def intronic(inputfile=None,
outputfile=None,
names='transcript_id',
separator="_",
intron_nb_in_name=False,
no_feature_name=False,
by_transcript=False):
"""
Extract intronic regions.
"""
message("Searching for intronic regions.")
# Need to load if the gtf comes from
# <stdin>
gtf = GTF(inputfile, check_ensembl_format=False)
if not by_transcript:
introns_bo = gtf.get_introns()
for i in introns_bo:
write_properly(chomp(str(i)), outputfile)
else:
introns_bo = gtf.get_introns(by_transcript=True,
name=names.split(","),
sep=separator,
intron_nb_in_name=intron_nb_in_name,
feat_name=not no_feature_name)
for i in introns_bo:
write_properly(chomp(str(i)), outputfile)
gc.disable()
close_properly(outputfile, inputfile)
def bed_to_gtf(inputfile=None,
outputfile=None,
ft_type="transcript",
source="Unknown"):
"""
Convert a bed file to a gtf. This will make the poor bed feel as if it was a
nice gtf (but with lots of empty fields...). May be helpful sometimes...
"""
message("Converting the bed file into GTF file.")
if inputfile.name == '<stdin>':
tmp_file = make_tmp_file(prefix="input_bed", suffix=".bed")
for i in inputfile:
write_properly(chomp(str(i)), tmp_file)
tmp_file.close()
inputfile.close()
bed_obj = BedTool(tmp_file.name)
else:
bed_obj = BedTool(inputfile.name)
n = 1
for i in bed_obj:
if i.strand == "":
i.strand = "."
if i.name == "":
i.name = str("feature_" + str(n))
if i.score == "":
i.score = "0"
if ft_type == "exon":
key_value = "gene_id \"" + i.name + "\"; " + \
"transcript_id \"" + i.name + "\"; " + \
"exon_id \"" + i.name + "\";"
elif ft_type == "gene":
key_value = "gene_id \"" + i.name + "\";"
else:
key_value = "gene_id \"" + i.name + "\"; " + \
"transcript_id \"" + i.name + "\";"
if pygtftk.utils.ADD_CHR == 1:
chrom_out = "chr" + i.chrom
else:
chrom_out = i.chrom
list_out = [
chrom_out, source, ft_type,
str(i.start + 1),
str(i.end),
str(i.score), i.strand, ".", key_value
]
write_properly("\t".join(list_out), outputfile)
n += 1
gc.disable()
close_properly(outputfile)
def add_attr_and_write(self, key, val, outputfile):
"""
Add a key/attribute record and write to a file.
:Example:
>>> from pygtftk.utils import get_example_feature
>>> from pygtftk.utils import make_tmp_file
>>> feat = get_example_feature()
>>> tmp_file = make_tmp_file()
>>> feat.add_attr_and_write("foo", "bar", tmp_file)
>>> tmp_file.close()
>>> from pygtftk.gtf_interface import GTF
>>> gtf = GTF(tmp_file.name, check_ensembl_format=False)
>>> assert gtf.extract_data("foo", as_list=True) == ['bar']
"""
tok_list = list()
for key_cur, val_cur in list(self.attr.items()):
tok_list.append(''.join([key_cur, ' "', str(val_cur), '";']))
tok_list.append(''.join([key, ' "', str(val), '";']))
tok = ' '.join(tok_list)
token = [
self.chrom, self.src, self.ft_type,
str(self.start),
str(self.end),
str(self.score), self.strand,
str(self.frame), tok
]
write_properly('\t'.join(token), outputfile)
def midpoints(inputfile=None,
outputfile=None,
ft_type="transcript",
names="transcript_id",
separator="|"):
"""
Get the midpoint coordinates for the requested feature.
"""
message("Loading input file...")
if inputfile.name == '<stdin>':
is_gtf = True
else:
region_bo = BedTool(inputfile.name)
if len(region_bo) == 0:
message("Unable to find requested regions", type="ERROR")
if region_bo.file_type == 'gff':
is_gtf = True
else:
is_gtf = False
if is_gtf:
gtf = GTF(inputfile.name, check_ensembl_format=False)
bed_obj = gtf.select_by_key("feature", ft_type).get_midpoints(
name=names.split(","), sep=separator)
for line in bed_obj:
write_properly(chomp(str(line)), outputfile)
else:
for line in region_bo:
diff = line.end - line.start
if diff % 2 != 0:
# e.g 10-13 (zero based) -> 11-13 one based
# mipoint is 12 (one-based) -> 11-12 (zero based)
# e.g 949-1100 (zero based) -> 950-1100 one based
# mipoint is 1025 (one-based) -> 1024-1025 (zero based)
# floored division (python 2)...
line.end = line.start + int(diff // 2) + 1
line.start = line.end - 1
else:
# e.g 10-14 (zero based) -> 11-14 one based
# mipoint is 12-13 (one-based) -> 11-13 (zero based)
# e.g 9-5100 (zero based) -> 10-5100 one based
# mipoint is 2555-2555 (one-based) -> 2554-2555 (zero based)
# floored division (python 2)...
# No real center. Take both
line.start = line.start + int(diff // 2) - 1
line.end = line.start + 2
outputfile.write(str(line))
gc.disable()
close_properly(outputfile, inputfile)
def select_by_go(inputfile=None,
outputfile=None,
go_id=None,
https_proxy=None,
http_proxy=None,
list_datasets=None,
species=None,
invert_match=False):
""" Select lines from a GTF file based using a Gene Ontology ID (e.g GO:0050789).
"""
if not go_id.startswith("GO:"):
go_id = "GO:" + go_id
is_associated = OrderedDict()
bm = Biomart(http_proxy=http_proxy,
https_proxy=https_proxy)
bm.get_datasets('ENSEMBL_MART_ENSEMBL')
if list_datasets:
for i in sorted(bm.datasets):
write_properly(i.replace("_gene_ensembl", ""), outputfile)
sys.exit()
else:
if species + "_gene_ensembl" not in bm.datasets:
message("Unknow dataset/species.", type="ERROR")
bm.query({'query': XML.format(species=species, go=go_id)})
for i in bm.response.content.decode().split("\n"):
i = i.rstrip("\n")
if i != '':
is_associated[i] = 1
gtf = GTF(inputfile)
gtf_associated = gtf.select_by_key("gene_id",
",".join(list(is_associated.keys())),
invert_match)
gtf_associated.write(outputfile,
gc_off=True)
def intergenic(inputfile=None, outputfile=None, chrom_info=None):
"""
Extract intergenic regions.
"""
message("Searching for intergenic regions.")
gtf = GTF(inputfile)
intergenic_regions = gtf.get_intergenic(chrom_info)
nb_intergenic_region = 1
for i in intergenic_regions:
i.name = "region_" + str(nb_intergenic_region)
write_properly(chomp(str(i)), outputfile)
nb_intergenic_region += 1
gc.disable()
close_properly(outputfile, inputfile)
def tabulate(inputfile=None,
outputfile=None,
key=None,
no_unset=False,
unique=False,
no_basic=False,
accept_undef=False,
select_gene_ids=False,
select_gene_names=False,
select_transcript_ids=False,
select_exon_ids=False,
separator="\t",
no_header=False):
"""Convert a GTF to tabulated format.
"""
# ----------------------------------------------------------------------
# Check mode
# ----------------------------------------------------------------------
if select_transcript_ids:
key = "transcript_id"
elif select_gene_ids:
key = "gene_id"
elif select_gene_names:
key = "gene_id"
elif select_exon_ids:
key = "exon_id"
no_undef = False
if not accept_undef:
no_undef = True
# ----------------------------------------------------------------------
# REad GTF and process
# ----------------------------------------------------------------------
gtf = GTF(inputfile, check_ensembl_format=False)
if key in ["all", "*"]:
if no_basic:
attr_list = gtf.get_attr_list(add_basic=False)
else:
attr_list = gtf.get_attr_list(add_basic=True)
tab = gtf.extract_data(attr_list)
else:
tab = gtf.extract_data(key)
if not no_header:
message("Writing header")
write_properly(separator.join(tab.colnames),
outputfile)
message("Writing")
try:
if not unique:
if no_unset:
if no_undef:
for i in tab:
if any([True for x in i.fields if x in [".", "?"]]):
continue
i.write(outputfile, separator)
else:
for i in tab:
if any([True for x in i.fields if x in ["."]]):
continue
i.write(outputfile, separator)
else:
if no_undef:
for i in tab:
if any([True for x in i.fields if x in ["?"]]):
continue
i.write(outputfile, separator)
else:
for i in tab:
i.write(outputfile, separator)
else:
printed = {}
if no_unset:
if no_undef:
for i in tab:
t = tuple(i)
if t not in printed:
if any([True for x in i.fields if x in [".", "?"]]):
continue
i.write(outputfile, separator)
printed[t] = 1
else:
for i in tab:
t = tuple(i)
if t not in printed:
if any([True for x in i.fields if x in ["."]]):
continue
i.write(outputfile, separator)
printed[t] = 1
else:
if no_undef:
for i in tab:
t = tuple(i)
if t not in printed:
if any([True for x in i.fields if x in ["?"]]):
continue
i.write(outputfile, separator)
printed[t] = 1
else:
for i in tab:
t = tuple(i)
if t not in printed:
i.write(outputfile, separator)
printed[t] = 1
except (BrokenPipeError, IOError):
def _void_f(*args, **kwargs):
pass
message("Received a boken pipe signal", type="WARNING")
sys.stdout.write = _void_f
sys.stdout.flush = _void_f
gc.disable()
close_properly(outputfile, inputfile)
def feature_size(inputfile=None,
outputfile=None,
ft_type="transcript",
names="transcript_id",
key_name='feature_size',
separator="|",
bed=False):
"""
Get the size and limits (start/end) of features enclosed in the GTF. If bed
format is requested returns the limits zero-based half open and the size as a score.
Otherwise output GTF file with 'feat_size' as a new key and size as value.
"""
message("Computing feature sizes.")
gtf = GTF(inputfile)
feat_list = gtf.get_feature_list(nr=True) + ['mature_rna']
if ft_type not in feat_list + ["*"]:
message("Unable to find requested feature.", type="ERROR")
names = names.split(",")
if ft_type != 'mature_rna':
if bed:
bed_obj = gtf.select_by_key("feature",
ft_type).to_bed(name=names,
sep=separator,
add_feature_type=True)
for i in bed_obj:
i.score = str(i.end - i.start)
write_properly(chomp(str(i)), outputfile)
else:
tmp_file = make_tmp_file(prefix="feature_size", suffix=".txt")
elmt = gtf.extract_data("feature,start,end",
as_list_of_list=True,
no_na=False,
hide_undef=False)
for i in elmt:
if i[0] != ft_type and ft_type != "*":
tmp_file.write("?\n")
else:
tmp_file.write(str(int(i[2]) - int(i[1]) + 1) + "\n")
tmp_file.close()
gtf.add_attr_column(tmp_file, key_name).write(outputfile,
gc_off=True)
else:
tx_size = gtf.get_transcript_size()
if bed:
bed_obj = gtf.select_by_key("feature", 'transcript').to_bed(
['transcript_id'] + names,
add_feature_type=False,
sep=separator,
more_name=['mature_rna'])
for i in bed_obj:
names = i.name.split(separator)
tx_id = names.pop(0)
i.score = tx_size[tx_id]
i.name = separator.join(names)
write_properly(chomp(str(i)), outputfile)
else:
if len(tx_size):
gtf = gtf.add_attr_from_dict(feat="transcript",
key="transcript_id",
a_dict=tx_size,
new_key=key_name)
gtf.write(outputfile, gc_off=True)
close_properly(outputfile, inputfile)
def col_from_tab(inputfile=None,
outputfile=None,
columns=None,
invert_match=False,
no_header=False,
unique=False,
more_col=None,
output_separator="\t",
separator="\t"):
"""Select columns from a tabulated file based on their names."""
line_set = dict()
if re.search(",", columns):
columns = columns.split(",")
else:
columns = [columns]
if more_col:
more_col_name, more_col_value = more_col.split(":")
else:
more_col_name = more_col_value = None
for p, line in enumerate(inputfile):
line = chomp(line)
line = line.split(separator)
if p == 0:
if not invert_match:
pos_list = list()
for i in range(len(columns)):
pos = line.index(columns[i]) if columns[i] in line else -1
if pos > -1:
pos_list.append(pos)
else:
message("Column " + columns[i] + " not found",
type="ERROR")
else:
pos_list = list(range(len(line)))
for i in range(len(columns)):
pos = line.index(columns[i]) if columns[i] in line else -1
if pos > -1:
pos_list.remove(pos)
else:
message("Column " + columns[i] + " not found",
type="ERROR")
if not no_header:
header_list = [line[k] for k in pos_list]
if more_col:
header_list += [more_col_name]
header = output_separator.join(header_list)
write_properly(header, outputfile)
else:
out_list = [line[k] for k in pos_list]
if more_col:
out_list += [more_col_value]
out = output_separator.join(out_list)
if unique:
if out not in line_set:
write_properly(out, outputfile)
line_set[out] = 1
else:
write_properly(out, outputfile)
def get_5p_3p_coords(inputfile=None,
outputfile=None,
ft_type="transcript",
names="transcript_id",
separator="|",
more_names='',
transpose=0,
invert=False,
explicit=False):
"""
Get the 5p or 3p coordinate for each feature (e.g TSS or TTS for a transcript).
"""
if more_names is None:
more_names = []
else:
more_names = more_names.split(',')
if not invert:
message("Computing 5' coordinates of '" + ft_type + "'.")
else:
message("Computing 3' coordinates of '" + ft_type + "'.")
gtf = GTF(inputfile, check_ensembl_format=False)
if names != "*":
nms = names.split(",")
else:
nms = gtf.select_by_key("feature", "transcript").get_attr_list(add_basic=False)
if not invert:
bed_obj = gtf.get_5p_end(feat_type=ft_type,
name=nms,
sep=separator,
more_name=more_names,
explicit=explicit)
else:
bed_obj = gtf.get_3p_end(feat_type=ft_type,
name=nms,
sep=separator,
more_name=more_names,
explicit=explicit)
if not len(bed_obj):
message("Requested feature could not be found. Use convert_ensembl maybe.",
type="ERROR")
if transpose == 0:
for i in bed_obj:
write_properly(chomp(str(i)), outputfile)
else:
for i in bed_obj:
out_list = list()
if i.strand == "+":
out_list = [i.chrom,
str(i.start + transpose),
str(i.end + transpose),
i.name,
i.score,
i.strand]
elif i.strand == "-":
out_list = [i.chrom,
str(i.start - transpose),
str(i.end - transpose),
i.name,
i.score,
i.strand]
outputfile.write("\t".join(out_list) + "\n")
gc.disable()
close_properly(outputfile, inputfile)