def test_predict_self_defined_motifs_Bradi1g00260_2_p(self):
dataset = []
for m in phytolrr_predictor.default_train_motifs:
dataset.append(m[:11])
test_seq = 'MEMITMTLAVFVSCFHLPLLWSFLLQYLNFFSHSTYWKLSLVSCPVVLLSWHKLNSESLHGFARGRKYYHLSDMPSQWPGYWMVMLVLVVIMALSFISIHGQPDSHGFISIDCGYTASKQYVDSRTGLTYASDDGFIDAGLVHTVDSANLQPDLAVRYFNLRYFPSGPRNCYTFRSLTAGGKYLVRAAFGYGDYDKLNRLPTFDLYFGVNYWTTVTIVSSSTAYLFESIAVSPADFLQICLVNTGSGTPFISALDLRSLTANLYPEANVTQSMVLLSFFRDTVGFGPNRYHFGTNYQHIRFPDDPYDRIWQRYEDIASWTDLPNKSNGEIQNPPNDTYDAPSAVMRSASTPLNASAMDLSWSSDSSMSVGVNPTYILVLYFAELDASQDLRQFDVSVDNDLLLASAFSPKFLLATVLSGIVRGSGEHSISLTTTSNSVLDPLISAMEIFMVRPVNESATDSVDAWTMMTIQTKYSVKRNWVGDPCVPTSLAWDGLNCSYTPSSAPRITGLNMSSSGLVSEIDASFGQILLLQHLDLSHNSLSGSIPDFLGQLPALKFLDLSSNNLSGSIPCNLLEKSQNGLLALRVDNPNLHGDCAPRPVGSKNKI'
matrix = pssm_matrix.calc_pssm_matrix(dataset)
ms = phytolrr_predictor.predict(test_seq, matrix)
ms.sort(key=lambda m: m.offset)
ms = motifs_tools.found_no_overlapped_motifs(ms, 11)
self.assertEqual(len(ms), 6)
def test_default_predict_Bradi1g00260_2_p(self):
test_seq = 'MEMITMTLAVFVSCFHLPLLWSFLLQYLNFFSHSTYWKLSLVSCPVVLLSWHKLNSESLHGFARGRKYYHLSDMPSQWPGYWMVMLVLVVIMALSFISIHGQPDSHGFISIDCGYTASKQYVDSRTGLTYASDDGFIDAGLVHTVDSANLQPDLAVRYFNLRYFPSGPRNCYTFRSLTAGGKYLVRAAFGYGDYDKLNRLPTFDLYFGVNYWTTVTIVSSSTAYLFESIAVSPADFLQICLVNTGSGTPFISALDLRSLTANLYPEANVTQSMVLLSFFRDTVGFGPNRYHFGTNYQHIRFPDDPYDRIWQRYEDIASWTDLPNKSNGEIQNPPNDTYDAPSAVMRSASTPLNASAMDLSWSSDSSMSVGVNPTYILVLYFAELDASQDLRQFDVSVDNDLLLASAFSPKFLLATVLSGIVRGSGEHSISLTTTSNSVLDPLISAMEIFMVRPVNESATDSVDAWTMMTIQTKYSVKRNWVGDPCVPTSLAWDGLNCSYTPSSAPRITGLNMSSSGLVSEIDASFGQILLLQHLDLSHNSLSGSIPDFLGQLPALKFLDLSSNNLSGSIPCNLLEKSQNGLLALRVDNPNLHGDCAPRPVGSKNKI'
ms = phytolrr_predictor.predict(test_seq)
ms.sort(key=lambda m: m.offset)
self.assertEqual(len(ms), 3)
self.assertEqual([m.offset for m in ms], [506, 530, 554])
numpy.testing.assert_array_almost_equal(
[m.score for m in ms],
[14.0695893079814, 44.675678790101, 46.0801846076546])
def test_default_predict_AT1G05700_1(self):
test_seq = 'MEEFRFLYLIYSAAFALCLVVSVLAQDQSGFISIDCGIPSGSSYKDDTTGINYVSDSSFVETGVSKSIPFTAQRQLQNLRSFPEGSRNCYTLIPIQGKGKKYLIRASFMYGNYDGENGSPEFDLFLGGNIWDTVLLSNGSSIVSKEVVYLSQSENIFVCLGNKGKGTPFISTLELRFLGNDNTTYDSPNGALFFSRRWDLRSLMGSPVRYDDDVYDRIWIPRNFGYCREINTSLPVTSDNNSYSLSSLVMSTAMTPINTTRPITMTLENSDPNVRYFVYMHFAEVEDLSLKPNQTREFDISINGVTVAAGFSPKYLQTNTFFLNPESQSKIAFSLVRTPKSTLPPIVNALEIYVANSFSQSLTNQEDGDAVTSLKTSYKVKKNWHGDPCLPNDYIWEGLNCSYDSLTPPRITSLNLSSSGLTGHISSSFSNLTMIQELDLSNNGLTGDIPEFLSKLKFLRVLNLENNTLTGSVPSELLERSNTGSFSLRLGENPGLCTEISCRKSNSKK'
ms = phytolrr_predictor.predict(test_seq)
ms.sort(key=lambda m: m.offset)
self.assertEqual(len(ms), 3)
self.assertEqual([m.offset for m in ms], [410, 434, 458])
numpy.testing.assert_array_almost_equal(
[m.score for m in ms],
[27.3503114204658, 39.1562104586186, 36.3811748663529])
def test_default_predict_AL1G15610_1(self):
test_seq = 'METFRFLYLALFALCLIVSVLAQDQSGFISIDCGIPSGSSYKDDTTGINYVSDSSFVETGISKSIPFTSQRQLQNLRSFPEGSRNCYTLIPVQGKGRKYLIRASFMYGNYDGENDSPEFDLFLGGNIWDTVLLSNESSIVSKEVVYLSQSENIFVCLGNKGKGTPFISTLELRFLGNDNTTYDSPNGTLFFSRRWDFGSLMDSPVRYDEDVYDRIWIPRNFGYCREINTSLPVTSDNNSYSLSSLVMSTAMTPTNTTRPITMTLEDSDPNVRYFVYMHLAEVEDLSLKPNQTREFDIGINGVTVAAGFSPKYLQTNTFFLNPESQSKIVFSLVRTPKSTLPPIVNALEIYIANRVSQSLTNQEDGDAVTSLKTIYKVKKNWQGDPCLPNDYIWEGLNCSYDSLTPPRITSLNLSSSGLTGHISPSFSNLTMIQELDLSNNGLTGDIPEFLSKLKFLRVLNLEKNKLTGSVPSELLERSNSGSFLLRVGENPGLCIEISCRKSNSKK'
ms = phytolrr_predictor.predict(test_seq)
ms.sort(key=lambda m: m.offset)
self.assertEqual(len(ms), 3)
self.assertEqual([m.offset for m in ms], [407, 431, 455])
numpy.testing.assert_array_almost_equal(
[m.score for m in ms],
[27.3503114204658, 39.1562104586186, 36.2951803140635])
def test_default_predict_evm_27_model_AmTr_v1_0_scaffold00002_363(self):
test_seq = 'MGILFFLFALTLTLSSLSSSVFGLTQDGEALLEMKRGLNDTKGLLSNWKDTDINPCNWTRISCHLHDQRVRVINLPFLRLGGTISPSIGKITRLHRLAIHENSLHGTIPAEIGNCTELRALYLRANYLEGGIPTELGRLSNLIILDLSSNSLRGSIPPSIGHLGRLRFLNLSTNFLSGEIPKTGVLGSFGNFSFIGNLDLCGPQVQRLCKGSLGFPAVLPGHVNDDEAVPPRRATS'
ms = phytolrr_predictor.predict(test_seq)
ms.sort(key=lambda m: m.offset)
self.assertEqual(len(ms), 6)
self.assertEqual([m.offset for m in ms], [6, 69, 93, 117, 141, 165])
numpy.testing.assert_array_almost_equal([m.score for m in ms], [
10.7748224913081, 19.331539280432, 25.5300148879452,
29.7083175881698, 40.8018286797862, 40.0297399657356
])
def predict_seqs(seqs: List[Sequence],
training_dataset=None) -> List[PredictResult]:
if training_dataset is None:
matrix = None
else:
matrix = pssm_matrix.calc_pssm_matrix(training_dataset)
rets = []
for seq in seqs:
ret = phytolrr_predictor.predict(seq.seq, matrix)
if ret is None:
raise Exception("Unexpected error, None result when predicting " +
seq.id)
rets.append(PredictResult(seq, ret))
return rets