def intron_sizes(
inputfile=None,
outputfile=None,
key_name=None):
"""
Add a new key to transcript features containing a comma-separated list of intron sizes.
"""
gtf = GTF(inputfile, check_ensembl_format=False)
all_tx_ids = gtf.get_tx_ids(nr=True)
intron_bo = gtf.get_introns(by_transcript=True,
name=["transcript_id"],
intron_nb_in_name=False,
feat_name=False)
strands = gtf.select_by_key("feature",
"transcript").extract_data("transcript_id,strand",
as_dict_of_values=True,
no_na=True,
nr=True,
hide_undef=True)
intron_size = {tx: [] for tx in all_tx_ids}
for bed_line in intron_bo:
intron_size[bed_line.name] += [str(bed_line.end - bed_line.start)]
for tx_id in intron_size:
if len(intron_size[tx_id]):
if strands[tx_id] == "-":
intron_size[tx_id] = ",".join(reversed(intron_size[tx_id]))
else:
intron_size[tx_id] = ",".join(intron_size[tx_id])
else:
intron_size[tx_id] = "0"
if len(intron_size):
gtf = gtf.add_attr_from_dict(feat="transcript",
key="transcript_id",
a_dict=intron_size,
new_key=key_name)
gtf.write(outputfile,
gc_off=True)
close_properly(outputfile, inputfile)
def exon_sizes(inputfile=None, outputfile=None, key_name=None):
"""
Add a new key to transcript features containing a comma-separated list of exon-size.
"""
gtf = GTF(inputfile)
all_tx_ids = gtf.get_tx_ids(nr=True)
tx_to_size_list = dict()
exons_starts = gtf.select_by_key("feature", "exon").extract_data(
"transcript_id,start",
as_dict_of_merged_list=True,
no_na=True,
nr=False)
if not len(exons_starts):
message("No exon found.", type="ERROR")
exons_ends = gtf.select_by_key("feature", "exon").extract_data(
"transcript_id,end", as_dict_of_merged_list=True, no_na=True, nr=False)
strands = gtf.select_by_key("feature", "transcript").extract_data(
"transcript_id,strand",
as_dict_of_values=True,
no_na=True,
nr=True,
hide_undef=True)
for tx_id in all_tx_ids:
size_list = []
for s, e in zip(exons_starts[tx_id], exons_ends[tx_id]):
size = str(int(e) - int(s) + 1)
size_list += [size]
if strands[tx_id] == "-":
size_list = reversed(size_list)
tx_to_size_list[tx_id] = ",".join(size_list)
if len(tx_to_size_list):
gtf = gtf.add_attr_from_dict(feat="transcript",
key="transcript_id",
a_dict=tx_to_size_list,
new_key=key_name)
gtf.write(outputfile, gc_off=True)
close_properly(outputfile, inputfile)
def nb_exons(inputfile=None,
outputfile=None,
key_name=None,
text_format=False):
"""
Count the number of exons in the gtf file.
"""
gtf = GTF(inputfile)
n_exons = defaultdict(int)
# -------------------------------------------------------------------------
# Computing number of exon for each transcript in input GTF file
#
# -------------------------------------------------------------------------
message("Computing number of exons for each transcript in input GTF file.")
exon = gtf.select_by_key("feature", "exon")
fields = exon.extract_data("transcript_id")
for i in fields:
tx_id = i[0]
n_exons[tx_id] += 1
if text_format:
for tx_id in n_exons:
outputfile.write(tx_id + "\t" + str(n_exons[tx_id]) +
"\ttranscript\n")
else:
if len(n_exons):
gtf = gtf.add_attr_from_dict(feat="transcript",
key="transcript_id",
a_dict=n_exons,
new_key=key_name)
gtf.write(outputfile, gc_off=True)
close_properly(outputfile, inputfile)
def divergent(
inputfile=None,
outputfile=None,
key_name=None,
upstream=1500,
downstream=1500,
chrom_info=None,
no_strandness=False,
no_annotation=False):
"""
Find transcript with divergent promoters.
"""
message("Using -u " + str(upstream) + ".")
message("Using -d " + str(downstream) + ".")
tx_with_divergent = dict()
dist_to_divergent = dict()
tss_pos = dict()
message("Loading GTF.")
gtf = GTF(inputfile)
message("Getting transcript coordinates.")
tx_feat = gtf.select_by_key("feature",
"transcript")
message("Getting tss coordinates.")
tss_bo = tx_feat.get_tss(name=["transcript_id", "gene_id"],
sep="||")
# get tss position
for i in tss_bo:
tx_id_tss, gn_id_tss = i.name.split("||")
tss_pos[tx_id_tss] = int(i.start)
message("Getting promoter coordinates.")
promoter_bo = tss_bo.slop(s=True,
l=upstream,
r=downstream,
g=chrom_info.name).cut([0, 1,
2, 3,
4, 5])
message("Intersecting...")
if no_strandness:
prom_with_tss_bo = promoter_bo.intersect(tss_bo,
wb=True,
s=False,
S=False)
else:
prom_with_tss_bo = promoter_bo.intersect(tss_bo,
wb=True,
s=False,
S=True)
tmp_file = make_tmp_file("promoter_slop", ".bed")
promoter_bo.saveas(tmp_file.name)
tmp_file = make_tmp_file("promoter_intersection_with_tss_as_", ".bed")
prom_with_tss_bo.saveas(tmp_file.name)
for i in prom_with_tss_bo:
tx_id_tss, gn_id_tss = i.fields[9].split("||")
tx_id_prom, gene_id_prom = i.fields[3].split("||")
if gene_id_prom != gn_id_tss:
if tx_id_prom in tx_with_divergent:
dist = abs(tss_pos[tx_id_prom] - tss_pos[tx_id_tss])
if dist < dist_to_divergent[tx_id_prom]:
dist_to_divergent[tx_id_prom] = dist
tx_with_divergent[tx_id_prom] = tx_id_tss
else:
dist = abs(tss_pos[tx_id_prom] - tss_pos[tx_id_tss])
dist_to_divergent[tx_id_prom] = dist
tx_with_divergent[tx_id_prom] = tx_id_tss
if not no_annotation:
if key_name is None:
key_name = "divergent"
key_name_dist = "dist_to_divergent"
else:
key_name_dist = "dist_" + key_name
if len(tx_with_divergent):
gtf = gtf.add_attr_from_dict(feat="transcript",
key="transcript_id",
a_dict=tx_with_divergent,
new_key=key_name)
gtf = gtf.add_attr_from_dict(feat="transcript",
key="transcript_id",
a_dict=dist_to_divergent,
new_key=key_name_dist)
gtf.write(outputfile,
gc_off=True)
else:
gtf.select_by_key("transcript_id",
",".join(list(tx_with_divergent.keys()))).write(outputfile, gc_off=True)
close_properly(outputfile, inputfile)
def overlapping(
inputfile=None,
outputfile=None,
key_name=None,
upstream=1500,
downstream=1500,
chrom_info=None,
feature_type='transcript',
same_strandedness=False,
diff_strandedness=False,
annotate_gtf=False,
bool=False,
annotate_all=False,
invert_match=False):
"""
Description: Find transcripts whose body/TSS/TTS do or do not overlap with any
transcript from another gene.
"""
# ----------------------------------------------------------------------
# Prepare key names
# ----------------------------------------------------------------------
if annotate_gtf:
if key_name is None:
key_info = ["overlap",
feature_type,
"u" + str(upstream / 1000) + "k",
"d" + str(downstream / 1000) + "k"
]
key_name = "_".join(key_info)
if invert_match:
message("--annotate-gtf and --invert-match are "
"mutually exclusive.",
type="ERROR")
if same_strandedness and diff_strandedness:
message("--same-strandedness and --diff-strandedness are "
"mutually exclusive.",
type="ERROR")
message("Using -u " + str(upstream))
message("Using -d " + str(downstream))
overlapping_tx = defaultdict(list)
# Load the GTF so that it won't be lost
# if GTF stream comes from stdin
gtf = GTF(inputfile)
message("Getting transcript in bed format")
tx_feat = gtf.select_by_key("feature",
"transcript")
if annotate_all:
overlapping_tx = gtf.extract_data(keys=["transcript_id"], as_dict=True, default_val="0")
for i in overlapping_tx:
overlapping_tx[i] = []
# ----------------------------------------------------------------------
# Get transcript limits
# ----------------------------------------------------------------------
tx_bed = tx_feat.to_bed(name=["transcript_id", "gene_id"], sep="||")
message("Getting " + feature_type + " and 'slopping'.")
if feature_type == "transcript":
bed_obj = tx_bed.slop(s=True,
l=upstream,
r=downstream,
g=chrom_info.name).cut([0, 1, 2, 3, 4, 5])
elif feature_type == "promoter":
bed_obj = tx_feat.get_tss(name=["transcript_id", "gene_id"],
sep="||").slop(s=True,
l=upstream,
r=downstream,
g=chrom_info.name).cut([0, 1,
2, 3,
4, 5])
elif feature_type == "tts":
bed_obj = tx_feat.get_tts(name=["transcript_id", "gene_id"],
sep="||").slop(s=True,
l=upstream,
r=downstream,
g=chrom_info.name).cut([0, 1,
2, 3,
4, 5])
else:
message("Not implemented yet", type="ERROR")
tmp_file = make_tmp_file(feature_type + "_slopped_region", ".bed")
bed_obj.saveas(tmp_file.name)
overlap_regions = bed_obj.intersect(tx_bed,
wb=True,
s=same_strandedness,
S=diff_strandedness)
tmp_file = make_tmp_file(feature_type + "_overlapping_regions", ".bed")
overlap_regions.saveas(tmp_file.name)
for i in overlap_regions:
tx_other, gn_other = i.fields[9].split("||")
tx_id, gene_id = i.fields[3].split("||")
if gene_id != gn_other:
overlapping_tx[tx_id] += [tx_other]
if bool:
for k, _ in overlapping_tx.items():
if not len(overlapping_tx[k]):
overlapping_tx[k] = "0"
else:
overlapping_tx[k] = "1"
if not invert_match:
if not annotate_gtf:
value = ",".join(set(overlapping_tx.keys()))
gtf.select_by_key("transcript_id",
value).write(outputfile,
gc_off=True)
else:
if len(overlapping_tx):
gtf = gtf.add_attr_from_dict(feat="transcript",
key="transcript_id",
a_dict=overlapping_tx,
new_key=key_name)
gtf.write(outputfile,
gc_off=True)
else:
values = ",".join(set(overlapping_tx.keys()))
gtf.select_by_key("transcript_id",
values,
invert_match).write(outputfile, gc_off=True)
gc.disable()
close_properly(outputfile, inputfile)
def select_by_intron_size(inputfile=None,
outputfile=None,
intron_size=0,
merged=False,
invert_match=False,
delete_monoexonic=False,
add_intron_size=False):
"""
Select genes which contain an intron of size at least s or whose sum of intron size is at least s
"""
message("Searching for intronic regions.")
gtf = GTF(inputfile, check_ensembl_format=False)
introns_bo = gtf.get_introns(by_transcript=True,
name=["transcript_id"],
intron_nb_in_name=False).sort()
# Get the list of transcripts
all_tx_ids = gtf.get_tx_ids(nr=True)
# The list of transcripts
# to be deleted
to_delete = OrderedDict()
if merged:
# Create a dict that will contain the sum of introns for
# each transcript
intron_sum_dict = OrderedDict.fromkeys(all_tx_ids, 0)
for i in introns_bo:
size = i.end - i.start
tx_id = i.name
intron_sum_dict[tx_id] += size
for tx_id, sum_intron in list(intron_sum_dict.items()):
if sum_intron != 0:
if not invert_match:
if sum_intron < intron_size:
to_delete[tx_id] = 1
else:
if sum_intron >= intron_size:
to_delete[tx_id] = 1
else:
if delete_monoexonic:
to_delete[tx_id] = 1
if add_intron_size:
gtf = gtf.add_attr_from_dict(feat="transcript",
key="transcript_id",
a_dict=intron_sum_dict,
new_key="intron_size_sum")
else:
# Create a dict that will contain a list introns size
# for each transcript
intron_size_dict = defaultdict(list)
for tx_id in all_tx_ids:
intron_size_dict[tx_id] = []
for i in introns_bo:
size = i.end - i.start
tx_id = i.name
intron_size_dict[tx_id] += [size]
for tx_id, list_size in list(intron_size_dict.items()):
if not list_size:
intron_size_dict[tx_id] = [0]
if delete_monoexonic:
to_delete[tx_id] = 1
continue
for size in intron_size_dict[tx_id]:
if not invert_match:
if size < intron_size:
to_delete[tx_id] = 1
else:
if size >= intron_size:
to_delete[tx_id] = 1
if add_intron_size:
for tx_id, list_size in list(intron_size_dict.items()):
list_size = [str(x) for x in list_size]
intron_size_dict[tx_id] = ",".join(list_size)
gtf = gtf.add_attr_from_dict(feat="transcript",
key="transcript_id",
a_dict=intron_size_dict,
new_key="intron_size")
all_tx_ids = gtf.get_tx_ids(nr=True)
all_tx_ids = [x for x in all_tx_ids if x not in to_delete]
msg_list = ",".join(list(to_delete.keys()))
nb_char = min([len(msg_list), 40])
msg_list = msg_list[0:nb_char]
message("Deleting: " + msg_list + "...")
gtf = gtf.select_by_key("transcript_id", ",".join(all_tx_ids))
gtf.write(outputfile, gc_off=True)
close_properly(outputfile, inputfile)
def closest_genes(
inputfile=None,
outputfile=None,
from_region_type=None,
no_header=False,
nb_neighbors=1,
to_region_type=None,
same_strandedness=False,
diff_strandedness=False,
text_format=False,
identifier="gene_id",
collapse=False):
"""
Find the n closest genes for each gene.
"""
if same_strandedness and diff_strandedness:
message("--same-strandedness and --diff-strandedness are "
"mutually exclusive.",
type="ERROR")
# ----------------------------------------------------------------------
# load GTF
# ----------------------------------------------------------------------
gtf = GTF(inputfile)
gn_gtf = gtf.select_by_key("feature", "gene")
gn_ids = gn_gtf.get_gn_ids(nr=True)
if len(gn_gtf) == 0:
message("No gene feature found. Please use convert_ensembl.",
type="ERROR")
if nb_neighbors >= (len(gn_gtf) - 1):
message("Two much neighbors",
type="ERROR")
all_ids = gn_gtf.extract_data(identifier, as_list=True, no_na=False)
if "." in all_ids:
message("Some identifiers are undefined ('.').",
type="ERROR")
if len(all_ids) == 0:
message("The identifier was not found.",
type="ERROR")
# ----------------------------------------------------------------------
# load GTF and requested regions (for source/'from' transcript)
# ----------------------------------------------------------------------
if from_region_type == 'tss':
from_regions = gn_gtf.get_5p_end(feat_type="gene",
name=[identifier],
).cut([0, 1, 2,
3, 4, 5]).sort()
elif from_region_type == 'tts':
from_regions = gn_gtf.get_3p_end(feat_type="gene",
name=[identifier],
).cut([0, 1, 2,
3, 4, 5]).sort()
elif from_region_type == 'gene':
from_regions = gn_gtf.to_bed(name=[identifier],
).cut([0, 1, 2,
3, 4, 5]).sort()
else:
message("Unknown type.", type="ERROR")
# ----------------------------------------------------------------------
# load GTF and requested regions (for dest/'to' transcript)
# ----------------------------------------------------------------------
if to_region_type == 'tss':
to_regions = gn_gtf.get_5p_end(feat_type="gene",
name=[identifier],
).cut([0, 1, 2,
3, 4, 5]).sort()
elif to_region_type == 'tts':
to_regions = gn_gtf.get_3p_end(feat_type="gene",
name=[identifier],
).cut([0, 1, 2,
3, 4, 5]).sort()
elif to_region_type == 'gene':
to_regions = gn_gtf.to_bed(name=[identifier],
).cut([0, 1, 2,
3, 4, 5]).sort()
else:
message("Unknown type.", type="ERROR")
# ----------------------------------------------------------------------
# Search closest genes
# ----------------------------------------------------------------------
gene_closest = defaultdict(list)
gene_closest_dist = defaultdict(list)
closest_bo = from_regions.closest(b=to_regions,
k=nb_neighbors,
N=True,
s=same_strandedness,
S=diff_strandedness,
d=True)
for i in closest_bo:
gene_closest[i[3]] += [i[9]]
gene_closest_dist[i[3]] += [i[12]]
if not text_format:
if len(gene_closest):
gtf = gtf.add_attr_from_dict(feat="gene",
key=identifier,
a_dict=gene_closest,
new_key="closest_gn")
gtf = gtf.add_attr_from_dict(feat="gene",
key=identifier,
a_dict=gene_closest_dist,
new_key="closest_dist")
gtf.write(outputfile, gc_off=True)
else:
if not no_header:
outputfile.write("genes\tclosest_genes\tdistances\n")
for gene in gn_ids:
if not collapse:
outputfile.write("\t".join([gene,
",".join(gene_closest[gene]),
",".join(gene_closest_dist[gene])]) + "\n")
else:
for closest, dist in zip(gene_closest[gene],
gene_closest_dist[gene]):
outputfile.write("\t".join([gene,
closest,
dist]) + "\n")
gc.disable()
close_properly(outputfile, inputfile)
def convergent(inputfile=None,
outputfile=None,
upstream=1500,
downstream=1500,
chrom_info=None):
"""
Find transcript with convergent tts.
"""
message("Using -u " + str(upstream) + ".")
message("Using -d " + str(downstream) + ".")
tx_to_convergent_nm = dict()
dist_to_convergent = dict()
tts_pos = dict()
message("Loading GTF.")
gtf = GTF(inputfile)
message("Getting transcript coordinates.")
tx_feat = gtf.select_by_key("feature", "transcript")
message("Getting tts coordinates.")
tts_bo = tx_feat.get_tts(name=["transcript_id", "gene_id"], sep="||")
# get tts position
for i in tts_bo:
tx_id_ov, gn_id_ov = i.name.split("||")
tts_pos[tx_id_ov] = int(i.start)
message("Getting tts coordinates.")
tts_region_bo = tts_bo.slop(s=True,
l=upstream,
r=downstream,
g=chrom_info.name).cut([0, 1, 2, 3, 4, 5])
message("Intersecting...")
tts_intersect_bo = tts_region_bo.intersect(tts_bo,
wb=True,
s=False,
S=True)
tmp_file = make_tmp_file("tts_slop", ".bed")
tts_region_bo.saveas(tmp_file.name)
tmp_file = make_tmp_file("tts_slop_intersection_with_tts_as_", ".bed")
tts_intersect_bo.saveas(tmp_file.name)
for i in tts_intersect_bo:
tx_id_main, gene_id_main = i.fields[3].split("||")
tx_id_ov, gn_id_ov = i.fields[9].split("||")
if gene_id_main != gn_id_ov:
if tx_id_main in tx_to_convergent_nm:
dist = abs(tts_pos[tx_id_main] - tts_pos[tx_id_ov])
if dist < dist_to_convergent[tx_id_main]:
dist_to_convergent[tx_id_main] = dist
tx_to_convergent_nm[tx_id_main] = tx_id_ov
else:
dist = abs(tts_pos[tx_id_main] - tts_pos[tx_id_ov])
dist_to_convergent[tx_id_main] = dist
tx_to_convergent_nm[tx_id_main] = tx_id_ov
if len(tx_to_convergent_nm):
gtf = gtf.add_attr_from_dict(feat="transcript",
key="transcript_id",
a_dict=tx_to_convergent_nm,
new_key="convergent")
gtf = gtf.add_attr_from_dict(feat="transcript",
key="transcript_id",
a_dict=dist_to_convergent,
new_key="dist_to_convergent")
gtf.write(outputfile, gc_off=True)
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)