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main.nf
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params.help = false
help_message = """
Usage:
nextflow run main.nf --matrix <path> [options]
Description:
ASARUSIM - SINGLE CELL NANOPORE READS SIMULATOR PIPELINE
Simulates nanopore reads from single-cell data.
Required parameters:
--matrix <path> Path to the expression matrix.
--transcriptome <path> Path to the transcriptome file.
--features <value> Features type provided in the count matrix (The matrix row names)
(transcript_id, gene_id or gene_name)[default: transcript_id]
Optional parameters:
--barcodes_counts <value> Distribution of barcode counts.
--full_length <boolean> Indicates if transcripts are full length [default: false].
--simulate_cell_types <boolean> Simulate cell types [default: false].
--cell_type_annotation <path> Path to cell type annotation.
--gtf <path> Path to the GTF file.
--trained_model <path> Pre-trained error model.
--identity_model <value> Identity model for Badread.
--error_model <path> Custom error model.
--qscore_model <path> Q score model.
--build_model <boolean> Build an error model [default: false].
--fastq_model <path> Path to the FASTQ model.
--intron_retention <boolean> Simulate intron retention process
--ir_model <path> intron retention MC model (.CSV file). [default: bin/models/SC3pv3_GEX_Human_IR_markov_model]
--unspliced_ratio <value> Percentage of transcrits to be unspliced.
--ref_genome <path> Path to the reference genome.
--umi_duplication <value> UMI duplication.
--pcr_cycles <int> Number of PCR amplification cycles.
--pcr_dup_rate <value> PCR duplication rate.
--pcr_error_rate <value> PCR error rate.
--pcr_total_reads <int> Total number of PCR reads.
--truncation_model <path> Path to truncation probabilities (.CSV file). [default: bin/models/truncation_default_model.csv].
--outdir <path> Output directory.
Example:
nextflow run main.nf --matrix 'path/to/matrix.csv' --transcriptome 'path/to/transcriptome.fa' --outdir 'results'
"""
if (params.help) {
println help_message
System.exit(0)
}
log.info """\
ASARU - SINGLE CELL NANOPORE READS SIMULATOR P I P E L I N E
===================================
matrix : ${params.matrix}
transcriptome : ${params.transcriptome}
matrix rownames : ${params.features}
barcodes counts distribution : ${params.bc_counts}
full length transcripts : ${params.full_length}
UMI duplication : ${params.umi_duplication}
novel transcripts : ${params.novel_transcripts}
simulate cell types : ${params.sim_celltypes}
cell type annotation : ${params.cell_types_annotation}
GTF : ${params.gtf}
pre-trained error model : ${params.trained_model}
badread sequence identity : ${params.badread_identity}
sequence identity model : ${params.identity_model}
error model : ${params.error_model}
Qscore model : ${params.qscore_model}
build new model : ${params.build_model}
FASTQ model : ${params.fastq_model}
Truncation model : ${params.truncation_model}
simulate intron retention : ${params.intron_retention}
intron retention model : ${params.ir_model}
unspliced ratio : ${params.unspliced_ratio}
reference genome : ${params.ref_genome}
PCR amplification cycles : ${params.pcr_cycles} Cycles
PCR duplication rate : ${params.pcr_dup_rate}
PCR error rate : ${params.pcr_error_rate}
Poly dT mean length : ${params.dT_LENGTH} nt
Adapter sequence : ${params.ADPTER_SEQ}
TSO sequence : ${params.TSO_SEQ}
Threads : ${params.threads} CPUs
outdir : ${params.outdir}
"""
.stripIndent()
logo_ch = channel.fromPath('./images/asarusim_v2.png')
config_params_ch = Channel.value(params)
.map { p -> p.collect { key, value -> "--conf_params $key:$value" }}
workflow_params_ch = Channel.from(workflow).map { p -> p.collect { value -> "--work_params '$value'" }}
include { SUBSAMPLE } from './modules/errorModel.nf'
include { ALIGNMENT } from './modules/errorModel.nf'
include { ERROR_MODLING } from './modules/errorModel.nf'
include { IDENTITY_ESTIMATION } from './modules/errorModel.nf'
include { LENGTH_ESTIMATION } from './modules/errorModel.nf'
include { BATCH_SIZE_FOR_BADREAD } from './modules/errorModel.nf'
include { TRUNCATION_ESTIMATION } from './modules/truncationModel.nf'
include { GFF_TO_FASTA } from './modules/gff_to_fasta.nf'
include { COUNT_SIMULATOR } from './modules/modules.nf'
include { TEMPLATE_MAKER } from './modules/modules.nf'
include { PCR_SIMULATOR } from './modules/PCR.nf'
include { ERRORS_SIMULATOR } from './modules/modules.nf'
include { GROUND_TRUTH } from './modules/modules.nf'
include { QC } from './modules/modules.nf'
def validSPARSIMOptions = ['Bacher', 'Camp', 'Chu', 'Engel', 'Horning', 'Tung', 'Zheng', 'Macosko', 'Saunders']
validSPARSIMOptions = validSPARSIMOptions.collect { it + '_param_preset' }
workflow {
if (params.matrix in validSPARSIMOptions){
matrix_ch = file(params.matrix, type: "file")
} else {
matrix_ch = params.matrix != null ? file(params.matrix, checkIfExists: true) :
file("no_matrix_counts", type: "file")
}
barcodes_ch = params.bc_counts != null ? file(params.bc_counts, checkIfExists: true) :
file("no_barcode_counts", type: "file")
if (barcodes_ch.name == "no_barcode_counts" && matrix_ch.name == "no_matrix_counts" ) {
error("\u001B[31mPlease provide one of the parameters: 'matrix counts' or 'barcodes counts'.\u001B[0m")
System.exit(1)
}
transcriptome_ch = params.transcriptome != null ? file(params.transcriptome, checkIfExists: true) :
file("no_transcriptome", type: "file")
gtf_ch = (params.features != "transcript_id" || params.transcriptome == null || params.intron_retention) ? Channel.fromPath(params.gtf, checkIfExists: true) :
file("no_gtf", type: "file")
cell_types_ch = params.sim_celltypes == true && (params.matrix !in validSPARSIMOptions)? Channel.fromPath(params.cell_types_annotation, checkIfExists: true) :
file("no_cell_types", type: "file")
error_model_ch = params.error_model != null ? file(params.error_model) :
file("no_error_model", type: "file")
qscore_model_ch = params.qscore_model != null ? file(params.qscore_model) :
file("no_qscore_model", type: "file")
identity_ch = params.badread_identity != null ? channel.from(params.badread_identity) :
channel.from("96,2,98")
length_dist_ch = params.length_dist != null ? channel.from(params.length_dist) :
channel.from("0.37,0.0,824.94")
truncation_ch = params.truncation_model != null ? file(params.truncation_model) :
file("bin/models/truncation_default_model.csv", type: "file")
intron_retention_ch = params.ir_model != null ? file(params.ir_model) :
file("bin/models/SC3pv3_GEX_Human_IR_markov_model", type: "file")
genome_ch = params.ref_genome != null ? file(params.ref_genome) :
file("no_genome", type: "file")
if (params.intron_retention || params.transcriptome == null){
if (genome_ch.name == "no_genome") {
println "\u001B[31mPlease provide the path to the reference genome file using the '--ref_genome' option. This is REQUIRED when intron retention mode is enabled or when NO TRANSCRIPTOME file provided.\u001B[0m"
System.exit(1)
}
}
if (params.transcriptome == null) {
if (params.gtf == null){
error("\u001B[31mPlease provide an annotation file in GTF format! e.g '--gtf annoation.gtf' or transcriptome in FASTA format.\u001B[0m")
System.exit(1)
}else{
transcriptome_ch = GFF_TO_FASTA(gtf_ch, genome_ch)
}
}
if (params.build_model) {
fastq_ch = Channel.fromPath(params.fastq_model, checkIfExists: true)
sub_fastq_ch = SUBSAMPLE(fastq_ch)
paf_ch = ALIGNMENT(sub_fastq_ch, transcriptome_ch)
ERROR_MODLING(sub_fastq_ch, transcriptome_ch, paf_ch)
identity_ch = IDENTITY_ESTIMATION(paf_ch)
truncation_ch = TRUNCATION_ESTIMATION(paf_ch)
if (params.features != "transcript_id") {
length_dist_ch = LENGTH_ESTIMATION(fastq_ch)
}
error_model_ch = ERROR_MODLING.out.error_model_ch
qscore_model_ch = ERROR_MODLING.out.qscore_model_ch
}
if (params.sim_celltypes) {
counts_ch = COUNT_SIMULATOR(matrix_ch, cell_types_ch)
TEMPLATE_MAKER(counts_ch, transcriptome_ch, barcodes_ch, gtf_ch, length_dist_ch, truncation_ch, intron_retention_ch, genome_ch)
} else {
TEMPLATE_MAKER(matrix_ch, transcriptome_ch, barcodes_ch, gtf_ch, length_dist_ch, truncation_ch, intron_retention_ch, genome_ch)
}
template_fa_ch = TEMPLATE_MAKER.out.template
template_log_ch = TEMPLATE_MAKER.out.logfile
if (params.pcr_cycles > 0) {
PCR_SIMULATOR(template_fa_ch, template_log_ch)
template_fa_ch = PCR_SIMULATOR.out.fasta
}
gr_truth_ch = GROUND_TRUTH(template_fa_ch)
batch_size_ch = BATCH_SIZE_FOR_BADREAD(template_fa_ch)
ERRORS_SIMULATOR(template_fa_ch, error_model_ch, qscore_model_ch, identity_ch, batch_size_ch.toInteger())
QC(ERRORS_SIMULATOR.out.fastq, config_params_ch, workflow_params_ch, logo_ch, template_log_ch)
}
workflow.onComplete {
log.info ( workflow.success ? "\nDone! Open the following report in your browser --> $params.outdir/${params.projetctName}.html\n" : "Oops .. something went wrong" )
}