def tss_dist(inputfile=None, outputfile=None):
"""
Computes the distance between TSS of gene transcripts.
"""
gtf = GTF(inputfile, check_ensembl_format=True)
gn_tss_dist = defaultdict(dict)
message("Getting TSSs.")
tss = gtf.get_tss(name=["transcript_id", "gene_id"], as_dict=True)
for k in tss:
tx_id, gn_id = k.split("|")
gn_tss_dist[gn_id][tx_id] = int(tss[k])
gn_to_tx_to_tss = gtf.get_gn_to_tx(as_dict_of_dict=True)
message("Computing distances.")
outputfile.write("\t".join([
"gene_id", "transcript_id_1", "transcript_id_2", "dist", "tss_num_1",
"tss_num_2"
]) + "\n")
try:
for gn_id in sorted(gn_tss_dist.keys()):
tx_list = sorted(list(gn_tss_dist[gn_id].keys()))
for i in range(len(tx_list) - 1):
for j in range(i + 1, len(tx_list)):
dist = str(
abs(gn_tss_dist[gn_id][tx_list[i]] -
gn_tss_dist[gn_id][tx_list[j]]))
tss_1 = gn_to_tx_to_tss[gn_id][tx_list[i]]
tss_2 = gn_to_tx_to_tss[gn_id][tx_list[j]]
if tss_1 < tss_2:
str_out = "\t".join([
gn_id, tx_list[i], tx_list[j], dist,
str(tss_1),
str(tss_2)
]) + "\n"
outputfile.write(str_out)
else:
str_out = "\t".join([
gn_id, tx_list[j], tx_list[i], dist,
str(tss_2),
str(tss_1)
]) + "\n"
outputfile.write(str_out)
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
close_properly(outputfile, inputfile)
def tss_numbering(inputfile=None,
outputfile=None,
compute_dist=False,
key_name='tss_number',
key_name_dist='dist_to_first_tss',
add_nb_tss_to_gene=False,
gene_key='nb_tss'):
"""
Computes the distance between TSS of gene transcripts.
"""
gtf = GTF(inputfile, check_ensembl_format=True)
gn_tss_dist = defaultdict(dict)
message("Getting TSSs.")
tss = gtf.get_tss(name=["transcript_id"], as_dict=True)
tx_to_gn = gtf.get_tx_to_gn()
for k in tss:
gn_id = tx_to_gn[k]
gn_tss_dist[gn_id][k] = int(tss[k])
# if_dict_of_dict is true, get_gn_to_tx() returns a dict of dict
# that maps gene_id to transcript_id and transcript_id to TSS
# numbering (1 for most 5', then 2...). For transcripts having
# the same TSSs, the tss number will be the same.
gn_to_tx_to_tss = gtf.get_gn_to_tx(as_dict_of_dict=True)
message("Numbering TSSs.")
tss_number_file = make_tmp_file(prefix='tx_to_tss_number', suffix='.txt')
gn_how_many_tss = dict()
for gn_id in gn_to_tx_to_tss:
for tx_id in gn_to_tx_to_tss[gn_id]:
tss_num = str(gn_to_tx_to_tss[gn_id][tx_id])
tss_number_file.write(tx_id + "\t" + tss_num + "\n")
if gn_id not in gn_how_many_tss:
gn_how_many_tss[gn_id] = tss_num
else:
if int(tss_num) > int(gn_how_many_tss[gn_id]):
gn_how_many_tss[gn_id] = tss_num
tss_number_file.close()
gtf = gtf.add_attr_from_file(feat='transcript',
key='transcript_id',
new_key=key_name,
inputfile=open(tss_number_file.name),
has_header=False)
if add_nb_tss_to_gene:
gn_how_many_tss_file = make_tmp_file(prefix='gn_how_many_tss',
suffix='.txt')
for a_key, a_val in gn_how_many_tss.items():
gn_how_many_tss_file.write(a_key + "\t" + a_val + "\n")
gn_how_many_tss_file.close()
gtf = gtf.add_attr_from_file(feat='gene',
key='gene_id',
new_key=gene_key,
inputfile=open(gn_how_many_tss_file.name),
has_header=False)
if compute_dist:
gn_to_tx_ordered_by_tss = gtf.get_gn_to_tx(ordered_5p=True)
tss_dist_file = make_tmp_file(prefix='tx_tss_dist_to_first_tss',
suffix='.txt')
for gn_id in gn_to_tx_to_tss:
tx_list = gn_to_tx_ordered_by_tss[gn_id]
tx_first = tx_list.pop(0)
# The first tss as distance 0 to the
# first tss...
tss_dist_file.write(tx_first + "\t0\n")
for tx_id in tx_list:
dist_to_first = abs(int(tss[tx_first]) - int(tss[tx_id]))
tss_dist_file.write(tx_id + "\t" + str(dist_to_first) + "\n")
tss_dist_file.close()
gtf = gtf.add_attr_from_file(feat='transcript',
key='transcript_id',
new_key=key_name_dist,
inputfile=open(tss_dist_file.name),
has_header=False)
gtf.write(outputfile, gc_off=True)
close_properly(outputfile, inputfile)
def coverage(
inputfile=None,
outputfile=None,
bw_list=None,
labels=None,
pseudo_count=1,
nb_window=1,
ft_type="promoter",
n_highest=None,
downstream=1000,
key_name="cov",
zero_to_na=False,
name_column=None,
upstream=1000,
chrom_info=None,
nb_proc=1,
matrix_out=False,
stat='mean'):
"""
Compute transcript coverage with one or several bigWig.
"""
# -------------------------------------------------------------------------
# Create a list of labels.
# Take user input in account
# -------------------------------------------------------------------------
bw_list = [x.name for x in bw_list]
if len(bw_list) != len(set(bw_list)):
message("Found the same bigwigs several times.",
type="ERROR")
message('Checking labels.')
if labels is not None:
labels = labels.split(",")
# Ensure the number of labels is the same as the number of bw files.
if len(labels) != len(bw_list):
message("The number of labels should be the same as the number of"
" bigwig files.", type="ERROR")
# Ensure labels are non-redondant
if len(labels) > len(set(labels)):
message("Labels must be unique.", type="ERROR")
else:
labels = []
for i in range(len(bw_list)):
labels += [
os.path.splitext(
os.path.basename(
bw_list[i]))[0]]
# -------------------------------------------------------------------------
# Check the number of windows
#
# -------------------------------------------------------------------------
if n_highest is None:
n_highest = nb_window
message('Number of bins: %d' % nb_window)
message('N highest values: %d' % n_highest)
if n_highest > nb_window:
message('The number of window used for computing the score'
' (-n) can not be greater than the number of'
' windows (-w)', type="ERROR")
sys.exit()
# -------------------------------------------------------------------------
# Check input file is in bed or GTF format
#
# -------------------------------------------------------------------------
message("Loading input file...")
if inputfile.name == '<stdin>':
gtf = GTF(inputfile.name)
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':
gtf = GTF(inputfile.name)
is_gtf = True
else:
is_gtf = False
# -------------------------------------------------------------------------
# Get regions of interest
#
# -------------------------------------------------------------------------
name_column = name_column.split(",")
if is_gtf:
message("Getting regions of interest...")
if ft_type.lower() == "intergenic":
region_bo = gtf.get_intergenic(chrom_info, 0, 0).slop(s=True,
l=upstream,
r=downstream,
g=chrom_info.name).sort()
elif ft_type.lower() == "intron":
region_bo = gtf.get_introns().slop(s=True,
l=upstream,
r=downstream,
g=chrom_info.name).sort()
elif ft_type == "intron_by_tx":
region_bo = gtf.get_introns(by_transcript=True,
name=name_column,
).slop(s=True,
l=upstream,
r=downstream,
g=chrom_info.name).sort()
elif ft_type.lower() in ["promoter", "tss"]:
region_bo = gtf.get_tss(name=name_column, ).slop(s=True,
l=upstream,
r=downstream,
g=chrom_info.name).sort()
elif ft_type.lower() in ["tts", "terminator"]:
region_bo = gtf.get_tts(name=name_column).slop(s=True,
l=upstream,
r=downstream,
g=chrom_info.name).sort()
else:
region_bo = gtf.select_by_key(
"feature",
ft_type, 0
).to_bed(name=name_column).slop(s=True,
l=upstream,
r=downstream,
g=chrom_info.name).sort()
if len(region_bo) == 0:
message("Unable to find requested regions",
type="ERROR")
else:
region_bo = region_bo.slop(s=True,
l=upstream,
r=downstream,
g=chrom_info.name).sort()
region_bed = make_tmp_file(prefix="region", suffix=".bed")
region_bo.saveas(region_bed.name)
# -------------------------------------------------------------------------
# Compute coverage
#
# -------------------------------------------------------------------------
result_bed = bw_cov_mp(bw_list=bw_list,
region_file=open(region_bed.name),
labels=labels,
bin_nb=nb_window,
pseudo_count=pseudo_count,
zero_to_na=zero_to_na,
nb_proc=nb_proc,
n_highest=n_highest,
stat=stat,
verbose=pygtftk.utils.VERBOSITY)
if matrix_out:
result_bed.close()
df_first = pd.read_csv(result_bed.name, sep="\t", header=None)
df_first = df_first.ix[:, [0, 1, 2, 3, 5, 4]]
df_list = []
for i in range(len(labels)):
# create a sub data frame containing the coverage values of the
# current bwig
str_to_find = r"^" + labels[i] + r"\|"
tmp_df = df_first[df_first[3].str.match(str_to_find)].copy()
to_replace = r"^" + labels[i] + r"\|"
tmp_df.iloc[:, 3] = tmp_df.iloc[:, 3].replace(to_replace,
r"", regex=True)
df_list += [tmp_df]
df_final = df_list.pop(0)
for i in df_list:
# Add columns to df final by joining on
# chrom, start, end, transcript_id, strand
df_final = df_final.merge(i.iloc[:,
list(range(6))], on=[0, 1,
2, 3, 5])
df_final.columns = ["chrom",
"start",
"end",
"name",
"strand"] + labels
df_final.to_csv(outputfile, sep="\t", index=False)
else:
nb_line = 0
for i in result_bed:
outputfile.write(i)
nb_line += 1
if nb_line == 0:
message("No line available in output...",
type="ERROR")
gc.disable()
close_properly(inputfile, outputfile)
def great_reg_domains(inputfile=None,
outputfile=None,
go_id="GO:0003700",
species="hsapiens",
upstream=1000,
downstream=1000,
chrom_info=None,
distal=1000000,
mode='basal_plus_extension',
http_proxy=None,
https_proxy=None):
""" Given a GTF and a GO term, attempt compute labeled regions using GREAT 'association rule'. """
# -------------------------------------------------------------------------
# chrom_len will store the chromosome sizes.
# -------------------------------------------------------------------------
chrom_len = chrom_info_as_dict(chrom_info)
# -------------------------------------------------------------------------
# Read the GTF
# -------------------------------------------------------------------------
gtf = GTF(inputfile, check_ensembl_format=False)
# -------------------------------------------------------------------------
# Get the TSSs -- Extend them by upstream/dowstream
# -------------------------------------------------------------------------
message("Defining basal regulatory domains.", type="INFO")
basal_reg_bed = gtf.get_tss(name=['gene_id', 'gene_name']).slop(
s=True, l=upstream, r=downstream, g=chrom_info.name).sort()
basal_reg_bed_file = make_tmp_file(prefix='basal_reg', suffix='.bed')
basal_reg_bed.saveas(basal_reg_bed_file.name)
if mode == 'basal_plus_extension':
# -------------------------------------------------------------------------
# Search for upstream limits of each basal_reg_bed
# Here we ignore overlapping basal_reg_bed as the way they
# are proceded is not documented in GREAT to our knowledge
# -------------------------------------------------------------------------
message("Defining regulatory domains upstream regions.", type="INFO")
regulatory_region_start = dict()
regulatory_region_end = dict()
chroms = dict()
strands = dict()
basal_reg_bed_upstream = basal_reg_bed.closest(
basal_reg_bed,
# Ignore features in B that overlap A
io=True,
# In addition to the closest feature in B report distance
# use negative distances to report upstream features.
# Report distance with respect to A.
# When A is on the - strand, "upstream" means B has a
# higher(start, stop).
D="a",
# Ignore features in B that are downstream of features in A
id=True,
# How ties are handled. "first" Report the first tie
t="first",
# Require that the query and the closest hit have different names/gene_ids.
N=True)
basal_reg_bed_upstream_file = make_tmp_file(
prefix='basal_reg_bed_upstream', suffix='.bed')
basal_reg_bed_upstream.saveas(basal_reg_bed_upstream_file.name)
for line in basal_reg_bed_upstream:
gene_id = line.name
strand = line.strand
end = line.end
start = line.start
gene_id = "|".join([gene_id, str(start), str(end), strand])
chroms[gene_id] = line.chrom
strands[gene_id] = strand
if strand == '+':
# if the feature chromosome in B is
# '.' we have reached the start of the chr
if line.fields[6] == '.':
regulatory_region_start[gene_id] = max(
0, line.start - distal)
else:
padding = min(distal, abs(int(line.fields[12])) - 1)
regulatory_region_start[gene_id] = line.start - padding
elif strand == '-':
# if the feature chromosome in B is
# '.' we have reached the end of the chr
if line.fields[6] == '.':
regulatory_region_end[gene_id] = min(
int(chrom_len[line.chrom]), line.end + distal)
else:
padding = min(distal, abs(int(line.fields[12])) - 1)
regulatory_region_end[gene_id] = line.end + padding
else:
message("Cannot process genes without strand", type="WARNING")
message("Please check:" + gene_id, type="ERROR")
# -------------------------------------------------------------------------
# Search for downstream limits of each basal_reg_bed
# Here we ignore overlapping basal_reg_bed as the way they
# are proceded is not documented in GREAT to our knowledge
# -------------------------------------------------------------------------
message("Defining regulatory domains downstream regions.", type="INFO")
basal_reg_bed_downstream = basal_reg_bed.closest(
basal_reg_bed,
# Ignore features in B that overlap A
io=True,
# In addition to the closest feature in B report distance
# use negative distances to report upstream features.
# Report distance with respect to A.
# When A is on the - strand, "upstream" means B has a
# higher(start, stop).
D="a",
# Ignore features in B that are upstream of features in A
iu=True,
# How ties are handled. "first" Report the first tie
t="first",
# Require that the query and the closest hit have different names/gene_ids.
N=True)
basal_reg_bed_downstream_file = make_tmp_file(
prefix='basal_reg_bed_upstream', suffix='.bed')
basal_reg_bed_downstream.saveas(basal_reg_bed_downstream_file.name)
for line in basal_reg_bed_downstream:
gene_id = line.name
strand = line.strand
end = line.end
start = line.start
gene_id = "|".join([gene_id, str(start), str(end), strand])
chroms[gene_id] = line.chrom
strands[gene_id] = strand
if strand == '+':
# if the feature chromosome in B is
# '.' we have reached the start of the chr
if line.fields[6] == '.':
regulatory_region_end[gene_id] = min(
int(chrom_len[line.chrom]), line.end + distal)
else:
padding = min(distal, abs(int(line.fields[12])) - 1)
regulatory_region_end[gene_id] = line.end + padding
elif strand == '-':
if line.fields[6] == '.':
# sys.stderr.write(str(line.start - distal + 1) + "\n")
# sys.stderr.write(gene_id + "\n")
regulatory_region_start[gene_id] = max(
0, line.start - distal)
else:
padding = min(distal, abs(int(line.fields[12])) - 1)
regulatory_region_start[gene_id] = max(
0, line.start - padding)
else:
message("Cannot process genes without strand", type="WARNING")
message("Please check:" + gene_id, type="ERROR")
# print(regulatory_region_start)
else:
message(
"Only 'basal_plus_extension' association rule is currently supported.",
type='ERROR')
# -------------------------------------------------------------------------
# Print the regulatory regions of all genes
# By default print all genes
# -------------------------------------------------------------------------
if go_id is None:
for gene_id in regulatory_region_start:
outlist = [
chroms[gene_id],
str(regulatory_region_start[gene_id]),
str(regulatory_region_end[gene_id]),
gene_id.split("|")[0], "0", strands[gene_id]
]
outputfile.write("\t".join(outlist) + "\n")
else:
# -------------------------------------------------------------------------
# Get the list of gene/transcript associated with a particular GO term
# -------------------------------------------------------------------------
message("Getting Gene Ontology annotations.")
if not go_id.startswith("GO:"):
go_id = "GO:" + go_id
is_associated = set()
bm = Biomart(http_proxy=http_proxy, https_proxy=https_proxy)
bm.get_datasets('ENSEMBL_MART_ENSEMBL')
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.add(i)
for gene_id in regulatory_region_start:
gene_id_short = gene_id.split("|")[0]
if gene_id_short in is_associated:
outlist = [
chroms[gene_id],
str(regulatory_region_start[gene_id]),
str(regulatory_region_end[gene_id]),
gene_id.split("|")[0], "0", strands[gene_id]
]
outputfile.write("\t".join(outlist) + "\n")
def rm_dup_tss(inputfile=None, outputfile=None):
"""If several transcripts of a gene share the same tss, select only one."""
# ----------------------------------------------------------------------
# Get the TSS
# ----------------------------------------------------------------------
gtf = GTF(inputfile)
tss_bo = gtf.get_tss(["gene_id", "transcript_id"])
# ----------------------------------------------------------------------
# Sort the file by name (4th col) to ensure reproducibility between calls.
# ----------------------------------------------------------------------
with open(tss_bo.fn) as f:
lines = [line.split('\t') for line in f]
tmp_file = make_tmp_file(prefix="tss_sorted_by_tx_id", suffix=".bed")
for line in sorted(lines, key=operator.itemgetter(3)):
tmp_file.write('\t'.join(line))
tmp_file.close()
tss_bo = BedTool(tmp_file.name)
# ----------------------------------------------------------------------
# Get the list of non redundant TSSs
# ----------------------------------------------------------------------
gene_dict = defaultdict(dict)
to_delete = []
message("Looking for redundant TSS (gene-wise).")
for line in tss_bo:
tss = line.start
name = line.name
gene_id, tx_id = name.split("|")
if gene_id in gene_dict:
if tss not in gene_dict[gene_id]:
gene_dict[gene_id][tss] = tx_id
else:
to_delete += [tx_id]
else:
gene_dict[gene_id][tss] = tx_id
message("Deleted transcripts: " +
",".join(to_delete[1:min(10, len(to_delete))]) + "...",
type="DEBUG")
# ----------------------------------------------------------------------
# Write
# ----------------------------------------------------------------------
gtf.select_by_key("feature", "gene",
invert_match=True).select_by_key(
"transcript_id",
",".join(to_delete),
invert_match=True).write(outputfile, gc_off=True)
