def _sort_cogs(cogs1, cogs2):
cogs1 = cogs1[1] # discard seed info
cogs2 = cogs2[1] # discard seed info
cog_sizes1 = [len(cog) for cog in cogs1]
cog_sizes2 = [len(cog) for cog in cogs2]
mx1, mn1, avg1 = _max(cog_sizes1), _min(cog_sizes1), round(_mean(cog_sizes1))
mx2, mn2, avg2 = _max(cog_sizes2), _min(cog_sizes2), round(_mean(cog_sizes2))
# we want to maximize all these values in the following order:
for i, j in ((mx1, mx2), (avg1, avg2), (len(cogs1), len(cogs2))):
v = -1 * cmp(i, j)
if v != 0:
break
return v
def _sort_cogs(cogs1, cogs2):
cogs1 = cogs1[1] # discard seed info
cogs2 = cogs2[1] # discard seed info
cog_sizes1 = [len(cog) for cog in cogs1]
cog_sizes2 = [len(cog) for cog in cogs2]
mx1, mn1, avg1 = _max(cog_sizes1), _min(cog_sizes1), round(
_mean(cog_sizes1))
mx2, mn2, avg2 = _max(cog_sizes2), _min(cog_sizes2), round(
_mean(cog_sizes2))
# we want to maximize all these values in the following order:
for i, j in ((mx1, mx2), (avg1, avg2), (len(cogs1), len(cogs2))):
v = -1 * cmp(i, j)
if v != 0:
break
return v
def get_identity(fname):
s = SeqGroup(fname)
seqlen = len(six.itervalues(s.id2seq))
ident = list()
for i in range(seqlen):
states = defaultdict(int)
for seq in six.itervalues(s.id2seq):
if seq[i] != "-":
states[seq[i]] += 1
values = list(states.values())
if values:
ident.append(float(max(values)) / sum(values))
return (_max(ident), _min(ident), _mean(ident), _std(ident))
def get_identity(fname):
s = SeqGroup(fname)
seqlen = len(six.itervalues(s.id2seq))
ident = list()
for i in range(seqlen):
states = defaultdict(int)
for seq in six.itervalues(s.id2seq):
if seq[i] != "-":
states[seq[i]] += 1
values = list(states.values())
if values:
ident.append(float(max(values)) / sum(values))
return (_max(ident), _min(ident), _mean(ident), _std(ident))
def get_seqs_identity(alg, seqs):
''' Returns alg statistics regarding a set of sequences'''
seqlen = len(alg.get_seq(seqs[0]))
ident = list()
for i in range(seqlen):
states = defaultdict(int)
for seq_id in seqs:
seq = alg.get_seq(seq_id)
if seq[i] != "-":
states[seq[i]] += 1
values = list(states.values())
if values:
ident.append(float(max(values)) / sum(values))
return (_max(ident), _min(ident), _mean(ident), _std(ident))
def get_seqs_identity(alg, seqs):
""" Returns alg statistics regarding a set of sequences"""
seqlen = len(alg.get_seq(seqs[0]))
ident = list()
for i in range(seqlen):
states = defaultdict(int)
for seq_id in seqs:
seq = alg.get_seq(seq_id)
if seq[i] != "-":
states[seq[i]] += 1
values = list(states.values())
if values:
ident.append(float(max(values)) / sum(values))
return (_max(ident), _min(ident), _mean(ident), _std(ident))
def get_best_selection(cogs_selections, species):
ALL_SPECIES = set(species)
def _compare_cog_selection(cs1, cs2):
seed_1, missing_sp_allowed_1, candidates_1, sp2hits_1 = cs1
seed_2, missing_sp_allowed_2, candidates_2, sp2hits_2 = cs2
score_1, min_cov_1, max_cov_1, median_cov_1, cov_std_1, cog_cov_1 = get_cog_score(
candidates_1, sp2hits_1, median_cogs, ALL_SPECIES - set([seed_1]))
score_2, min_cov_2, max_cov_2, median_cov_2, cov_std_2, cog_cov_2 = get_cog_score(
candidates_2, sp2hits_2, median_cogs, ALL_SPECIES - set([seed_2]))
sp_represented_1 = len(sp2hits_1)
sp_represented_2 = len(sp2hits_1)
cmp_rpr = cmp(sp_represented_1, sp_represented_2)
if cmp_rpr == 1:
return 1
elif cmp_rpr == -1:
return -1
else:
cmp_score = cmp(score_1, score_2)
if cmp_score == 1:
return 1
elif cmp_score == -1:
return -1
else:
cmp_mincov = cmp(min_cov_1, min_cov_2)
if cmp_mincov == 1:
return 1
elif cmp_mincov == -1:
return -1
else:
cmp_maxcov = cmp(max_cov_1, max_cov_2)
if cmp_maxcov == 1:
return 1
elif cmp_maxcov == -1:
return -1
else:
cmp_cand = cmp(len(candidates_1), len(candidates_2))
if cmp_cand == 1:
return 1
elif cmp_cand == -1:
return -1
else:
return 0
min_score = 0.5
max_cogs = _max([len(data[2]) for data in cogs_selections])
median_cogs = _median([len(data[2]) for data in cogs_selections])
cogs_selections.sort(_compare_cog_selection)
cogs_selections.reverse()
header = [
'seed', 'missing sp allowed', 'spcs covered', '#COGs',
'mean sp coverage)', '#COGs for worst sp.', '#COGs for best sp.',
'sp. in COGS(avg)', 'SCORE'
]
print_header = True
best_cog_selection = None
cog_analysis = StringIO()
for i, cogs in enumerate(cogs_selections):
seed, missing_sp_allowed, candidates, sp2hits = cogs
sp_percent_coverages = [
(100 * sp2hits.get(sp, 0)) / float(len(candidates))
for sp in species
]
sp_coverages = [sp2hits.get(sp, 0) for sp in species]
score, min_cov, max_cov, median_cov, cov_std, cog_cov = get_cog_score(
candidates, sp2hits, median_cogs, ALL_SPECIES - set([seed]))
if best_cog_selection is None:
best_cog_selection = i
flag = "*"
else:
flag = " "
data = (candidates,
flag+"%10s" %seed, \
missing_sp_allowed, \
"%d (%0.1f%%)" %(len(set(sp2hits.keys()))+1, 100*float(len(ALL_SPECIES))/(len(sp2hits)+1)) , \
len(candidates), \
"%0.1f%% +- %0.1f" %(_mean(sp_percent_coverages), _std(sp_percent_coverages)), \
"% 3d (%0.1f%%)" %(min(sp_coverages),100*min(sp_coverages)/float(len(candidates))), \
"% 3d (%0.1f%%)" %(max(sp_coverages),100*max(sp_coverages)/float(len(candidates))), \
cog_cov,
score
)
if print_header:
print_as_table([data[1:]],
header=header,
print_header=True,
stdout=cog_analysis)
print_header = False
else:
print_as_table([data[1:]],
header=header,
print_header=False,
stdout=cog_analysis)
#raw_input("Press")
print(cog_analysis.getvalue())
#best_cog_selection = int(raw_input("choose:"))
return cogs_selections[best_cog_selection], cog_analysis
def brh_cogs2(DB, species, missing_factor=0.0, seed_sp=None, min_score=0):
"""It scans all precalculate BRH relationships among the species
passed as an argument, and detects Clusters of Orthologs
according to several criteria:
min_score: the min coverage/overalp value required for a
blast to be a reliable hit.
missing_factor: the min percentage of species in which a
given seq must have orthologs.
"""
def _sort_cogs(cogs1, cogs2):
seed1, mx1, avg1, ncogs1 = cogs1
seed2, mx2, avg2, ncogs2 = cogs2
for i, j in ((mx1, mx2), (avg1, avg2), (ncogs1, ncogs2)):
v = -1 * cmp(i, j)
if v != 0:
break
return v
log.log(26, "Searching BRH orthologs")
species = set(map(str, species))
min_species = len(species) - round(missing_factor * len(species))
if seed_sp == "auto":
sp_to_test = list(species)
elif seed_sp == "largest":
cmd = """SELECT taxid, size FROM species"""
db.seqcursor.execute(cmd)
sp2size = {}
for tax, counter in db.seqcursor.fetchall():
if tax in species:
sp2size[tax] = counter
sorted_sp = sorted(list(sp2size.items()), lambda x, y: cmp(x[1], y[1]))
log.log(24, sorted_sp[:6])
largest_sp = sorted_sp[-1][0]
sp_to_test = [largest_sp]
log.log(28, "Using %s as search seed. Proteome size=%s genes" %\
(largest_sp, sp2size[largest_sp]))
else:
sp_to_test = [str(seed_sp)]
analysis_txt = StringIO()
if sp_to_test:
log.log(26, "Finding best COG selection...")
seed2size = get_sorted_seeds(seed_sp, species, sp_to_test, min_species,
DB)
size_analysis = []
for seedname, content in six.iteritems(seed2size):
cog_sizes = [size for seq, size in content]
mx, avg = _max(cog_sizes), round(_mean(cog_sizes))
size_analysis.append([seedname, mx, avg, len(content)])
size_analysis.sort(_sort_cogs)
#print '\n'.join(map(str, size_analysis))
seed = size_analysis[0][0]
print_as_table(
size_analysis[:25],
stdout=analysis_txt,
header=["Seed", "largest COG", "avg COG size", "total COGs"])
if size_analysis[0][1] < len(species) - 1:
print(size_analysis[0][1])
raise ValueError(
"Current COG selection parameters do not permit to cover all species"
)
log.log(28, analysis_txt.getvalue())
# The following loop tests each possible seed if none is
# specified.
log.log(28, "Computing Clusters of Orthologs groups (COGs)")
log.log(28, "Min number of species per COG: %d" % min_species)
cogs_selection = []
log.log(26, "Using seed species:%s", seed)
species_side1 = ','.join(
map(quote, [s for s in species if str(s) > str(seed)]))
species_side2 = ','.join(
map(quote, [s for s in species if str(s) < str(seed)]))
pairs1 = []
pairs2 = []
# Select all ids with matches in the target species, and
# return the total number of species covered by each of
# such ids.
if species_side1 != "":
cmd = """SELECT seqid1, taxid1, seqid2, taxid2 from ortho_pair WHERE
taxid1="%s" AND taxid2 IN (%s) """ % (seed, species_side1)
DB.orthocursor.execute(cmd)
pairs1 = DB.orthocursor.fetchall()
if species_side2 != "":
cmd = """SELECT seqid2, taxid2, seqid1, taxid1 from ortho_pair WHERE
taxid1 IN (%s) AND taxid2 = "%s" """ % (species_side2, seed)
DB.orthocursor.execute(cmd)
pairs2 = DB.orthocursor.fetchall()
cog_candidates = defaultdict(set)
for seq1, sp1, seq2, sp2 in pairs1 + pairs2:
s1 = (sp1, seq1)
s2 = (sp2, seq2)
cog_candidates[(sp1, seq1)].update([s1, s2])
all_cogs = [
cand for cand in list(cog_candidates.values())
if len(cand) >= min_species
]
# CHECK CONSISTENCY
seqs = set()
for cand in all_cogs:
seqs.update([b for a, b in cand if a == seed])
pre_selected_seqs = set([v[0] for v in seed2size[seed]])
if len(seqs & pre_selected_seqs) != len(set(seed2size[seed])) or\
len(seqs & pre_selected_seqs) != len(seqs):
print("old method seqs", len(seqs), "new seqs",
len(set(seed2size[seed])), "Common",
len(seqs & pre_selected_seqs))
raise ValueError("ooops")
cog_sizes = [len(cog) for cog in all_cogs]
cog_spsizes = [len(set([e[0] for e in cog])) for cog in all_cogs]
if [1 for i in range(len(cog_sizes)) if cog_sizes[i] != cog_spsizes[i]]:
raise ValueError("Inconsistent COG found")
if cog_sizes:
cogs_selection.append([seed, all_cogs])
log.log(26, "Found %d COGs" % len(all_cogs))
recoded_cogs = []
for cog in all_cogs:
named_cog = [
"%s%s%s" % (x[0], GLOBALS["spname_delimiter"], x[1]) for x in cog
]
recoded_cogs.append(named_cog)
return recoded_cogs, analysis_txt.getvalue()
def get_best_selection(cogs_selections, species):
ALL_SPECIES = set(species)
def _compare_cog_selection(cs1, cs2):
seed_1, missing_sp_allowed_1, candidates_1, sp2hits_1 = cs1
seed_2, missing_sp_allowed_2, candidates_2, sp2hits_2 = cs2
score_1, min_cov_1, max_cov_1, median_cov_1, cov_std_1, cog_cov_1 = get_cog_score(candidates_1, sp2hits_1, median_cogs, ALL_SPECIES-set([seed_1]))
score_2, min_cov_2, max_cov_2, median_cov_2, cov_std_2, cog_cov_2 = get_cog_score(candidates_2, sp2hits_2, median_cogs, ALL_SPECIES-set([seed_2]))
sp_represented_1 = len(sp2hits_1)
sp_represented_2 = len(sp2hits_1)
cmp_rpr = cmp(sp_represented_1, sp_represented_2)
if cmp_rpr == 1:
return 1
elif cmp_rpr == -1:
return -1
else:
cmp_score = cmp(score_1, score_2)
if cmp_score == 1:
return 1
elif cmp_score == -1:
return -1
else:
cmp_mincov = cmp(min_cov_1, min_cov_2)
if cmp_mincov == 1:
return 1
elif cmp_mincov == -1:
return -1
else:
cmp_maxcov = cmp(max_cov_1, max_cov_2)
if cmp_maxcov == 1:
return 1
elif cmp_maxcov == -1:
return -1
else:
cmp_cand = cmp(len(candidates_1), len(candidates_2))
if cmp_cand == 1:
return 1
elif cmp_cand == -1:
return -1
else:
return 0
min_score = 0.5
max_cogs = _max([len(data[2]) for data in cogs_selections])
median_cogs = _median([len(data[2]) for data in cogs_selections])
cogs_selections.sort(_compare_cog_selection)
cogs_selections.reverse()
header = ['seed',
'missing sp allowed',
'spcs covered',
'#COGs',
'mean sp coverage)',
'#COGs for worst sp.',
'#COGs for best sp.',
'sp. in COGS(avg)',
'SCORE' ]
print_header = True
best_cog_selection = None
cog_analysis = StringIO()
for i, cogs in enumerate(cogs_selections):
seed, missing_sp_allowed, candidates, sp2hits = cogs
sp_percent_coverages = [(100*sp2hits.get(sp,0))/float(len(candidates)) for sp in species]
sp_coverages = [sp2hits.get(sp, 0) for sp in species]
score, min_cov, max_cov, median_cov, cov_std, cog_cov = get_cog_score(candidates, sp2hits, median_cogs, ALL_SPECIES-set([seed]))
if best_cog_selection is None:
best_cog_selection = i
flag = "*"
else:
flag = " "
data = (candidates,
flag+"%10s" %seed, \
missing_sp_allowed, \
"%d (%0.1f%%)" %(len(set(sp2hits.keys()))+1, 100*float(len(ALL_SPECIES))/(len(sp2hits)+1)) , \
len(candidates), \
"%0.1f%% +- %0.1f" %(_mean(sp_percent_coverages), _std(sp_percent_coverages)), \
"% 3d (%0.1f%%)" %(min(sp_coverages),100*min(sp_coverages)/float(len(candidates))), \
"% 3d (%0.1f%%)" %(max(sp_coverages),100*max(sp_coverages)/float(len(candidates))), \
cog_cov,
score
)
if print_header:
print_as_table([data[1:]], header=header, print_header=True, stdout=cog_analysis)
print_header = False
else:
print_as_table([data[1:]], header=header, print_header=False, stdout=cog_analysis)
#raw_input("Press")
print(cog_analysis.getvalue())
#best_cog_selection = int(raw_input("choose:"))
return cogs_selections[best_cog_selection], cog_analysis
def brh_cogs2(DB, species, missing_factor=0.0, seed_sp=None, min_score=0):
"""It scans all precalculate BRH relationships among the species
passed as an argument, and detects Clusters of Orthologs
according to several criteria:
min_score: the min coverage/overalp value required for a
blast to be a reliable hit.
missing_factor: the min percentage of species in which a
given seq must have orthologs.
"""
def _sort_cogs(cogs1, cogs2):
seed1, mx1, avg1, ncogs1 = cogs1
seed2, mx2, avg2, ncogs2 = cogs2
for i, j in ((mx1, mx2), (avg1, avg2), (ncogs1, ncogs2)):
v = -1 * cmp(i, j)
if v != 0:
break
return v
log.log(26, "Searching BRH orthologs")
species = set(map(str, species))
min_species = len(species) - round(missing_factor * len(species))
if seed_sp == "auto":
sp_to_test = list(species)
elif seed_sp == "largest":
cmd = """SELECT taxid, size FROM species"""
db.seqcursor.execute(cmd)
sp2size = {}
for tax, counter in db.seqcursor.fetchall():
if tax in species:
sp2size[tax] = counter
sorted_sp = sorted(list(sp2size.items()), lambda x,y: cmp(x[1],y[1]))
log.log(24, sorted_sp[:6])
largest_sp = sorted_sp[-1][0]
sp_to_test = [largest_sp]
log.log(28, "Using %s as search seed. Proteome size=%s genes" %\
(largest_sp, sp2size[largest_sp]))
else:
sp_to_test = [str(seed_sp)]
analysis_txt = StringIO()
if sp_to_test:
log.log(26, "Finding best COG selection...")
seed2size = get_sorted_seeds(seed_sp, species, sp_to_test, min_species, DB)
size_analysis = []
for seedname, content in six.iteritems(seed2size):
cog_sizes = [size for seq, size in content]
mx, avg = _max(cog_sizes), round(_mean(cog_sizes))
size_analysis.append([seedname, mx, avg, len(content)])
size_analysis.sort(_sort_cogs)
#print '\n'.join(map(str, size_analysis))
seed = size_analysis[0][0]
print_as_table(size_analysis[:25], stdout=analysis_txt,
header=["Seed","largest COG", "avg COG size", "total COGs"])
if size_analysis[0][1] < len(species)-1:
print(size_analysis[0][1])
raise ValueError("Current COG selection parameters do not permit to cover all species")
log.log(28, analysis_txt.getvalue())
# The following loop tests each possible seed if none is
# specified.
log.log(28, "Computing Clusters of Orthologs groups (COGs)")
log.log(28, "Min number of species per COG: %d" %min_species)
cogs_selection = []
log.log(26,"Using seed species:%s", seed)
species_side1 = ','.join(map(quote, [s for s in species if str(s)>str(seed)]))
species_side2 = ','.join(map(quote, [s for s in species if str(s)<str(seed)]))
pairs1 = []
pairs2 = []
# Select all ids with matches in the target species, and
# return the total number of species covered by each of
# such ids.
if species_side1 != "":
cmd = """SELECT seqid1, taxid1, seqid2, taxid2 from ortho_pair WHERE
taxid1="%s" AND taxid2 IN (%s) """ % (seed, species_side1)
DB.orthocursor.execute(cmd)
pairs1 = DB.orthocursor.fetchall()
if species_side2 != "":
cmd = """SELECT seqid2, taxid2, seqid1, taxid1 from ortho_pair WHERE
taxid1 IN (%s) AND taxid2 = "%s" """ % (species_side2, seed)
DB.orthocursor.execute(cmd)
pairs2 = DB.orthocursor.fetchall()
cog_candidates = defaultdict(set)
for seq1, sp1, seq2, sp2 in pairs1 + pairs2:
s1 = (sp1, seq1)
s2 = (sp2, seq2)
cog_candidates[(sp1, seq1)].update([s1, s2])
all_cogs = [cand for cand in list(cog_candidates.values()) if
len(cand) >= min_species]
# CHECK CONSISTENCY
seqs = set()
for cand in all_cogs:
seqs.update([b for a,b in cand if a == seed])
pre_selected_seqs = set([v[0] for v in seed2size[seed]])
if len(seqs & pre_selected_seqs) != len(set(seed2size[seed])) or\
len(seqs & pre_selected_seqs) != len(seqs):
print("old method seqs", len(seqs), "new seqs", len(set(seed2size[seed])), "Common", len(seqs & pre_selected_seqs))
raise ValueError("ooops")
cog_sizes = [len(cog) for cog in all_cogs]
cog_spsizes = [len(set([e[0] for e in cog])) for cog in all_cogs]
if [1 for i in range(len(cog_sizes)) if cog_sizes[i] != cog_spsizes[i]]:
raise ValueError("Inconsistent COG found")
if cog_sizes:
cogs_selection.append([seed, all_cogs])
log.log(26, "Found %d COGs" % len(all_cogs))
recoded_cogs = []
for cog in all_cogs:
named_cog = ["%s%s%s" %(x[0], GLOBALS["spname_delimiter"],x[1]) for x in cog]
recoded_cogs.append(named_cog)
return recoded_cogs, analysis_txt.getvalue()
