New retrieval logic
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hyginn
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@ -3,12 +3,13 @@
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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: 1.3
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# Version: 1.4
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#
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# Date: 2016 09 - 2020 09
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# Date: 2016 09 - 2021 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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# 1.4 New retrieval logic
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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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@ -18,8 +19,6 @@
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#
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# TODO:
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#
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# type out workflow
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#
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# ==============================================================================
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#
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# DO NOT source() THIS FILE!
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@ -40,15 +39,15 @@
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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 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> 1 The strategy 55
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#TOC> 2 PACKAGES AND INITIALIZATIONS 67
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#TOC> 3 ENSEMBL FUNGI 75
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#TOC> 3.1 Import 78
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#TOC> 4 BLAST SEARCH 155
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#TOC> 4.1 find homologous proteins 161
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#TOC> 4.2 Identify species in "hits" 192
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#TOC> 5 MERGE ENSEMBL AND BLAST RESULTS 282
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#TOC> 6 STUDENT NUMBERS 375
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#TOC>
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#TOC> ==========================================================================
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@ -196,7 +195,7 @@ BLASThits <- readRDS(file = "data/BLASThits.rds")
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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(BLASThits$hits)
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length(BLASThits$hits) # 1,134
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# Let's look at a hit somewhere down the list
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str(BLASThits$hit[[277]])
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@ -239,7 +238,7 @@ sum(eVals == 0)
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plot(log(eVals[eVals > 0]), col = "#CC0000")
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# This is very informative: I would suspect that the first ten or so are
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# virtually identical to the yeast protein, then we have about 700 hits with
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# virtually identical to the yeast protein, then we have about 800 hits with
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# decreasing similarity, and then about 200 more that may actually be false
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# positives. Also - we plotted them by index, that means the table is SORTED:
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# Lower E-values strictly come before higher E-values.
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@ -271,7 +270,7 @@ BLASTspecies <- BLASTspecies[ ! duplicated(BLASTspecies)]
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# check the number again:
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length(BLASTspecies)
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# Think a bit about this: what may be the biological reason to find that
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# on average, in 300 fungi across the entire phylogenetic tree, we have
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# on average, in 388 fungi across the entire phylogenetic tree, we have
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# three sequences that are homologous to yeast Mbp1?
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# Let's look at the distribution of E-values in this selection (Subsetting FTW):
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@ -342,6 +341,11 @@ length(unique(sDat$species))
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length(unique(sDat$genus))
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length(unique(sDat$order))
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# I need an extra species for admin purposes later on ...
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sel <- grep("Sporothrix schenckii", sDat$species)
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SPOSCdat <- sDat[sel, ]
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sDat <- sDat[-sel, ]
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# To get the final dataset, we remove the reference species with their
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# entire orders ...
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REForders <- unique(sDat$order[sDat$species %in% REFspecies])
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@ -355,9 +359,14 @@ sDat <- sDat[ ! sel , ]
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# ... but all of them
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sum(REFspecies %in% REFdat$species)
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# ... and we have enough left in sDat to prune sDat to unique genus ...
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# ... and we have enough left in sDat to prune sDat to unique genus
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sDat <- sDat[ ! duplicated(sDat$genus) , ]
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nrow(sDat) # 84
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# I add back "Sporothrix schenckii" ...
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sDat <- rbind(SPOSCdat, sDat)
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# ... and save for future use.
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# saveRDS(sDat, file = "data/sDat.rds")
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# saveRDS(REFdat, file = "data/REFdat.rds")
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@ -366,44 +375,58 @@ sDat <- sDat[ ! duplicated(sDat$genus) , ]
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# = 6 STUDENT NUMBERS =====================================================
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#
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# An asymmetric function to retrieve a MYSPE species
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#
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sDat <- readRDS(file = "data/sDat.rds")
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students <- read.csv("../BCH441-2020-students.csv")
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students <- read.csv("../BCH441-2021-students.csv")
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sN <- students$Integration.ID
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sN <- sN[! is.na(sN)]
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sN <- as.character(sN)
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sN <- c("1003141593", sN) # will map to "Sporothrix schenckii"
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sN <- students$Student.Number
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range(sN)
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any(duplicated(gsub(".+(.......)$", "\\1", sN)))
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N <- 7
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x <- numeric(N)
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for (i in 1:N) {
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x[i] <- H(substr(gsub(".+(.......)$", "\\1", sN), i, i))
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}
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plot(x, col = "#BB0000", type = "b")
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keys <- as.numeric(gsub(".+(....).$", "\\1", sN))
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any(duplicated(keys))
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# =====
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set.seed(112358)
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names(sN) <- sample(1:nrow(sDat), length(sN))
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theseSpecies <- sDat[sample(1:nrow(sDat)), ]
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all(sort(theseSpecies$name) == sort(sDat$name))
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nrow((theseSpecies))
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(iX <- grep("Sporothrix schenckii", theseSpecies$name))
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theseSpecies <- rbind(theseSpecies[iX, ], theseSpecies[-iX, ])
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rndMin <- 992000000
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rndMax <- 1020000000
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N <- 10000
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keys <- as.character(sample(rndMin:rndMax, N + 1000))
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keys <- keys[! (keys %in% sN)]
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keys <- keys[1:N]
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keys[1:length(sN)] <- sN
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MYSPEmap <- data.frame(keys = sprintf("%04d", 0:9999),
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iMYSPE = sample(1:nrow(sDat), 10000, replace = TRUE))
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rownames(MYSPEmap) <- MYSPEmap$keys
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nRep <- floor(N/nrow(theseSpecies))
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MYSPEdat <- theseSpecies
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for(i in 1:nRep) {
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MYSPEdat <- rbind(MYSPEdat, theseSpecies)
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}
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MYSPEdat <- MYSPEdat[1:N, ]
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for (i in 1:N) {
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rownames(MYSPEdat)[i] <- digest::digest(keys[i], algo = "md5")
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}
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set.seed(NULL)
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MYSPEdat <- MYSPEdat[sample(1:N), ]
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for (i in 1:length(sN)) {
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rMap <- gsub(".+(....).$", "\\1", sN[i])
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MYSPEmap[rMap, "iMYSPE"] <- as.integer(names(sN)[i])
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# saveRDS(MYSPEdat, file = "data/MYSPEdat.rds")
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# === validate
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x <- character()
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for (n in sN) {
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sp <- getMYSPE(n)
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if (length(sp) != 1) {
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stop(print(as.character(n)))
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} else {
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x <- c(x, sp)
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}
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}
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# saveRDS(MYSPEmap, "./data/MYSPEmap.rds")
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# === species for late-comers
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y <- unique(MYSPEdat$species)
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print(y[!(y %in% x)])
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getMYSPE <- function(x) {
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dat <- readRDS("./data/sDat.rds")
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map <- readRDS("./data/MYSPEmap.rds")
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key <- gsub(".+(....).$", "\\1", x)
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return(dat$species[map[key, "iMYSPE"]])
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}
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# === validate
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l <- length(sN)
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