def find_best_translation_by_similarity(mapped_sequences,
reference_og_sequence,
exclude_species=["not_me"]):
'''Given a list of sequences that are derived from mapped reads to multiple seq of a OG
we find the best corresponding mapped seq by comparing it with a representative sequence of the original OG using
pyopa local alignment and return the sequence with its highest score!'''
best_score = 0
best_sequence = None
s1 = pyopa.Sequence(str(reference_og_sequence.seq))
for record in mapped_sequences:
if record.id[0:5] not in exclude_species:
# print(record)
frames = [
record.seq[i:].translate(table='Standard',
stop_symbol='*',
to_stop=True,
cds=False,
gap="N") for i in range(3)
]
best_seq_idx = 0
for i, seq in enumerate(frames):
s2 = pyopa.Sequence(str(seq))
# calculating local and global scores for the given sequences
local_double = pyopa.align_double(s1, s2, env)
# print('Local score: %f' % local_double[0])
if local_double[0] > best_score:
best_score = local_double[0]
best_seq_idx = i
best_sequence = SeqRecord(frames[best_seq_idx],
id="simul",
description=record.description,
name=record.name)
# print(best_sequence)
return best_sequence
def _predict_best_protein_pyopa(self, record, og):
"""
Given a list of sequences that are derived from mapped reads to multiple seq of a OG
we find the best corresponding mapped seq by comparing it with a representative sequence of the original OG using
pyopa local alignment and return the sequence with its highest score!
:return:
"""
ref_og_seq = og.aa[0]
s1 = pyopa.Sequence(str(ref_og_seq.seq))
best_score = 0
try:
frames = [
record.seq[i:].translate(table='Standard',
stop_symbol='X',
to_stop=False,
cds=False) for i in range(3)
]
best_seq_idx = 0
for i, seq in enumerate(frames):
s2 = pyopa.Sequence(str(seq))
# calculating local and global scores for the given sequences
local_double = pyopa.align_double(s1, s2, self.env)
# print('Local score: %f' % local_double[0])
if local_double[0] > best_score:
best_score = local_double[0]
best_seq_idx = i
best_translation = SeqRecord.SeqRecord(
frames[best_seq_idx],
id=self._species_name,
description=record.description,
name=record.name)
except:
raise ValueError("Problem with sequence format!", ref_og_seq.seq)
return best_translation
def test_create_env(self):
simple_score = 3.78
simple_scores = [[simple_score]]
env_a = pyopa.create_environment(-2, -1, 20, simple_scores, 'A')
env_b = pyopa.create_environment(-2, -1, 20, simple_scores, 'B')
s_short1 = pyopa.Sequence('AAA')
s_short2 = pyopa.Sequence('BBB')
s_short3 = pyopa.Sequence('A')
s_short4 = pyopa.Sequence('B')
s_short5 = pyopa.Sequence('')
self.assertEqual(pyopa.align_double(s_short1, s_short1, env_a)[0], 3 * simple_score)
self.assertEqual(pyopa.align_double(s_short2, s_short2, env_b)[0], 3 * simple_score)
self.assertEqual(pyopa.align_double(s_short3, s_short3, env_a)[0], 1 * simple_score)
self.assertEqual(pyopa.align_double(s_short4, s_short4, env_b)[0], 1 * simple_score)
self.assertEqual(pyopa.align_double(s_short3, s_short5, env_a)[0], 0.0)
self.assertEqual(pyopa.align_double(s_short5, s_short5, env_a)[0], 0.0)
self.assertEqual(pyopa.align_double(s_short3, s_short1, env_a)[0], 1 * simple_score)
self.assertEqual(pyopa.align_double(s_short1, s_short3, env_a)[0], 1 * simple_score)
self.assertRaises(ValueError, pyopa.create_environment, -2, -1, 20, simple_scores, 'AB')
self.assertRaises(ValueError, pyopa.create_environment, -2, -1, 20, simple_scores, '')
bad_matrix = [[0.23], [0.65, -12.32]]
self.assertRaises(ValueError, pyopa.create_environment, -2, -1, 20, bad_matrix, 'A')
self.assertRaises(ValueError, pyopa.create_environment, -2, -1, 20, bad_matrix, 'AB')
def setUp(self):
self.s1 = pyopa.Sequence('PDVRTQYSRTKTIKLAQVRKCGAWRVLCLDLIPDLTAKNNHMRTKWTEVQYLAFVVSIVKKRPLSHSLVLITTGKAWNGTWRALPRLSNKLIETAFKEIQAEETVYDTKAFVAGKKPRWVSPFICYGLPFVISRFDFAQYRLKDMLILFSDMLLSRICNFYNGNTGPVPNSKTNEDTDLFFDGLSGMLKLNLKRSDAICHVICYEAPIARVKFGREVKDKFSLPKGGKNPSRRISWNILGILIDRTMFIRPRLVARKEAIHLFDLIGENIDAITQRLRAHKTLMVHESQVVEQPLKVKNLDLRPELVGEEEKNRHGRAKQLDRMANGNMAQIKNGHFKQTYLISVFRPQWLQLQGGCLIAEGFHSEVGGTVDGLKGTPCAQGPVVKGLFAVWRRCDTLAGRYYQKAADIDKLGDILLASLYYIPQGAIITLSEEMAKRIGANVLLVGLINVRYSGIGYEACVGDLAPEVSWLNAGHGNIQMVLHTIDGDGCQTPHGLKIYTDKRLLDLYQGAQLKVTVATTGSVKVSKSMGWLQEGGLDYFALAGRFYRADLREIEHPRAMAVSAHLCAVGLNWVFLADIICDPNEAFKFGKDFEPRTLTYGFANEDENPKNGGATTTSFAVAVYKIKTVATLKVIGKALWKGIQMRTQQGSGPTCQWALRKGKNSILLLAQDSRGGIPKNEFTILGDLPEGQTTTCTHTEIKTRLLYGATVFFMRGDLVGLYADGCSHLYRSSNLMSQACAAAKTILCSLDGERANFSNPTDFAMYNAVFRPRLYTVSFGVFDNNVDVLQAALYYLIMMAMKQYWGVKQGGLEGTLYTWSKVSGKKETSDSRNNPSICVSVCKNPLKDVQLRIAALKRFAEAEEIGKPAVVIRALEPGLTLYILLSSHGSEGKKTHNPILVSAFVVTTVADTSKPKVTYHKDQEMAIYQVLGNNPAGYEVELAFLLPTASSKQQSGRTRKFMDTASGELKEMPIQSSHEITQAADINNLRQLPRTYKKESAKVKVAACKQPPAALNTGIEKVPSHPDGLQLIIEDEWKLLEASSMSQYNEQAKEWPFHKGGIFFKGHEQKCIDASELPRGITRDLRVILINEALVLNTFCGERKLQNEATLILLRAYVWGRHLLANYFRAPNEQDGVLVDIPQGRSTLKSDHLRASIPLFLYTTIETCTSNVTIHKRVQPMIILDIAVAGEGVCDMKNGQVFKRRMARSNDRRLPPGARMKIILFRRNHECYPLQKHQEQWILGAIRTPYGLYNLQEKATLTTRYLIKLQINNRNDLVTTLVSLLMHTRESYIRFTKERRTTESPIDVLAATLYQEFTREVRRAGEQRAGIFFSQDTNYEQAIFETKMAAYPPFGANSWNPTLRYEAWTIIKTPNSKGQEFFLEHMQDVGYGKIASSKYQEKDDDEEVARGRIVPAWY')
self.s2 = pyopa.Sequence('PPFQPDKKLAGIELVLCNADLPGRSIYLRKVLQANANKRASASKRCTDDDIIKVDSAPDPQRKLVQAGKVPRVLYNGDVSNIISQILICAYVTGASRNFQHVMLLMDKGWGRGFTLMVNYPCPKVLEEFNPTLLTALVIISVYLNSIECERAGVTIAALNVKLEATDRLALLGRQTANTVMRAPLLLLCQGDSAKNTLNWSLEDLAIVFGRAATRVCKNLALLLNSQVFFQKTTGYKSQLGKNVINFDLYKPLVCDLVDATKYMKFYGTNDDSTDIQGRSSEKAAALAAAAMGVVGWHFLAPTGLVGAGSTFSPVFCIKGNAQLCCKRFDIDEWKALLTLQKSKIANIDYLRYRTGAVIEVGANYDGCSGQPKLQCFYDYLIRYPETVLGTNRQERVMTDEGGEHVRDLILRNVLENPTGFIGSGTHPGNISCTLETTNADLIIGSTDYDGVGSYLIIMGTCFMVTGCVVFTYAVMELVRPLKIHIFACAKVILQEADGSQKTNLRGRGKVSSFGDLPVRFRTLDGIATPSTTHAEMGASFDAAVLVIGRTGTAKFRQFATLDNRNLACNINLSSIRRYFNDNNWLEAGAKNAAEILVNHADKSLTPWVVGLGPLLKPGDIACPMIAVSYLVLVIMDMYLASYSDSFAKHLKNKHRTTTSAHKPSNQQLALDGALTAKRSSQAASIIFEAEEWGFLEWAMIGHLQTKMIYDDAFRLNSPEEELLTQATTHKIKPNYLIALQMLHRDFCIGFFHTLIHASVADSIVYASRLKQNAAIIDRGKTARQDLLGIALKLIVSASTKNAASFNRDFKLPVDVMFRFLDKMLNHGVNTIVHGGQDPKNGNPVGAGLPSWAKNIKVELQVTMFQLFESVDCTSELRLLSTAVDTTLHGEVQVMSAKDLFGRFRYRILSAGESLMENGISPKSFVEALKYFIMYYWTDITEPRCRGSALYPITIQPNLYKRTSATSLHPKGERWLPFEETSRTTISTVLMNNALLGICLYKSYQLLDHDFLGDKKQSNKRVSENSFLGIQTLHDPTGYLQKLDHSRLSKFNRDIRWGQGKSPEQWAVTLVPTLFVKKGTNAWRKKNNAEPIIVTTGTNTAPLEELHKAWMQLAHDGIVVSTLTENEKLEFFSFQDGMPSLVLFSIMAETNQLRYIGNKIYASRKWMADAQKASWVYASLPTNSCNWTAVEVAFEPKGECQMAKKFDLHSMAIVMVRLLAQERSDGADGMNNASSVKWLRKEANEKVCKWWFASPKINAMFQTVKIQSSGKYLARNPKAATKDVKKVEQDLLSRIQTQEHGLLWFYVRLIGEISEVPILSCNKALFLTIKLFNKFIRWNIAPLEITSGVDAWHTIFTSSRFSETDTGIEMTALDLTLPQGNWGTMKKKVALAATGFILFLAYSMGTLSKKFEGNHHWTWVYPFFITITVQLYIFNGHTAWVLFNFVEIPGEAIVSLRTGYLNGGRDKTFVEGLVFNSDVGRTYGGYTSNIK')
defaults = pyopa.load_default_environments()
self.envs = defaults['environments']
self.log_pam1 = defaults['log_pam1']
self.env = self.envs[515]
def test_runtime_single_matrix(self):
env = self.aligner.environment_at_distance(self.data['distance'])
s1 = pyopa.Sequence(str(self.data['s1']))
s2 = pyopa.Sequence(str(self.data['s2']))
t0 = time.time()
nr_runs = 5
for x in range(nr_runs):
#yep.start('align_{}.prof'.format(x))
double_alignment = pyopa.align_double(s1, s2, env, stop_at_threshold=False,
is_global=False, calculate_ranges=True)
as1, as2 = pyopa.align_strings(s1, s2, env, False, double_alignment)
#yep.stop()
print("Avg time used to compute alignment on fixed matrix: {}sec"
.format((time.time()-t0) / nr_runs))
print("Darwin's run time for this alignment: {}sec".format(self.data['time_single_matrix_align']))
self.assertEqual(str(self.data['as1']), as1)
self.assertEqual(str(self.data['as2']), as2)
def setUp(self):
self.precision = 10
#resource.setrlimit(resource.RLIMIT_STACK, (resource.RLIM_INFINITY, resource.RLIM_INFINITY))
with open(
os.path.join(os.path.dirname(__file__), 'data',
'testseqs.txt')) as f:
self.sequences = f.readlines()
self.sequences = list(
map(lambda s: pyopa.Sequence(s.strip()), self.sequences))
self.darwin_results = []
defaults = pyopa.load_default_environments()
self.alignment_environments = defaults['environments']
self.log_pam1 = defaults['log_pam1']
self.dms = pyopa.MutipleAlEnv(self.alignment_environments,
self.log_pam1)
"""
write_all_env_files(self.alignment_environments)
with open(os.path.dirname(__file__) + '/data/matrices/json/logPAM1.json') as lp:
json_data = json.load(lp)
#json_data["Scores"] = map(lambda l: map(lambda s: s/(2048*2048*2048), l), json_data["Scores"])
logPAM1 = pyopa.read_env_json(json_data, self.alignment_environments[0].columns)
write_env_file(logPAM1, "logPAM1")
"""
with open(
os.path.join(os.path.dirname(__file__), 'data',
'reference_test_results.dat')) as f:
#skip header
next(f)
reader = csv.reader(f, delimiter='\t')
for s1, s2, matrix_nr, pam, threshold, score_d, score_f, score_s,\
score_b, als1, als2, ep_sim, ep_pamn, ep_var, in reader:
curr = DarwinResult()
curr.s1_id = int(s1)
curr.s2_id = int(s2)
curr.matrix_nr = int(matrix_nr)
curr.pam = float(pam)
curr.threshold = float(threshold)
curr.score_double = float(score_d)
curr.score_float = float(score_f)
curr.score_short = float(score_s)
curr.score_byte = float(score_b)
curr.als1 = als1
curr.als2 = als2
curr.ep_sim = float(ep_sim)
curr.ep_pamn = float(ep_pamn)
curr.ep_var = float(ep_var)
self.darwin_results.append(curr)
'''
def test_sequence(self):
s_string = 'TE_ST'
s_normalized = pyopa.normalize_sequence(s_string)
# checking non-normalized constructor
s = pyopa.Sequence(s_string)
self.assertEqual(s.convert_readable(), s_string)
# normalized constructor
s = pyopa.Sequence(s_normalized, True)
self.assertEqual(s.convert_readable(), s_string)
# wrong type exception
if (sys.version_info < (3,)):
s_bytes = array.array('B', s_string)
self.assertRaises(ValueError, pyopa.Sequence, s_bytes, True)
else:
s_bytes = s_string.encode('utf-8')
self.assertRaises(ValueError, pyopa.Sequence, s_bytes, True)
# normalized and non-normalized byte list constructor
self.assertEqual('ACA_', pyopa.Sequence([0, 2, 0, ord('_')], True))
self.assertEqual('ACA_', pyopa.Sequence([65, 67, 65, ord('_')], False))
for i in range(20):
for j in range(20):
qsum[i] += json_data['scores'][j][i]
for i in range(20):
json_data['scores'][i][i] = -qsum[i]
return json_data
json_matrix = convert_to_json(os.path.dirname(os.path.abspath(__file__)) + '/test/data/jtt.dat')
for i in range(20):
print(json_matrix['scores'][i])
log_pam1 = pyopa.read_env_json(
os.path.dirname(os.path.abspath(__file__)) + '/test/data/matrices/json/logPAM1.json')
envs = pyopa.read_all_env_json(
os.path.dirname(os.path.abspath(__file__)) + '/test/data/matrices/json/all_matrices.json')
generated_envs = pyopa.generate_all_env(log_pam1, 1266)
mul1 = pyopa.MutipleAlEnv(envs, log_pam1)
mul2 = pyopa.MutipleAlEnv(generated_envs, log_pam1)
s1 = 'PDVRTQYSRTKTIKLAQVRKCGAWRVLCLDLIPDLTAKNNHMRTKWTEVQYLAFVVSIVKKRPLSHSLVLITTGKAWNGTWRALPRLSNKLIETAFKEIQAEETVYDTKAFVAGKKPRWVSPFICYGLPFVISRFDFAQYRLKDMLILFSDMLLSRICNFYNGNTGPVPNSKTNEDTDLFFDGLSGMLKLNLKRSDAICHVICYEAPIARVKFGREVKDKFSLPKGGKNPSRRISWNILGILIDRTMFIRPRLVARKEAIHLFDLIGENIDAITQRLRAHKTLMVHESQVVEQPLKVKNLDLRPELVGEEEKNRHGRAKQLDRMANGNMAQIKNGHFKQTYLISVFRPQWLQLQGGCLIAEGFHSEVGGTVDGLKGTPCAQGPVVKGLFAVWRRCDTLAGRYYQKAADIDKLGDILLASLYYIPQGAIITLSEEMAKRIGANVLLVGLINVRYSGIGYEACVGDLAPEVSWLNAGHGNIQMVLHTIDGDGCQTPHGLKIYTDKRLLDLYQGAQLKVTVATTGSVKVSKSMGWLQEGGLDYFALAGRFYRADLREIEHPRAMAVSAHLCAVGLNWVFLADIICDPNEAFKFGKDFEPRTLTYGFANEDENPKNGGATTTSFAVAVYKIKTVATLKVIGKALWKGIQMRTQQGSGPTCQWALRKGKNSILLLAQDSRGGIPKNEFTILGDLPEGQTTTCTHTEIKTRLLYGATVFFMRGDLVGLYADGCSHLYRSSNLMSQACAAAKTILCSLDGERANFSNPTDFAMYNAVFRPRLYTVSFGVFDNNVDVLQAALYYLIMMAMKQYWGVKQGGLEGTLYTWSKVSGKKETSDSRNNPSICVSVCKNPLKDVQLRIAALKRFAEAEEIGKPAVVIRALEPGLTLYILLSSHGSEGKKTHNPILVSAFVVTTVADTSKPKVTYHKDQEMAIYQVLGNNPAGYEVELAFLLPTASSKQQSGRTRKFMDTASGELKEMPIQSSHEITQAADINNLRQLPRTYKKESAKVKVAACKQPPAALNTGIEKVPSHPDGLQLIIEDEWKLLEASSMSQYNEQAKEWPFHKGGIFFKGHEQKCIDASELPRGITRDLRVILINEALVLNTFCGERKLQNEATLILLRAYVWGRHLLANYFRAPNEQDGVLVDIPQGRSTLKSDHLRASIPLFLYTTIETCTSNVTIHKRVQPMIILDIAVAGEGVCDMKNGQVFKRRMARSNDRRLPPGARMKIILFRRNHECYPLQKHQEQWILGAIRTPYGLYNLQEKATLTTRYLIKLQINNRNDLVTTLVSLLMHTRESYIRFTKERRTTESPIDVLAATLYQEFTREVRRAGEQRAGIFFSQDTNYEQAIFETKMAAYPPFGANSWNPTLRYEAWTIIKTPNSKGQEFFLEHMQDVGYGKIASSKYQEKDDDEEVARGRIVPAWY'
s2 = 'PPFQPDKKLAGIELVLCNADLPGRSIYLRKVLQANANKRASASKRCTDDDIIKVDSAPDPQRKLVQAGKVPRVLYNGDVSNIISQILICAYVTGASRNFQHVMLLMDKGWGRGFTLMVNYPCPKVLEEFNPTLLTALVIISVYLNSIECERAGVTIAALNVKLEATDRLALLGRQTANTVMRAPLLLLCQGDSAKNTLNWSLEDLAIVFGRAATRVCKNLALLLNSQVFFQKTTGYKSQLGKNVINFDLYKPLVCDLVDATKYMKFYGTNDDSTDIQGRSSEKAAALAAAAMGVVGWHFLAPTGLVGAGSTFSPVFCIKGNAQLCCKRFDIDEWKALLTLQKSKIANIDYLRYRTGAVIEVGANYDGCSGQPKLQCFYDYLIRYPETVLGTNRQERVMTDEGGEHVRDLILRNVLENPTGFIGSGTHPGNISCTLETTNADLIIGSTDYDGVGSYLIIMGTCFMVTGCVVFTYAVMELVRPLKIHIFACAKVILQEADGSQKTNLRGRGKVSSFGDLPVRFRTLDGIATPSTTHAEMGASFDAAVLVIGRTGTAKFRQFATLDNRNLACNINLSSIRRYFNDNNWLEAGAKNAAEILVNHADKSLTPWVVGLGPLLKPGDIACPMIAVSYLVLVIMDMYLASYSDSFAKHLKNKHRTTTSAHKPSNQQLALDGALTAKRSSQAASIIFEAEEWGFLEWAMIGHLQTKMIYDDAFRLNSPEEELLTQATTHKIKPNYLIALQMLHRDFCIGFFHTLIHASVADSIVYASRLKQNAAIIDRGKTARQDLLGIALKLIVSASTKNAASFNRDFKLPVDVMFRFLDKMLNHGVNTIVHGGQDPKNGNPVGAGLPSWAKNIKVELQVTMFQLFESVDCTSELRLLSTAVDTTLHGEVQVMSAKDLFGRFRYRILSAGESLMENGISPKSFVEALKYFIMYYWTDITEPRCRGSALYPITIQPNLYKRTSATSLHPKGERWLPFEETSRTTISTVLMNNALLGICLYKSYQLLDHDFLGDKKQSNKRVSENSFLGIQTLHDPTGYLQKLDHSRLSKFNRDIRWGQGKSPEQWAVTLVPTLFVKKGTNAWRKKNNAEPIIVTTGTNTAPLEELHKAWMQLAHDGIVVSTLTENEKLEFFSFQDGMPSLVLFSIMAETNQLRYIGNKIYASRKWMADAQKASWVYASLPTNSCNWTAVEVAFEPKGECQMAKKFDLHSMAIVMVRLLAQERSDGADGMNNASSVKWLRKEANEKVCKWWFASPKINAMFQTVKIQSSGKYLARNPKAATKDVKKVEQDLLSRIQTQEHGLLWFYVRLIGEISEVPILSCNKALFLTIKLFNKFIRWNIAPLEITSGVDAWHTIFTSSRFSETDTGIEMTALDLTLPQGNWGTMKKKVALAATGFILFLAYSMGTLSKKFEGNHHWTW'
print(mul1.estimate_pam(pyopa.Sequence(s1), pyopa.Sequence(s2)))
print(mul2.estimate_pam(pyopa.Sequence(s1), pyopa.Sequence(s2)))
import os
import threading
#---------------------------------------------------------------------------------------------------
data = {
'gap_open': -20.56,
'gap_ext': -3.37,
'pam_distance': 150.87,
'scores': [[10.0]],
'column_order': 'A',
'threshold': 50.0
}
env = pyopa.create_environment(**data)
s1 = pyopa.Sequence('AAA')
s2 = pyopa.Sequence('TTT')
#prints [30.0, 2, 2, 0, 0], the first element is the score
print(pyopa.align_double(s1, s1, env))
#prints [0.0, -1, -1, 0, 0], the score is 0
# since the score for 'A -> T' is undefined
print(pyopa.align_double(s2, s1, env))
#---------------------------------------------------------------------------------------------------
#loading the default environments from the data directory
# created at installation time
defaults = pyopa.load_default_environments()
env_list = defaults['environments']
log_pam1_env = defaults['log_pam1']
def read_sequences(seq_file):
with open(seq_file) as f:
sequences = f.readlines()
return [pyopa.Sequence(s.strip()) for s in sequences]
def calc_distvar(sequence1, sequence2):
sp1 = pyopa.Sequence(sequence1)
sp2 = pyopa.Sequence(sequence2)
aligned_pair = align_sequence(sp1, sp2)
distvar = find_dist_var(aligned_pair)
return distvar