def alignment_text(x, y, pred, truth, stats):
""" Used to visualize alignment as text
Parameters
----------
x : str
Protein X
y : str
Protein Y
pred : list of int
Predicted states
truth : list of int
Ground truth states
stats : list of float
List of statistics from roc_edges
"""
# TODO: we got the truth and prediction edges swapped somewhere earlier
true_alignment = states2alignment(truth, x, y)
pred_alignment = states2alignment(pred, x, y)
cols = ['tp', 'fp', 'fn', 'perc_id', 'ppv', 'fnr', 'fdr']
stats = list(map(lambda x: np.round(x, 2), stats))
s = list(map(lambda x: f'{x[0]}: {x[1]}', list(zip(cols, stats))))
stats_viz = ' '.join(s)
truth_viz = ('# Ground truth\n'
f' {true_alignment[0]}\n {true_alignment[1]}')
pred_viz = ('# Prediction\n'
f' {pred_alignment[0]}\n {pred_alignment[1]}')
s = stats_viz + '\n' + truth_viz + '\n' + pred_viz
return s
def test_states2alignment_8(self):
x = 'HECDDCSKQFSRNNHLAKHLRAH'
y = 'YRCHKVCPYTFVGKSDLDLHQFITAH'
s = np.array([
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 1
])
states2alignment(s, y, x)
def test_states2alignment_10(self):
gen = 'YACSGGCGQNFRTMSEFNEHMIRLVH'
oth = 'LICPKHTRDCGKVFKRNSSLRVHEH'
pred = np.array([
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1, 0, 1,
2, 0, 1, 1, 1, 1
])
states2alignment(pred, gen, oth)
def test_states2alignment_11(self):
gen = 'LNCKEIKKYCEMSFRNPDDIRKHRGAIH'
oth = 'YTCSSCNESLRTAWCLNKHLR'
pred = np.array([
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0
])
states2alignment(pred, gen, oth)
def test_decoding2(self):
X = 'HECDRKTCDESFSTKGNLRVHKLGH'
Y = 'LKCSGCGKNFKSQYAYKRHEQTH'
needle = NeedlemanWunschDecoder(self.operator)
dm = torch.Tensor(np.loadtxt(get_data_path('dm.txt')))
decoded = needle.traceback(dm)
pred_x, pred_y, pred_states = list(zip(*decoded))
states2alignment(np.array(pred_states), X, Y)
def test_states2alignment_3(self):
x = ('XSDHGDVSLPPEDRVRALSQLGSAVEVNEDIPPRRYFRSGVEIIRMA'
'SIYSEEGNIEHAFILYNKYITLFIEKLPKHRDYKSAVIPEKKDTVK'
'KLKEIAFPKAEELKAELLKRYTKEYTEYNEEKKKEAEELARNMAIQ'
'QELX')
y = ('XIDVLRAKAAKERAERRLQSQQDDIDFKRAELALKRAMNRLSVAEMKX')
s = np.array([
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 1, 1, 2, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
])
states2alignment(s, x, y)
def test_states2alignment_7(self):
x = ('XGSSGSSGFDENWGADEELLLIDACETLGLGNWADIADYVGNARTKEECRDHYLKTYIEX')
y = ('XGEIRVGNRYQADITDLLKEGEEDGRDQSRLETQVWEAHNPLTDKQIDQFLVVARSVGTF'
'ARALDSLHMSAAAASRDITLFHAMDTLHKNIYDISKAISALVPQGGPVLCRDEMEEWSAS'
'EANLFEEALEKYGKDFTDIQQDFLPWKSLTSIIEYYYMWKTTX')
s = np.array([
1, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
])
states2alignment(s, x, y)
def test_states2alignment_2(self):
s = "111:::111"
s = np.array(list(map(tmstate_f, s)))
X = "123456789"
Y = "abc"
exp_x = "123456789"
exp_y = "---abc---"
res_x, res_y = states2alignment(s, X, Y)
self.assertEqual(res_x, exp_x)
self.assertEqual(res_y, exp_y)
def test_states2alignment_1(self):
s = "111:::222"
s = np.array(list(map(tmstate_f, s)))
X = "123456"
Y = "abcdef"
exp_x = "123456---"
exp_y = "---abcdef"
res_x, res_y = states2alignment(s, X, Y)
self.assertEqual(res_x, exp_x)
self.assertEqual(res_y, exp_y)
def deepblast_align(
pairings: List[Tuple[str, str]],
query_by_id: Dict[str, str],
target_by_id: Dict[str, str],
model_file: str,
device: torch.device,
batch_size: int,
) -> List[Tuple[str, str, str, str]]:
"""Aligns the given pairings using DeepBLAST
Returns a list of query id, target id, query aligned, target aligned
The model on its own takes between 740MiB (Titan X, torch 1.5) and 1284MiB (RTX 8000, torch 1.7)
Note that the batch size has much less of an impact for DeepBLAST than for the embedders
"""
model = LightningAligner.load_from_checkpoint(model_file).to(device)
tokenizer = UniprotTokenizer()
alignments = []
# Naive batching
batches = numpy.array_split(pairings,
math.ceil(len(pairings) / batch_size))
for batch in tqdm(batches):
# noinspection PyArgumentList
queries = [
torch.Tensor(tokenizer(query_by_id[query].encode())).long()
for query, _ in batch
]
# noinspection PyArgumentList
targets = [
torch.Tensor(tokenizer(target_by_id[target].encode())).long()
for _, target in batch
]
seqs, order = pack_sequences(queries, targets)
gen = model.aligner.traceback(seqs.to(device), order)
for (decoded, _), (query, target) in zip(gen, batch):
pred_x, pred_y, pred_states = zip(*decoded)
pred_alignment = "".join(list(map(revstate_f, pred_states)))
x_aligned, y_aligned = states2alignment(pred_alignment,
query_by_id[query],
target_by_id[target])
alignments.append((query, target, x_aligned, y_aligned))
return alignments
def test_states2alignment_9(self):
x = 'HCH'
y = 'HCAH'
s = np.array([1, 1, 0, 1])
states2alignment(s, y, x)
seqs={}
with open(args.input) as f:
for i in f:
i=i.rstrip()
if i[0]=='>':
ID=i[1:]
continue
seqs[ID]=seqs.get(ID,'')+i
keys_list = list(seqs)
x = seqs[keys_list[0]]
y = seqs[keys_list[1]]
pred_alignment = model.align(x, y)
x_aligned, y_aligned = states2alignment(pred_alignment, x, y)
file = open(args.output,"w")
file.write(">%s\n%s\n>%s\n%s" % (keys_list[0], x_aligned, keys_list[1], y_aligned))
file.close()
print(x_aligned)
print(pred_alignment)
print(y_aligned)
x_ = torch.Tensor(model.tokenizer(str.encode(x))).long()
y_ = torch.Tensor(model.tokenizer(str.encode(y))).long()
seq, order = pack_sequences([x_], [y_])
score = model.aligner.score(seq, order).item()