def main(go_file, hp_file, terms_file, preds_file, gene):
go = Ontology(go_file, with_rels=True)
print('GO loaded')
hp = Ontology(hp_file, with_rels=True)
print('HP loaded')
terms_df = pd.read_pickle(terms_file)
global terms
terms = terms_df['terms'].values.flatten()
labels = terms_df['labels'].values.flatten()
print('Phenotypes', len(terms))
global term_set
term_set = set(terms)
terms_dict = {v: i for i, v in enumerate(terms)}
df = pd.read_pickle(preds_file)
row = df.loc[df['genes'] == gene]
with open(f'data/{gene}.deepgo_annotations.txt', 'w') as f:
dg = [x.split('|') for x in row['deepgo_annotations'].values[0]]
dg = sorted(dg, key=lambda x: float(x[1]), reverse=True)
for go_id, score in dg:
name = go.get_term(go_id)['name']
f.write(f'{go_id}\t{name}\t{score}\n')
with open(f'data/{gene}.go_annotations.txt', 'w') as f:
dg = [x for x in row['go_annotations'].values[0]]
for go_id in dg:
name = go.get_term(go_id)['name']
f.write(f'{go_id}\t{name}\n')
with open(f'data/{gene}.deeppheno_annotations.txt', 'w') as f:
dp = [(terms[i], score)
for i, score in enumerate(row['preds'].values[0])]
dp = sorted(dp, key=lambda x: x[1], reverse=True)
for hp_id, score in dp:
name = hp.get_term(hp_id)['name']
f.write(f'{hp_id}\t{name}\t{score}\n')
if score < 0.01:
break
def main(in_file, out_file, go_file, model_file, terms_file, annotations_file,
chunk_size, diamond_file, threshold, batch_size, alpha):
# Load GO and read list of all terms
go = Ontology(go_file, with_rels=True)
terms_df = pd.read_pickle(terms_file)
terms = terms_df['terms'].values.flatten()
# Read known experimental annotations
annotations = {}
df = pd.read_pickle(annotations_file)
for row in df.itertuples():
annotations[row.proteins] = set(row.prop_annotations)
go.calculate_ic(annotations.values())
diamond_preds = {}
mapping = {}
with gzip.open(diamond_file, 'rt') as f:
for line in f:
it = line.strip().split()
if it[0] not in mapping:
mapping[it[0]] = {}
mapping[it[0]][it[1]] = float(it[2])
for prot_id, sim_prots in mapping.items():
annots = {}
allgos = set()
total_score = 0.0
for p_id, score in sim_prots.items():
allgos |= annotations[p_id]
total_score += score
allgos = list(sorted(allgos))
sim = np.zeros(len(allgos), dtype=np.float32)
for j, go_id in enumerate(allgos):
s = 0.0
for p_id, score in sim_prots.items():
if go_id in annotations[p_id]:
s += score
sim[j] = s / total_score
for go_id, score in zip(allgos, sim):
annots[go_id] = score
diamond_preds[prot_id] = annots
# Load CNN model
model = load_model(model_file)
# Alphas for the latest model
alphas = {NAMESPACES['mf']: 0.55, NAMESPACES['bp']: 0.59, NAMESPACES['cc']: 0.46}
# Alphas for the cafa2 model
# alphas = {NAMESPACES['mf']: 0.63, NAMESPACES['bp']: 0.68, NAMESPACES['cc']: 0.48}
start_time = time.time()
total_seq = 0
w = gzip.open(out_file, 'wt')
for prot_ids, sequences in read_fasta(in_file, chunk_size):
total_seq += len(prot_ids)
deep_preds = {}
ids, data = get_data(sequences)
preds = model.predict(data, batch_size=batch_size)
assert preds.shape[1] == len(terms)
for i, j in enumerate(ids):
prot_id = prot_ids[j]
if prot_id not in deep_preds:
deep_preds[prot_id] = {}
for l in range(len(terms)):
if preds[i, l] >= 0.01: # Filter out very low scores
if terms[l] not in deep_preds[prot_id]:
deep_preds[prot_id][terms[l]] = preds[i, l]
else:
deep_preds[prot_id][terms[l]] = max(
deep_preds[prot_id][terms[l]], preds[i, l])
# Combine diamond preds and deepgo
for prot_id in prot_ids:
annots = {}
if prot_id in diamond_preds:
for go_id, score in diamond_preds[prot_id].items():
annots[go_id] = score * alphas[go.get_namespace(go_id)]
for go_id, score in deep_preds[prot_id].items():
if go_id in annots:
annots[go_id] += (1 - alphas[go.get_namespace(go_id)]) * score
else:
annots[go_id] = (1 - alphas[go.get_namespace(go_id)]) * score
# Propagate scores with ontology structure
gos = list(annots.keys())
for go_id in gos:
for g_id in go.get_anchestors(go_id):
if g_id in annots:
annots[g_id] = max(annots[g_id], annots[go_id])
else:
annots[g_id] = annots[go_id]
sannots = sorted(annots.items(), key=lambda x: x[1], reverse=True)
for go_id, score in sannots:
if score >= threshold:
w.write(prot_id + '\t' + go_id + '\t' + go.get_term(go_id)['name'] + '\t%.2f' % go.get_ic(go_id) + '\t%.3f\n' % score)
w.write('\n')
w.close()
total_time = time.time() - start_time
print('Total prediction time for %d sequences is %d' % (total_seq, total_time))