def main():
fmtdate = '%H:%M:%S %d-%m'
now = datetime.datetime.now().strftime(fmtdate)
home = os.path.expanduser("~")
args = arguments.setting()
if args.pasa_db == "":
pasadb = ''.join(random.sample(string.ascii_lowercase, 5))
else:
pasadb = args.pasa_db
augustus_species = logistic.augustus_species_func()
if not augustus_species.get(args.species) and args.long_reads == "" and args.short_reads == "":
sys.exit("#####PLEASE DEFINE A SPECIES NAME OR ANY KIND OF RNA-SEQ AND RE-RUN\t" + now + "\t#####\n")
max_threads = multiprocessing.cpu_count()
gmap_name = args.reference + '_GMAPindex'
pasa_name = 'assembler-' + pasadb
if args.upgrade == "":
protein_loc = os.path.abspath(args.proteins)
iprscan_log = iprscan.check_iprscan()
# Useful variables for later
root = os.getcwd()
#if args.out_dir != "":# and args.out_dir.startswith("/"):
# output_dir = os.path.join(root, "LoReAn" + args.out_dir)
#else:
output_dir = os.path.join(root, "LoReAn_" + args.out_dir)
logistic.check_create_dir(output_dir)
if args.keep_tmp or args.verbose:
wd = os.path.join(output_dir, "run/")
logistic.check_create_dir(wd)
else:
temp_dir = tempfile.TemporaryDirectory(prefix='run_', dir=output_dir, suffix="/", )
wd = temp_dir.name
if args.upgrade == "":
#if not os.path.isfile(home + "/.gm_key"):
# sys.exit("#####LOREAN STOPS HERE. CHECK THAT THE gm_key IS IN THE HOME FOLDER#####\n")
if args.proteins == "":
if not args.keep_tmp or not args.verbose:
shutil.rmtree(wd)
sys.exit("#####LOREAN STOPS HERE. CHECK THAT THE PROTEIN OPTION IS SET#####\n")
if args.long_reads != "":
if args.stranded or args.adapter:
if args.adapter == '':
adapter_value = True
sys.stdout.write('### RUNNING IN STRAND MODE AND FINDING ADAPTER AUTOMATICALLY ###\n')
stranded_value = True
else:
adapter_value = args.adapter
sys.stdout.write('### RUNNING IN STRAND MODE AND USING ADAPTER PROVIDED ###\n')
stranded_value = True
else:
stranded_value = False
sys.stdout.write('### RUNNING IN NON-STRAND MODE ###\n')
adapter_value = False
ref_orig = os.path.abspath(args.reference)
ref_link = os.path.join(wd, args.reference)
if not os.path.exists(ref_link):
shutil.copyfile(ref_orig, ref_link)
long_reads = args.long_reads
fasta = (".fasta", ".fa", ".fas", ".fsta")
fastq = (".fastq", ".fq")
'''Core of the program'''
# Parse the arguments
if int(args.threads) > max_threads:
threads_use = str(max_threads)
sys.stdout.write(('### MAX NUMBER OF USED THREADS IS ' + str(max_threads) + ' AND NOT ' + args.threads + ' AS SET ###\n'))
else:
threads_use = args.threads
if args.external:
external_file = args.external
else:
external_file = ''
if args.upgrade == "":
if args.species == "":
sys.exit("#####PLEASE DEFINE A SPECIES NAME\t" + now + "\t#####\n")
else:
if args.short_reads == '' and long_reads == '':
if external_file.endswith("gff3") or external_file.endswith(fasta):
weights_dic = {'Augustus': args.augustus_weigth, 'GeneMark.hmm': args.genemark_weigth, 'exonerate': args.exonerate_weigth,
'external' : args.external_weigth}
else:
weights_dic = {'Augustus': args.augustus_weigth, 'GeneMark.hmm': args.genemark_weigth, 'exonerate': args.exonerate_weigth}
elif args.short_reads != '' or long_reads != '':
if external_file.endswith("gff3") or external_file.endswith(fasta):
weights_dic = {'Augustus': args.augustus_weigth, pasa_name: args.pasa_weigth, 'GeneMark.hmm': args.genemark_weigth,
'exonerate': args.exonerate_weigth, gmap_name: args.trinity_weigth, 'external' : args.external_weigth}
else:
weights_dic = {'Augustus': args.augustus_weigth, pasa_name: args.pasa_weigth, 'GeneMark.hmm': args.genemark_weigth,
'exonerate': args.exonerate_weigth, gmap_name: args.trinity_weigth}
final_files = [] # STORE THE IMPORTANT OUTPUT FILES
logistic.check_create_dir(wd)
logistic.check_file(ref_link)
gmap_wd = os.path.join(wd ,'gmap_output/')
exonerate_wd = os.path.join(wd , 'exonerate')
pasa_dir = os.path.join(wd , 'PASA/')
star_out = os.path.join(wd , 'STAR/')
trin_dir = os.path.join(wd , 'Trinity/')
evm_inputs_dir = os.path.join(wd , 'evm_inputs/')
braker_folder = os.path.join(wd , 'braker/')
evm_output_dir = os.path.join(wd , 'evm_output/')
interproscan_out_dir = os.path.join(wd , 'interproscan')
wd_split = os.path.join(wd , 'split/')
logistic.check_create_dir(wd_split)
logistic.check_create_dir(evm_inputs_dir)
logistic.check_create_dir(evm_output_dir)
logistic.check_create_dir(trin_dir)
logistic.check_create_dir(star_out)
logistic.check_create_dir(pasa_dir)
logistic.check_create_dir(gmap_wd)
logistic.check_create_dir(exonerate_wd)
if args.interproscan:
logistic.check_create_dir(interproscan_out_dir)
if long_reads:
consensus_wd = os.path.join(wd , 'consensus/')
logistic.check_create_dir(consensus_wd)
if long_reads != "" or args.short_reads != "":
logistic.check_gmap(threads_use, 'samse', args.min_intron_length, args.max_intron_length, args.end_exon, gmap_wd,
args.verbose)
if args.repeat_masked != "":
sys.stdout.write(('###MASKING THE GENOME STARTED AT:\t' + now + '\t###\n'))
masked_ref = mseq.maskedgenome(wd_split, ref_link, args.repeat_masked, args.repeat_lenght, args.verbose)
elif args.mask_genome:
sys.stdout.write(('###RUNNNG REPEATSCOUT AND REPEATMASK TO MASK THE GENOME STARTED AT:\t' + now + '\t###\n'))
masked_ref, repeats_families, repeats_gff = mseq.repeatsfind(ref_link, wd_split, threads_use, args.verbose)
if os.path.exists(repeats_families):
final_files.append(repeats_families)
if os.path.exists(repeats_gff):
final_files.append(repeats_gff)
else:
masked_ref = ref_link
list_fasta_names, dict_ref_name, ref_rename = multiple.single_fasta(masked_ref, wd_split)
if args.short_reads or long_reads:
if int(threads_use) > 1:
trinity_cpu = int(int(threads_use) / int(2))
else:
trinity_cpu = int(threads_use)
now = datetime.datetime.now().strftime(fmtdate)
# SHORT READS
if args.short_reads.endswith(fastq):
sys.stdout.write(('###STAR MAPPING STARTED AT:\t' + now + '\t###\n'))
if ',' in args.short_reads:
paired_end_files = args.short_reads.split(',')
short_1 = os.path.abspath(paired_end_files[0])
short_2 = os.path.abspath(paired_end_files[1])
short_reads_file = [short_1, short_2]
else:
short_reads_file = os.path.abspath(args.short_reads)
# Map with STAR
short_bam = mapping.star(ref_rename, short_reads_file, threads_use, args.max_intron_length, star_out,
args.verbose)
short_sorted_bam = mapping.samtools_sort(short_bam, threads_use, wd, args.verbose)
final_mapping_star = mapping.change_chr(short_sorted_bam, dict_ref_name, star_out, threads_use, args.verbose, "short")
default_bam = short_sorted_bam
# Keep the output
final_files.append(final_mapping_star)
# TRANSCRIPT ASSEMBLY
# TRINITY
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###TRINITY STARTS AT:\t' + now + '\t###\n'))
trinity_out = transcripts.trinity(short_sorted_bam, trin_dir, args.max_intron_length, trinity_cpu, args.verbose)
if args.upgrade == "":
trinity_gff3 = mapping.gmap('trin', ref_rename, trinity_out, threads_use, 'gff3_gene',
args.min_intron_length, args.max_intron_length, args.end_exon, gmap_wd,
args.verbose, Fflag=True)
trinity_path = trinity_gff3
long_sorted_bam = False
# BAM SORTED FILES GET IN HERE
elif args.short_reads.endswith("bam") or long_reads.endswith("bam"):
logistic.check_create_dir(star_out)
if args.short_reads.endswith("bam"):
map_reads = os.path.abspath(args.short_reads)
short_sorted_bam = mapping.change_chr_to_seq(map_reads, dict_ref_name, star_out, threads_use, args.verbose)
else:
map_reads = os.path.abspath(long_reads)
short_sorted_bam = mapping.change_chr_to_seq(map_reads, dict_ref_name, star_out, threads_use, args.verbose)
mapping.samtools_index(short_sorted_bam, star_out, args.verbose)
long_reads = transcripts.bamtofastq(short_sorted_bam, args.verbose)
#short_sorted_bam = os.path.abspath(args.short_reads)
default_bam = short_sorted_bam
# TRANSCRIPT ASSEMBLY
# TRINITY
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###TRINITY STARTS AT:\t' + now + '\t###\n'))
trinity_out = transcripts.trinity(short_sorted_bam, trin_dir, args.max_intron_length, trinity_cpu, args.verbose)
if args.upgrade == "":
trinity_gff3 = mapping.gmap('trin', ref_rename, trinity_out, threads_use, 'gff3_gene',
args.min_intron_length, args.max_intron_length, args.end_exon, gmap_wd,
args.verbose, Fflag=True)
trinity_path = trinity_gff3
long_sorted_bam = False
# LONG READS
elif long_reads.endswith(fastq) or long_reads.endswith(fasta):
# with this operation, reads are filtered for their length.
# Nanopore reads can be chimeras or sequencing artefacts.
# filtering on length reduces the amount of sequencing
# artefacts
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(("###FILTERING OUT LONG READS STARTED AT:\t" + now + "\t###\n"))
long_fasta, stranded_value = mseq.filterLongReads(long_reads, args.assembly_overlap_length, args.max_long_read, gmap_wd,
adapter_value, threads_use, args.adapter_match_score, ref_rename,
args.max_intron_length, args.verbose, stranded_value)
# If short reads have been mapped dont do it
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###GMAP\t' + now + 't###\n'))
if args.minimap2:
long_sam = mapping.minimap(ref_rename, long_fasta, threads_use, args.max_intron_length, gmap_wd, args.verbose)
else:
long_sam = mapping.gmap('sam', ref_rename, long_fasta, threads_use, 'samse',
args.min_intron_length, args.max_intron_length, args.end_exon, gmap_wd,
args.verbose, Fflag=False)
# Convert to sorted BAM
long_sorted_bam = mapping.sam_to_sorted_bam(long_sam, threads_use, gmap_wd, args.verbose)
sam_orig_id = mapping.change_chr(long_sorted_bam, dict_ref_name, gmap_wd, threads_use, args.verbose, "long")
default_bam = long_sorted_bam
# Keep the output
final_files.append(sam_orig_id)
# TRANSCRIPT ASSEMBLY
# TRINITY
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###TRINITY STARTS AT:\t' + now + '\t###\n'))
trinity_out = transcripts.trinity(long_sorted_bam, trin_dir, args.max_intron_length, trinity_cpu, args.verbose)
if args.upgrade == "":
trinity_gff3 = mapping.gmap('trin', ref_rename, trinity_out, threads_use, 'gff3_gene',
args.min_intron_length, args.max_intron_length, args.end_exon, gmap_wd,
args.verbose, Fflag=True)
trinity_path = trinity_gff3
else:
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###NO LONG READS FILE OR SHORT READS\t' + now + '\t###\n'))
# PASA Pipeline
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###PASA STARTS AT:\t' + now + '\t###\n'))
# Create PASA folder and configuration file
#align_pasa_conf = pasa.pasa_configuration(pasa_dir, pasadb, args.verbose)
# Launch PASA
if args.upgrade == "":
#if os.path.isfile(home + "/.gm_key") and args.proteins != "":
if args.proteins != "":
pasa_gff3 = pasa.pasa_call(pasa_dir, pasadb, ref_rename, trinity_out, args.max_intron_length,
threads_use, args.verbose)
final_files.append(grs.trasform_gff(pasa_gff3, dict_ref_name))
# HERE WE PARALLELIZE PROCESSES WHEN MULTIPLE THREADS ARE USED
if args.species in augustus_species:
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###AUGUSTUS, GENEMARK-ES AND EXONERATE STARTED AT:' + now + '\t###\n'))
queue = Queue()
for software in range(3):
queue.put(software) # QUEUE WITH A ZERO AND A ONE
for software in range(3):
t = Thread(target=handler.august_gmes_exonerate, args=(queue, ref_rename, args.species, protein_loc,
threads_use, args.fungus, list_fasta_names, wd, exonerate_wd,
args.verbose))
t.daemon = True
t.start()
queue.join()
augustus_file = wd + 'augustus/augustus.gff'
augustus_gff3 = inputEvm.convert_augustus(augustus_file, wd)
final_files.append(grs.trasform_gff(augustus_gff3, dict_ref_name))
genemark_file = wd + 'gmes/genemark.gtf'
genemark_gff3 = inputEvm.convert_genemark(genemark_file, wd)
final_files.append(grs.trasform_gff(genemark_gff3, dict_ref_name))
merged_prot_gff3 = wd + 'exonerate/protein_evidence.gff3'
final_files.append(grs.trasform_gff(merged_prot_gff3, dict_ref_name))
elif args.short_reads or long_reads: # USING PROTEINS AND SHORT READS
logistic.check_create_dir(braker_folder)
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###BRAKER1 (USING SHORT READS) AND EXONERATE STARTED AT:\t' + now + '\t###\n'))
queue = Queue()
for software in range(2):
queue.put(software) # QUEUE WITH A ZERO AND A ONE
for software in range(2):
t = Thread(target=handler.braker_exonerate, args=(queue, ref_rename, default_bam, args.species, protein_loc,
threads_use, args.fungus, wd,
braker_folder, exonerate_wd, args.verbose))
t.daemon = True
t.start()
queue.join()
augustus_file, genemark_file = inputEvm.braker_folder_find(braker_folder)
augustus_gff3 = inputEvm.convert_augustus(augustus_file, wd)
genemark_gff3 = inputEvm.convert_genemark(genemark_file, wd)
merged_prot_gff3 = wd + 'exonerate/protein_evidence.gff3'
final_files.append(grs.trasform_gff(augustus_gff3, dict_ref_name))
final_files.append(grs.trasform_gff(genemark_gff3, dict_ref_name))
final_files.append(grs.trasform_gff(merged_prot_gff3, dict_ref_name))
else: # USING PROTEINS AND LONG READS
queue = Queue()
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###BRAKER1 (USING LONG READS) AND EXONERATE STARTED AT: \t' + now + '\t###\n'))
logistic.check_create_dir(braker_folder)
for software in range(2):
queue.put(software) # QUEUE WITH A ZERO AND A ONE
for software in range(2):
t = Thread(target=handler.braker_exonerate,
args=(queue, ref_rename, long_sorted_bam, args.species, protein_loc,
threads_use, args.fungus, wd, braker_folder, exonerate_wd, args.verbose))
t.daemon = True
t.start()
queue.join()
augustus_file, genemark_file = inputEvm.braker_folder_find(braker_folder)
augustus_gff3 = inputEvm.convert_augustus(augustus_file, wd)
genemark_gff3 = inputEvm.convert_genemark(genemark_file, wd)
merged_prot_gff3 = wd + 'exonerate/protein_evidence.gff3'
final_files.append(grs.trasform_gff(augustus_gff3, dict_ref_name))
final_files.append(grs.trasform_gff(genemark_gff3, dict_ref_name))
final_files.append(grs.trasform_gff(merged_prot_gff3, dict_ref_name))
elif args.species in augustus_species or args.species != "" or args.upgrade != "":
#if os.path.isfile(home + "/.gm_key") and args.proteins != "":
if args.proteins != "":
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###AUGUSTUS, GENEMARK-ES AND EXONERATE STARTED AT:' + now + '\t###\n'))
queue = Queue()
for software in range(3):
queue.put(software) # QUEUE WITH A ZERO AND A ONE
for software in range(3):
t = Thread(target=handler.august_gmes_exonerate, args=(queue, ref_rename, args.species, protein_loc,
threads_use, args.fungus, list_fasta_names, wd, exonerate_wd,
args.verbose))
t.daemon = True
t.start()
queue.join()
augustus_file = wd + 'augustus/augustus.gff'
augustus_gff3 = inputEvm.convert_augustus(augustus_file, wd)
genemark_file = wd + 'gmes/genemark.gtf'
genemark_gff3 = inputEvm.convert_genemark(genemark_file, wd)
merged_prot_gff3 = wd + 'exonerate/protein_evidence.gff3'
final_files.append(grs.trasform_gff(augustus_gff3, dict_ref_name))
final_files.append(grs.trasform_gff(genemark_gff3, dict_ref_name))
final_files.append(grs.trasform_gff(merged_prot_gff3, dict_ref_name))
else:
now = datetime.datetime.now().strftime(fmtdate)
sys.exit("#####UNRECOGNIZED SPECIES FOR AUGUSTUS AND NO READS\t" + now + "\t#####\n")
# Prepare EVM input files
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###EVM STARTED AT:\t' + now + '\t###\n'))
# HERE WE CONVERT FILES FOR EVM AND PLACE THEM IN INPUT FOLDER
round_n = 0
if args.upgrade == "":
if not args.short_reads and not long_reads:
if external_file:
if external_file.endswith(fasta):
external_file_gff3 = mapping.gmap('ext', ref_rename, external_file, threads_use, 'gff3_gene',
args.min_intron_length, args.max_intron_length, args.end_exon,
gmap_wd, args.verbose, Fflag=True)
external_file_changed = update.external(external_file_gff3, gmap_wd, args.verbose)
elif external_file.endswith("gff3"):
external_file_changed = update.external(external_file, gmap_wd, args.verbose)
evm_inputs = {'augustus': augustus_gff3, 'genemark': genemark_gff3, 'exonerate': merged_prot_gff3,
'external': external_file_changed}
else:
evm_inputs = {'augustus': augustus_gff3, 'genemark': genemark_gff3, 'exonerate': merged_prot_gff3}
elif args.short_reads or long_reads:
if args.external:
external_file = args.external
if external_file.endswith(fasta):
external_file_gff3 = mapping.gmap('ext', ref_rename, external_file, threads_use, 'gff3_gene',
args.min_intron_length, args.max_intron_length, args.end_exon,
gmap_wd, args.verbose, Fflag=True)
external_file_changed = update.external(external_file_gff3, gmap_wd, args.verbose)
elif external_file.endswith("gff3"):
external_file_changed = update.external(external_file, gmap_wd, args.verbose)
evm_inputs = {'pasa': pasa_gff3, 'augustus': augustus_gff3, 'genemark': genemark_gff3,
'exonerate': merged_prot_gff3, 'gmap': trinity_path,'external': external_file_changed}
else:
evm_inputs = {'pasa': pasa_gff3, 'augustus': augustus_gff3, 'genemark': genemark_gff3,
'exonerate': merged_prot_gff3, 'gmap': trinity_path}
# HERE WE RUN EVM; WE PREPARE FILES THAT ARE REQUIRED BY EVM LIKE
# WEIGTH TABLE
list_soft, pred_file, transcript_file, protein_file = inputEvm.group_EVM_inputs(evm_inputs_dir, evm_inputs)
weight_file = inputEvm.evm_weight(evm_inputs_dir, weights_dic, list_soft, pasa_name, gmap_name)
# EVM PIPELINE
if args.short_reads or long_reads: # WE HAVE SHORT READS AND PROTEINS
evm_gff3 = evmPipeline.evm_pipeline(evm_output_dir, threads_use, ref_rename, weight_file, pred_file,
transcript_file, protein_file, args.segmentSize, args.overlap_size,
args.verbose)
final_evm = grs.genename_evm(evm_gff3, args.verbose, evm_output_dir, dict_ref_name, args.upgrade)
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###UPDATE WITH PASA DATABASE STARTED AT:\t ' + now + '\t###\n'))
round_n += 1
final_output = pasa.update_database(threads_use, str(round_n), pasa_dir, pasadb, ref_rename, trinity_out,
final_evm, args.verbose)
if long_reads == '':
final_update_all = grs.genename_last(final_output, args.prefix_gene, args.verbose, pasa_dir, dict_ref_name, "pasa")
final_update_stats = evmPipeline.gff3_stats(final_update_all, pasa_dir)
final_files.append(final_update_all)
final_files.append(final_update_stats)
if "command" not in (iprscan_log.decode("utf-8")) and args.interproscan:
annot, bad_models = iprscan.iprscan(masked_ref, final_update_all, interproscan_out_dir, args.threads)
final_files.append(annot)
final_files.append(bad_models)
final_output_dir = os.path.join(output_dir, args.out_dir + '_output')
logistic.check_create_dir(final_output_dir)
for filename in final_files:
if filename != '':
logistic.copy_file(filename, final_output_dir)
cmdstring = "chmod -R 775 %s" % wd
os.system(cmdstring)
now = datetime.datetime.now().strftime(fmtdate)
sys.exit("#####LOREAN FINISHED WITHOUT USING LONG READS\t" + now + "\t. GOOD BYE.#####\n")
else:
final_keep = grs.genename_last(final_output, args.prefix_gene, args.verbose, pasa_dir, dict_ref_name, "pasa")
final_keep_stats = evmPipeline.gff3_stats(final_keep, pasa_dir)
final_files.append(final_keep)
final_files.append(final_keep_stats)
elif not args.short_reads and not long_reads: # WE HAVE PROTEINS BUT NOT SHORT READS
transcript_file = ''
evm_gff3 = evmPipeline.evm_pipeline(evm_output_dir, threads_use, ref_rename, weight_file, pred_file,
transcript_file, protein_file, args.segmentSize, args.overlap_size,
args.verbose)
final_update_all = grs.genename_last(evm_gff3, args.prefix_gene, args.verbose, pasa_dir, dict_ref_name, "pasa")
final_update_stats = evmPipeline.gff3_stats(final_update_all, pasa_dir)
final_files.append(final_update_all)
final_files.append(final_update_stats)
now = datetime.datetime.now().strftime(fmtdate)
if "command" not in (iprscan_log.decode("utf-8")) and args.interproscan:
annot, bad_models = iprscan.iprscan(masked_ref, final_update_all, interproscan_out_dir, args.threads)
final_files.append(annot)
final_files.append(bad_models)
final_output_dir = os.path.join(output_dir, args.out_dir + '_output')
logistic.check_create_dir(final_output_dir)
for filename in final_files:
if filename != '':
logistic.copy_file(filename, final_output_dir)
cmdstring = "chmod -R 775 %s" % wd
os.system(cmdstring)
now = datetime.datetime.now().strftime(fmtdate)
sys.exit("##### EVM FINISHED AT:\t" + now + "\t#####\n")
else:
final_evm = grs.genename_evm(args.upgrade, args.verbose, evm_output_dir, dict_ref_name, args.upgrade)
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###UPDATE WITH PASA DATABASE STARTED AT:\t ' + now + '\t###\n'))
round_n += 1
final_output = pasa.update_database(threads_use, str(round_n), pasa_dir, pasadb, ref_rename, trinity_out,
final_evm, args.verbose)
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###RUNNING iASSEMBLER\t' + now + '\t###\n'))
if not long_sorted_bam:
#print("line 430")
long_fasta, stranded_value_new = mseq.filterLongReads(long_reads, args.assembly_overlap_length, args.max_long_read, gmap_wd,
adapter_value, threads_use, args.adapter_match_score, ref_rename,
args.max_intron_length, args.verbose, stranded_value)
if stranded_value != stranded_value_new:
stranded_value = stranded_value_new
if args.minimap2:
long_sam = mapping.minimap(ref_rename, long_fasta, threads_use, args.max_intron_length, gmap_wd, args.verbose)
else:
long_sam = mapping.gmap('sam', ref_rename, long_fasta, threads_use, 'samse',
args.min_intron_length, args.max_intron_length, args.end_exon, gmap_wd,
args.verbose, Fflag=False)
long_sorted_bam = mapping.sam_to_sorted_bam(long_sam, threads_use, wd, args.verbose)
sam_orig_id = mapping.change_chr(long_sorted_bam, dict_ref_name, gmap_wd, threads_use, args.verbose, "long")
final_files.append(sam_orig_id)
# HERE WE MERGE THE GMAP OUTPUT WITH THE EVM OUTPUT TO HAVE ONE # FILE
# HERE WE CHECK IF WE HAVE THE PASA UPDATED FILE OR THE EVM
# ORIGINAL FILE
mergedmap_gff3 = logistic.catTwoBeds(long_sorted_bam, final_evm, args.verbose, consensus_wd)
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(("\t###GFFREAD\t" + now + "\t###\n"))
# HERE WE TRANSFORM THE COODINATES INTO SEQUENCES USING THE
# REFERENCE
gffread_fasta_file = consensus.gffread(mergedmap_gff3, ref_rename, consensus_wd, args.verbose)
# HERE WE STORE THE SEQUENCE IN A DICTIONARY
gffread_dict = consensus.fasta2Dict(gffread_fasta_file)
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(("\t#CLUSTERING\t" + now + "\t###\n"))
# HERE WE CLUSTER THE SEQUENCES BASED ON THE GENOME POSITION
cluster_list = consensus.cluster_pipeline(mergedmap_gff3, stranded_value, args.verbose)
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(("\t#CONSENSUS FOR EACH CLUSTER\t" + now + "\t###\n"))
# HERE WE MAKE CONSENSUS FOR EACH CLUSTER
tmp_wd = consensus_wd + 'tmp/'
logistic.check_create_dir(tmp_wd)
tmp_assembly_file = tmp_wd + 'assembly.fasta'
if os.path.isfile(tmp_assembly_file):
sys.stdout.write('No assembly')
else:
consensus.generate_fasta(cluster_list, gffread_dict, args.cluster_min_evidence,
args.cluster_max_evidence, args.assembly_overlap_length, stranded_value, tmp_wd)
consensus.assembly(args.assembly_overlap_length, args.assembly_percent_identity, threads_use, tmp_wd,
args.verbose)
utrs.lengthSupport(tmp_wd, threads_use)
# WITH THE ELSE, WE ALLOW THE USER TO DECIDE TO CHANGE THE ASSEMBLY
# PARAMETERS AND COLLECT DIFFERENT ASSEMBLED SEQUENCES WITHOT RUNNING
# THE FULL PIPELINE
# HERE WE COLLECT THE ASSEMBLED SEQUENCES. WE COLLECT ONLY SEQUENCE
# THAT PASS THE FILTER
tmp_consensus = os.path.join(consensus_wd , 'tmp/')
collect.parse_only(args.assembly_read_threshold, tmp_consensus, args.verbose)
tmp_assembly = collect.cat_assembled(tmp_consensus)
tmp_assembly_all = collect.cat_assembled_all(tmp_consensus)
# HERE WE COLLECT THE NEW ASSEMBLED SEQUENCES AND WE COLLECT THE OLD
# EVM DATA
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(("###MAPPING CONSENSUS ASSEMBLIES\t" + now + "\t###\n"))
# HERE WE MAP ALL THE FASTA FILES TO THE GENOME USING GMAP
consensus_mapped_gff3 = mapping.gmap('cons', ref_rename, tmp_assembly, threads_use, 'gff3_gene',
args.min_intron_length, args.max_intron_length, args.end_exon, gmap_wd,
args.verbose, Fflag=True)
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(("###GETTING THE STRAND RIGHT\t" + now + "\t###\n"))
merged_gff3 = collect.add_EVM(final_output, gmap_wd, consensus_mapped_gff3)
#print(merged_gff3)
update2 = grs.exonerate(ref_rename, merged_gff3, threads_use, exonerate_wd, args.verbose)
print(ref_rename, update2)
update3_1 = grs.remove_redudant(ref_rename, update2)
print(update3_1)
update3 = grs.genename_lorean(update3_1, args.verbose, exonerate_wd)
print(update3)
# HERE WE COMBINE TRINITY OUTPUT AND THE ASSEMBLY OUTPUT TO RUN AGAIN
# PASA TO CORRECT SMALL ERRORS
sys.stdout.write(("###FIXING GENES NON STARTING WITH MET\t" + now + "\t###\n"))
fasta_all = logistic.cat_two_fasta(trinity_out, tmp_assembly_all, long_fasta, pasa_dir)
round_n += 1
update5 = pasa.update_database(threads_use, str(round_n), pasa_dir, pasadb, ref_rename, fasta_all,
update3, args.verbose)
if args.verbose:
sys.stdout.write(update5)
round_n += 1
update6 = pasa.update_database(threads_use, str(round_n), pasa_dir, pasadb, ref_rename, fasta_all,
update5, args.verbose)
if args.verbose:
sys.stdout.write(update6)
final_update_update = grs.genename_last(update6, args.prefix_gene, args.verbose, pasa_dir, dict_ref_name, "lorean")
final_files.append(final_update_update)
final_update_stats = evmPipeline.gff3_stats(final_update_update, pasa_dir)
final_files.append(final_update_stats)
if "command" not in (iprscan_log.decode("utf-8")) and args.interproscan:
annot, bad_models = iprscan.iprscan(masked_ref, final_update_update, interproscan_out_dir, args.threads)
final_files.append(annot)
final_files.append(bad_models)
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(('###CREATING OUTPUT DIRECTORY\t' + now + '\t###\n'))
final_output_dir = os.path.join(output_dir, args.out_dir + '_output')
logistic.check_create_dir(final_output_dir)
now = datetime.datetime.now().strftime(fmtdate)
sys.stdout.write(("##PLACING OUTPUT FILES IN OUTPUT DIRECTORY\t" + now + "\t###\n"))
for filename in final_files:
if os.path.exists(filename):
logistic.copy_file(filename, final_output_dir)
cmdstring = "chmod -R 775 %s" % wd
os.system(cmdstring)
sys.exit("##### LOREAN FINISHED HERE. GOOD BYE. #####\n")
def adapter_find(reference_database, reads, threads, max_intron_length, working_dir, verbose):
subset_fasta = reads + "subset.10000.fasta"
count_reads = 0
with open(subset_fasta, "w") as fh:
for rec in SeqIO.parse(reads, "fasta"):
if int(rec.id) < 10000:
SeqIO.write(rec, fh, "fasta")
bam = mapping.minimap(reference_database, subset_fasta, threads, max_intron_length, working_dir, verbose)
#soft_clip_regions = soft_clip(bam)
fasta_gz = bam + ".fasta.gz"
cmd = "extractSoftclipped %s > %s" % (bam, fasta_gz)
if verbose:
sys.stderr.write('Executing: %s\n\n' % cmd)
extract_clip = subprocess.Popen(cmd, cwd=working_dir, shell=True)
extract_clip.communicate()
list_short = []
list_long = []
dict_uniq = {}
with gzip.open(fasta_gz, "rt") as handle:
for rec in SeqIO.parse(handle, "fasta"):
name_seq = str(rec.id)
name = name_seq.split("_")[0]
if name in dict_uniq:
if len(dict_uniq[name].seq) > len(rec.seq):
list_long.append(dict_uniq[name])
list_short.append(rec)
else:
list_short.append(dict_uniq[name])
list_long.append(rec)
else:
dict_uniq[name] = rec
long_file = fasta_gz + ".long.fasta"
with open(long_file, "w") as fh:
SeqIO.write(list_long, fh, "fasta")
short_file = fasta_gz + ".short.fasta"
with open(short_file, "w") as fh:
SeqIO.write(list_short, fh, "fasta")
list_file_clip = [(long_file, "long"),(short_file,"short")]
list_file_adapter = []
for clip_file in list_file_clip:
kmer_start = 21
list_kmer = []
while kmer_start < 120:
cmd = "jellyfish count -s 10000000 -m %s -o %s.%s.kmer %s" % (kmer_start, kmer_start, clip_file[1], clip_file[0])
if verbose:
sys.stderr.write('Executing: %s\n\n' % cmd)
jelly_count = subprocess.Popen(cmd, cwd=working_dir, shell=True)
jelly_count.communicate()
cmd = "jellyfish dump -L 2 -ct %s.%s.kmer | sort -k2n | tail -n 1" % (kmer_start, clip_file[1])
if verbose:
sys.stderr.write('Executing: %s\n\n' % cmd)
jelly_dump = subprocess.Popen(cmd, cwd=working_dir, stdout=subprocess.PIPE, shell=True)
out_dump = jelly_dump.communicate()[0].decode('utf-8')
mer = out_dump.split("\t")[0]
a_count = mer.count("A")
t_count = mer.count("T")
if a_count > t_count:
bias_count = a_count
else:
bias_count = t_count
data_kmer = (kmer_start, mer, GC(mer), (bias_count/kmer_start)*100, (bias_count/kmer_start)*100 - GC(mer) )
list_kmer.append(data_kmer)
kmer_start += 5
value_adapter = 0
for i in list_kmer:
if i[4] > int(value_adapter):
value_adapter = i[4]
kmer_done = i[1]
adapter_file = "adapter.fasta"
with open(adapter_file, "w") as fh:
record = SeqRecord(Seq(str(kmer_done)), id="adapter")
SeqIO.write(record, fh, "fasta")
return adapter_file
def adapter_find(reference_database, reads, threads, max_intron_length,
working_dir, verbose):
subset_fasta = reads + "subset.10000.fasta"
with open(subset_fasta, "w") as fh:
for rec in SeqIO.parse(reads, "fasta"):
if int(rec.id) < 10000:
SeqIO.write(rec, fh, "fasta")
bam = mapping.minimap(reference_database, subset_fasta, threads,
max_intron_length, working_dir, verbose)
#soft_clip_regions = soft_clip(bam)
fasta_gz = bam + ".fasta"
cmd = "extractSoftclipped %s | zcat | fastqToFa /dev/stdin %s" % (bam,
fasta_gz)
if verbose:
sys.stderr.write('Executing: %s\n\n' % cmd)
extract_clip = subprocess.Popen(cmd, cwd=working_dir, shell=True)
extract_clip.communicate()
list_short = []
list_long = []
dict_uniq = {}
with open(fasta_gz, "r") as handle:
for rec in SeqIO.parse(handle, "fasta"):
name_seq = str(rec.id)
name = name_seq.split("_")[0]
if name in dict_uniq:
if len(dict_uniq[name].seq) > len(rec.seq):
list_long.append(dict_uniq[name])
list_short.append(rec)
else:
list_short.append(dict_uniq[name])
list_long.append(rec)
else:
dict_uniq[name] = rec
long_file = fasta_gz + ".long.fasta"
with open(long_file, "w") as fh:
SeqIO.write(list_long, fh, "fasta")
short_file = fasta_gz + ".short.fasta"
with open(short_file, "w") as fh:
SeqIO.write(list_short, fh, "fasta")
list_file_clip = [(long_file, "long"), (short_file, "short")]
for clip_file in list_file_clip:
kmer_start = 21
list_kmer = []
while kmer_start < 120:
cmd = "jellyfish count -s 10000000 -m %s -o %s.%s.kmer %s" % (
kmer_start, kmer_start, clip_file[1], clip_file[0])
if verbose:
sys.stderr.write('Executing: %s\n\n' % cmd)
jelly_count = subprocess.Popen(cmd, cwd=working_dir, shell=True)
jelly_count.communicate()
cmd = "jellyfish dump -L 2 -ct %s.%s.kmer | sort -k2n | tail -n 1" % (
kmer_start, clip_file[1])
if verbose:
sys.stderr.write('Executing: %s\n\n' % cmd)
jelly_dump = subprocess.Popen(cmd,
cwd=working_dir,
stdout=subprocess.PIPE,
shell=True)
out_dump = jelly_dump.communicate()[0].decode('utf-8')
mer = out_dump.split("\t")[0]
a_count = mer.count("A")
t_count = mer.count("T")
if a_count > t_count:
bias_count = a_count
else:
bias_count = t_count
data_kmer = (kmer_start, mer, GC(mer),
(bias_count / kmer_start) * 100,
(bias_count / kmer_start) * 100 - GC(mer))
list_kmer.append(data_kmer)
kmer_start += 5
value_adapter = 0
for i in list_kmer:
if i[4] > int(value_adapter):
value_adapter = i[4]
kmer_done = i[1]
adapter_file = os.path.join(working_dir, "adapter.fasta")
if value_adapter > 0:
with open(adapter_file, "w") as fh:
record = SeqRecord(Seq(str(kmer_done)), id="adapter")
SeqIO.write(record, fh, "fasta")
return adapter_file