def prepare(self):
# parse_mapfile
self.fq_dict, self.col4_dict = parse_map_col4(self.args.mapfile,
self.col4_default)
# link
link_data(self.outdir, self.fq_dict)
# mk log dir
self.logdir = self.outdir + '/log'
os.system('mkdir -p %s' % (self.logdir))
# script init
self.sjm_cmd = 'log_dir %s\n' % (self.logdir)
self.sjm_order = ''
self.shell_dict = defaultdict(str)
# outdir dict
self.outdir_dic = {}
for sample in self.fq_dict:
self.outdir_dic[sample] = {}
index = 0
for step in self.__STEPS__:
step_outdir = f"{self.outdir}/{sample}/{index:02d}.{step}"
self.outdir_dic[sample].update({step: step_outdir})
index += 1
def main():
# init
assay = __ASSAY__
steps = __STEPS__
conda = __CONDA__
app = 'celescope'
# parser
parser = multi_opts(assay)
parser.add_argument('--starMem', help='starMem', default=30)
parser.add_argument('--genomeDir', help='genome index dir', required=True)
parser.add_argument(
'--gtf_type',
help='Specify attribute type in GTF annotation, default=exon',
default='exon')
parser.add_argument('--thread', help='thread', default=6)
parser.add_argument('--probe_file', help="probe fasta file")
args = parser.parse_args()
# read args
outdir = args.outdir
chemistry = args.chemistry
pattern = args.pattern
whitelist = args.whitelist
linker = args.linker
lowQual = args.lowQual
lowNum = args.lowNum
mod = args.mod
rm_files = args.rm_files
# parse mapfile
fq_dict, match_dict = parse_map_col4(args.mapfile, None)
# link
link_data(outdir, fq_dict)
# custom args
thread = args.thread
genomeDir = args.genomeDir
starMem = args.starMem
gtf_type = args.gtf_type
probe_file = args.probe_file
# mk log dir
logdir = outdir + '/log'
os.system('mkdir -p %s' % (logdir))
# script init
sjm_cmd = 'log_dir %s\n' % (logdir)
sjm_order = ''
shell_dict = defaultdict(str)
# outdir dict
for sample in fq_dict:
outdir_dic = {}
index = 0
for step in steps:
step_outdir = f"{outdir}/{sample}/{index:02d}.{step}"
outdir_dic.update({step: step_outdir})
index += 1
# sample
step = "sample"
cmd = (
f'{app} {assay} {step} '
f'--chemistry {chemistry} '
f'--sample {sample} --outdir {outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda)
shell_dict[sample] += cmd + '\n'
last_step = step
# barcode
arr = fq_dict[sample]
step = "barcode"
cmd = (
f'{app} {assay} {step} '
f'--fq1 {arr[0]} --fq2 {arr[1]} --chemistry {chemistry} '
f'--pattern {pattern} --whitelist {whitelist} --linker {linker} '
f'--sample {sample} --lowQual {lowQual} --thread {thread} '
f'--lowNum {lowNum} --outdir {outdir_dic[step]} --assay {assay} '
f'--probe_file {probe_file} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=thread)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
# adapt
step = "cutadapt"
fq = f'{outdir_dic["barcode"]}/{sample}_2.fq.gz'
cmd = (f'{app} {assay} {step} '
f'--fq {fq} --sample {sample} --outdir '
f'{outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=1)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
# STAR
step = 'STAR'
fq = f'{outdir_dic["cutadapt"]}/{sample}_clean_2.fq.gz'
cmd = (f'{app} {assay} {step} '
f'--fq {fq} --sample {sample} '
f'--genomeDir {genomeDir} --thread {thread} '
f'--outdir {outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd,
f'{step}_{sample}',
conda,
m=starMem,
x=thread)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
# featureCounts
step = 'featureCounts'
input = f'{outdir_dic["STAR"]}/{sample}_Aligned.sortedByCoord.out.bam'
cmd = (
f'{app} {assay} {step} '
f'--input {input} --gtf_type {gtf_type} '
f'--sample {sample} --thread {thread} --outdir {outdir_dic[step]} '
f'--genomeDir {genomeDir} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=8, x=thread)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
# count
step = 'count_capture_rna'
bam = f'{outdir_dic["featureCounts"]}/{sample}_name_sorted.bam'
cmd = (f'{app} {assay} {step} '
f'--bam {bam} --sample {sample} --cells auto '
f'--outdir {outdir_dic[step]} --assay {assay} '
f'--match_dir {match_dict[sample]} '
f'--genomeDir {genomeDir}')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=8, x=thread)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
# analysis
step = 'analysis'
matrix_file = f'{outdir_dic["count_capture_rna"]}/{sample}_matrix.tsv.gz'
cmd = (f'{app} {assay} {step} '
f'--matrix_file {matrix_file} --sample {sample} '
f'--outdir {outdir_dic[step]} '
f'--assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=15, x=1)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
# merged report
if mod == 'sjm':
step = 'merge_report'
merge_report(
fq_dict,
steps,
last_step,
sjm_cmd,
sjm_order,
logdir,
conda,
outdir,
rm_files,
)
if mod == 'shell':
os.system('mkdir -p ./shell/')
for sample in shell_dict:
with open(f'./shell/{sample}.sh', 'w') as f:
f.write(shell_dict[sample])
def main():
# init
assay = __ASSAY__
steps = __STEPS__
conda = __CONDA__
app = 'celescope'
# parser
parser = multi_opts(assay)
parser.add_argument('--starMem', help='starMem', default=10)
parser.add_argument('--thread', help='thread', default=6)
parser.add_argument('--genomeDir', help='fusion genomeDir', required=True)
parser.add_argument(
"--fusion_pos",
help="first base position of the second gene(0-start),tsv file",
required=True)
parser.add_argument("--UMI_min", default=1)
args = parser.parse_args()
# read args
outdir = args.outdir
chemistry = args.chemistry
pattern = args.pattern
whitelist = args.whitelist
linker = args.linker
lowQual = args.lowQual
lowNum = args.lowNum
mod = args.mod
rm_files = args.rm_files
# parse mapfile
fq_dict, match_dict = parse_map_col4(args.mapfile, None)
# link
link_data(outdir, fq_dict)
# custom args
thread = args.thread
genomeDir = args.genomeDir
starMem = args.starMem
fusion_pos = args.fusion_pos
UMI_min = args.UMI_min
# mk log dir
logdir = outdir + '/log'
os.system('mkdir -p %s' % (logdir))
# script init
sjm_cmd = 'log_dir %s\n' % (logdir)
sjm_order = ''
shell_dict = defaultdict(str)
# outdir dict
for sample in fq_dict:
outdir_dic = {}
index = 0
for step in steps:
step_outdir = f"{outdir}/{sample}/{index:02d}.{step}"
outdir_dic.update({step: step_outdir})
index += 1
# sample
step = "sample"
cmd = (
f'{app} {assay} {step} '
f'--chemistry {chemistry} '
f'--sample {sample} --outdir {outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda)
shell_dict[sample] += cmd + '\n'
last_step = step
# barcode
arr = fq_dict[sample]
step = "barcode"
cmd = (
f'{app} {assay} {step} '
f'--fq1 {arr[0]} --fq2 {arr[1]} --chemistry {chemistry} '
f'--pattern {pattern} --whitelist {whitelist} --linker {linker} '
f'--sample {sample} --lowQual {lowQual} --thread {thread} '
f'--lowNum {lowNum} --outdir {outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=thread)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
# adapt
step = "cutadapt"
fq = f'{outdir_dic["barcode"]}/{sample}_2.fq.gz'
cmd = (f'{app} {assay} {step} '
f'--fq {fq} --sample {sample} --outdir '
f'{outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=1)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
# STAR_fusion
step = 'STAR_fusion'
input_read = f'{outdir_dic["cutadapt"]}/{sample}_clean_2.fq.gz'
cmd = (
f'{app} {assay} {step} '
f'--outdir {outdir_dic[step]} --assay {assay} --sample {sample} '
f'--thread {thread} '
f'--input_read {input_read} '
f'--genomeDir {genomeDir} ')
sjm_cmd += generate_sjm(cmd,
f'{step}_{sample}',
conda,
m=starMem,
x=thread)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
# count_fusion
step = 'count_fusion'
bam = f'{outdir_dic["STAR_fusion"]}/{sample}_Aligned.sortedByCoord.out.bam'
cmd = (
f'{app} {assay} {step} '
f'--outdir {outdir_dic[step]} --assay {assay} --sample {sample} '
f'--bam {bam} '
f'--UMI_min {UMI_min} '
f'--match_dir {match_dict[sample]} '
f'--fusion_pos {fusion_pos} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=20, x=thread)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
last_step = step
# merged report
if mod == 'sjm':
step = 'merge_report'
merge_report(
fq_dict,
steps,
last_step,
sjm_cmd,
sjm_order,
logdir,
conda,
outdir,
rm_files,
)
if mod == 'shell':
os.system('mkdir -p ./shell/')
for sample in shell_dict:
with open(f'./shell/{sample}.sh', 'w') as f:
f.write(shell_dict[sample])
def main():
# init
assay = __ASSAY__
steps = __STEPS__
conda = __CONDA__
app = 'celescope'
# parser
parser = multi_opts(assay)
parser.add_argument('--thread', help='thread', default=6)
parser.add_argument(
"--UMI_min",
help="cells have SMK_UMI>=UMI_min are considered as valid cell",
default="auto")
parser.add_argument("--dim", help="SMK tag dimension", default=1)
parser.add_argument("--SNR_min",
help="minimum signal to noise ratio",
default="auto")
parser.add_argument("--SMK_pattern", help="SMK read2 pattern")
parser.add_argument("--SMK_linker", help="SMK read2 linker fasta path")
parser.add_argument("--SMK_barcode", help="SMK read2 barcode fasta path ")
args = parser.parse_args()
# read args
outdir = args.outdir
chemistry = args.chemistry
pattern = args.pattern
whitelist = args.whitelist
linker = args.linker
lowQual = args.lowQual
lowNum = args.lowNum
mod = args.mod
rm_files = args.rm_files
# parse mapfile
fq_dict, match_dict = parse_map_col4(args.mapfile, "auto")
# link
link_data(outdir, fq_dict)
# custom args
thread = args.thread
UMI_min = args.UMI_min
dim = args.dim
SNR_min = args.SNR_min
SMK_pattern = args.SMK_pattern
SMK_linker = args.SMK_linker
SMK_barcode = args.SMK_barcode
# mk log dir
logdir = outdir + '/log'
os.system('mkdir -p %s' % (logdir))
# script init
sjm_cmd = 'log_dir %s\n' % (logdir)
sjm_order = ''
shell = ''
# run
for sample in fq_dict:
outdir_dic = {}
index = 0
for step in steps:
step_outdir = f"{outdir}/{sample}/{index:02d}.{step}"
outdir_dic.update({step: step_outdir})
index += 1
# sample
step = "sample"
cmd = (
f'{app} {assay} {step} '
f'--chemistry {chemistry} '
f'--sample {sample} --outdir {outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda)
shell += cmd + '\n'
last_step = step
# barcode
arr = fq_dict[sample]
step = "barcode"
cmd = (
f'{app} {assay} {step} '
f'--fq1 {arr[0]} --fq2 {arr[1]} --chemistry {chemistry} '
f'--pattern {pattern} --whitelist {whitelist} --linker {linker} '
f'--sample {sample} --lowQual {lowQual} --thread {thread} '
f'--lowNum {lowNum} --outdir {outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=thread)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell += cmd + '\n'
last_step = step
# adapt
step = "cutadapt"
fq = f'{outdir_dic["barcode"]}/{sample}_2.fq.gz'
cmd = (f'{app} {assay} {step} '
f'--fq {fq} --sample {sample} --outdir '
f'{outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=1)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell += cmd + '\n'
last_step = step
# mapping_smk
step = 'mapping_smk'
SMK_read2 = f'{outdir_dic["cutadapt"]}/{sample}_clean_2.fq.gz'
cmd = (f'{app} {assay} {step} '
f'--sample {sample} '
f'--outdir {outdir_dic[step]} '
f'--assay {assay} '
f'--SMK_read2 {SMK_read2} '
f'--SMK_pattern {SMK_pattern} '
f'--SMK_barcode {SMK_barcode} '
f'--SMK_linker {SMK_linker} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=1)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell += cmd + '\n'
last_step = step
# count_smk
step = 'count_smk'
read_file = f'{outdir_dic["mapping_smk"]}/{sample}_read_count.tsv'
cmd = (f'{app} {assay} {step} '
f'--sample {sample} '
f'--outdir {outdir_dic[step]} '
f'--assay {assay} '
f'--match_dir {match_dict[sample]} '
f'--read_file {read_file} '
f'--dim {dim} '
f'--UMI_min {UMI_min} '
f'--SNR_min {SNR_min} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=1)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell += cmd + '\n'
last_step = step
# analysis_smk
step = 'analysis_smk'
tsne_tag_file = f'{outdir_dic["count_smk"]}/{sample}_tsne_tag.tsv'
cmd = (f'{app} {assay} {step} '
f'--sample {sample} '
f'--outdir {outdir_dic[step]} '
f'--assay {assay} '
f'--match_dir {match_dict[sample]} '
f'--tsne_tag_file {tsne_tag_file} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=1)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell += cmd + '\n'
last_step = step
# merged report
if mod == 'sjm':
step = 'merge_report'
merge_report(
fq_dict,
steps,
last_step,
sjm_cmd,
sjm_order,
logdir,
conda,
outdir,
rm_files,
)
if mod == 'shell':
os.system('mkdir -p ./shell/')
with open(f'./shell/{sample}.sh', 'w') as f:
f.write(shell)
def main():
# init
assay = __ASSAY__
steps = __STEPS__
conda = __CONDA__
app = 'celescope'
# parser
parser = multi_opts(assay)
parser.add_argument('--thread', help='thread', default=6)
parser.add_argument("--fq_pattern",
help="tag read2 pattern",
required=True)
parser.add_argument("--linker_fasta", help="linker fasta")
parser.add_argument("--barcode_fasta", help="barcode fasta", required=True)
args = parser.parse_args()
# read args
outdir = args.outdir
chemistry = args.chemistry
pattern = args.pattern
whitelist = args.whitelist
linker = args.linker
lowQual = args.lowQual
lowNum = args.lowNum
mod = args.mod
rm_files = args.rm_files
minimum_length = args.minimum_length
# parse mapfile
fq_dict, match_dict = parse_map_col4(args.mapfile, None)
# link
link_data(outdir, fq_dict)
# custom args
thread = args.thread
fq_pattern = args.fq_pattern
linker_fasta = args.linker_fasta
barcode_fasta = args.barcode_fasta
# mk log dir
logdir = outdir + '/log'
os.system('mkdir -p %s' % (logdir))
# script init
sjm_cmd = 'log_dir %s\n' % (logdir)
sjm_order = ''
shell_dict = defaultdict(str)
# run
for sample in fq_dict:
outdir_dic = {}
index = 0
for step in steps:
step_outdir = f"{outdir}/{sample}/{index:02d}.{step}"
outdir_dic.update({step: step_outdir})
index += 1
# sample
step = "sample"
cmd = (
f'{app} {assay} {step} '
f'--chemistry {chemistry} '
f'--sample {sample} --outdir {outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda)
shell_dict[sample] += cmd + '\n'
last_step = step
# barcode
arr = fq_dict[sample]
step = "barcode"
cmd = (
f'{app} {assay} {step} '
f'--fq1 {arr[0]} --fq2 {arr[1]} --chemistry {chemistry} '
f'--pattern {pattern} --whitelist {whitelist} --linker {linker} '
f'--sample {sample} --lowQual {lowQual} --thread {thread} '
f'--lowNum {lowNum} --outdir {outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=thread)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
# adapt
step = "cutadapt"
fq = f'{outdir_dic["barcode"]}/{sample}_2.fq.gz'
cmd = (f'{app} {assay} {step} '
f'--fq {fq} --sample {sample} --outdir '
f'{outdir_dic[step]} --assay {assay} '
f'--minimum_length {minimum_length} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=1)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
# mapping_tag
step = 'mapping_tag'
fq = f'{outdir_dic["cutadapt"]}/{sample}_clean_2.fq.gz'
cmd = (f'{app} {assay} {step} '
f'--sample {sample} '
f'--outdir {outdir_dic[step]} '
f'--assay {assay} '
f'--fq {fq} '
f'--fq_pattern {fq_pattern} '
f'--barcode_fasta {barcode_fasta} '
f'--linker_fasta {linker_fasta} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=1)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
step = 'count_cite'
read_count_file = f'{outdir_dic["mapping_tag"]}/{sample}_read_count.tsv'
cmd = (f'{app} {assay} {step} '
f'--sample {sample} '
f'--outdir {outdir_dic[step]} '
f'--assay {assay} '
f'--match_dir {match_dict[sample]} '
f'--read_count_file {read_count_file} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=1)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
step = 'analysis_cite'
citeseq_mtx = f'{outdir_dic["count_cite"]}/{sample}_citeseq.mtx.gz'
cmd = (f'{app} {assay} {step} '
f'--sample {sample} '
f'--outdir {outdir_dic[step]} '
f'--assay {assay} '
f'--match_dir {match_dict[sample]} '
f'--citeseq_mtx {citeseq_mtx} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=1)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell_dict[sample] += cmd + '\n'
last_step = step
# merged report
if mod == 'sjm':
step = 'merge_report'
merge_report(
fq_dict,
steps,
last_step,
sjm_cmd,
sjm_order,
logdir,
conda,
outdir,
rm_files,
)
if mod == 'shell':
os.system('mkdir -p ./shell/')
for sample in shell_dict:
with open(f'./shell/{sample}.sh', 'w') as f:
f.write(shell_dict[sample])
def main():
# init
assay = __ASSAY__
steps = __STEPS__
conda = __CONDA__
app = 'celescope'
# parser
parser = multi_opts(assay)
parser.add_argument('--thread', help='thread', default=6)
args = parser.parse_args()
# read args
outdir = args.outdir
chemistry = args.chemistry
pattern = args.pattern
whitelist = args.whitelist
linker = args.linker
lowQual = args.lowQual
lowNum = args.lowNum
mod = args.mod
rm_files = args.rm_files
# parse mapfile
fq_dict, match_dict = parse_map_col4(args.mapfile, None)
# link
link_data(outdir, fq_dict)
# custom args
thread = args.thread
# mk log dir
logdir = outdir + '/log'
os.system('mkdir -p %s' % (logdir))
# script init
sjm_cmd = 'log_dir %s\n' % (logdir)
sjm_order = ''
shell = ''
# outdir dict
for sample in fq_dict:
outdir_dic = {}
index = 0
for step in steps:
step_outdir = f"{outdir}/{sample}/{index:02d}.{step}"
outdir_dic.update({step: step_outdir})
index += 1
# sample
step = "sample"
cmd = (
f'{app} {assay} {step} '
f'--chemistry {chemistry} '
f'--sample {sample} --outdir {outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda)
shell += cmd + '\n'
last_step = step
# barcode
arr = fq_dict[sample]
step = "barcode"
cmd = (
f'{app} {assay} {step} '
f'--fq1 {arr[0]} --fq2 {arr[1]} --chemistry {chemistry} '
f'--pattern {pattern} --whitelist {whitelist} --linker {linker} '
f'--sample {sample} --lowQual {lowQual} --thread {thread} '
f'--lowNum {lowNum} --outdir {outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=thread)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell += cmd + '\n'
last_step = step
# adapt
step = "cutadapt"
fq = f'{outdir_dic["barcode"]}/{sample}_2.fq.gz'
cmd = (f'{app} {assay} {step} '
f'--fq {fq} --sample {sample} --outdir '
f'{outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=5, x=1)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell += cmd + '\n'
last_step = step
# mapping_hla
step = 'mapping_hla'
fq = f'{outdir_dic["cutadapt"]}/{sample}_clean_2.fq.gz'
cmd = (f'{app} {assay} {step} '
f'--fq {fq} --sample {sample} '
f'--thread {thread} '
f'--match_dir {match_dict[sample]} '
f'--outdir {outdir_dic[step]} --assay {assay} ')
sjm_cmd += generate_sjm(cmd, f'{step}_{sample}', conda, m=30, x=thread)
sjm_order += f'order {step}_{sample} after {last_step}_{sample}\n'
shell += cmd + '\n'
last_step = step
# merged report
if mod == 'sjm':
step = 'merge_report'
merge_report(
fq_dict,
steps,
last_step,
sjm_cmd,
sjm_order,
logdir,
conda,
outdir,
rm_files,
)
if mod == 'shell':
os.system('mkdir -p ./shell/')
with open(f'./shell/{sample}.sh', 'w') as f:
f.write(shell)