def test_trimal(key, hash3, hash07, alb_resources, hf):
alignbuddy = alb_resources.get_one(key)
tester1, tester2 = Alb.make_copy(alignbuddy), Alb.make_copy(alignbuddy)
Alb.trimal(tester1, 3)
assert hf.buddy2hash(tester1) == hash3, alignbuddy.write("error_files%s%s" % (hash3, os.path.sep))
tester1, tester2 = Alb.make_copy(alignbuddy), Alb.make_copy(alignbuddy)
Alb.trimal(tester1, 0.7)
assert hf.buddy2hash(tester1) == hash07, alignbuddy.write("error_files%s%s" % (hash07, os.path.sep))
def score_sequences(seq_pair):
seq1, seq2 = seq_pair.records
id_regex = "^%s$|^%s$" % (seq1.id, seq2.id)
sb_copy = Sb.make_copy(seqbuddy)
Sb.delete_records(sb_copy, id_regex)
sb_copy = Sb.SeqBuddy(sb_copy.records + [seq1, seq2], out_format="gb", alpha=sb_copy.alpha)
alignbuddy = Alb.generate_msa(sb_copy, tool="mafft", params=" --globalpair", quiet=True)
if not in_args.no_msa_trim:
alignbuddy = Alb.trimal(alignbuddy, threshold="gappyout")
alignbuddy = Alb.pull_records(alignbuddy, id_regex)
_score = 0
seq1, seq2 = alignbuddy.records()
prev_aa1 = "-"
prev_aa2 = "-"
for aa_pos in range(alignbuddy.lengths()[0]):
aa1 = seq1.seq[aa_pos]
aa2 = seq2.seq[aa_pos]
if aa1 == "-" or aa2 == "-":
if prev_aa1 == "-" or prev_aa2 == "-":
_score += gap_extend
else:
_score += gap_open
else:
_score += BLOSUM45[aa1, aa2]
prev_aa1 = str(aa1)
prev_aa2 = str(aa2)
return _score
def test_trimal2(alb_resources, hf):
tester = Alb.trimal(alb_resources.get_one("o p n"), 'all')
assert hf.buddy2hash(tester) == "8faaf09741ddb3137653cb77ee66974a"
tester = alb_resources.get_one("o p n")
tester.alignments[0]._records = tester.alignments[0]._records[:5]
Alb.trimal(tester, 'clean')
assert hf.buddy2hash(tester) == "93a2aa21e6baf5ca70eb2de52ae8dbea"
tester = alb_resources.get_one("o p n")
tester_dir = TEMPDIR.subdir()
tester.write("%s%strimal" % (tester_dir, os.path.sep))
assert hf.buddy2hash(Alb.trimal(
tester, 'gappyout')) == "2877ecfb201fc35211a4625f34c7afdd"
""" Probably not a good idea to be calling binaries like this...
real_trimal = Popen("trimal -in %s%strimal -gappyout" % (tester_dir, os.path.sep),
stdout=PIPE, shell=True).communicate()
real_trimal = real_trimal[0].decode()
with open("%s%strimal" % (tester_dir, os.path.sep), "w") as ofile:
ofile.write(real_trimal)
tester = Alb.AlignBuddy("%s%strimal" % (tester_dir, os.path.sep))
assert hf.buddy2hash(tester) == "2877ecfb201fc35211a4625f34c7afdd"
"""
records = [
SeqRecord(Seq("A--G-")),
SeqRecord(Seq("--T--")),
SeqRecord(Seq("--TG-")),
SeqRecord(Seq("A---C"))
]
tester = Alb.AlignBuddy([MultipleSeqAlignment(records)])
Alb.trimal(tester, "gappyout")
assert "".join([str(rec.seq) for rec in tester.records()]) == ""
def create_all_by_all_scores(seqbuddy, quiet=False):
"""
Generate a multiple sequence alignment and pull out all-by-all similarity graph
:param seqbuddy: SeqBuddy object
:param quiet: Supress multicore output
:return:
"""
if len(seqbuddy) == 1:
alignment = Alb.AlignBuddy(str(seqbuddy))
sim_scores = pd.DataFrame(data=None, columns=["seq1", "seq2", "score"])
else:
alignment = Alb.generate_msa(Sb.make_copy(seqbuddy), tool="mafft", params="--globalpair --thread -1", quiet=True)
# Need to specify what columns the PsiPred files map to now that there are gaps.
psi_pred_files = {}
for rec in alignment.records_iter():
ss_file = pd.read_csv("%s/psi_pred/%s.ss2" % (in_args.outdir, rec.id), comment="#",
header=None, delim_whitespace=True)
ss_file.columns = ["indx", "aa", "ss", "coil_prob", "helix_prob", "sheet_prob"]
ss_counter = 0
for indx, residue in enumerate(rec.seq):
if residue != "-":
ss_file.set_value(ss_counter, "indx", indx)
ss_counter += 1
psi_pred_files[rec.id] = ss_file
alignment = Alb.trimal(alignment, "gappyout")
# Re-update PsiPred files, now that some columns are removed
for rec in alignment.records_iter():
new_psi_pred = []
for row in psi_pred_files[rec.id].itertuples():
if alignment.alignments[0].position_map[int(row[1])][1]:
new_psi_pred.append(list(row)[1:])
psi_pred_files[rec.id] = pd.DataFrame(new_psi_pred, columns=["indx", "aa", "ss", "coil_prob",
"helix_prob", "sheet_prob"])
ids1 = [rec.id for rec in alignment.records_iter()]
ids2 = [rec.id for rec in alignment.records_iter()]
all_by_all = []
for rec1 in ids1:
del ids2[ids2.index(rec1)]
for rec2 in ids2:
all_by_all.append((rec1, rec2))
outfile = MyFuncs.TempFile()
outfile.write("seq1,seq2,score")
printer.clear()
MyFuncs.run_multicore_function(all_by_all, score_sequences, [alignment, psi_pred_files, outfile.path], quiet=quiet)
sim_scores = pd.read_csv(outfile.path, index_col=False)
return alignment, sim_scores
