def publocus(args):
"""
%prog publocus idsfile > idsfiles.publocus
Given a list of model identifiers, convert each into a GenBank approved
pub_locus.
Example output:
Medtr1g007020.1 MTR_1g007020
Medtr1g007030.1 MTR_1g007030
Medtr1g007060.1 MTR_1g007060A
Medtr1g007060.2 MTR_1g007060B
"""
from jcvi.utils.cbook import AutoVivification
p = OptionParser(publocus.__doc__)
p.add_option("--locus_tag",
default="MTR_",
help="GenBank locus tag [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
locus_tag = opts.locus_tag
index = AutoVivification()
idsfile, = args
fp = must_open(idsfile)
for row in fp:
locus, chrom, sep, rank, iso = atg_name(
row, retval="locus,chr,sep,rank,iso")
if None in (locus, chrom, sep, rank, iso):
logging.warning(
"{0} is not a valid gene model identifier".format(row))
continue
if locus not in index.keys():
pub_locus = gene_name(chrom, rank, prefix=locus_tag, sep=sep)
index[locus]['pub_locus'] = pub_locus
index[locus]['isos'] = set()
index[locus]['isos'].add(int(iso))
for locus in index:
pub_locus = index[locus]['pub_locus']
Index[locus]['isos'] = sorted(index[locus]['isos'])
if len(index[locus]['isos']) > 1:
new = [chr(n + 64) for n in index[locus]['isos'] if n < 27]
for i, ni in zip(index[locus]['isos'], new):
print "\t".join(x for x in ("{0}.{1}".format(locus, i), \
"{0}{1}".format(pub_locus, ni)))
else:
print "\t".join(x for x in ("{0}.{1}".format(locus, index[locus]['isos'][0]), \
pub_locus))
def publocus(args):
"""
%prog publocus idsfile > idsfiles.publocus
Given a list of model identifiers, convert each into a GenBank approved
pub_locus.
Example output:
Medtr1g007020.1 MTR_1g007020
Medtr1g007030.1 MTR_1g007030
Medtr1g007060.1 MTR_1g007060A
Medtr1g007060.2 MTR_1g007060B
"""
from jcvi.utils.cbook import AutoVivification
p = OptionParser(publocus.__doc__)
p.add_option("--locus_tag", default="MTR_",
help="GenBank locus tag [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
locus_tag = opts.locus_tag
index = AutoVivification()
idsfile, = args
fp = must_open(idsfile)
for row in fp:
locus, chrom, sep, rank, iso = atg_name(row, retval="locus,chr,sep,rank,iso")
if None in (locus, chrom, sep, rank, iso):
logging.warning("{0} is not a valid gene model identifier".format(row))
continue
if locus not in index.keys():
pub_locus = gene_name(chrom, rank, prefix=locus_tag, sep=sep)
index[locus]['pub_locus'] = pub_locus
index[locus]['isos'] = set()
index[locus]['isos'].add(int(iso))
for locus in index:
pub_locus = index[locus]['pub_locus']
Index[locus]['isos'] = sorted(index[locus]['isos'])
if len(index[locus]['isos']) > 1:
new = [chr(n+64) for n in index[locus]['isos'] if n < 27]
for i, ni in zip(index[locus]['isos'], new):
print "\t".join(x for x in ("{0}.{1}".format(locus, i), \
"{0}{1}".format(pub_locus, ni)))
else:
print "\t".join(x for x in ("{0}.{1}".format(locus, index[locus]['isos'][0]), \
pub_locus))
def group(args):
"""
%prog group tabfile > tabfile.grouped
Given a tab-delimited file, either group all elements within the file or
group the elements in the value column(s) based on the key (groupby) column
For example, convert this | into this
---------------------------------------
a 2 3 4 | a,2,3,4,5,6
a 5 6 | b,7,8
b 7 8 | c,9,10,11
c 9 |
c 10 11 |
If grouping by a particular column,
convert this | into this:
---------------------------------------------
a 2 3 4 | a 2,5 3,6 4
a 5 6 | b 7 8
b 7 8 | c 9,10 11
c 9 |
c 10 11 |
By default, it uniqifies all the grouped elements
"""
from jcvi.utils.cbook import AutoVivification
from jcvi.utils.grouper import Grouper
p = OptionParser(group.__doc__)
p.set_sep()
p.add_option("--groupby",
default=None,
type="int",
help="Default column to groupby")
p.add_option("--groupsep",
default=",",
help="Separator to join the grouped elements")
p.add_option(
"--nouniq",
default=False,
action="store_true",
help="Do not uniqify the grouped elements",
)
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
(tabfile, ) = args
sep = opts.sep
groupby = opts.groupby
groupsep = opts.groupsep
cols = []
grouper = AutoVivification() if groupby is not None else Grouper()
fp = must_open(tabfile)
for row in fp:
row = row.rstrip()
atoms = row.split(sep)
if groupby is not None:
if len(cols) < len(atoms):
cols = [x for x in range(len(atoms))]
if groupby not in cols:
logging.error(
"groupby col index `{0}` is out of range".format(groupby))
sys.exit()
key = atoms[groupby]
for col in cols:
if col == groupby:
continue
if not grouper[key][col]:
grouper[key][col] = [] if opts.nouniq else set()
if col < len(atoms):
if groupsep in atoms[col]:
for atom in atoms[col].split(groupsep):
if opts.nouniq:
grouper[key][col].append(atom)
else:
grouper[key][col].add(atom)
else:
if opts.nouniq:
grouper[key][col].append(atoms[col])
else:
grouper[key][col].add(atoms[col])
else:
grouper.join(*atoms)
for key in grouper:
if groupby is not None:
line = []
for col in cols:
if col == groupby:
line.append(key)
elif col in grouper[key].keys():
line.append(groupsep.join(grouper[key][col]))
else:
line.append("na")
print(sep.join(line))
else:
print(groupsep.join(key))
"""
Connect to databases (Sybase, MySQL and PostgreSQL database backends)
"""
import os.path as op
import sys
import logging
import re
from jcvi.formats.base import must_open
from jcvi.apps.base import OptionParser, ActionDispatcher, sh, getusername
from jcvi.utils.cbook import AutoVivification
# set up valid database connection params
valid_dbconn = AutoVivification()
for (dbconn, port, module, host) in zip(("Sybase", "MySQL", "PostgreSQL", "Oracle"), \
(2025, 3306, 5432, 1521), \
("Sybase", "MySQLdb", "psycopg2", "cx_Oracle"), \
("SYBPROD", "mysql-lan-dev", "pgsql-lan-dev", "DBNAME.tacc.utexas.edu")):
valid_dbconn[dbconn]['port'] = port
valid_dbconn[dbconn]['module'] = module
valid_dbconn[dbconn]['hostname'] = host
def db_defaults(connector='Sybase'):
"""
JCVI legacy Sybase, MySQL and PostgreSQL database connection defaults
"""
return valid_dbconn[connector]['hostname'], "access", "access"
def group(args):
"""
%prog group tabfile > tabfile.grouped
Given a tab-delimited file, either group all elements within the file or
group the elements in the value column(s) based on the key (groupby) column
For example, convert this | into this
---------------------------------------
a 2 3 4 | a,2,3,4,5,6
a 5 6 | b,7,8
b 7 8 | c,9,10,11
c 9 |
c 10 11 |
If grouping by a particular column,
convert this | into this:
---------------------------------------------
a 2 3 4 | a 2,5 3,6 4
a 5 6 | b 7 8
b 7 8 | c 9,10 11
c 9 |
c 10 11 |
By default, it uniqifies all the grouped elements
"""
from jcvi.utils.cbook import AutoVivification
from jcvi.utils.grouper import Grouper
p = OptionParser(group.__doc__)
p.set_sep()
p.add_option("--groupby", default=None, type='int',
help="Default column to groupby [default: %default]")
p.add_option("--groupsep", default=',',
help="Separator to join the grouped elements [default: `%default`]")
p.add_option("--nouniq", default=False, action="store_true",
help="Do not uniqify the grouped elements [default: %default]")
opts, args = p.parse_args(args)
if len(args) != 1:
sys.exit(not p.print_help())
tabfile, = args
sep = opts.sep
groupby = opts.groupby
groupsep = opts.groupsep
cols = []
grouper = AutoVivification() if groupby is not None else Grouper()
fp = must_open(tabfile)
for row in fp:
row = row.rstrip()
atoms = row.split(sep)
if groupby is not None:
if len(cols) < len(atoms):
cols = [x for x in xrange(len(atoms))]
if groupby not in cols:
logging.error("groupby col index `{0}` is out of range".format(groupby))
sys.exit()
key = atoms[groupby]
for col in cols:
if col == groupby:
continue
if not grouper[key][col]:
grouper[key][col] = [] if opts.nouniq else set()
if col < len(atoms):
if groupsep in atoms[col]:
for atom in atoms[col].split(groupsep):
if opts.nouniq:
grouper[key][col].append(atom)
else:
grouper[key][col].add(atom)
else:
if opts.nouniq:
grouper[key][col].append(atoms[col])
else:
grouper[key][col].add(atoms[col])
else:
grouper.join(*atoms)
for key in grouper:
if groupby is not None:
line = []
for col in cols:
if col == groupby:
line.append(key)
elif col in grouper[key].keys():
line.append(groupsep.join(grouper[key][col]))
else:
line.append("na")
print sep.join(line)
else:
print groupsep.join(key)
def reindex(args):
"""
%prog reindex gffile pep.fasta ref.pep.fasta
Reindex the splice isoforms (mRNA) in input GFF file, preferably
generated after PASA annotation update
In the input GFF file, there can be several types of mRNA within a locus:
* CDS matches reference, UTR extended, inherits reference mRNA ID
* CDS (slightly) different from reference, inherits reference mRNA ID
* Novel isoform added by PASA, have IDs like "LOCUS.1.1", "LOCUS.1.2"
* Multiple mRNA collapsed due to shared structure, have IDs like "LOCUS.1-LOCUS.1.1"
In the case of multiple mRNA which have inherited the same reference mRNA ID,
break ties by comparing the new protein with the reference protein using
EMBOSS `needle` to decide which mRNA retains ID and which is assigned a new ID.
All mRNA identifiers should follow the AGI naming conventions.
When reindexing the isoform identifiers, order mRNA based on:
* decreasing transcript length
* decreasing support from multiple input datasets used to run pasa.consolidate()
"""
from jcvi.formats.gff import make_index
from jcvi.formats.fasta import Fasta
from jcvi.apps.emboss import needle
from jcvi.formats.base import FileShredder
from tempfile import mkstemp
p = OptionParser(reindex.__doc__)
p.add_option("--scores", type="str", \
help="read from existing EMBOSS `needle` scores file")
p.set_outfile()
opts, args = p.parse_args(args)
if len(args) != 3:
sys.exit(not p.print_help())
gffile, pep, refpep, = args
gffdb = make_index(gffile)
reffasta = Fasta(refpep)
if not opts.scores:
fh, pairsfile = mkstemp(prefix='pairs', suffix=".txt", dir=".")
fw = must_open(pairsfile, "w")
conflict, novel = AutoVivification(), {}
for gene in gffdb.features_of_type('gene', order_by=('seqid', 'start')):
geneid = atg_name(gene.id, retval='locus')
novel[geneid] = []
updated_mrna, hybrid_mrna = [], []
for mrna in gffdb.children(gene, featuretype='mRNA', order_by=('seqid', 'start')):
if re.match(atg_name_pat, mrna.id) is not None and "_" not in mrna.id:
pf, mrnaid = parse_prefix(mrna.id)
mlen = gffdb.children_bp(mrna, child_featuretype='exon')
if "-" in mrna.id:
hybrid_mrna.append((mrna.id, mrna.start, mlen, len(pf)))
else:
updated_mrna.append((mrna.id, mrna.start, mlen, len(pf)))
for mrna in sorted(updated_mrna, key=lambda k:(k[1], -k[2], -k[3])):
pf, mrnaid = parse_prefix(mrna[0])
mstart, mlen = mrna[1], mrna[2]
iso = atg_name(mrnaid, retval='iso')
newiso = "{0}{1}".format(iso, re.sub(atg_name_pat, "", mrnaid))
if iso == newiso:
if iso not in conflict[geneid]:
conflict[geneid][iso] = []
conflict[geneid][iso].append((mrna[0], iso, newiso, \
mstart, mlen, len(pf)))
else:
novel[geneid].append((mrna[0], None, newiso, \
mstart, mlen, len(pf)))
for mrna in sorted(hybrid_mrna, key=lambda k:(k[1], -k[2], -k[3])):
pf, mrnaid = parse_prefix(mrna[0])
mstart, mlen = mrna[1], mrna[2]
_iso, _newiso = [], []
for id in sorted(mrnaid.split("-")):
a = atg_name(id, retval='iso')
b = "{0}{1}".format(a, re.sub(atg_name_pat, "", id))
_iso.append(a)
_newiso.append(b)
_novel = None
newiso = "-".join(str(x) for x in set(_newiso))
for iso, niso in zip(_iso, _newiso):
if iso == niso:
if iso not in conflict[geneid]:
conflict[geneid][iso] = \
[(mrna[0], iso, newiso, mstart, mlen, len(pf))]
_novel = None
break
_novel = True
if _novel is not None:
novel[geneid].append((mrna[0], None, newiso, \
mstart, mlen, len(pf)))
if not opts.scores:
for isoform in sorted(conflict[geneid]):
mrnaid = "{0}.{1}".format(geneid, isoform)
if mrnaid in reffasta.keys():
for mrna in conflict[geneid][isoform]:
print >> fw, "\t".join(str(x) for x in (mrnaid, mrna[0]))
scoresfile = None
if not opts.scores:
fw.close()
needle([pairsfile, refpep, pep])
FileShredder([pairsfile], verbose=False)
scoresfile = "{0}.scores".format(pairsfile.rsplit(".")[0])
else:
scoresfile = opts.scores
scores = read_scores(scoresfile, sort=True, trimsuffix=False)
primary = {}
for geneid in conflict:
primary[geneid] = []
for iso in sorted(conflict[geneid]):
conflict[geneid][iso].sort(key=lambda k:(k[3], -k[4], -k[5]))
_iso = "{0}.{1}".format(geneid, iso)
if _iso not in scores:
novel[geneid].extend(conflict[geneid][iso])
continue
top_score = scores[_iso][0][1]
result = next((i for i, v in enumerate(conflict[geneid][iso]) if v[0] == top_score), None)
if result is not None:
primary[geneid].append(conflict[geneid][iso][result])
del conflict[geneid][iso][result]
if geneid not in novel:
novel[geneid] = []
novel[geneid].extend(conflict[geneid][iso])
novel[geneid].sort(key=lambda k:(k[3], -k[4], -k[5]))
fw = must_open(opts.outfile, 'w')
for gene in gffdb.features_of_type('gene', order_by=('seqid', 'start')):
geneid = gene.id
print >> fw, gene
seen = []
if geneid in primary:
all_mrna = primary[geneid]
all_mrna.extend(novel[geneid])
for iso, mrna in enumerate(all_mrna):
_mrna = gffdb[mrna[0]]
_iso = mrna[1]
if mrna not in novel[geneid]:
seen.append(int(mrna[1]))
else:
mseen = 0 if len(seen) == 0 else max(seen)
_iso = (mseen + iso + 1) - len(seen)
_mrnaid = "{0}.{1}".format(geneid, _iso)
_mrna['ID'], _mrna['_old_ID'] = [_mrnaid], [_mrna.id]
print >> fw, _mrna
for c in gffdb.children(_mrna, order_by=('start')):
c['Parent'] = [_mrnaid]
print >> fw, c
else:
for feat in gffdb.children(gene, order_by=('seqid', 'start')):
print >> fw, feat
fw.close()
def consolidate(args):
"""
%prog consolidate gffile1 gffile2 ... > consolidated.out
Given 2 or more gff files generated by pasa annotation comparison,
iterate through each locus (shared locus name or overlapping CDS)
and identify same/different isoforms (shared splicing structure)
across the input datasets.
If `slop` is enabled, consolidation will collapse any variation
in terminal UTR lengths, keeping the longest as representative.
"""
from jcvi.formats.base import longest_unique_prefix
from jcvi.formats.gff import make_index, match_subfeats
from jcvi.utils.cbook import AutoVivification
from jcvi.utils.grouper import Grouper
from itertools import combinations, product
supported_modes = ["name", "coords"]
p = OptionParser(consolidate.__doc__)
p.add_option("--slop", default=False, action="store_true",
help="allow minor variation in terminal 5'/3' UTR" + \
" start/stop position [default: %default]")
p.add_option("--inferUTR", default=False, action="store_true",
help="infer presence of UTRs from exon coordinates")
p.add_option("--mode", default="name", choices=supported_modes,
help="method used to determine overlapping loci")
p.add_option("--summary", default=False, action="store_true",
help="Generate summary table of consolidation process")
p.add_option("--clusters", default=False, action="store_true",
help="Generate table of cluster members after consolidation")
p.set_outfile()
opts, args = p.parse_args(args)
slop = opts.slop
inferUTR = opts.inferUTR
mode = opts.mode
if len(args) < 2:
sys.exit(not p.print_help())
gffdbx = {}
for gffile in args:
dbn = longest_unique_prefix(gffile, args)
gffdbx[dbn] = make_index(gffile)
loci = Grouper()
for dbn in gffdbx:
odbns = [odbn for odbn in gffdbx if dbn != odbn]
for gene in gffdbx[dbn].features_of_type('gene', order_by=('seqid', 'start')):
if mode == "name":
loci.join(gene.id, (gene.id, dbn))
else:
if (gene.id, dbn) not in loci:
loci.join((gene.id, dbn))
gene_cds = list(gffdbx[dbn].children(gene, \
featuretype='CDS', order_by=('start')))
gene_cds_start, gene_cds_stop = gene_cds[0].start, \
gene_cds[-1].stop
for odbn in odbns:
for ogene_cds in gffdbx[odbn].region(seqid=gene.seqid, \
start=gene_cds_start, end=gene_cds_stop, \
strand=gene.strand, featuretype='CDS'):
for ogene in gffdbx[odbn].parents(ogene_cds, featuretype='gene'):
loci.join((gene.id, dbn), (ogene.id, odbn))
gfeats = {}
mrna = AutoVivification()
for i, locus in enumerate(loci):
gene = "gene_{0:0{pad}}".format(i, pad=6) \
if mode == "coords" else None
for elem in locus:
if type(elem) == tuple:
_gene, dbn = elem
if gene is None: gene = _gene
g = gffdbx[dbn][_gene]
if gene not in gfeats:
gfeats[gene] = g
gfeats[gene].attributes['ID'] = [gene]
else:
if g.start < gfeats[gene].start:
gfeats[gene].start = g.start
if g.stop > gfeats[gene].stop:
gfeats[gene].stop = g.stop
c = list(gffdbx[dbn].children(_gene, featuretype='mRNA', order_by='start'))
if len(c) > 0:
mrna[gene][dbn] = c
fw = must_open(opts.outfile, "w")
print("##gff-version 3", file=fw)
seen = {}
if opts.summary:
summaryfile = "{0}.summary.txt".format(opts.outfile.rsplit(".")[0])
sfw = must_open(summaryfile, "w")
summary = ["id"]
summary.extend(gffdbx.keys())
print("\t".join(str(x) for x in summary), file=sfw)
if opts.clusters:
clustersfile = "{0}.clusters.txt".format(opts.outfile.rsplit(".")[0])
cfw = must_open(clustersfile, "w")
clusters = ["id", "dbns", "members", "trlens"]
print("\t".join(str(x) for x in clusters), file=cfw)
for gene in mrna:
g = Grouper()
dbns = list(combinations(mrna[gene], 2))
if len(dbns) > 0:
for dbn1, dbn2 in dbns:
dbx1, dbx2 = gffdbx[dbn1], gffdbx[dbn2]
for mrna1, mrna2 in product(mrna[gene][dbn1], mrna[gene][dbn2]):
mrna1s, mrna2s = mrna1.stop - mrna1.start + 1, \
mrna2.stop - mrna2.start + 1
g.join((dbn1, mrna1.id, mrna1s))
g.join((dbn2, mrna2.id, mrna2s))
if match_subfeats(mrna1, mrna2, dbx1, dbx2, featuretype='CDS'):
res = []
ftypes = ['exon'] if inferUTR else ['five_prime_UTR', 'three_prime_UTR']
for ftype in ftypes:
res.append(match_subfeats(mrna1, mrna2, dbx1, dbx2, featuretype=ftype, slop=slop))
if all(r == True for r in res):
g.join((dbn1, mrna1.id, mrna1s), (dbn2, mrna2.id, mrna2s))
else:
for dbn1 in mrna[gene]:
for mrna1 in mrna[gene][dbn1]:
g.join((dbn1, mrna1.id, mrna1.stop - mrna1.start + 1))
print(gfeats[gene], file=fw)
for group in g:
group.sort(key=lambda x: x[2], reverse=True)
dbs, mrnas = [el[0] for el in group], [el[1] for el in group]
d, m = dbs[0], mrnas[0]
dbid, _mrnaid = "|".join(str(x) for x in set(dbs)), []
for x in mrnas:
if x not in _mrnaid: _mrnaid.append(x)
mrnaid = "{0}|{1}".format(dbid, "-".join(_mrnaid))
if mrnaid not in seen:
seen[mrnaid] = 0
else:
seen[mrnaid] += 1
mrnaid = "{0}-{1}".format(mrnaid, seen[mrnaid])
_mrna = gffdbx[d][m]
_mrna.attributes['ID'] = [mrnaid]
_mrna.attributes['Parent'] = [gene]
children = gffdbx[d].children(m, order_by='start')
print(_mrna, file=fw)
for child in children:
child.attributes['ID'] = ["{0}|{1}".format(dbid, child.id)]
child.attributes['Parent'] = [mrnaid]
print(child, file=fw)
if opts.summary:
summary = [mrnaid]
summary.extend(['Y' if db in set(dbs) else 'N' for db in gffdbx])
print("\t".join(str(x) for x in summary), file=sfw)
if opts.clusters:
clusters = [mrnaid]
clusters.append(",".join(str(el[0]) for el in group))
clusters.append(",".join(str(el[1]) for el in group))
clusters.append(",".join(str(el[2]) for el in group))
print("\t".join(str(x) for x in clusters), file=cfw)
fw.close()
if opts.summary: sfw.close()
if opts.clusters: cfw.close()
def consolidate(args):
"""
%prog consolidate gffile1 gffile2 ... > consolidated.out
Given 2 or more gff files generated by pasa annotation comparison,
iterate through every gene locus and identify all cases of same and
different isoforms across the different input datasets.
"""
from jcvi.formats.base import longest_unique_prefix
from jcvi.formats.gff import make_index
from jcvi.utils.cbook import AutoVivification
from jcvi.utils.grouper import Grouper
from itertools import combinations, product
p = OptionParser(consolidate.__doc__)
p.add_option("--slop", default=False, action="store_true",
help="allow minor variation in terminal 5'/3' UTR" + \
" start/stop position [default: %default]")
p.set_outfile()
opts, args = p.parse_args(args)
slop = opts.slop
if len(args) < 2:
sys.exit(not p.print_help())
gffdbx = {}
gene_coords = {}
mrna = AutoVivification()
for gffile in args:
dbn = longest_unique_prefix(gffile, args)
gffdbx[dbn] = make_index(gffile)
for gene in gffdbx[dbn].features_of_type('gene',
order_by=('seqid', 'start')):
if gene.id not in gene_coords:
gene_coords[gene.id] = []
gene_coords[gene.id].extend([gene.start, gene.stop])
c = list(gffdbx[dbn].children(gene,
featuretype='mRNA',
order_by='start'))
if len(c) > 0:
mrna[gene.id][dbn] = c
fw = must_open(opts.outfile, "w")
print >> fw, "##gff-version 3"
summary = ["id"]
summary.extend(gffdbx.keys())
print >> sys.stderr, "\t".join(str(x) for x in summary)
for gene in mrna:
g = Grouper()
dbns = list(combinations(mrna[gene], 2))
if len(dbns) > 0:
for dbn1, dbn2 in dbns:
for mrna1, mrna2 in product(mrna[gene][dbn1],
mrna[gene][dbn2]):
g.join((dbn1, mrna1.id))
g.join((dbn2, mrna2.id))
fUTR, tUTR = None, None
if match_subfeats(mrna1, mrna2, gffdbx[dbn1],
gffdbx[dbn2]):
fUTR = match_subfeats(mrna1, mrna2, gffdbx[dbn1], gffdbx[dbn2], \
featuretype='five_prime_UTR', slop=slop)
tUTR = match_subfeats(mrna1, mrna2, gffdbx[dbn1], gffdbx[dbn2], \
featuretype='three_prime_UTR', slop=slop)
if fUTR and tUTR:
g.join((dbn1, mrna1.id), (dbn2, mrna2.id))
else:
for dbn1 in mrna[gene]:
for mrna1 in mrna[gene][dbn1]:
g.join((dbn1, mrna1.id))
dbn = mrna[gene].keys()[0]
gene_coords[gene].sort()
_gene = gffdbx[dbn][gene]
_gene.start, _gene.stop = gene_coords[gene][0], gene_coords[gene][-1]
print >> fw, _gene
logging.debug(list(g))
for group in g:
dbs, mrnas = [el[0] for el in group], [el[1] for el in group]
d, m = dbs[0], mrnas[0]
if slop:
mlen = 0
for D, M in zip(dbs, mrnas):
_mrna = gffdbx[D][M]
_mlen = (_mrna.stop - _mrna.start) + 1
if _mlen > mlen:
d, m, mlen = D, M, _mlen
dbid, _mrnaid = "".join(str(x) for x in set(dbs)), []
_mrnaid = [x for x in mrnas if x not in _mrnaid]
mrnaid = "{0}:{1}".format(dbid, "-".join(_mrnaid))
_mrna = gffdbx[d][m]
_mrna.attributes['ID'] = [mrnaid]
children = gffdbx[d].children(m, order_by='start')
print >> fw, _mrna
for child in children:
child.attributes['ID'] = ["{0}:{1}".format(dbid, child.id)]
child.attributes['Parent'] = [mrnaid]
print >> fw, child
summary = [mrnaid]
summary.extend(['Y' if db in set(dbs) else 'N' for db in gffdbx])
print >> sys.stderr, "\t".join(str(x) for x in summary)
fw.close()