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sgRNA_library_evaluation.R
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#### Target-site detection and reporting for given sgRNAs ####
# Author: Vincent de Bakker
# Veening Lab, DMF, FBM, University of Lausanne, Switzerland
####
#### 1. Settings ####
## REQUIRED ##
input_genome <- "INPUT GENOME HERE"
sgRNA_file <- "INPUT sgRNA LIBRARY HERE"
outdir <- "OUTPUT DIRECTORY HERE"
TINDRidir <- "TINDRi.py DIRECTORY HERE"
## OPTIONAL ##
path_ncbi_downloads <- NA
regions <- c(7, 2, 11)
max_mismatch_cum <- c(1, 2, 11)
reprAct_penalties <- "HawkinsBsuMedian"
pen_func <- prod
bad_seeds <- c("ACCCA", "TGGAA")
cut_sites <- c(BsmBI = "CGTCTC", tandemT = "TTTTT")
oligoForwardOverhang <- "TATA"
oligoReverseOverhang <- "AAAC"
PAM <- "NGG"
output_exact <- TRUE
output_full <- FALSE
output_sgRNAs_fasta <- FALSE
output_sites_fasta <- FALSE
detect_offtarget_genes_full <- FALSE
keep_TINDRi_input_sgRNAs <- FALSE
keep_TINDRi_input_sites <- FALSE
keep_TINDRi_matches <- FALSE
path_python <- NULL
feature_type <- "locus_tag"
#### 2. Preliminaries ####
# check inputs
if(any(endsWith(input_genome, c(".gb", ".gbf", ".gbff", ".gbk")))){
input_type <- "gbfile"
} else{
if(any(startsWith(input_genome, c("GCA", "GCF")))){
input_type <- "accessionnr"
} else{
stop("Input should either be one of .gb, .gbf, .gbff or .gbk files or an NCBI genome assembly accession number (GCA_ or GCF_).")
}
}
if(input_type == "accessionnr" & is.na(path_ncbi_downloads)){
stop("Please provide the path to the directory to save downloaded genomes and annotations to input parameter path_ncbi_downloads.")
}
if(input_type == "gbfile" & !file.exists(input_genome)){
stop("Input parameter input_genome: no such file. Please specify correct and complete path to a .gb, .gbf .gbff or .gbk file, or give an NCBI assembly accession number (e.g. GCA_003003495.1).")
}
if(!file.exists(paste0(TINDRidir, "/TINDRi.py"))){
stop("Please save the Python script TINDRi.py in the directory specified for input parameter 'TINDRidir'.")
}
if(input_type == "accessionnr"){
db <- switch(substr(input_genome, 1, 3),
GCA = "genbank",
GCF = "refseq")
message(paste("Using", db, "data base. (Please use GCA accession for genbank and GCF for refseq.)"))
}
if(length(regions) != length(max_mismatch_cum)){
stop("Input parameters regions and max_mismatch_cum need to be of equal length.")
}
if(!output_exact & !output_full){
stop("Neither exact nor full binding site list for given sgRNAs specified as output. Please set at least one of output_exact or output_full to TRUE to retrieve output.")
}
# set penalties for reprAct
pen_ls <- list("HawkinsEcoMean" = c(0.97, 0.93, 0.89, 0.91, 0.75, 0.82, 0.83, 0.87, 0.78, 0.74, 0.72, 0.66, 0.59, 0.45, 0.38, 0.47, 0.46, 0.46, 0.32, 0.19),
"HawkinsBsuMean" = c(0.99, 0.91, 0.84, 0.85, 0.77, 0.88, 0.85, 0.88, 0.85, 0.81, 0.79, 0.76, 0.69, 0.6, 0.56, 0.58, 0.58, 0.53, 0.4, 0.3),
"HawkinsEcoMedian" = c(0.98, 0.97, 0.93, 0.96, 0.78, 0.94, 0.87, 0.88, 0.88, 0.85, 0.79, 0.72, 0.69, 0.41, 0.36, 0.46, 0.39, 0.35, 0.29, 0.18),
"HawkinsBsuMedian" = c(0.99, 0.96, 0.94, 0.94, 0.84, 0.95, 0.92, 0.95, 0.94, 0.91, 0.9, 0.83, 0.67, 0.57, 0.53, 0.57, 0.53, 0.5, 0.46, 0.35),
"Qi" = c(0.78, 0.41, 0.44, 0.63, 0.65, 0.68, 0.65, 0.63,
0.30, 0.25, 0.24, 0.22, 0.24,
0.01, 0.12, 0.10, 0.07, 0.06, 0.09, 0.05))
pen_ls$QiMean <- rep(tapply(pen_ls$Qi, rep(c("1III", "2II", "3I"), c(8, 5, 7)), mean), c(8, 5, 7))
pen_ls$QiMedian <- rep(tapply(pen_ls$Qi, rep(c("1III", "2II", "3I"), c(8, 5, 7)), median), c(8, 5, 7))
if(!(reprAct_penalties %in% names(pen_ls)) & (!(is.numeric(reprAct_penalties)) | length(reprAct_penalties) != sum(regions))){
stop(paste("For custom penalties, please set reprAct_penalties to a numeric vector of the length of the sgRNA spacer (i.e. sum(regions)). Otherwise set it to one of these standard options:",
paste(names(pen_ls), collapse = ", ")))
}
if(reprAct_penalties %in% names(pen_ls)){
penalties <- pen_ls[[match(reprAct_penalties, names(pen_ls))]]
} else{
penalties <- rev(reprAct_penalties) # custom input given from PAM-proximal to -distal
}
# install required packages if needed
#message(paste0(Sys.time(), ": Loading packages (and installing if needed)..."))
required_packages_CRAN <- c("BiocManager", "reticulate")
required_packages_BioC <- switch(input_type,
gbfile = c("Biostrings", "genbankr", "CRISPRseek"),
accessionnr = c("Biostrings", "biomartr", "CRISPRseek"))
for(i in seq.int(required_packages_CRAN)){
if(!requireNamespace(required_packages_CRAN[i], quietly = TRUE)){install.packages(required_packages_CRAN[i])}
}
for(i in seq.int(required_packages_BioC)){
if(!requireNamespace(required_packages_BioC[i], quietly = TRUE)){BiocManager::install(required_packages_BioC[i])}
}
# load packages
invisible(suppressWarnings(suppressPackageStartupMessages(lapply(c(required_packages_BioC,
required_packages_CRAN[-1]),
library, character.only = TRUE)
)))
library(parallel)
if(!is.null(path_python)){
if(file.exists(path_python)){
use_python(path_python)
} else{
stop("Path to Python3 installation invalid: python.exe was not found. Please ensure full and complete path specification if path_python is provided.")
}
}
# check if required Python modules available
py_required_modules <- c("numpy", "numba", "time", "multiprocessing", "datetime", "os", "sys")
py_required_modules_available <- unlist(lapply(py_required_modules, py_module_available))
if(!py_available()){
stop("Path to Python3 installation invalid: python was not found. Please ensure full and complete path specification if path_python is provided. Note that if the parameter is set to NULL, the script will look for installed python instances itself.")
}
if(any(!py_required_modules_available)){
message("Trying to install required Python modules...")
try(py_install(py_required_modules[!py_required_modules_available]), silent = TRUE)
}
if(any(!py_required_modules_available)){
stop(paste("Not all Python modules are available or could be installed automatically. Please install manually:",
paste(py_required_modules[!py_required_modules_available], collapse = ",")))
}
# set n_cores
n_cores <- ifelse(detectCores() > 1, detectCores() - 1, 1)
# detect OS
platform <- .Platform$OS.type
#### 3. Get genome, features and extract feature sequences ####
if(input_type == "accessionnr"){
if(file.exists(paste0(path_ncbi_downloads, "/_ncbi_downloads/genomes/", input_genome,"_genomic_", db, ".fna.gz"))){
genome_path <- paste0(path_ncbi_downloads, "/_ncbi_downloads/genomes/", input_genome,"_genomic_", db, ".fna.gz")
} else{
genome_path <- getGenome(db = db,
organism = input_genome,
reference = FALSE,
path = paste0(path_ncbi_downloads, "/_ncbi_downloads/genomes/"))
}
genome <- read_genome(genome_path)
if(file.exists(paste0(path_ncbi_downloads, "/_ncbi_downloads/annotation/", input_genome, "_genomic_", db, ".gff.gz"))){
gffPath <- paste0(path_ncbi_downloads, "/_ncbi_downloads/annotation/", input_genome, "_genomic_", db, ".gff.gz")
} else{
gffPath <- getGFF(db = db,
organism = input_genome,
reference = FALSE,
path = paste0(path_ncbi_downloads, "/_ncbi_downloads/annotation/"))
}
GFF <- suppressMessages(read_gff(gffPath))
# extract all features of type feature_type
genes <- GFF[unlist(lapply(GFF$attribute, grepl, pattern = feature_type)), ]
rm(GFF)
# replace split features with same attributes by first with total range for start and end
# e.g. annotions of "joined feature span"
# (https://www.ncbi.nlm.nih.gov/genbank/genomesubmit_annotation/)
if(any(duplicated(genes$attribute))){
# if any, then per unique attribute...
genes <- do.call(rbind, lapply(split(genes, genes$attribute), function(x){
# ...find total range of start and end
tmp_range <- range(x[, c("start", "end")])
# return only first row of attribute...
res <- x[1, ]
# ...with total chromosomal location span
res$start <- tmp_range[1]
res$end <- tmp_range[2]
res
}))
}
# find duplicates of same feature
## add ";" at end in case feature_type is last attribute (regular expression requires ending character)
genes$locus_tag <- sub(paste0(".*?", feature_type, "=(.*?);.*"), "\\1", paste0(genes$attribute, ";"))
genes_tags <- genes$locus_tag
# remove duplicates
genes <- genes[!duplicated(genes_tags), ]
# add gene names where possible for final output
genes$gene <- ifelse(grepl("gene=", genes$attribute),
sub(paste0(".*?", "gene", "=(.*?);.*"), "\\1", paste0(genes$attribute, ";")),
genes$locus_tag)
} else{
# made generic for multi-chromosome cases (plasmids)
genome_txt <- readLines(input_genome)
# chromosomes are split in .gbff files by //
chromsep <- which(genome_txt == "//")
if(length(chromsep) > 100){message("Reading in many (", length(chromsep), ") chromosomes...")}
# read in genbank object for each chromosome separately
genome_gb_ls <- lapply(seq.int(length(chromsep)), function(chrom){
chrom_txt <- if(chrom == 1){
genome_txt[seq.int(chromsep[chrom])]
} else{
genome_txt[seq.int(chromsep[chrom - 1] + 1,
chromsep[chrom],
by = 1)]
}
# prokka sometimes doesn't put space between ID and length in LOCUS
chrom_txt[1] <- sub("([0123456789]+\\sbp)", " \\1", chrom_txt[1])
# readGenBank CANNOT HANDLE a 't' in the LOCUS line
chrom_txt[1] <- gsub("t", "", chrom_txt[1])
chrom_gb <- suppressWarnings(readGenBank(text = chrom_txt))
return(chrom_gb)
})
# merge all chromosome information
genome <- do.call(c, lapply(seq.int(length(genome_gb_ls)), function(chrom){
chrom_seq <- getSeq(genome_gb_ls[[chrom]])
chrom_loc <- strsplit(locus(genome_gb_ls[[chrom]]), " ")[[1]]
# add chrom nr to ensure unique IDs
names(chrom_seq) <- paste0(chrom_loc[chrom_loc != ""][2], "_", chrom)
return(chrom_seq)
}))
# make sure resulting gene tables can be merged by selecting same columns
genes_cols <- c("seqnames", "start", "end", "strand", "locus_tag")
genes <- do.call(rbind, lapply(seq.int(length(genome_gb_ls)), function(chrom){
# find features on each contig
if(isEmpty(genes(genome_gb_ls[[chrom]]))){
if(isEmpty(transcripts(genome_gb_ls[[chrom]])) & isEmpty(otherFeatures(genome_gb_ls[[chrom]]))){
message("No features detected to design sgRNAs for on contig ", names(genome)[chrom])
NULL
} else{
if(isEmpty(otherFeatures(genome_gb_ls[[chrom]]))){
genes_tmp <- as.data.frame(transcripts(genome_gb_ls[[chrom]]))
data.frame(genes_tmp[, genes_cols],
"gene" = if("gene" %in% colnames(genes_tmp)){genes_tmp$gene}else{NA},
"seqid" = names(genome)[chrom])
} else{
# exclude other features without locus_tag
othfeat <- as.data.frame(otherFeatures(genome_gb_ls[[chrom]]))
# use product for column "gene" if is.na(gene); replace spaces by _
othfeat$gene <- gsub(" ", "_",
ifelse(is.na(othfeat$gene), othfeat$product, othfeat$gene))
if(isEmpty(transcripts(genome_gb_ls[[chrom]]))){
if("locus_tag" %in% colnames(othfeat)){
data.frame(othfeat[!is.na(othfeat$locus_tag), genes_cols],
"seqid" = names(genome)[chrom])
} else{
NULL
}
} else{
genes_tmp <- as.data.frame(transcripts(genome_gb_ls[[chrom]]))
if("locus_tag" %in% colnames(othfeat)){
data.frame(rbind(othfeat[!is.na(othfeat$locus_tag), genes_cols],
data.frame(genes_tmp[, genes_cols]),
"gene" = if("gene" %in% colnames(genes_tmp)){genes_tmp$gene}else{NA}),
"seqid" = names(genome)[chrom])
} else{
# superfluous?
genes_tmp <- as.data.frame(transcripts(genome_gb_ls[[chrom]]))
data.frame(genes_tmp[, genes_cols],
"gene" = if("gene" %in% colnames(genes_tmp)){genes_tmp$gene}else{NA},
"seqid" = names(genome)[chrom])
}
}
}
}
} else{
# prokka does not always annotate gene flags, but if present, easier:
genes_tmp <- as.data.frame(genes(genome_gb_ls[[chrom]]))
data.frame(genes_tmp[, genes_cols],
"gene" = if("gene" %in% colnames(genes_tmp)){genes_tmp$gene}else{NA},
"seqid" = names(genome)[chrom])
}
}))
}
genes <- genes[, c("seqid", "strand", "start", "end", "locus_tag", "gene")]
genomeID <- switch(input_type,
accessionnr = input_genome,
gbfile = sub("\\..*", "", sub(".*/", "", input_genome)))
rm(genome_gb_ls, genome_txt)
#### 4. Read and prepare sgRNAs ####
sgRNAs <- read.csv(sgRNA_file, header = FALSE)
colnames(sgRNAs) <- c("tag", "sgRNA")
## keep only sgRNAs of correct length, also removing comments as e.g. "No PAM"
sgRNAs <- sgRNAs[unlist(lapply(sgRNAs$sgRNA, nchar)) == sum(regions), ]
## check if no duplicates in either tag or sgRNA
if(any(duplicated(sgRNAs$sgRNA))){
warning(paste("Duplicate sgRNA sequences:", paste(sgRNAs$tag[duplicated(sgRNAs$sgRNA) | duplicated(sgRNAs$sgRNA, fromLast = TRUE)], collapse = ", ")))
}
if(any(duplicated(sgRNAs$tag))){stop("Duplicate sgRNA names, please ensure all names are unique")}
## attach PAM and make sure all bases are upper case
sgRNAs$sgRNA <- apply(sgRNAs, 1, function(x){paste0(toupper(substr(x[2], 1, sum(regions))), PAM)})
# format for CRISPRseek::searchHits
gRNAs <- DNAStringSet(unlist(sgRNAs[, 2]), use.names = FALSE)
names(gRNAs) <- unlist(sgRNAs[, 1])
rm(sgRNAs)
# output if wanted
if(output_sgRNAs_fasta){writeXStringSet(gRNAs, paste0(outdir, "/", genomeID, "_sgRNAs.fasta"))}
# write
write.table(cbind(1:length(gRNAs),
as.character(reverse(narrow(gRNAs, 1, sum(regions))))),
paste0(outdir, "/sg_candidates_within_genes.csv"),
sep = ",",
col.names = FALSE,
row.names = FALSE, quote = FALSE)
#### 5. Find all possible binding sites ####
if(platform == "windows"){
# socket built-in for function
all_sites <- findgRNAs(genome, annotatePaired = FALSE,
n.cores.max = n_cores,
enable.multicore = ifelse(n_cores > 1, TRUE, FALSE),
PAM = PAM,
PAM.size = nchar(PAM),
gRNA.size = sum(regions))
} else{
all_sites <- do.call(c, mclapply(seq.int(genome), function(chrom){
findgRNAs(genome[chrom],
annotatePaired = FALSE,
n.cores.max = 1,
enable.multicore = FALSE,
PAM = PAM,
PAM.size = nchar(PAM),
gRNA.size = sum(regions))
}, mc.cores = n_cores))
}
rm(genome)
if(output_sites_fasta){writeXStringSet(all_sites, paste0(outdir, "/", genomeID, "_all_binding_sites.fasta"))}
# write
write.table(cbind(1:length(all_sites),
as.character(reverse(narrow(all_sites, 1, sum(regions))))),
paste0(outdir, "/allsgcandidates.csv"),
sep = ",",
col.names = FALSE,
row.names = FALSE, quote = FALSE)
#### 6. Match candidate sgRNAs to binding sites ####
if(is.null(path_python)){path_python <- "python"}
if(platform == "windows"){
par_TINDRi <- gsub(" ", ",", paste(cumsum(regions), max_mismatch_cum, collapse = ";"))
} else{
par_TINDRi <- gsub(" ", ",", paste(cumsum(regions), max_mismatch_cum, collapse = "\\;"))
}
system(paste(path_python, paste0(TINDRidir, "/TINDRi.py"), outdir, par_TINDRi),
wait = TRUE)
# remove temp files not needed anymore, unless requested to keep
if(!keep_TINDRi_input_sgRNAs){
invisible(file.remove(paste0(outdir, "/sg_candidates_within_genes.csv")))
} else{
invisible(file.rename(paste0(outdir, "/sg_candidates_within_genes.csv"),
paste0(outdir, "/", genomeID, "_sgRNAs.csv")))
}
if(!keep_TINDRi_input_sites){
invisible(file.remove(paste0(outdir, "/allsgcandidates.csv")))
} else{
invisible(file.rename(paste0(outdir, "/allsgcandidates.csv"),
paste0(outdir, "/", genomeID, "_all_binding_sites.csv")))
}
#### 7. Compute scores needed in any case ####
# compute scores for mismatch vectors
candidate_mm <- unlist(read.table(paste0(outdir, "/missmatch_matrix_end.txt"),
colClasses = c("NULL", "character", "NULL"),
sep = "\t",
header = FALSE),
use.names = FALSE)
# compute scores separately to save RAM, but takes longer
if(platform == "windows"){
# needed anyway
cl <- makeCluster(n_cores)
clusterExport(cl, c("penalties", "pen_func"))
reprAct <- unlist(parLapply(cl, candidate_mm, function(mm){
# leave penalty function customizable
do.call(pen_func, list(rev(penalties)[as.integer(unlist(strsplit(mm, ","))) == 1]))
}))
stopCluster(cl)
if(output_full){
write(reprAct, file = paste0(outdir, "/tmp_reprAct.tsv"), sep = "\t", ncolumns = 1)
}
# nmismatch
cl <- makeCluster(n_cores)
nmm <- unlist(parLapply(cl, candidate_mm, function(mm){
sum(as.integer(unlist(strsplit(mm, ","))))
}))
stopCluster(cl)
#candidate_ID <- which(nmm == 0)
if(output_full){
write(nmm, file = paste0(outdir, "/tmp_nmm.tsv"), sep = "\t", ncolumns = 1)
}
} else{
reprAct <- unlist(mclapply(candidate_mm, function(mm){
# leave penalty function customizable
do.call(pen_func, list(rev(penalties)[as.integer(unlist(strsplit(mm, ","))) == 1]))
}, mc.cores = n_cores))
if(output_full){
write(reprAct, file = paste0(outdir, "/tmp_reprAct.tsv"), sep = "\t", ncolumns = 1)
}
# nmismatch
nmm <- unlist(mclapply(candidate_mm, function(mm){
sum(as.integer(unlist(strsplit(mm, ","))))
}, mc.cores = n_cores))
#candidate_ID <- which(nmm == 0)
if(output_full){
write(nmm, file = paste0(outdir, "/tmp_nmm.tsv"), sep = "\t", ncolumns = 1)
}
}
rm(candidate_mm)
# required for either output
# CRISPRseek offset in within-target bp locus of sgRNA names
offset_emp <- 3
#### 8. Get zero-mismatch sites per sgRNA plus scores ####
if(output_exact){
# all information comes from all_sites ID, except reprAct
sg_hits <- read.table(paste0(outdir, "/missmatch_matrix_end.txt"),
colClasses = c("integer", "NULL", "integer"),
sep = "\t",
header = FALSE)
# get 1st- and 2nd-best target IDs
#names(reprAct) <- sg_hits[, 2]
if(platform == "windows"){
cl <- makeCluster(n_cores)
maxreprAct <- parLapply(cl, split(data.frame(sg_hits[, 2], reprAct, nmm), sg_hits[, 1]), function(sg_ra){
sg_max1 <- sg_ra[, 2] == max(sg_ra[, 2])
sg_max2 <- sg_ra[, 2] == max(sg_ra[!sg_max1, 2])
list("sites_1st" = sg_ra[sg_max1, 1],
"reprAct_1st" = sg_ra[sg_max1, 2][1],
"sites_2nd" = sg_ra[sg_max2, 1],
"reprAct_2nd" = sg_ra[sg_max2, 2][1],
"nmm_1st" = sg_ra[sg_max1, 3],
"nmm_2nd" = sg_ra[sg_max2, 3])
})
stopCluster(cl)
} else{
maxreprAct <- mclapply(split(data.frame(sg_hits[, 2], reprAct, nmm), sg_hits[, 1]), function(sg_ra){
sg_max1 <- sg_ra[, 2] == max(sg_ra[, 2])
sg_max2 <- sg_ra[, 2] == max(sg_ra[!sg_max1, 2])
list("sites_1st" = sg_ra[sg_max1, 1],
"reprAct_1st" = sg_ra[sg_max1, 2][1],
"sites_2nd" = sg_ra[sg_max2, 1],
"reprAct_2nd" = sg_ra[sg_max2, 2][1],
"nmm_1st" = sg_ra[sg_max1, 3],
"nmm_2nd" = sg_ra[sg_max2, 3])
}, mc.cores = n_cores)
}
rm(reprAct, nmm)
# compute binding site scores 1st-best hits
if(platform == "windows"){
cl <- makeCluster(n_cores)
clusterExport(cl, c("genes", "regions", "offset_emp"))
NTgene_ls <- parLapply(cl, names(all_sites[unlist(lapply(maxreprAct, function(x){x[[1]]}))]), function(site){
chrom_tmp <- gsub(" ", "", sub("\\_gR[0-9]+[rf]$", "", site))
strand_tmp <- ifelse(substr(site, nchar(site), nchar(site)) == "f", "+", "-")
coord_tmp <- as.integer(sub(".*_gR([0-9]+).*", "\\1", site))
start_tmp <- ifelse(strand_tmp == "+",
yes = coord_tmp - (sum(regions) - offset_emp - 1),
no = coord_tmp - offset_emp)
end_tmp <- start_tmp + sum(regions) - 1
tmp_i <- start_tmp - genes$end <= 0 &
end_tmp - genes$start >= 0 &
strand_tmp != genes$strand &
unlist(lapply(gsub(" ", "", genes$seqid), grepl, x = chrom_tmp))
# find gene hits on site, if any (can be 0, or >1 if overlap)
if(any(tmp_i)){
c(paste(genes$locus_tag[which(tmp_i)], collapse = ","),
paste(ifelse(is.na(genes$gene[which(tmp_i)]), genes$locus_tag[which(tmp_i)], genes$gene[which(tmp_i)]), collapse = ","),
paste(chrom_tmp, collapse = ","),
paste(strand_tmp, collapse = ","),
mapply(paste, start_tmp, end_tmp, MoreArgs = list(sep = "..", collapse = ",")))
} else{
c(NA,
NA,
paste(chrom_tmp, collapse = ","),
paste(strand_tmp, collapse = ","),
mapply(paste, start_tmp, end_tmp, MoreArgs = list(sep = "..", collapse = ",")))
}
})
# split per sgRNA
hitinfo1 <- do.call(rbind, parLapply(cl,
split(NTgene_ls,
rep(seq.int(maxreprAct),
unlist(lapply(maxreprAct, function(x){length(x[[1]])})))),
function(hits){
# cannot paste with sep or col = ","...
gsub(" ", ",", do.call(paste, hits))
}))
stopCluster(cl)
} else{
NTgene_ls <- mclapply(names(all_sites[unlist(lapply(maxreprAct, function(x){x[[1]]}))]), function(site){
chrom_tmp <- gsub(" ", "", sub("\\_gR[0-9]+[rf]$", "", site))
strand_tmp <- ifelse(substr(site, nchar(site), nchar(site)) == "f", "+", "-")
coord_tmp <- as.integer(sub(".*_gR([0-9]+).*", "\\1", site))
start_tmp <- ifelse(strand_tmp == "+",
yes = coord_tmp - (sum(regions) - offset_emp - 1),
no = coord_tmp - offset_emp)
end_tmp <- start_tmp + sum(regions) - 1
tmp_i <- start_tmp - genes$end <= 0 &
end_tmp - genes$start >= 0 &
strand_tmp != genes$strand &
unlist(lapply(gsub(" ", "", genes$seqid), grepl, x = chrom_tmp))
# find gene hits on site, if any (can be 0, or >1 if overlap)
if(any(tmp_i)){
c(paste(genes$locus_tag[which(tmp_i)], collapse = ","),
paste(ifelse(is.na(genes$gene[which(tmp_i)]), genes$locus_tag[which(tmp_i)], genes$gene[which(tmp_i)]), collapse = ","),
paste(chrom_tmp, collapse = ","),
paste(strand_tmp, collapse = ","),
mapply(paste, start_tmp, end_tmp, MoreArgs = list(sep = "..", collapse = ",")))
} else{
c(NA,
NA,
paste(chrom_tmp, collapse = ","),
paste(strand_tmp, collapse = ","),
mapply(paste, start_tmp, end_tmp, MoreArgs = list(sep = "..", collapse = ",")))
}
}, mc.cores = n_cores)
# split per sgRNA
hitinfo1 <- do.call(rbind, mclapply(split(NTgene_ls,
rep(seq.int(maxreprAct),
unlist(lapply(maxreprAct, function(x){length(x[[1]])})))),
function(hits){
# cannot paste with sep or coll = ","...
gsub(" ", ",", do.call(paste, hits))
}, mc.cores = n_cores))
}
colnames(hitinfo1) <- c("targets_1st", "target_names_1st", "chroms_1st", "strands_1st", "ranges_1st")
# compute binding site scores 2nd-best hits
site_index <- unlist(lapply(maxreprAct, function(x){
if(isEmpty(x[[3]])){
NA
} else{
names(all_sites[x[[3]]])
}
}))
if(platform == "windows"){
cl <- makeCluster(n_cores)
clusterExport(cl, c("genes", "regions", "offset_emp"))
NTgene_ls <- parLapply(cl, site_index, function(site){
# old: names(all_sites[unlist(lapply(maxreprAct, function(x){x[[3]]}))])
chrom_tmp <- gsub(" ", "", sub("\\_gR[0-9]+[rf]$", "", site))
strand_tmp <- ifelse(substr(site, nchar(site), nchar(site)) == "f", "+", "-")
coord_tmp <- as.integer(sub(".*_gR([0-9]+).*", "\\1", site))
start_tmp <- ifelse(strand_tmp == "+",
yes = coord_tmp - (sum(regions) - offset_emp - 1),
no = coord_tmp - offset_emp)
end_tmp <- start_tmp + sum(regions) - 1
tmp_i <- start_tmp - genes$end <= 0 &
end_tmp - genes$start >= 0 &
strand_tmp != genes$strand &
unlist(lapply(gsub(" ", "", genes$seqid), grepl, x = chrom_tmp))
# find gene hits on site, if any (can be 0, or >1 if overlap)
if(any(tmp_i)){
c(paste(genes$locus_tag[which(tmp_i)], collapse = ","),
paste(ifelse(is.na(genes$gene[which(tmp_i)]), genes$locus_tag[which(tmp_i)], genes$gene[which(tmp_i)]), collapse = ","),
paste(chrom_tmp, collapse = ","),
paste(strand_tmp, collapse = ","),
mapply(paste, start_tmp, end_tmp, MoreArgs = list(sep = "..", collapse = ",")))
} else{
c(NA,
NA,
paste(chrom_tmp, collapse = ","),
paste(strand_tmp, collapse = ","),
mapply(paste, start_tmp, end_tmp, MoreArgs = list(sep = "..", collapse = ",")))
}
})
# split per sgRNA
hitinfo2 <- do.call(rbind, parLapply(cl,
split(NTgene_ls,
rep(seq.int(maxreprAct),
# old: lapply(maxreprAct, function(x){length(x[[3]])})
unlist(lapply(maxreprAct, function(x){ifelse(length(x[[3]]) == 0, 1, length(x[[3]]))})))),
function(hits){
# cannot paste with sep or col = ","...
gsub(" ", ",", do.call(paste, hits))
}))
stopCluster(cl)
} else{
# old: names(all_sites[unlist(lapply(maxreprAct, function(x){x[[3]]}))])
NTgene_ls <- mclapply(site_index, function(site){
chrom_tmp <- gsub(" ", "", sub("\\_gR[0-9]+[rf]$", "", site))
strand_tmp <- ifelse(substr(site, nchar(site), nchar(site)) == "f", "+", "-")
coord_tmp <- as.integer(sub(".*_gR([0-9]+).*", "\\1", site))
start_tmp <- ifelse(strand_tmp == "+",
yes = coord_tmp - (sum(regions) - offset_emp - 1),
no = coord_tmp - offset_emp)
end_tmp <- start_tmp + sum(regions) - 1
tmp_i <- start_tmp - genes$end <= 0 &
end_tmp - genes$start >= 0 &
strand_tmp != genes$strand &
unlist(lapply(gsub(" ", "", genes$seqid), grepl, x = chrom_tmp))
# find gene hits on site, if any (can be 0, or >1 if overlap)
if(any(tmp_i)){
c(paste(genes$locus_tag[which(tmp_i)], collapse = ","),
paste(ifelse(is.na(genes$gene[which(tmp_i)]), genes$locus_tag[which(tmp_i)], genes$gene[which(tmp_i)]), collapse = ","),
paste(chrom_tmp, collapse = ","),
paste(strand_tmp, collapse = ","),
mapply(paste, start_tmp, end_tmp, MoreArgs = list(sep = "..", collapse = ",")))
} else{
c(NA,
NA,
paste(chrom_tmp, collapse = ","),
paste(strand_tmp, collapse = ","),
mapply(paste, start_tmp, end_tmp, MoreArgs = list(sep = "..", collapse = ",")))
}
}, mc.cores = n_cores)
# split per sgRNA
hitinfo2 <- do.call(rbind, mclapply(split(NTgene_ls,
rep(seq.int(maxreprAct),
unlist(lapply(maxreprAct, function(x){ifelse(length(x[[3]]) == 0, 1, length(x[[3]]))})))),
function(hits){
# cannot paste with sep or coll = ","...
gsub(" ", ",", do.call(paste, hits))
}, mc.cores = n_cores))
}
colnames(hitinfo2) <- c("targets_2nd", "target_names_2nd", "chroms_2nd", "strands_2nd", "ranges_2nd")
# get site sequences
seqs1 <- all_sites[unlist(lapply(maxreprAct, function(x){x[[1]]}))]
# old: seqs2 <- all_sites[unlist(lapply(maxreprAct, function(x){x[[3]]}))]
seqs2 <- unlist(DNAStringSetList(lapply(maxreprAct, function(x){
if(isEmpty(x[[3]])){
DNAStringSet(paste(rep("N", sum(regions) + nchar(PAM)), collapse = ""))
} else{
all_sites[x[[3]]]
}
})))
seqs1 <- do.call(rbind, lapply(split(as.character(seqs1),
rep(seq.int(maxreprAct),
unlist(lapply(maxreprAct, function(x){length(x[[1]])})))),
function(sg_site){
c(paste(substr(sg_site, 1, sum(regions)), collapse = ","),
paste(substr(sg_site, sum(regions) + 1, sum(regions) + 1 + nchar(PAM)), collapse = ","))
}))
colnames(seqs1) <- c("seq_1st", "PAM_1st")
seqs2 <- do.call(rbind, lapply(split(as.character(seqs2),
rep(seq.int(maxreprAct),
# old: function(x){length(x[[3]])}
unlist(lapply(maxreprAct, function(x){ifelse(length(x[[3]]) == 0, 1, length(x[[3]]))})))),
function(sg_site){
c(paste(substr(sg_site, 1, sum(regions)), collapse = ","),
paste(substr(sg_site, sum(regions) + 1, sum(regions) + 1 + nchar(PAM)), collapse = ","))
}))
colnames(seqs2) <- c("seq_2nd", "PAM_2nd")
# bad seed flag
# cannot be multiple per sgRNA, use that
bad_seed_found <- lapply(bad_seeds, function(seed){
which(narrow(gRNAs[sort(unique(sg_hits[, 1]))],
sum(regions) - nchar(seed) + 1,
sum(regions)) %in% seed)
})
sgRNA_bad_seed <- rep(NA, length(unique(sg_hits[, 1])))
for(i in seq.int(bad_seeds)){
sgRNA_bad_seed[bad_seed_found[[i]]] <- bad_seeds[i]
}
# cut_site flag
spacers_ext <- xscat(oligoForwardOverhang,
narrow(gRNAs[sort(unique(sg_hits[, 1]))],
1, sum(regions)),
reverseComplement(DNAString(oligoReverseOverhang)))
cut_sites_plusrev <- c(reverseComplement(DNAStringSet(cut_sites)), cut_sites)
names(cut_sites_plusrev) <- c(paste0("reverse_", names(cut_sites)), names(cut_sites))
cut_site_found <- vwhichPDict(cut_sites_plusrev, spacers_ext)
sgRNA_cut_site <- unlist(lapply(cut_site_found, function(sg_cut){
ifelse(isEmpty(sg_cut), NA, paste(names(cut_sites_plusrev[sg_cut]), collapse = ","))
}))
# make data frame (ordered by sorted unique(candidate_sgID))
all_df <- data.frame("sgRNA_name" = names(gRNAs[sort(unique(sg_hits[, 1]))]),
"sgRNA_seq" = substr(gRNAs[sort(unique(sg_hits[, 1]))], 1, sum(regions)),
"sgRNA_GC" = as.numeric(letterFrequency(narrow(gRNAs[sort(unique(sg_hits[, 1]))], 1, sum(regions)), letters = "GC") / sum(regions)),
"bad_seed" = sgRNA_bad_seed,
"cut_site" = sgRNA_cut_site,
"reprAct_1st" = unlist(lapply(maxreprAct, function(x){x[2]})),
"nmismatch_1st" = unlist(lapply(maxreprAct, function(x){paste(x[[5]], collapse = ",")})),
hitinfo1,
seqs1,
"reprAct_2nd" = unlist(lapply(maxreprAct, function(x){x[4]})),
"nmismatch_2nd" = unlist(lapply(maxreprAct, function(x){paste(x[[6]], collapse = ",")})),
hitinfo2,
seqs2)
# save
write.csv(all_df, paste0(outdir, "/", genomeID, "_sgRNA_hits_summary.csv"),
row.names = FALSE)
rm(all_df, hitinfo1, hitinfo2, NTgene_ls, seqs1, seqs2, maxreprAct, sg_hits)
}
#### 9. Get full table plus scores ####
if(output_full){
# reading whole file
full_df <- read.table(paste0(outdir, "/missmatch_matrix_end.txt"),
header = FALSE, sep = "\t")
colnames(full_df) <- c("sgRNA_ID", "mismatches", "site_ID")
# add sgRNA name
full_df$sgRNA_name <- names(gRNAs[full_df$sgRNA_ID])
# add n_mismatch
full_df$n_mismatch <- scan(paste0(outdir, "/tmp_nmm.tsv"),
what = integer(), quiet = TRUE)
# add reprAct
full_df$reprAct <- scan(paste0(outdir, "/tmp_reprAct.tsv"),
quiet = TRUE)
full_df$chrom <- gsub(" ", "", sub("\\_gR[0-9]+[rf]$", "", names(all_sites[full_df$site_ID])))
full_df$strand <- ifelse(substr(names(all_sites[full_df$site_ID]),
nchar(names(all_sites[full_df$site_ID])),
nchar(names(all_sites[full_df$site_ID]))) == "f",
"+", "-")
coord_tmp <- as.numeric(sub(".*_gR([0-9]+).*", "\\1", names(all_sites[full_df$site_ID])))
full_df$start <- ifelse(full_df$strand == "+",
yes = coord_tmp - (sum(regions) - offset_emp - 1),
no = coord_tmp - offset_emp)
rm(coord_tmp)
full_df$end <- full_df$start + sum(regions) - 1
full_df$sgRNA_seq <- substr(gRNAs[full_df$sgRNA_ID], 1, sum(regions))
full_df$site_seq <- substr(all_sites[full_df$site_ID], 1, sum(regions))
full_df$site_PAM <- substr(all_sites[full_df$site_ID], sum(regions) + 1, sum(regions) + nchar(PAM))
full_df$sgRNA_GC <- as.numeric(letterFrequency(gRNAs[full_df$sgRNA_ID], letters = "GC") / sum(regions))
# need lots of RAM for detecting non-template strand gene overlaps of sites
if(detect_offtarget_genes_full){
if(platform == "windows"){
cl <- makeCluster(n_cores)
clusterExport(cl, "genes")
NTgene <- do.call(rbind, parLapply(cl, split(full_df[, c("chrom", "strand", "start", "end")],
seq.int(nrow(full_df))), function(site){
tmp_i <- as.integer(site["start"]) - genes$end <= 0 &
as.integer(site["end"]) - genes$start >= 0 &
as.character(site["strand"]) != genes$strand &
unlist(lapply(gsub(" ", "", genes$seqid), grepl, x = site["chrom"]))
# find gene hits on site, if any (can be 0, or >1 if overlap)
if(any(tmp_i)){
c(paste(genes$locus_tag[which(tmp_i)], collapse = ","),
paste(ifelse(is.na(genes$gene[which(tmp_i)]), genes$locus_tag[which(tmp_i)], genes$gene[which(tmp_i)]), collapse = ","),
paste(site["chrom"], collapse = ","),
paste(site["strand"], collapse = ","),
mapply(paste, site["start"], site["end"], MoreArgs = list(sep = "..", collapse = ",")))
} else{
c(NA,
NA,
paste(site["chrom"], collapse = ","),
paste(site["strand"], collapse = ","),
mapply(paste, site["start"], site["end"], MoreArgs = list(sep = "..", collapse = ",")))
}
}))
stopCluster(cl)
} else{
NTgene <- do.call(rbind, mclapply(split(full_df[, c("chrom", "strand", "start", "end")], seq.int(nrow(full_df))), function(site){
tmp_i <- as.integer(site["start"]) - genes$end <= 0 &
as.integer(site["end"]) - genes$start >= 0 &
as.character(site["strand"]) != genes$strand &
unlist(lapply(gsub(" ", "", genes$seqid), grepl, x = site["chrom"]))
# find gene hits on site, if any (can be 0, or >1 if overlap)
if(any(tmp_i)){
c(paste(genes$locus_tag[which(tmp_i)], collapse = ","),
paste(ifelse(is.na(genes$gene[which(tmp_i)]), genes$locus_tag[which(tmp_i)], genes$gene[which(tmp_i)]), collapse = ","),
paste(site["chrom"], collapse = ","),
paste(site["strand"], collapse = ","),
mapply(paste, site["start"], site["end"], MoreArgs = list(sep = "..", collapse = ",")))
} else{
c(NA,
NA,
paste(site["chrom"], collapse = ","),
paste(site["strand"], collapse = ","),
mapply(paste, site["start"], site["end"], MoreArgs = list(sep = "..", collapse = ",")))
}
}, mc.cores = n_cores))
}
colnames(NTgene) <- c("all_targets", "all_target_names", "all_chroms", "all_strands", "all_ranges")
full_df <- data.frame(full_df, NTgene)
}
# write
write.csv(full_df,
file = paste0(outdir, "/", genomeID, "_sgRNA_hits_full.csv"),
row.names = FALSE)
# can remove tmp files here
file.remove(paste0(outdir, "/tmp_nmm.tsv"))
file.remove(paste0(outdir, "/tmp_reprAct.tsv"))
}
# remove file as not needed anymore
if(!keep_TINDRi_matches){
file.remove(paste0(outdir, "/missmatch_matrix_end.txt"))
} else{
file.rename(paste0(outdir, "/missmatch_matrix_end.txt"),
paste0(outdir, "/", genomeID, "_missmatch_matrix_end.txt"))
}