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backend.R
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backend.R
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# --------------------------------------------------------------
library(Biostrings)
library(httr)
library(InterMineR)
library(jsonlite)
library(stringi)
library(stringr)
library(yaml)
# --------------------------------------------------------------
# Global settings
# setwd("/srv/shiny-server/Funnotate") # already there
settings <- read_yaml("settings.yml")
# Global data:
# Read table of gene families
getGeneFamilies <- function() {
gfFile <- tempfile()
download.file(settings$gene_families_data, gfFile)
df.gf <- read.table(gzfile(gfFile, "rt"), header = FALSE, sep = "\t", quote = "", stringsAsFactors = FALSE)
unlink(gfFile)
names(df.gf) <- c("name", "descriptor")
df.gf$name <- gsub("-consensus", "", df.gf$name)
df.gf
}
df.geneFamilies <- getGeneFamilies()
legfed_prefix <- "legfed_v1_0."
# LegumeMine service
legumeMine <- initInterMine(mine = listMines()["LegumeMine"])
# --------------------------------------------------------------
# For scrubbing upload files
scrubber <- list()
scrubber$n <- list(
sequenceType = "nucleotide",
goodChars = "acgtn",
changeTo = "n"
)
scrubber$p <- list(
sequenceType = "protein",
goodChars = "abcdefghiklmnpqrstuvwxyz",
changeTo = "x"
)
# Returns total number of bad characters in ss0
# compared to scrubbed characters in ss1
countBadChars <- function(ss0, ss1) {
# assumes ss0 and ss1 have the same number of sequences (ns),
# and each sequence has the same number of characters,
# as ss1 = ss0 with characters replaced
ns <- length(ss0) # == length(ss1)
cc0 <- str_split(ss0, "")
cc1 <- str_split(ss1, "")
sum(sapply(1:ns, function(i) sum(cc0[[i]] != cc1[[i]]) ))
}
isProbablyNucleotideSequence <- function(lines) {
ss <- lines[!startsWith(lines, ">")]
ss <- tolower(str_split_1(str_flatten(ss), ""))
nn <- str_split_1(scrubber$n$goodChars, "")
all(ss %in% nn)
}
# Create directory if it does not exist
requireDirectory <- function(dirpath) {
if (!dir.exists(dirpath)) dir.create(dirpath)
}
# File exists and is not empty
fileReallyExists <- function(filename) {
fileExists <- file.exists(filename)
result <- (fileExists && file.info(filename)$size > 0)
if (fileExists && !result) file.remove(filename)
result
}
hmmFileReallyExists <- function(filename) {
fileExists <- fileReallyExists(filename)
if (!fileExists) return(FALSE)
ll <- readLines(filename)
ll <- ll[!startsWith(ll, "#")]
result <- (length(ll) > 0)
if (!result) file.remove(filename)
result
}
# --------------------------------------------------------------
# Current and elapsed time as string
currentTimeString <- function() {
as.character(Sys.time())
}
elapsedTimeString <- function(t0, t1) {
elapsedTime <- as.POSIXct(t1) - as.POSIXct(t0)
ss <- as.integer(round(as.double(elapsedTime, units = "secs")))
hh <- ss %/% 3600
ss <- ss - hh*3600
mm <- ss %/% 60
ss <- ss - mm*60
if (hh > 0) {
ets <- sprintf("%d:%02d:%02d", hh, mm, ss)
} else {
ets <- sprintf("%d:%02d", mm, ss)
}
ets
}
logError <- function(errMsg) {
errFile <- "static/error.log"
write(currentTimeString(), errFile, append = TRUE)
write(errMsg, errFile, append = TRUE)
write("----------------------------------------", errFile, append = TRUE)
}
# --------------------------------------------------------------
# fiData =
# 1. For uploaded FASTA files, the value of a Shiny fileInput control (name, size, datapath).
# 2. For FASTA sequences pasted into the text input, (name = source, size, datapath = temporary file).
# 3. For FASTA sequences posted from InterMine, (name = source, size, seqNames, sequences)
# to avoid creating the upload files until necessary.
# seqType = n for nucleotide/DNA, p for protein sequence
createNewUpload <- function(fiData, seqType) {
scrub <- scrubber[[seqType]]
uploadDir <- "static/upload"
requireDirectory(uploadDir)
indexFile <- sprintf("%s/index", uploadDir)
if (!file.exists(indexFile)) {
write("1", indexFile)
}
nextIndex <- scan(indexFile, what = integer())
# Create the upload - fields set to NA will be filled in below
upload <- list(
index = nextIndex,
inputFileName = fiData$name,
inputFileSize = fiData$size,
seqType = seqType,
sequenceType = scrub$sequenceType,
numSequences = NA,
totalSequenceLength = NA,
totalBadChars = NA,
uploadFile = sprintf("%s/upload_%d", uploadDir, nextIndex), # metadata for existing uploads
inputFileScrubbed = sprintf("%s/input_%d", uploadDir, nextIndex),
messages = c()
)
if (!is.null(fiData$datapath)) {
# Read the sequences (to a Biostrings::DNAStringSet or Biostrings::AAStringSet)
if (seqType == "n") {
fasta <- readDNAStringSet(fiData$datapath)
} else {
fasta <- readAAStringSet(fiData$datapath)
}
seqNames <- names(fasta)
sequences <- as.character(fasta)
} else {
# We already extracted the sequences from the POST
seqNames <- fiData$seqNames
sequences <- fiData$sequences
}
upload$numSequences <- length(sequences) # or length(seqNames)
upload$totalSequenceLength <- sum(width(sequences))
# probablyNucleotides <- all(!is.na(stri_match_first(sequences, regex = "^[acgtn]*$")))
# updateRadioButtons(session, "seqType", value = ifelse(probablyNucleotides, "nucleotide", "protein"))
# Scrub the uploaded sequences
badChars <- sprintf("(?i)[^%s]", scrub$goodChars)
sequencesScrubbed <- gsub(badChars, scrub$changeTo, sequences)
upload$totalBadChars <- countBadChars(sequences, sequencesScrubbed)
if (seqType == "n") {
fastaScrubbed <- DNAStringSet(sequencesScrubbed)
} else {
fastaScrubbed <- AAStringSet(sequencesScrubbed)
}
names(fastaScrubbed) <- seqNames
writeXStringSet(fastaScrubbed, upload$inputFileScrubbed, width = 60) # TODO: match original width if possible
# Error checking
if (upload$totalSequenceLength > 100000) {
upload$messages <- c(upload$messages, "Total sequence length exceeds 100 kbp.")
}
if (upload$totalBadChars > 0) {
upload$messages <- c(upload$messages,
sprintf("%d invalid characters (other than %s) were found. These will be changed to '%s'.",
upload$totalBadChars, scrub$goodChars, scrub$changeTo)
)
}
if (seqType == "n") {
bb <- (width(sequences) %% 3 != 0)
if (FALSE && any(bb)) {
upload$messages <- c(upload$messages, "The following sequences do not look like nucleotide sequences:", sequences[bb])
}
# } else if (...) {
# scrubbingErrors <- append(scrubbingErrors, list(
# "The following sequences do not look like protein sequences:",
# sequences[bb]
# ))
}
write(as.character(nextIndex + 1), indexFile)
write_yaml(upload, upload$uploadFile)
upload
}
# --------------------------------------------------------------
createNewJob <- function(upload, useInterpro) {
# Generate an as-yet-unused job id
jobsDir <- "static/job"
requireDirectory(jobsDir)
dd <- list.dirs(jobsDir)
while (TRUE) {
# Why is job id like "A1B2C"? Andrew says it is to guard against "obscenities".
aa <- sample(LETTERS, 3, replace = TRUE)
nn <- sample(1:9, 2, replace = TRUE) # why not allow 0s? Easily confused with Os?
jobId <- paste(aa[1], nn[1], aa[2], nn[2], aa[3], sep = "")
if (!(jobId %in% dd)) break
}
jobDir <- sprintf("%s/%s", jobsDir, jobId)
# Create the job - fields set to NA will be filled in later
job <- list(
id = jobId,
dir = jobDir,
inputFileName = upload$inputFileName,
inputFileSize = upload$inputFileSize,
sequenceType = upload$sequenceType,
numSequences = upload$numSequences,
totalSequenceLength = upload$totalSequenceLength,
totalBadChars = upload$totalBadChars,
useInterpro = useInterpro,
inputFile = upload$inputFileScrubbed,
jobFile = sprintf("%s/job_%s", jobDir, jobId), # metadata for existing jobs
blastStatus = sprintf("%s: Queued", basename(settings$blast$dbs)),
ahrdStatus = "AHRD: Queued",
hmmStatus = "HMMer: Queued",
summaryStatus = "Postprocessing: Queued",
blastFiles = sprintf("%s/blast_%s_%d", jobDir, jobId, 1:length(settings$blast$dbs)),
ahrdFile = sprintf("%s/ahrd_%s.txt", jobDir, jobId),
hmmFile = sprintf("%s/hmm_%s.tbl", jobDir, jobId),
summaryFile = sprintf("%s/summary_%s.txt", jobDir, jobId),
status = "new",
startTime = currentTimeString(),
endTime = ""
)
# Insert optional fields in the right position
if (upload$sequenceType == "nucleotide") {
j <- which(names(job) == "jobFile")
job <- append(job, list(estscanStatus = "ESTScan: Queued"), j)
}
if (job$useInterpro) {
j <- which(names(job) == "ahrdStatus")
job <- append(job, list(iprStatus = "InterPro: Queued"), j)
j <- which(names(job) == "ahrdFile")
job <- append(job, list(iprFile = sprintf("%s/ipr_%s.txt", job$dir, job$id)), j)
}
job
}
createNewJobWithGeneFamily <- function(upload, geneFamily) {
# Generate an as-yet-unused job id
jobsDir <- "static/job"
requireDirectory(jobsDir)
dd <- list.dirs(jobsDir)
while (TRUE) {
# Why is job id like "A1B2C"? Andrew says it is to guard against "obscenities".
aa <- sample(LETTERS, 3, replace = TRUE)
nn <- sample(1:9, 2, replace = TRUE) # why not allow 0s? Easily confused with Os?
jobId <- paste(aa[1], nn[1], aa[2], nn[2], aa[3], sep = "")
if (!(jobId %in% dd)) break
}
jobDir <- sprintf("%s/%s", jobsDir, jobId)
# Create the job - fields set to NA will be filled in later
job <- list(
id = jobId,
dir = jobDir,
inputFileName = upload$inputFileName,
inputFileSize = upload$inputFileSize,
sequenceType = upload$sequenceType,
numSequences = upload$numSequences,
totalSequenceLength = upload$totalSequenceLength,
totalBadChars = upload$totalBadChars,
inputFile = upload$inputFileScrubbed,
geneFamily = geneFamily,
jobFile = sprintf("%s/job_%s", jobDir, jobId), # metadata for existing jobs
summaryFile = sprintf("%s/summary_%s.txt", jobDir, jobId),
status = "new",
startTime = currentTimeString(),
endTime = ""
)
job
}
isActive <- function(job) {
job$status != "failure"
}
isDone <- function(job) {
job$status %in% c("failure", "success")
}
isRunning <- function(job) {
is.null(job) || !isDone(job)
}
failJob <- function(job) {
job$status <- "failure"
job$endTime <- currentTimeString()
job
}
# --------------------------------------------------------------
runESTScan <- function(job) {
inputFileTrans <- paste0(job$inputFile, ".trans")
estscanCmd <- sprintf("%s -M %s -t %s %s", # -o /dev/null
settings$estscan$exe, settings$estscan$matrix, inputFileTrans, job$inputFile)
job$estscanStatus <- "ESTScan: Running"
writeJob(job)
system(estscanCmd)
# Remove (first) semicolon after sequence name, if any
system(sprintf("perl -pi -e 's/;//' %s", inputFileTrans))
# TODO: Clean up the header (sequence name)?
if (fileReallyExists(inputFileTrans)) {
j <- which(names(job) == "inputFile") - 1
job <- append(job, list(originalInputFile = job$inputFile), j)
job$inputFile <- inputFileTrans
job$estscanStatus <- "ESTScan: Done"
} else {
job$estscanStatus <- "ESTScan: Failed, no output file"
job$status <- "failure"
job$endTime <- currentTimeString()
}
writeJob(job)
job
}
runBLAST <- function(job) {
for (i in 1:length(settings$blast$dbs)) {
blastDb.i <- basename(settings$blast$dbs[i])
job$blastStatus[i] <- sprintf("%s: Running", blastDb.i)
writeJob(job)
#blastCmd.i <- sprintf("%s -db %s -query %s -out %s -outfmt 6 -num_threads %d",
# settings$blast$exe, settings$blast$dbs[i], inputFile, job$blastFiles[i], settings$num_threads)
blastCmd.i <- sprintf("%s -p blastp -d %s -i %s -o %s -m 8",
# blastCmd.i <- sprintf("%s -p blastp -d %s -i %s -o %s -e 0.0001 -v 200 -b 200 -m 0 -a 4",
settings$blast$exe, settings$blast$dbs[i], job$inputFile, job$blastFiles[i])
system(blastCmd.i)
if (fileReallyExists(job$blastFiles[i])) {
job$blastStatus[i] <- sprintf("%s: Done", blastDb.i)
} else {
job$blastStatus[i] <- sprintf("%s: Failed, no output file", blastDb.i)
#job$status <- "failure"
job$endTime <- currentTimeString()
}
writeJob(job)
}
if (sum(file.exists(job$blastFiles)) == 0) {
job$status <- "failure"
writeJob(job)
}
job
}
runAHRD <- function(job) {
ahrdTmpYmlFile <- NA # for cleanup
# Read YAML file and update certain parameters
ahrdYml <- read_yaml(settings$ahrd$yml)
ahrdYml$proteins_fasta <- job$inputFile
ahrdYml$output <- job$ahrdFile
job$ahrdStatus <- "AHRD: Running"
writeJob(job)
b <- 1
for (bdb in ahrdYml$blast_dbs) {
# AHRD expects the databases in FASTA (text) format
db <- bdb$database
# match db with index
i <- which(basename(settings$blast$dbs) == basename(db))
ahrdYml$blast_dbs[[b]]$file <- job$blastFiles[i]
b <- b + 1
}
# remove missing blast_dbs
bb <- sapply(ahrdYml$blast_dbs, function(bdb) file.exists(bdb$file))
ahrdYml$blast_dbs <- ahrdYml$blast_dbs[bb]
# write the modified YAML to a temporary file
ahrdTmpYmlFile <- tempfile()
write_yaml(ahrdYml, ahrdTmpYmlFile)
ahrdCmd <- sprintf("%s -Xmx2g -jar %s %s", settings$ahrd$java, settings$ahrd$jar, ahrdTmpYmlFile)
system(ahrdCmd)
# clean up temporary file
if (!is.na(ahrdTmpYmlFile) && file.exists(ahrdTmpYmlFile)) unlink(ahrdTmpYmlFile)
# done
if (fileReallyExists(job$ahrdFile)) {
job$ahrdStatus <- "AHRD: Done"
} else {
job$ahrdStatus <- "AHRD: Failed, no output file"
job$status <- "failure"
job$endTime <- currentTimeString()
}
writeJob(job)
job
}
runInterPro <- function(job) {
iprXml <- tempfile()
iprCmdXml <- sprintf("%s -i %s -o %s -f XML %s", settings$interpro$exe, job$inputFile, iprXml, settings$interpro$params)
job$iprStatus <- "InterPro: Running"
writeJob(job)
system(iprCmdXml)
if (file.exists(iprXml)) {
iprCmdRaw <- sprintf("%s -i %s -mode convert -f RAW -o %s", settings$interpro$exe, iprXml, job$iprFile)
system(iprCmdRaw)
# clean up temporary file
if (!is.na(iprXml) && file.exists(iprXml)) unlink(iprXml)
# done
if (fileReallyExists(job$iprFile)) {
job$iprStatus <- "InterPro: Done"
} else {
job$iprStatus <- "InterPro: Failed to convert XML to raw (txt) - no matches"
#job$status <- "failure"
job$endTime <- currentTimeString()
}
} else {
job$iprStatus <- "InterPro: Failed, no XML output"
#job$status <- "failure"
job$endTime <- currentTimeString()
}
writeJob(job)
job
}
runHMMer <- function(job) {
hmmCmd <- sprintf("%s --cpu %d --tblout %s %s %s",
settings$hmmer$exe, settings$num_threads, job$hmmFile, settings$hmmer$db, job$inputFile)
job$hmmStatus <- "HMMer: Running"
writeJob(job)
system(hmmCmd)
if (hmmFileReallyExists(job$hmmFile)) {
job$hmmStatus <- "HMMer: Done"
} else {
job$hmmStatus <- "HMMer: Failed, no output"
#job$status <- "failure"
job$endTime <- currentTimeString()
}
writeJob(job)
job
}
runJob <- function(job) {
requireDirectory(job$dir)
# ESTScan
if (job$sequenceType == "nucleotide") {
if (isActive(job)) job <- runESTScan(job)
}
# BLAST
if (isActive(job)) job <- runBLAST(job)
# AHRD
if (isActive(job)) job <- runAHRD(job)
# InterPro
if (job$useInterpro) {
if (isActive(job)) job <- runInterPro(job)
}
# HMMer
if (isActive(job)) job <- runHMMer(job)
# Job completed!
if (isActive(job)) {
job$status <- "success"
job$endTime <- currentTimeString()
}
writeJob(job)
job
}
runJobWithGeneFamily <- function(job) {
requireDirectory(job$dir)
# ESTScan
if (job$sequenceType == "nucleotide") {
if (isActive(job)) job <- runESTScan(job)
}
# Job completed!
if (isActive(job)) {
job$status <- "success"
job$endTime <- currentTimeString()
}
writeJob(job)
job
}
# --------------------------------------------------------------
# Read an existing upload
readUpload <- function(index) {
uploadFile <- sprintf("static/upload/upload_%d", index)
if (!file.exists(uploadFile)) return(NULL)
upload <- read_yaml(uploadFile)
upload
}
# Read an existing job
readJob <- function(jobId) {
jobFile <- sprintf("static/job/%s/job_%s", jobId, jobId)
if (!file.exists(jobFile)) return(NULL)
job <- read_yaml(jobFile)
job
}
# Write an existing job
writeJob <- function(job) {
write_yaml(job, job$jobFile)
}
# --------------------------------------------------------------
# Read output files and create summary table
createSummaryTable <- function(job) {
if (!endsWith(job$summaryStatus, "Done")) {
job$summaryStatus <- "Postprocessing: Running"
writeJob(job)
}
# AHRD
df.ahrd <- read.table(job$ahrdFile, skip = 2, header = TRUE, sep = "\t", comment.char = "", stringsAsFactors = FALSE)
df.summary <- df.summary.txt <- df.ahrd[, 1:4]
colnames.summary <- c("Query", "AHRD BLAST Hit", "AHRD Quality<sup>3</sup>", "AHRD Descriptor")
blankColumn <- rep("", nrow(df.summary))
# InterPro
if (job$useInterpro) {
if (!file.exists(job$iprFile)) {
df.i <- data.frame(iit1 = blankColumn, go1 = blankColumn, iit2 = blankColumn, go2 = blankColumn, stringsAsFactors = FALSE)
} else {
df.ipr <- read.table(job$iprFile, header = FALSE, sep = "\t", fill = TRUE, comment.char = "", stringsAsFactors = FALSE)
hasGOTermColumn <- (ncol(df.ipr) >= 14)
# Loop over all sequences, match with first column of df.ahrd
df.i <- as.data.frame(do.call(rbind, lapply(df.ahrd[, 1], function(q) {
df.ii <- df.ipr[df.ipr[, 1] == q, ]
iit <- setdiff(unique(df.ii[, 12]), "NULL")
iit1 <- ifelse(length(iit) == 0, "", paste(sprintf("<a href='https://www.ebi.ac.uk/interpro/entry/%s' target='_blank'>%s</a>", iit, iit), collapse = ", "))
iit2 <- ifelse(length(iit) == 0, "", paste(iit, collapse = ","))
if (hasGOTermColumn) {
go <- stri_match_all(df.ii[, 14], regex = "\\(GO:(\\d+)\\)")
go <- setdiff(unique(unlist(sapply(go, function(g) g[, 2], USE.NAMES = FALSE))), NA)
go1 <- ifelse(length(go) == 0, "", paste(sprintf("<a href='http://amigo.geneontology.org/amigo/term/GO:%s' target='_blank'>%s</a>", go, go), collapse = ", "))
go2 <- ifelse(length(go) == 0, "", paste(go, collapse = ","))
} else {
go1 <- go2 <- ""
}
# InterPro id, GO terms (HTML and text format)
c(iit1, go1, iit2, go2)
})), stringsAsFactors = FALSE)
}
df.summary <- cbind(df.summary, data.frame(iit = df.i[, 1], go = df.i[, 2], stringsAsFactors = FALSE))
df.summary.txt <- cbind(df.summary.txt, data.frame(iit = df.i[, 3], go = df.i[, 4], stringsAsFactors = FALSE))
colnames.summary <- c(colnames.summary, "InterPro-ID", "GO Terms")
nGo <- sum(df.i[, 2] != "")
} else {
nGo <- 0
}
df.simpleTable <- data.frame(
label = c("Number of annotated sequences", "Number with GO assignment"),
value = c(
sprintf("%d (%2.1f%%)", nrow(df.summary), 100*nrow(df.summary)/job$numSequences),
sprintf("%d (%2.1f%%)", nGo, 100*nGo/job$numSequences)
),
stringsAsFactors = FALSE
)
# HMMer
if (!file.exists(job$hmmFile)) {
df.h <- data.frame(gf1 = blankColumn, gfs1 = blankColumn, gf2 = blankColumn, gfs2 = blankColumn, stringsAsFactors = FALSE)
} else {
ll.hmm <- readLines(job$hmmFile)
ll.hmm <- ll.hmm[!startsWith(ll.hmm, "#")]
df.hmm <- as.data.frame(do.call(rbind, str_split(ll.hmm, "\\s+")), stringsAsFactors = FALSE)
# Loop over all sequences, match with first column of df.ahrd
df.h <- as.data.frame(do.call(rbind, lapply(df.ahrd[, 1], function(q) {
df.hi <- df.hmm[df.hmm[, 3] == q, ]
if (nrow(df.hi) == 0) {
gf1 <- gfs1 <- gf2 <- gfs2 <- ""
} else {
family <- df.hi[1, 1]
gf1 <- paste(
sprintf("<a href='?family=%s' title='View the phylotree for this family' target='_blank'>%s</a>", family, family),
sprintf("<a href='?job=%s&family=%s' title='Rebuild family phylotree including your sequence' target='%s.%s'><img src='static/tools-512.png' width='16px' height='16px' style='vertical-align: top'></a>", job$id, family, family, job$id)
)
gf2 <- df.hi[1, 1]
gfs1 <- gfs2 <- df.hi[1, 5]
}
# gene families, gene family score (HTML and text format)
c(gf1, gfs1, gf2, gfs2)
})), stringsAsFactors = FALSE)
}
df.summary <- cbind(df.summary, data.frame(gf = df.h[, 1], gfs = df.h[, 2], stringsAsFactors = FALSE))
df.summary.txt <- cbind(df.summary.txt, data.frame(gf = df.h[, 3], gfs = df.h[, 4], stringsAsFactors = FALSE))
colnames.summary <- c(colnames.summary, "Gene Family", "GF Score<sup>1</sup>")
# BLAST
df.blast <- list()
ii <- which(file.exists(job$blastFiles))
for (i in ii) {
df.blast[[i]] <- read.table(job$blastFiles[i], header = FALSE, sep = "\t", comment.char = "", stringsAsFactors = FALSE)
}
# Loop over all sequences, match with first column of df.ahrd
df.b <- as.data.frame(do.call(rbind, lapply(df.ahrd[, 1], function(q) {
# Loop over the BLAST results and choose the one with the highest score (column 12)
bbh <- bs <- score <- ""
for (i in ii) {
if (!(q %in% df.blast[[i]][, 1])) next
df.bi <- df.blast[[i]][df.blast[[i]][, 1] == q, ]
m <- which(df.bi[, 12] == max(df.bi[, 12]))[1]
if (score == "" || df.bi[m, 12] > score) {
score <- df.bi[m, 12]
bs <- df.bi[m, 11]
bbh <- df.bi[m, 2]
}
}
# best BLAST hit, BLAST score
c(bbh, bs)
})), stringsAsFactors = FALSE)
df.bl <- data.frame(bbh = df.b[, 1], bs = df.b[, 2], stringsAsFactors = FALSE)
df.summary <- cbind(df.summary, df.bl)
df.summary.txt <- cbind(df.summary.txt, df.bl)
colnames.summary <- c(colnames.summary, "Best BLAST Hit", "BLAST Score<sup>2</sup>")
# Sort by sequence name
df.summary <- df.summary[order(df.summary[, 1]), ]
df.summary.txt <- df.summary.txt[order(df.summary.txt[, 1]), ]
if (!endsWith(job$summaryStatus, "Done")) {
job$summaryStatus <- "Postprocessing: Done"
writeJob(job)
}
list(simpleTable = df.simpleTable, columnNames = colnames.summary,
summaryTable = df.summary, summaryTableOut = df.summary.txt)
}
# --------------------------------------------------------------
# Given an MSA and a Newick tree with the same sequences,
# return the MSA rearranged into the same order as in the tree
msaOrderedLikeTree <- function(msa_in, tree) {
# sequence names from the tree (top to bottom)
tree_names <- stri_match_all(tree, regex = "[\\(\\,]([^\\(\\,\\:]+)\\:")[[1]][, 2]
# msa_in is a single string with rows separated by newlines,
# so convert it to individual rows (like a FASTA file)
msa1 <- str_split_1(msa_in, "\n")
nr <- length(msa1) # total number of rows (lines)
ss <- which(startsWith(msa1, ">")) # indices of rows containing sequence name (= first row of each sequence)
ee <- c(ss[-1] - 1, nr) # indices of last row of each sequence
# put the sequences in the correct order (that of the tree)
oo <- order(match(substring(msa1[ss], 2), tree_names))
msa2 <- character(nr)
l <- 0
for (o in oo) {
nl <- ee[o] - ss[o] + 1 # number of lines in the oth sequence
msa2[l + 1:nl] <- msa1[ss[o]:ee[o]]
l <- l + nl
}
# combine all rows into a single string (separated by newlines)
msa_out <- paste(msa2, collapse = "\n")
msa_out
}
# Write sequences and their names for a given (job, gene family) to a temporary file for input to Lorax
# TODO: better description, refactoring
buildUserPhylogram <- function(job, family) {
# TODO: error checking...
# output: append user phylogram information or status/error messages, as appropriate
userPhylogramInfo <- list(family = family, done = FALSE)
i.match <- which(grepl(family, df.geneFamilies$name))
userPhylogramInfo$descriptor <- ifelse(length(i.match) == 0, "unknown", df.geneFamilies$descriptor[i.match])
if (is.null(job)) {
# Display precomputed phylotree and MSA for the given family
treeUrl <- sprintf("%s/trees/%s/FastTree/tree.nwk", settings$lorax$url, family)
treeResponse <- GET(treeUrl)
msaUrl <- sprintf("%s/trees/%s/alignment", settings$lorax$url, family)
msaResponse <- GET(msaUrl)
# failure
if (treeResponse$status_code != 200 || msaResponse$status_code != 200) return(NULL)
# success
newickTree <- rawToChar(treeResponse$content)
msa <- rawToChar(msaResponse$content)
userPhylogramInfo$tree <- trimws(newickTree)
userPhylogramInfo$msa <- msaOrderedLikeTree(trimws(msa), userPhylogramInfo$tree)
userPhylogramInfo$done <- TRUE
return(userPhylogramInfo)
}
# Check status of phylotree computation
# (wrap in tryCatch() to detect errors arising in Lorax)
tryCatch({
family_job <- paste(family, job$id, sep = ".")
statusUrl <- sprintf("%s/trees/%s/FastTree/status", settings$lorax$url, family_job)
statusResponse <- GET(statusUrl)
statusResult <- 42
tryCatch({
statusResult <- fromJSON(rawToChar(statusResponse$content))
}, error = function(e2) {
# ...
})
statusCode <- statusResponse$status_code
if (statusResult == -1) {
userPhylogramInfo$message <- "Computing phylogenetic tree, please be patient."
return(userPhylogramInfo)
} else if (statusResult == 0) {
# Done computing phylogenetic tree, now parse it
treeUrl <- sprintf("%s/trees/%s/FastTree/tree.nwk", settings$lorax$url, family_job)
treeResponse <- GET(treeUrl)
newickTree <- rawToChar(treeResponse$content)
userSeqNames <- paste0("USR.", stri_match_all(newickTree, regex = sprintf("%s\\.([^\\:]+)\\:", job$id))[[1]][, 2])
newickTree <- gsub(job$id, "USR", newickTree)
# Return the multiple sequence alignment as well
msaUrl <- sprintf("%s/trees/%s/alignment", settings$lorax$url, family_job)
msaResponse <- GET(msaUrl)
msa <- rawToChar(msaResponse$content)
msa <- gsub(job$id, "USR", msa)
# success
userPhylogramInfo$seqNames <- userSeqNames
userPhylogramInfo$tree <- trimws(newickTree)
userPhylogramInfo$msa <- msaOrderedLikeTree(trimws(msa), userPhylogramInfo$tree)
userPhylogramInfo$done <- TRUE
return(userPhylogramInfo)
} else if (statusCode == 404) {
# Phylogenetic tree computation not yet started
# Find matching sequences for family
df.summary.txt <- read.table(job$summaryFile, header = TRUE, sep = "\t", comment.char = "", stringsAsFactors = FALSE)
ff.matches <- (df.summary.txt$Gene.Family == family)
seqNames <- df.summary.txt$Query[ff.matches]
numMatchingSequences <- length(seqNames)
# If there are no matches, let the user know (and return)
if (numMatchingSequences == 0) {
userPhylogramInfo$message <- paste("No matching sequences for", family)
userPhylogramInfo$done <- TRUE
logError(sprintf("%s (job %s)", userPhylogramInfo$message, job$id))
return(userPhylogramInfo)
}
# Read the user's original (or translated) protein sequences
fasta <- readAAStringSet(job$inputFile)
allSeqNames <- names(fasta)
# To correctly match user sequences by name,
# remove any characters after and including the first whitespace
allSeqNames <- sapply(allSeqNames, function(sn) str_split_i(sn, " ", 1))
names(fasta) <- allSeqNames
allSequences <- as.character(fasta)
# Write matching sequences to a temporary file to upload to Lorax
seqFile <- tempfile()
file.create(seqFile)
for (sn in seqNames) {
sn_out <- gsub("[|:]", ".", sn) # to prevent downstream Newick tree parser from treating them as delimiters
write(paste0(">", sn_out), seqFile, append = TRUE)
# split FASTA sequences into lines of at most 60 characters
ss <- unlist(stri_match_all(allSequences[sn], regex = ".{1,60}"))
for (s in ss) write(s, seqFile, append = TRUE)
}
# Upload (POST) matching sequences to Lorax
sequencesUrl <- sprintf("%s/trees/%s/sequences", settings$lorax$url, family_job)
sequencesResponse <- POST(sequencesUrl, body = list(peptide = upload_file(seqFile)), verbose())
sequencesCode <- sequencesResponse$status_code
# Clean up
if (file.exists(seqFile)) unlink(seqFile)
if (sequencesCode == 500) {
# Internal Server Error
userPhylogramInfo$message <- sprintf("Error %d: Unable to compute tree for %s (no sequences).", sequencesCode, family)
userPhylogramInfo$done <- TRUE
logError(sprintf("%s (job %s)", userPhylogramInfo$message, job$id))
return(userPhylogramInfo)
} else if (sequencesCode != 200) {
userPhylogramInfo$message <- sprintf("Error %d: Sequence upload for tree computation was not successful.", sequencesCode)
userPhylogramInfo$done <- TRUE
logError(sprintf("%s (family %s, job %s)", userPhylogramInfo$message, family, job$id))
return(userPhylogramInfo)
} else {
# Tree computation using hmmalign
hmmalignUrl <- sprintf("%s/trees/%s/hmmalign_FastTree", settings$lorax$url, family_job)
hmmalignResponse <- GET(hmmalignUrl)
hmmalignCode <- hmmalignResponse$status_code
if (hmmalignCode != 200) {
userPhylogramInfo$message <- sprintf("Error %d: Launch of tree computation was not successful.", hmmalignCode)
userPhylogramInfo$done <- TRUE
logError(sprintf("%s (family %s, job %s)", userPhylogramInfo$message, family, job$id))
} else {
userPhylogramInfo$message <- "Tree computation launched successfully."
}
return(userPhylogramInfo)
}
} else {
userPhylogramInfo$message <- sprintf("Error %d: Unable to compute tree.", statusCode)
userPhylogramInfo$done <- TRUE
logError(sprintf("%s (family %s, job %s)", userPhylogramInfo$message, family, job$id))
return(userPhylogramInfo)
}
}, warning = function(w) {
# Report Lorax warnings (note that w alone returns a less specific message)
userPhylogramInfo$message <- paste(w, "<br>The Funnotate sysadmin has been notified.")
userPhylogramInfo$done <- TRUE
logError(sprintf("%s (family %s, job %s)", trimws(as.character(w)), family, job$id))
return(userPhylogramInfo)
}, error = function(e) {
# Report Lorax errors (note that e alone returns a less specific message)
userPhylogramInfo$message <- paste(e, "<br>The Funnotate sysadmin has been notified. Please try again later.")
userPhylogramInfo$done <- TRUE
logError(sprintf("%s (family %s, job %s)", trimws(as.character(e)), family, job$id))
})
}
# --------------------------------------------------------------
geneFamilySearchQuery <- function(keywords) {
keywords <- trimws(keywords)
if (nchar(keywords) == 0) return(NULL)
familyRequest <- sprintf("%s/service/search?q=%s&searchBag=&facet_Category=GeneFamily", legumeMine@mine, URLencode(keywords))
familyResponse <- GET(familyRequest)
json <- fromJSON(rawToChar(familyResponse$content))
results_per_page <- 100 # set somewhere in LegumeMine
num_pages <- 1 + (json$totalHits - 1) %/% results_per_page
familyResults <- json$results$fields
familyResults$relevance <- json$results$relevance
if (num_pages > 1) {
for (p in 2:num_pages) {
s <- (p - 1)*results_per_page # start field in URL must have zero offset
familyResponse_p <- GET(paste0(familyRequest, "&start=", s))
json_p <- fromJSON(rawToChar(familyResponse_p$content))
familyResults_p <- json_p$results$fields
familyResults_p$relevance <- json_p$results$relevance
familyResults <- rbind(familyResults, familyResults_p)
}
}
if (!is.null(familyResults)) {
families <- stri_match_first(familyResults$primaryIdentifier, regex = "^.+\\.(.+)$")[, 2]
familyResults$primaryIdentifier <- sprintf("<a href='?family=%s' target='_blank'>%s</a>", families, familyResults$primaryIdentifier)
}
familyResults
}
# --------------------------------------------------------------
genesToProteinsQuery <- function(family, genes) {
# add prefix to restore full-yuck gene family name for the query
family <- paste0(legfed_prefix, family)
# convert genes to character vector
genes <- str_split_1(URLdecode(genes), ",")
# find all genes in family
family_constraints = setConstraints(
paths = "GeneFamily.primaryIdentifier",
operators = "=",
values = list(family)
)
genes_query = setQuery(
select = c("GeneFamily.genes.primaryIdentifier"),
where = family_constraints
)
all_genes <- runQuery(legumeMine, genes_query)
# match against user-supplied genes
matched_genes <- base::intersect(all_genes$GeneFamily.genes.primaryIdentifier, genes)
if (length(matched_genes) == 0) return(NULL)
# match against proteins
gene_constraints = setConstraints(
paths = "Gene.primaryIdentifier",
operators = "=",
values = list(matched_genes)
)
protein_query = setQuery(
select = c("Gene.proteins.primaryIdentifier"),
where = gene_constraints
)
proteins <- runQuery(legumeMine, protein_query)
if (length(proteins) == 0) return(NULL)
proteins$Gene.proteins.primaryIdentifier
}
# --------------------------------------------------------------
genesToGeneFamiliesQuery <- function(genes) {
# convert genes to character vector
genes <- str_split_1(URLdecode(genes), ",")
# find gene families associated with genes
gene_constraints = setConstraints(
paths = "Gene.primaryIdentifier",
operators = "=",
values = list(genes)
)
gene_families_query = setQuery(
select = c(
"Gene.primaryIdentifier",
"Gene.geneFamilyAssignments.geneFamily.primaryIdentifier",
"Gene.geneFamilyAssignments.geneFamily.description"
),
where = gene_constraints
)
query_results <- runQuery(legumeMine, gene_families_query) # data frame if successful, list of length 0 if not
if (length(query_results) == 0) {
return(data.frame(geneFamily = "", description = "Not found",
genes = paste0(genes, collapse = "<br>"), gene_name = "", link = ""))
}
# rearrange query_results to be by gene family instead of by gene
names(query_results) <- c("gene", "geneFamily", "description")
query_results <- query_results[startsWith(query_results$geneFamily, legfed_prefix), ]
if (nrow(query_results) == 0) {
return(data.frame(geneFamily = "", description = "Not found",
genes = paste0(genes, collapse = "<br>"), gene_name = "", link = ""))
}
ff <- sort(unique(query_results$geneFamily))
dd <- sapply(ff, function(f) query_results$description[min(which(query_results$geneFamily == f))], USE.NAMES = FALSE)
gg_comma <- sapply(ff, function(f) paste(query_results$gene[which(query_results$geneFamily == f)], collapse = ","), USE.NAMES = FALSE)
gg_br <- gsub(",", "<br>", gg_comma)
df_gene_families <- data.frame(geneFamily = ff, description = dd, genes = gg_br, gene_name = gg_comma,
link = sprintf("<a href='?family=%s&gene_name=%s'>%s</a>", ff, gg_comma, ff))
# add non-family row for any genes not found
gg_found <- str_split_1(gg_comma, ",")
gg_not_found <- setdiff(genes, gg_found)
if (length(gg_not_found) > 0) {
df_gene_families <- rbind(df_gene_families, data.frame(geneFamily = "", description = "Not found",
genes = paste0(gg_not_found, collapse = "<br>"), gene_name = "", link = ""))
}
df_gene_families
}
# --------------------------------------------------------------