def test_read_genomic(self):
from mirtop.mirna import mapper
from mirtop.libs import logger
logger.initialize_logger("test_read_files", True, True)
map_mir = mapper.read_gtf_to_mirna("data/examples/annotate/hsa.gff3")
print(map_mir)
# if map_mir["hsa-let-7a-1"]["hsa-let-7a-5p"][0] != 5:
# raise ValueError("GFF is not loaded correctly.")
return True
def test_read_genomic(self):
from mirtop.mirna import mapper
from mirtop.libs import logger
logger.initialize_logger("test_read_files", True, True)
map_mir = mapper.read_gtf_to_mirna("data/examples/annotate/hsa.gff3")
print(map_mir)
# if map_mir["hsa-let-7a-1"]["hsa-let-7a-5p"][0] != 5:
# raise ValueError("GFF is not loaded correctly.")
return True
def _write(lines, header, fn, args=None):
out_handle = open(fn, 'w')
print(header, file=out_handle)
mapper = read_gtf_to_mirna(args.gtf)
for m in lines:
for s in sorted(lines[m].keys()):
for hit in lines[m][s]:
# TODO: convert to genomic if args.out_genomic
if args and args.out_genomic:
lifted = body.lift_to_genome(hit[4], mapper)
print(lifted, file=out_handle)
else:
print(hit[4], file=out_handle)
out_handle.close()
def test_spikeins(self):
"""Test spikeins reading and annotation"""
from mirtop.libs import spikeins
from mirtop.mirna.realign import get_mature_sequence
load = spikeins.read_spikeins("data/examples/spikeins/spikeins.fa")
print(load)
load1 = load['spikein-1']
mature_from_data = get_mature_sequence(load1['precursor'],
load1['position'],
exact=True)
if mature_from_data != load1['mature']:
raise ValueError("Sequences doesn't match \n%s\n%s" %
(mature_from_data, load1['mature']))
file_fasta = "data/examples/spikeins/spikeins_pre.fasta"
file_gff = "data/examples/spikeins/spikeins_pre.gff"
spikeins.write_precursors(load, file_fasta)
spikeins.write_gff(load, file_gff)
from mirtop.mirna import mapper, fasta
map_mir = mapper.read_gtf_to_mirna(file_gff)
print(map_mir)
fasta_precursor = fasta.read_precursor(file_fasta, None)
print(fasta_precursor)
def read_file(fn, args):
"""
Read isomiR-SEA file and convert to mirtop GFF format.
Args:
*fn(str)*: file name with isomiR-SEA output information.
*database(str)*: database name.
*args(namedtuple)*: arguments from command line.
See *mirtop.libs.parse.add_subparser_gff()*.
Returns:
*reads (nested dicts)*:gff_list has the format as
defined in *mirtop.gff.body.read()*.
"""
database = args.database
gtf = args.gtf
sep = " " if args.out_format == "gtf" else "="
map_mir = mapper.read_gtf_to_mirna(gtf)
reads = defaultdict(dict)
reads_in = 0
sample = os.path.splitext(os.path.basename(fn))[0]
hits = _get_hits(fn)
logger.debug("ISOMIRSEA::SAMPLE::%s" % sample)
with open(fn) as handle:
for line in handle:
cols = line.strip().split("\t")
attr = read_attributes(line, "=")
query_name = attr['TS']
query_sequence = attr['TS'].replace("U", "T")
start = int(cols[3])
end = int(cols[4])
isomirseq_iso = attr['ISO']
if query_name not in reads and query_sequence == None:
continue
if query_sequence and query_sequence.find("N") > -1:
continue
counts = attr["TC"]
chrom = cols[0]
# logger.debug("SEQBUSTER:: cigar {cigar}".format(**locals()))
cigar = attr['CI'].replace("U", "T")
idu = make_id(query_sequence)
isoformat = cigar2variants(cigar, query_sequence, attr['ISO'])
logger.debug("\nISOMIRSEA::NEW::query: {query_sequence}\n"
" precursor {chrom}\n"
" name: {query_name}\n"
" idu: {idu}\n"
" start: {start}\n"
" cigar: {cigar}\n"
" iso: {isoformat}\n"
" variant: {isoformat}".format(**locals()))
source = "isomiR" if isoformat != "NA" else "ref_miRNA"
strand = "+"
database = cols[1]
mirName = attr['MIN'].split()[0]
preName = attr['PIN'].split()[0]
score = "."
Filter = attr['FILTER']
isotag = attr['ISO']
tchrom, tstart = _genomic2transcript(map_mir[mirName], chrom,
start)
start = start if not tstart else tstart
chrom = chrom if not tstart else tchrom
end = start + len(query_sequence)
hit = hits[idu]
fields = {
'seq_name': query_sequence,
'idseq': idu,
'name': mirName,
'parent': preName,
'variant': isoformat,
'cigar': cigar,
'counts': counts,
'filter': Filter,
'hits': hit,
'chrom': chrom,
'start': start,
'end': end,
'database': database,
'source': source,
'score': score,
'strand': strand
}
# TODO: convert to genomic if args.out_genomic
line = feature(fields).line
if args.add_extra:
extra = variant_with_nt(line, args.precursors, args.matures)
line = "%s Changes %s;" % (line, extra)
line = paste_columns(feature(line), sep=sep)
if start not in reads[chrom]:
reads[chrom][start] = []
if Filter == "Pass":
reads_in += 1
reads[chrom][start].append([idu, chrom, counts, sample, line])
logger.info("Hits: %s" % reads_in)
return reads
def read_file(fn, precursors, database, mirna_gtf):
"""
read bam file and perform realignment of hits
"""
reads = defaultdict(dict)
sample = os.path.splitext(os.path.basename(fn))[0]
map_mir = mapper.read_gtf_to_mirna(mirna_gtf)
non_mirna = 0
non_chromosome_mirna = 0
outside_mirna = 0
lines_read = 0
with open(fn) as handle:
handle.readline()
for line in handle:
lines_read += 1
cols = line.strip().split("\t")
query_name = cols[0]
query_sequence = cols[0]
if len(cols) < 12:
non_mirna += 1
continue
miRNA = cols[11]
if not miRNA:
if cols[13]:
miRNA = cols[13]
elif cols[15]:
miRNA = cols[15]
else:
continue
if query_name not in reads and query_sequence == None:
continue
if query_sequence and query_sequence.find("N") > -1:
continue
for loc in cols[5].split(";")[:1]:
if loc.find("-") < 0:
non_chromosome_mirna += 1
continue
chrom = loc.split(":")[0]
start, end = loc.split(":")[1].split("-")
preName, reference_start = genomic2transcript(map_mir[miRNA], chrom, int(start))
if not chrom:
non_chromosome_mirna += 1
continue
# reference_start = int(cols[4]) - 1
logger.debug("\nPROST::NEW::query: {query_sequence}\n"
" precursor {chrom}\n"
" name: {query_name}\n"
" start: {start}\n"
" reference_start: {reference_start}\n"
" mirna: {miRNA}".format(**locals()))
Filter = "PASS"
hit = "NA"
isoformat = _make_variant(cols[19:])
idu = make_id(query_sequence)
strand = "."
counts = cols[9]
cigar = "NA"
score = "."
source = "isomiR" if isoformat != "NA" else "ref_miRNA"
attrb = ("Read {query_sequence}; UID {idu}; Name {miRNA}; Parent {preName}; Variant {isoformat}; Cigar {cigar}; Expression {counts}; Filter {Filter}; Hits {hit};").format(**locals())
res = ("{chrom}\t{database}\t{source}\t{start}\t{end}\t{score}\t{strand}\t.\t{attrb}").format(**locals())
if start not in reads[chrom]:
reads[chrom][start] = []
reads[chrom][start].append([idu, chrom, counts, sample, res])
logger.info("Lines loaded: %s" % lines_read)
logger.info("Skipped lines because non miRNA in line: %s" % non_mirna)
logger.info("Skipped lines because non chromosome in GTF: %s" % non_chromosome_mirna)
logger.info("Skipped lines because outside precursor: %s" % outside_mirna)
logger.info("Hits: %s" % len(reads))
return reads
def read_file(fn, hairpins, database, mirna_gtf):
"""
Read PROST! file and convert to mirtop GFF format.
Args:
*fn(str)*: file name with PROST output information.
*database(str)*: database name.
*args(namedtuple)*: arguments from command line.
See *mirtop.libs.parse.add_subparser_gff()*.
Returns:
*reads*: dictionary where keys are read_id and values are *mirtop.realign.hits*
"""
reads = defaultdict(hits)
sample = os.path.splitext(os.path.basename(fn))[0]
genomics = mapper.read_gtf_to_mirna(mirna_gtf)
matures = mapper.read_gtf_to_precursor(mirna_gtf)
non_mirna = 0
non_chromosome_mirna = 0
outside_mirna = 0
lines_read = 0
ann, ann_type = _group_seqs_by_ann(fn)
with open(fn) as handle:
handle.readline()
for line in handle:
lines_read += 1
cols = line.strip().split("\t")
query_name = cols[0]
query_sequence = cols[0]
if not ann[query_sequence]:
non_mirna += 1
continue
miRNA = ann_type[ann[query_sequence]][1]
preNames = ann_type[ann[query_sequence]][0]
if query_name not in reads and query_sequence==None:
continue
if query_sequence and query_sequence.find("N") > -1:
continue
reads[query_name].set_sequence(query_sequence)
reads[query_name].counts = cols[9]
for preName in preNames.split(","):
if preName in reads[query_name].precursors:
continue
if preName not in hairpins:
non_chromosome_mirna += 1
continue
reference_start = _align_to_mature(query_sequence, hairpins[preName], matures[preName][miRNA])
logger.debug("\nPROST!::NEW::query: {query_sequence}\n"
" precursor {preName}\n"
" name: {query_name}\n"
" reference_start: {reference_start}\n"
" mirna: {miRNA}".format(**locals()))
iso = isomir()
iso.align = line
iso.set_pos(reference_start, len(reads[query_name].sequence))
logger.debug("PROST!:: start %s end %s" % (iso.start, iso.end))
if len(hairpins[preName]) < reference_start + len(reads[query_name].sequence):
continue
iso.subs, iso.add, iso.cigar = filter.tune(
reads[query_name].sequence,
hairpins[preName],
reference_start, None)
logger.debug("PROST!::After tune start %s end %s" % (
iso.start, iso.end))
if len(iso.subs) < 2:
reads[query_name].set_precursor(preName, iso)
logger.info("Lines loaded: %s" % lines_read)
logger.info("Skipped lines because non miRNA in line: %s" % non_mirna)
logger.info("Skipped lines because non chromosome in GTF:"
" %s" % non_chromosome_mirna)
logger.info("Skipped lines because outside precursor: %s" % outside_mirna)
logger.info("Hits: %s" % len(reads))
return reads
def read_file(fn, database, gtf):
"""
read bam file and perform realignment of hits
"""
map_mir = mapper.read_gtf_to_mirna(gtf)
reads = defaultdict(dict)
reads_in = 0
sample = os.path.splitext(os.path.basename(gtf))[0]
hits = _get_hits(fn)
with open(fn) as handle:
for line in handle:
cols = line.strip().split("\t")
attr = read_attributes(line, "=")
query_name = attr['TS']
query_sequence = attr['TS'].replace("U", "T")
start = int(cols[3])
end = int(cols[4])
isomirseq_iso = attr['ISO']
if query_name not in reads and query_sequence == None:
continue
if query_sequence and query_sequence.find("N") > -1:
continue
counts = attr["TC"]
chrom = cols[0]
# logger.debug("SEQBUSTER:: cigar {cigar}".format(**locals()))
cigar = attr['CI'].replace("U", "T")
idu = make_id(query_sequence)
isoformat = cigar2variants(cigar, query_sequence, attr['ISO'])
logger.debug("\nSOMIRSEA::NEW::query: {query_sequence}\n"
" precursor {chrom}\n"
" name: {query_name}\n"
" idu: {idu}\n"
" start: {start}\n"
" cigar: {cigar}\n"
" iso: {isoformat}\n"
" variant: {isoformat}".format(**locals()))
source = "isomiR" if isoformat != "NA" else "ref_miRNA"
strand = "+"
database = cols[1]
mirName = attr['MIN'].split()[0]
preName = attr['PIN'].split()[0]
score = "."
Filter = attr['FILTER']
isotag = attr['ISO']
tchrom, tstart = genomic2transcript(map_mir[mirName], chrom, start)
start = start if not tstart else tstart
chrom = chrom if not tstart else tchrom
end = start + len(query_sequence)
hit = hits[idu]
attrb = (
"Read {query_sequence}; UID {idu}; Name {mirName}; Parent {preName}; Variant {isoformat}; Isocode {isotag}; Cigar {cigar}; Expression {counts}; Filter {Filter}; Hits {hit};"
).format(**locals())
res = (
"{chrom}\t{database}\t{source}\t{start}\t{end}\t{score}\t{strand}\t.\t{attrb}"
).format(**locals())
if start not in reads[chrom]:
reads[chrom][start] = []
if Filter == "Pass":
reads_in += 1
reads[chrom][start].append([idu, chrom, counts, sample, res])
logger.info("Hits: %s" % reads_in)
return reads
def read_file(fn, args):
"""
Read isomiR-SEA file and convert to mirtop GFF format.
Args:
*fn(str)*: file name with isomiR-SEA output information.
*database(str)*: database name.
*args(namedtuple)*: arguments from command line.
See *mirtop.libs.parse.add_subparser_gff()*.
Returns:
*reads (nested dicts)*:gff_list has the format as
defined in *mirtop.gff.body.read()*.
"""
database = args.database
gtf = args.gtf
sep = " " if args.out_format == "gtf" else "="
map_mir = mapper.read_gtf_to_mirna(gtf)
reads = defaultdict(dict)
reads_in = 0
sample = os.path.splitext(os.path.basename(fn))[0]
hits = _get_hits(fn)
logger.debug("ISOMIRSEA::SAMPLE::%s" % sample)
with open(fn) as handle:
for line in handle:
cols = line.strip().split("\t")
attr = read_attributes(line, "=")
query_name = attr['TS']
query_sequence = attr['TS'].replace("U", "T")
start = int(cols[3])
end = int(cols[4])
isomirseq_iso = attr['ISO']
if query_name not in reads and query_sequence == None:
continue
if query_sequence and query_sequence.find("N") > -1:
continue
counts = attr["TC"]
chrom = cols[0]
# logger.debug("SEQBUSTER:: cigar {cigar}".format(**locals()))
cigar = attr['CI'].replace("U", "T")
idu = make_id(query_sequence)
isoformat = cigar2variants(cigar, query_sequence, attr['ISO'])
logger.debug("\nISOMIRSEA::NEW::query: {query_sequence}\n"
" precursor {chrom}\n"
" name: {query_name}\n"
" idu: {idu}\n"
" start: {start}\n"
" cigar: {cigar}\n"
" iso: {isoformat}\n"
" variant: {isoformat}".format(**locals()))
source = "isomiR" if isoformat != "NA" else "ref_miRNA"
strand = "+"
database = cols[1]
mirName = attr['MIN'].split()[0]
preName = attr['PIN'].split()[0]
score = "."
Filter = attr['FILTER']
isotag = attr['ISO']
tchrom, tstart = _genomic2transcript(map_mir[mirName],
chrom, start)
start = start if not tstart else tstart
chrom = chrom if not tstart else tchrom
end = start + len(query_sequence)
hit = hits[idu]
attrb = ("Read {query_sequence}; UID {idu}; Name {mirName};"
" Parent {preName}; Variant {isoformat};"
" Isocode {isotag}; Cigar {cigar}; Expression {counts};"
" Filter {Filter}; Hits {hit};").format(**locals())
line = ("{chrom}\t{database}\t{source}\t{start}\t{end}\t"
"{score}\t{strand}\t.\t{attrb}").format(**locals())
if args.add_extra:
extra = variant_with_nt(line, args.precursors, args.matures)
line = "%s Changes %s;" % (line, extra)
line = paste_columns(read_gff_line(line), sep=sep)
if start not in reads[chrom]:
reads[chrom][start] = []
if Filter == "Pass":
reads_in += 1
reads[chrom][start].append([idu, chrom, counts, sample, line])
logger.info("Hits: %s" % reads_in)
return reads
def create_vcf(mirgff3, precursor, gtf, vcffile):
"""
Args:
'mirgff3(str)': File with mirGFF3 format that will be converted
'precursor(str)': Fasta format sequences of all miRNA hairpins
'gtf(str)': Genome coordinates
'vcffile': name of the file to be saved
Returns:
Nothing is returned, instead, a VCF file is generated
"""
#Check if the input files exist:
try:
gff3_file = open(mirgff3, "r", encoding="utf-8") if six.PY3 else open(
mirgff3, "r")
except IOError:
print("Can't read the file", end=mirgff3)
sys.exit()
with gff3_file:
data = gff3_file.read()
if six.PY2:
data = data.decode("utf-8-sig").encode("utf-8")
gff3_data = data.split("\n")
vcf_file = open(vcffile, "w")
ver = "v4.3" # Current VCF version formatting
vcf_file.write("##fileformat=VCF%s\n" % ver)
date = datetime.datetime.now().strftime("%Y%m%d")
vcf_file.write("##fileDate=%s\n" % date)
source = "\n".join(s for s in gff3_data
if "## source-ontology: " in s)[20:]
line = 0
sample_names = []
while gff3_data[line][:2] == "##":
if gff3_data[line][:19] == "## source-ontology:":
source = gff3_data[line][20:]
elif gff3_data[line][:11] == "## COLDATA:":
sample_names = gff3_data[line][12:].split(",")
line += 1
vcf_file.write("##source=%s\n" % source)
vcf_file.write(
'##INFO=<ID=NS,Type=Integer,Description="Number of samples"\n')
vcf_file.write("##FILTER=<ID=REJECT,Description='"
'Filter not passed'
"'>\n")
vcf_file.write(
'##FORMAT=<ID=TRC,Number=1,Type=Integer,Description="Total read count">\n'
)
vcf_file.write(
'##FORMAT=<ID=TSC,Number=1,Type=Integer,Description="Total SNP count">\n'
)
vcf_file.write(
'##FORMAT=<ID=TMC,Number=1,Type=Integer,Description="Total miRNA count">\n'
)
vcf_file.write(
'##FORMAT=<ID=GT,Number=1,Type=Integer,Description="Genotype">\n')
header = "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT"
# Adds Header
for s in range(len(sample_names)):
header = header + "\t" + sample_names[s]
vcf_file.write(header)
all_dict = dict(
) # initializing an empty dictionary where all info will be added
key_list = [
] # Initializing a list which will contain all the keys of the dictionary
mirna_dict = dict(
) # initializing an empty dictionary where mirna info will be put
n_SNP = 0
n_noSNP = 0
no_var = 0
hairpins = read_precursor(precursor)
gff3 = read_gtf_to_precursor(gtf)
gtf_dic = read_gtf_to_mirna(gtf)
for line in range(0, len(gff3_data)):
if not gff3_data[line]:
continue
if gff3_data[line][1] == "#":
continue
else: # Parsing the gff3 mirna lecture:
gff_fields = read_gff_line(gff3_data[line])
gtf_name = gff_fields['attrb']['Name']
gtf_parent = gff_fields['attrb']['Parent']
if gtf_parent not in gff3:
continue
if gtf_name not in gff3[gtf_parent]:
continue
parent_ini_pos = gff3[gtf_parent][gtf_name][0]
parent_end_pos = gff3[gtf_parent][gtf_name][1]
ref_seq = (hairpins[gtf_parent][parent_ini_pos:parent_end_pos + 1])
vcf_chrom = gtf_dic[gtf_name][gtf_parent][0]
vcf_pos = int(gff_fields['start']) + int(
gtf_dic[gtf_name][gtf_parent][1])
hairpin = hairpins[gtf_parent]
variants = gff_fields['attrb']['Variant'].split(",")
logger.debug("VCF::Variant::%s" % variants)
# Obtaining the iso_3p, iso_add3p and iso_5p values:
var3p = [s for s in variants if 'iso_3p' in s]
if len(var3p):
var3p = int(var3p[0][7:]) # Position of iso_3p value
else:
var3p = 0
var_add3p = [s for s in variants if 'iso_add3p' in s]
if len(var_add3p):
var_add3p = int(
var_add3p[0][10:]) # Position of iso_add3p value
else:
var_add3p = 0
var3p = var3p + var_add3p
logger.debug("VCF::VAR_3p::%s" % var3p)
var5p = [s for s in variants if 'iso_5p' in s]
if len(var5p):
var5p = int(var5p[0][7:]) # Position of iso_5p value
else:
var5p = 0 #
logger.debug("VCF::VAR_5p::%s" % var5p)
cigar = gff_fields['attrb']["Cigar"]
# Obtaining all the variants from the cigar:
if 1:
(key_pos, key_var, vcf_ref, vcf_alt) = cigar_2_key(
cigar, gff_fields['attrb']['Read'], ref_seq, vcf_pos,
var5p, var3p, parent_ini_pos, parent_end_pos, hairpin)
# Adding the variants to a dictionary and calculating all the fields of a vcf file format:
if len(key_var) > 0:
for s in range(len(key_var)):
key_dict = vcf_chrom + '-' + str(
key_pos[s]) + '-' + str(key_var[s])
raw_counts = gff_fields['attrb']['Expression']
raw_counts = [int(i) for i in raw_counts.split(',')]
nozero_counts = [
int(i > 0) for i in raw_counts
] # counts for every sample if expr != 0.
if gtf_name in mirna_dict: # Adding expression values to same mirnas
mirna_dict[gtf_name]['Z'] = [
sum(x) for x in zip(mirna_dict[gtf_name]['Z'],
raw_counts)
]
else:
mirna_dict[gtf_name] = {}
mirna_dict[gtf_name]["Z"] = raw_counts
if key_dict in all_dict:
if all_dict[key_dict]["Type"] in [
"A", "C", "T", "G"
]:
all_dict[key_dict]['X'] = [
sum(x) for x in zip(
all_dict[key_dict]['X'], nozero_counts)
]
all_dict[key_dict]['Y'] = [
sum(x) for x in zip(
all_dict[key_dict]['Y'], raw_counts)
]
else:
key_list.append(key_dict)
all_dict[key_dict] = {}
all_dict[key_dict]["Chrom"] = vcf_chrom
all_dict[key_dict]["Position"] = key_pos[s]
all_dict[key_dict]["mirna"] = gtf_name
all_dict[key_dict]["Type"] = key_var[s]
if key_var[s][0] in ["A", "C", "T", "G"]:
n_SNP += 1
all_dict[key_dict]["SNP"] = True
all_dict[key_dict]["ID"] = gff_fields['attrb'][
'Name'] + '-SNP' + str(n_SNP)
all_dict[key_dict]['X'] = nozero_counts
all_dict[key_dict]['Y'] = raw_counts
else:
n_noSNP += 1
all_dict[key_dict]["SNP"] = False
all_dict[key_dict]["ID"] = gff_fields['attrb'][
'Name'] + '-nonSNP' + str(n_noSNP)
all_dict[key_dict]["Ref"] = vcf_ref[s]
all_dict[key_dict]["Alt"] = vcf_alt[s]
all_dict[key_dict]["Qual"] = "."
all_dict[key_dict]["Filter"] = gff_fields['attrb'][
'Filter']
all_dict[key_dict]["Info"] = "NS=" + str(
len(sample_names))
else:
no_var += 1
# Writing the VCF file:
for s in key_list:
variant_line = (
"\n%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" %
(all_dict[s]["Chrom"], all_dict[s]["Position"], all_dict[s]["ID"],
all_dict[s]["Ref"], all_dict[s]["Alt"], all_dict[s]["Qual"],
all_dict[s]["Filter"], all_dict[s]["Info"]))
if all_dict[s]["Type"] in ["A", "T", "C", "G"]:
format_col = "TRC:TSC:TMC:GT"
variant_line = variant_line + "\t" + format_col
samples = ""
for n in range(len(sample_names)):
X = all_dict[s]["X"][n]
Y = all_dict[s]["Y"][n]
Z = mirna_dict[all_dict[s]["mirna"]]["Z"][n]
# Calculating the genotype:
if Y == 0:
GT = "0|0"
elif Z == Y:
GT = "1|1"
else:
GT = "1|0"
samples = samples + "\t" + str(X) + ":" + str(Y) + ":" + str(
Z) + ":" + GT
variant_line = variant_line + samples
else:
format_col = ""
variant_line = variant_line + format_col
vcf_file.write(variant_line)
vcf_file.close()
def test_read_mir2genomic(self):
from mirtop.mirna import mapper
from mirtop.libs import logger
logger.initialize_logger("test_read_files", True, True)
map_mir = mapper.read_gtf_to_mirna("data/examples/annotate/hsa.gff3")
print(map_mir)