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main.nf
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#! /usr/bin/env nextflow
nextflow.enable.dsl=2
params.chemistry = "custom" //"10x3v3" "10x3v2" "10x5v2"
params.technology = 'ONT' //"ONT" "PacBio"
params.whitelist = "NULL"
params.bambuPath = "bambu"
params.lowMemory = "FALSE"
params.ncore = 4
params.spatial = null
params.NDR = "NULL"
params.cleanReads = "TRUE"
params.keepChimericReads = "FALSE"
params.deduplicateUMIs = "TRUE"
params.bambuParams = tuple(params.cleanReads, params.keepChimericReads, params.deduplicateUMIs)
params.barcodeMap = "TRUE"
params.clusters = "auto"
params.resolution = 0.8
params.reads = null
params.bams = null
params.noEM = null
params.flexiplex_x = 'CTACACGACGCTCTTCCGATCT' //sequence Append flanking sequence to search for
params.flexiplex_b = '????????????????' //sequence Append the barcode pattern to search for
params.flexiplex_u = '????????????' //sequence Append the UMI pattern to search for
params.flexiplex_x2 = 'TTTTTTTTT' //sequence Append flanking sequence to search for
params.flexiplex_e = 1
params.outdir = "output"
process flexiplex{
publishDir "$params.outdir", mode: 'copy'
cpus params.ncore
maxForks params.ncore
input:
tuple val(sample), path(fastq), val(chemistry), val(technology), val(whitelist)
output:
tuple val(sample), path("new_reads.fastq"), val(chemistry), val(technology), emit: fastq
script:
"""
chem="$chemistry"
if [[ "$chemistry" == 10x3v3 ]]; then
chem="-x CTACACGACGCTCTTCCGATCT -b ???????????????? -u ???????????? -x TTTTTTTTT"
elif [[ "$chemistry" == 10x3v2 ]]; then
chem="-x CTACACGACGCTCTTCCGATCT -b ???????????????? -u ?????????? -x TTTTTTTTT"
elif [[ "$chemistry" == 10x5v2 ]]; then
chem="-x CTACACGACGCTCTTCCGATCT -b ???????????????? -u ?????????? -x TTTCTTATATGGG"
elif [[ "$chemistry" == 10x5v3 ]]; then
chem="-x CTACACGACGCTCTTCCGATCT -b ???????????????? -u ???????????? -x TTTCTTATATGGG"
fi
gunzip -c $fastq > reads.fastq
flexiplex -p $params.ncore \$chem -f 0 reads.fastq
if [[ $whitelist == "NULL" ]]; then
python /mnt/software/main.py --outfile my_filtered_barcode_list.txt flexiplex_barcodes_counts.txt
else
gunzip -c $whitelist > whitelist.txt
python /mnt/software/main.py --outfile my_barcode_list.txt flexiplex_barcodes_counts.txt
awk '{print \$1}' whitelist.txt my_barcode_list.txt | sort | uniq -d > my_filtered_barcode_list.txt
fi
flexiplex -p $params.ncore -k my_filtered_barcode_list.txt \$chem -f 8 -e $params.flexiplex_e reads.fastq > new_reads.fastq
rm reads.fastq
"""
}
process minimap{
publishDir "$params.outdir", mode: 'copy'
cpus params.ncore
maxForks params.ncore
input:
tuple val(sample),path(newfastq), val(chemistry), val(technology)
path(genome)
output:
tuple val(sample), path ('*.demultiplexed.bam')
script:
"""
if [[ $technology == PacBio ]]; then
minimap2 -ax splice:hq -t $params.ncore -d ref.mmi $genome
minimap2 -ax splice:hq -t $params.ncore -a ref.mmi $newfastq > demultiplexed.sam
else
minimap2 -ax splice -k14 -t $params.ncore -d ref.mmi $genome
minimap2 -ax splice -k14 -t $params.ncore -a ref.mmi $newfastq > demultiplexed.sam
fi
samtools sort -@ $params.ncore demultiplexed.sam -o ${sample}.demultiplexed.bam
samtools index -@ $params.ncore ${sample}.demultiplexed.bam
rm demultiplexed.sam
rm ref.mmi
"""
}
process bambu{
publishDir "$params.outdir", mode: 'copy'
cpus params.ncore
maxForks 1
input:
val(id)
val(bam)
path(genome)
path(annotation)
val(bambuPath)
tuple val(cleanReads), val(keepChimericReads), val(deduplicateUMIs)
val(NDR)
val(barcode_map)
val(whitelist)
output:
tuple val ('combined'), path ('*readClassFile.rds'), path ('*quantData.rds')
path ('*extended_annotations.rds')
path ('*extended_annotations_NDR1.rds')
path ('*extended_annotations_NDR1.gtf')
path ('*.mtx')
path ('*.tsv')
script:
"""
#!/usr/bin/env Rscript
#.libPaths("/usr/local/lib/R/site-library")
samples = "$bam"
samples = gsub("[][]","", gsub(' ','', samples))
samples = unlist(strsplit(samples, ','))
ids = "$id"
ids = gsub("[][]","", gsub(' ','', ids))
ids = unlist(strsplit(ids, ','))
if(length(ids)>1){runName = "combined_"
}else{runName = paste0(ids, "_")}
if(!isTRUE($barcode_map)){
x = gsub("[][]","",gsub(' ','', "$barcode_map"))
barcode_maps = unlist(strsplit(x, ','))
} else {
barcode_maps = TRUE
}
library(devtools)
if("$bambuPath" == "bambu") {
load_all("/mnt/software/bambu")
} else {
load_all("$bambuPath")
}
spatial = "$whitelist"
if(spatial == "FALSE"){spatial = NULL}
annotations <- prepareAnnotations("$annotation")
# Transcript discovery and generate readGrgList for each cell
readClassFile = bambu(reads = samples, annotations = annotations, genome = "$genome", ncore = $params.ncore, discovery = FALSE, quant = FALSE, demultiplexed = barcode_maps, verbose = TRUE, assignDist = FALSE, lowMemory = as.logical("$params.lowMemory"), yieldSize = 10000000, sampleNames = ids, cleanReads = as.logical($cleanReads), dedupUMI = as.logical($deduplicateUMIs))
saveRDS(readClassFile, paste0(runName, "_readClassFile.rds"))
if(isFALSE($NDR)){
extendedAnno = bambu(reads = readClassFile, annotations = annotations, genome = "$genome", ncore = $params.ncore, discovery = TRUE, quant = FALSE, demultiplexed = TRUE, verbose = FALSE, assignDist = FALSE)
} else{
extendedAnno = bambu(reads = readClassFile, annotations = annotations, genome = "$genome", ncore = $params.ncore, discovery = TRUE, quant = FALSE, demultiplexed = TRUE, verbose = FALSE, assignDist = FALSE, NDR = $NDR)
}
saveRDS(extendedAnno, paste0(runName, "_extended_annotations.rds"))
extendedAnno_NDR1 = bambu(reads = readClassFile, annotations = annotations, genome = "$genome", NDR = 1, ncore = $params.ncore, discovery = TRUE, quant = FALSE, demultiplexed = TRUE, verbose = FALSE, assignDist = FALSE)
saveRDS(extendedAnno_NDR1,paste0(runName, "_extended_annotations_NDR1.rds"))
writeToGTF(extendedAnno_NDR1, paste0(runName, "extended_annotations_NDR1.gtf"))
rm(extendedAnno_NDR1)
rm(annotations)
se = bambu(reads = readClassFile, annotations = extendedAnno, genome = "$genome", ncore = $params.ncore, discovery = FALSE, quant = FALSE, demultiplexed = TRUE, verbose = FALSE, opt.em = list(degradationBias = FALSE), assignDist = TRUE, spatial = spatial)
saveRDS(se, paste0(runName, "_quantData.rds"))
for(se.x in se){
if(as.logical("$params.lowMemory")){
writeBambuOutput(se.x, '.', prefix = metadata(se.x)\$sampleNames)
} else{
writeBambuOutput(se.x, '.', prefix = "combined_")
}
}
#writeBambuOutput(do.call(cbind, se), '.')
#write(runName, "runName.txt")
"""
}
process bambu_EM{
publishDir "$params.outdir", mode: 'copy'
cpus params.ncore
maxForks 1
input:
tuple val(sample),path(readClassFile), path(quantData)
path(extendedAnno)
path(extendedAnno_NDR1)
path(extended_annotations_NDR1_gtf)
path(counts)
path(metadata)
path(genome)
val(bambuPath)
val(clusters)
val(resolution)
output:
path ('*.rds')
path ('*.mtx')
path ('*.gtf')
path ('*.tsv')
script:
"""
#!/usr/bin/env Rscript
#.libPaths("/usr/local/lib/R/site-library")
library(devtools)
if("$bambuPath" == "bambu") {
load_all("/mnt/software/bambu")
} else {
load_all("$bambuPath")
}
if(".txt" %in% "$sample"){runName = readLines("$sample")
} else{runName = "$sample"}
extendedAnno <- readRDS("$extendedAnno")
quantDatas = readRDS("$quantData")
#if no clustering provided, automatically cluster
if("$clusters" == "auto"){
clusters = list()
cellMixs = list()
source("${projectDir}/utilityFunctions.R")
for(quantData in quantDatas){
quantData.gene = transcriptToGeneExpression(quantData)
for(sample in unique(colData(quantData)\$sampleName)){
i = which(colData(quantData)\$sampleName == sample)
counts = assays(quantData.gene)\$counts[,i]
cellMix = clusterCells(counts, resolution = $resolution)
x = setNames(names([email protected]), [email protected])
names(x) = paste0(sample,"_",names(x))
clusters = c(clusters, splitAsList(unname(x), names(x)))
cellMixs = c(cellMixs, cellMix)
}
}
saveRDS(clusters, paste0(runName, "_clusters.rds"))
saveRDS(cellMixs, paste0(runName, "_cellMixs.rds"))
}
if("$clusters" == "none"){
clusters = NULL
}
se = bambu( reads = "test.rds",
annotations = extendedAnno,
genome = "$genome",
quantData = quantDatas,
assignDist = FALSE,
ncore = $params.ncore,
discovery = FALSE,
quant = TRUE,
demultiplexed = TRUE,
verbose = FALSE,
opt.em = list(degradationBias = FALSE),
clusters = clusters)
saveRDS(se, paste0(runName, "_se.rds"))
writeBambuOutput(se, path = ".", prefix = paste0(runName, "_EM_"))
"""
}
process fusion_mode{
input:
output:
script:
"""
bpipe run -p refBase=$jaffal_ref_dir $jaffal_ref_dir/JAFFAL.groovy $fastq
"""
}
// This is the workflow to execute the process
workflow {
if (params.reads) {
//User can provide either 1 .fastq file or a .csv with .fastq files
fastq = file(params.reads, checkIfExists:true)
if(fastq.getExtension() == "csv") {
//TODO NEED TO CHECK THAT ALL CHEMISTRY AND TECHNOLOGY VALUES ARE VALID
//TODO NEED TO INCLUDE WHITELIST
readsChannel = Channel.fromPath(params.reads) \
| splitCsv(header:true, sep:',') \
| map { row-> tuple(row.sample, file(row.fastq),
row.containsKey("chemistry") ? row.chemistry : params.chemistry,
row.containsKey("technology") ? row.technology : params.technology,
row.containsKey("whitelist") ? row.whitelist : params.whitelist,
row.containsKey("barcode_map") ? row.barcode_map : params.barcodeMap,
row.containsKey("clusters") ? row.whitelist : params.clusters) }
flexiplex_out_ch = flexiplex(readsChannel.map{it[0..4]})
minimap_out_ch = minimap(flexiplex_out_ch, "$params.genome")
barcodeMaps = readsChannel.collect{it[6]}
barcodeMaps2 = barcodeMaps.map { it == null ? it : params.barcodeMap }
whitelists = readsChannel.collect{it[5]}
whiteLists2 = whitelists.map { it == null ? it : params.whitelist }
clusters = readsChannel.collect{it[7]}
clusters2 = clusters.map { it == null ? it : params.clusters }
}
else {
//if (params.chemistry != "10x3v3"| params.chemistry != "10x3v2" | params.chemistry != "10x5v2"){ exit 1, "--chemistry must be one of (3prime-v3/3prime-v2/5prime-v2)" }
//if (params.technology == false) { exit 1, "--technology must be one of (ONT/PacBio)" }
readsChannel = Channel.fromPath(params.reads)
readsChannel.set{ch_test}
ch_test = ch_test
.map {tuple("Bambu", it, params.chemistry, params.technology, params.whitelist)}
flexiplex_out_ch = flexiplex(ch_test)
minimap_out_ch = minimap(flexiplex_out_ch, "$params.genome")
barcodeMaps2 = params.barcodeMap
whiteLists2 = params.whitelist
clusters2 = params.clusters
}
sampleIds = minimap_out_ch.collect{it[0]}
bamsFiles = minimap_out_ch.collect{it[1]}
}
else{ //When starting from bam
bam = file(params.bams, checkIfExists:true)
if(bam.getExtension() == "csv") {
bamsChannel = Channel.fromPath(params.bams) \
| splitCsv(header:true, sep:',') \
| map { row->
def barcodeMap = row.containsKey("barcode_map") ? row.barcode_map : null
def spatialWhitelist = row.containsKey("spatial_whitelist") ? row.spatial_whitelist : null
def clusters = row.containsKey("clusters") ? row.clusters : null
tuple(row.sample, file(row.bam), barcodeMap, spatialWhitelist, clusters) }
barcodeMaps = bamsChannel.collect{it[2]}
whiteLists = bamsChannel.collect{it[3]}
clusters = bamsChannel.collect{it[4]}
barcodeMaps2 = barcodeMaps.map { it == null ? it : params.barcodeMap }
clusters2 = clusters.map { it == null ? it : params.clusters }
whiteLists2 = whiteLists.map { it == null ? it : params.whitelist }
}
if(bam.getExtension() == "bam"){
bamsChannel = Channel.fromPath(params.bams)
bamsChannel = bamsChannel
.map {["Bambu", it]}
barcodeMaps2 = params.barcodeMap
whiteLists2 = params.whitelist
clusters2 = params.clusters
}
sampleIds = bamsChannel.collect{it[0]}
bamsFiles = bamsChannel.collect{it[1]}
}
if(!params.spatial){
whiteLists2 = "FALSE"
}
bambu_out_ch = bambu(sampleIds, bamsFiles, "$params.genome", "$params.annotation", "$params.bambuPath", params.bambuParams,"$params.NDR",barcodeMaps2, whiteLists2)
if(!params.noEM){
bambuEM_out_ch = bambu_EM(bambu_out_ch, "$params.genome", "$params.bambuPath", clusters2, "$params.resolution")
}
}