPE <- function(x) {
|
||||
dat <- readRDS("./data/sDat.rds")
|
||||
map <- readRDS("./data/MYSPEmap.rds")
|
||||
key <- gsub(".+(....).$", "\\1", x)
|
||||
return(dat$species[map[key, "iMYSPE"]])
|
||||
}
|
||||
|
||||
# === validate
|
||||
l <- length(sN)
|
||||
sp <- character(l)
|
||||
for(i in 1:l) {
|
||||
sp[i] <- getMYSPE(sN[i])
|
||||
}
|
||||
any(duplicated(sp))
|
||||
length(unique(sp))
|
||||
which(! sDat$species %in% sp) # these can be assigned to late-comers
|
||||
|
||||
# Done.
|
||||
|
||||
# [END]
|
||||
|
||||
@@ -119,10 +119,10 @@ scCCnet <- scCCnet[! duplicated(x), ]
|
||||
# length(unique(c(mySubnet$protein1, mySubnet$protein2))) # 261, no change
|
||||
# Network has 261 nodes, 1280 edges
|
||||
|
||||
save(scCCnet, file = "./data/scCCnet.RData")
|
||||
saveRDS(scCCnet, file = "./data/scCCnet.rds")
|
||||
|
||||
# load("./data/scCCnet.RData") # <<<- use this to load the object when
|
||||
# needed
|
||||
# scCCnet <- readRDS("./data/scCCnet.rds") # <<<- use this to restore the
|
||||
# object when needed
|
||||
|
||||
|
||||
# [END]
|
||||
|
||||
@@ -4,23 +4,23 @@
|
||||
# This script uses the BLAST URL-API
|
||||
# (Application Programming Interface) at the NCBI.
|
||||
# Read about the constraints here:
|
||||
# https://ncbi.github.io/blast-cloud/dev/api.html
|
||||
# https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE=DeveloperInfo
|
||||
#
|
||||
#
|
||||
# Version: 3.1
|
||||
# Date: 2016 09 - 2019 01
|
||||
# Version: 3.2
|
||||
# Date: 2016 09 - 2020 09
|
||||
# Author: Boris Steipe
|
||||
#
|
||||
# Versions:
|
||||
# 3.2 2020 updates
|
||||
# 3.1 Change from require() to requireNamespace(),
|
||||
# use <package>::<function>() idiom throughout
|
||||
# 3 parsing logic had not been fully implemented; Fixed.
|
||||
# 3.0 parsing logic had not been fully implemented; Fixed.
|
||||
# 2.1 bugfix in BLAST(), bug was blanking non-split deflines;
|
||||
# refactored parseBLASTalignment() to handle lists with multiple hits.
|
||||
# 2.0 Completely rewritten because the interface completely changed.
|
||||
# Code adpated in part from NCBI Perl sample code:
|
||||
# $Id: web_blast.pl,v 1.10 2016/07/13 14:32:50 merezhuk Exp $
|
||||
#
|
||||
# 1.0 first version posted for BCH441 2016, based on BLAST - API
|
||||
#
|
||||
# ToDo:
|
||||
@@ -31,47 +31,50 @@
|
||||
# ==============================================================================
|
||||
|
||||
|
||||
if (! requireNamespace(httr, quietly = TRUE)) {
|
||||
if (! requireNamespace("httr", quietly = TRUE)) {
|
||||
install.packages("httr")
|
||||
}
|
||||
|
||||
|
||||
BLAST <- function(q,
|
||||
BLAST <- function(Q,
|
||||
db = "refseq_protein",
|
||||
nHits = 30,
|
||||
E = 0.1,
|
||||
limits = "",
|
||||
rid = "",
|
||||
query = "",
|
||||
quietly = FALSE,
|
||||
myTimeout = 120) {
|
||||
# Purpose:
|
||||
# Basic BLAST search
|
||||
#
|
||||
# Parameters:
|
||||
# q: query - either a valid ID or a sequence
|
||||
# Q: query - either a valid ID or a sequence
|
||||
# db: "refseq_protein" by default,
|
||||
# other legal valuses include: "nr", "pdb", "swissprot" ...
|
||||
# other legal values include: "nr", "pdb", "swissprot" ...
|
||||
# nHits: number of hits to maximally return
|
||||
# E: E-value cutoff. Do not return hits whose score would be expected
|
||||
# to occur E or more times in a database of random sequence.
|
||||
# limits: a valid ENTREZ filter
|
||||
# rid: a request ID - to retrieve earleir search results
|
||||
# rid: a request ID - to retrieve earlier search results
|
||||
# query: the actual query string (needed when retrieving results
|
||||
# with an rid)
|
||||
# quietly: controls printing of wait-time progress bar
|
||||
# timeout: how much longer _after_ rtoe to wait for a result
|
||||
# before giving up (seconds)
|
||||
# Value:
|
||||
# result: list of resulting hits and some metadata
|
||||
# result: list of process status or resulting hits, and some metadata
|
||||
|
||||
|
||||
EXTRAWAIT <- 10 # duration of extra wait cycles if BLAST search is not done
|
||||
|
||||
results <- list()
|
||||
results$query = query
|
||||
results$rid <- rid
|
||||
results$rtoe <- 0
|
||||
|
||||
if (rid == "") { # if rid is not the empty string we skip the
|
||||
# initial search and and proceed directly to retrieval
|
||||
|
||||
if (rid == "") { # If no rid is available, spawn a search.
|
||||
# Else, proceed directly to retrieval.
|
||||
|
||||
# prepare query, GET(), and parse rid and rtoe from BLAST server response
|
||||
results$query <- paste0("https://blast.ncbi.nlm.nih.gov/blast/Blast.cgi",
|
||||
@@ -141,7 +144,8 @@ BLAST <- function(q,
|
||||
|
||||
if (myTimeout <= 0) { # abort
|
||||
cat("BLAST search not concluded before timeout. Aborting.\n")
|
||||
cat(sprintf("You could check back later with rid \"%s\"\n",
|
||||
cat(sprintf("%s BLASThits <- BLAST(rid=\"%s\")\n",
|
||||
"Trying checking back later with >",
|
||||
results$rid))
|
||||
return(results)
|
||||
}
|
||||
@@ -370,7 +374,7 @@ if (FALSE) {
|
||||
nHits = 100,
|
||||
E = 0.001,
|
||||
rid = "",
|
||||
limits = "txid4751[ORGN]")
|
||||
limits = "txid4751[ORGN]") # Fungi
|
||||
str(test)
|
||||
length(test$hits)
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user