def predict_ecpred(input_seq, result):
timestamp = str(round(datetime.utcnow().timestamp() * 1000))
ecpred_input_path = f'/home/juyeon/Program/Multi_Pred/multi_pred/vendor/Input/{timestamp}.fasta'
file = open(ecpred_input_path, 'w')
file.write('>Sample\n' + input_seq)
file.close()
ecpred_path = "/home/juyeon/Program/Multi_Pred/multi_pred/vendor/ECPred/"
ecpred_execute_path = os.path.join(ecpred_path, "ECPred.jar")
ecpred_output_path = os.path.join("/home/juyeon/Program/Multi_Pred/multi_pred/vendor/Output/", f"{timestamp}_ecpred_output.tsv")
subprocess.run(["java", "-jar", ecpred_execute_path,
"blast", ecpred_input_path,
ecpred_path, "temp/",
ecpred_output_path])
file = open(ecpred_output_path, 'r')
result_ecpred = file.read()
result_ecpred_split = result_ecpred.split("\n")
ec_number_ecpred = result_ecpred_split[1].split("\t")
ecpred_ec = ec_number_ecpred[1]
ecpred_acc = float(ec_number_ecpred[2])
alpha_info = mysql_dao.connect_result(ecpred_ec)
ecpred_result_json = {"ecpred_ec": ecpred_ec, "ecpred_acc": ecpred_acc, "ecpred_name": alpha_info[0],
"ecpred_reac": alpha_info[1]}
file.close()
result.update({'ECPred': ecpred_result_json})
def predict_deepec(input_seq, result):
timestamp = str(round(datetime.utcnow().timestamp() * 1000))
deepec_input_path = f'/home/juyeon/Program/Multi_Pred/multi_pred/vendor/Input/{timestamp}.fasta'
file = open(deepec_input_path, 'w')
file.write('>Sample\n' + input_seq)
file.close()
deepec_path = "/home/juyeon/Program/Multi_Pred/multi_pred/vendor/deepec"
deepec_execute_path = os.path.join(deepec_path, "deepec.py")
deepec_output_path = os.path.join("/home/juyeon/Program/Multi_Pred/multi_pred/vendor/Output/",
f"{timestamp}_deepec_output")
subprocess.run(["python3", deepec_execute_path,
"-i", deepec_input_path,
"-o", deepec_output_path])
deepec_output_path = os.path.join(deepec_output_path, "log_files", "4digit_EC_prediction.txt")
file = open(deepec_output_path, 'r')
s = file.read()
s_split = s.split("\n")
s_double_split = s_split[1].split(":")
if len(s_double_split) < 2:
deepec_result_json = {"deepec_ec": "UnPredictable", "deepec_acc": "",
"deepec_name": "", "deepec_reac": ""}
else:
s_triple_split = s_double_split[1].split("\t")
deepec_ec = s_triple_split[0]
deepec_acc = float(s_triple_split[1])
alpha_info = mysql_dao.connect_result(deepec_ec)
deepec_result_json = {"deepec_ec": deepec_ec, "deepec_acc": deepec_acc, "deepec_name": alpha_info[0],
"deepec_reac": alpha_info[1]}
file.close()
result.update({'DeepEC': deepec_result_json})
def fun(ec_1, ec_2, ec_3, acc_1, acc_2, acc_3):
ec_list = []
acc_list = []
s_list = []
re_list = []
ec_list.append(ec_1)
ec_list.append(ec_2)
ec_list.append(ec_3)
acc_list.append(acc_1)
acc_list.append(acc_2)
acc_list.append(acc_3)
# S값 구하기
for i in ec_list:
cnt = ec_list.count(i)
s_list.append(cnt / 3)
# R값 구하기
for i in range(len(ec_list)):
temp = s_list[i] * acc_list[i]
re_list.append(temp)
sum_relist = sum(re_list)
finre_list = []
for i in range(len(ec_list)):
temp = re_list[i] / sum_relist
finre_list.append(round(temp, 3))
for v in range(0, len(ec_list)):
if (v == re_list.index(max(re_list))):
final_ec = ec_list[v]
rec_ec = mysql_dao.connect_result(final_ec)
final_result = {"final_ec": final_ec,
"final_name": rec_ec[0], "final_reac": rec_ec[1]}
return final_result
def predict_detect(input_seq, result):
timestamp = str(round(datetime.utcnow().timestamp() * 1000))
input_path = f'/home/juyeon/Program/Multi_Pred/multi_pred/vendor/Input/{timestamp}.fasta'
with open(input_path, 'w') as file:
file.write('>Sample\n' + input_seq)
output_base_path = "/home/juyeon/Program/Multi_Pred/multi_pred/vendor/Output"
output_default_file_path = f"{output_base_path}/{timestamp}_detect_output.out"
detect_path = "/home/juyeon/Program/Multi_Pred/multi_pred/vendor/DETECTv2/detect.py"
subprocess.run(["python", detect_path, input_path,
"--output_file", output_default_file_path,
"--num_threads", "4"])
file = open(output_default_file_path, 'r')
header = next(file)
detect_ec = None
detect_acc = 0.0
try:
for line in file:
line = line.strip().split("\t")
detect_ec = line[1]
detect_acc = float(line[2])
break
except StopIteration:
detect_ec = None
if detect_ec is None:
detect_result_json = {"detect_ec": "Unpredictable", "detect_acc": 0, "detect_name": "", "detect_reac": ""}
else:
alpha_info = mysql_dao.connect_result(detect_ec)
detect_result_json = {"detect_ec": detect_ec, "detect_acc": detect_acc, "detect_name": alpha_info[0],
"detect_reac": alpha_info[1]}
result.update({'DETECT': detect_result_json})
def predict_ecami(input_seq, result):
timestamp = str(round(datetime.utcnow().timestamp() * 1000))
ecami_input_path = f'/home/juyeon/Program/Multi_Pred/multi_pred/vendor/Input/{timestamp}.fasta'
file = open(ecami_input_path, 'w')
file.write('>Sample\n' + input_seq)
file.close()
ecami_path = "/home/juyeon/Program/Multi_Pred/multi_pred/vendor/eCAMI"
ecami_execute_path = os.path.join(ecami_path, "prediction.py")
ecami_kmer_db_path = os.path.join(ecami_path, "CAZyme")
ecami_output_path = os.path.join("/home/juyeon/Program/Multi_Pred/multi_pred/vendor/Output/",
f"{timestamp}_ecami_output.txt")
subprocess.run(["python3", ecami_execute_path,
"-input", ecami_input_path,
"-kmer_db", ecami_kmer_db_path,
"-output", ecami_output_path])
file = open(ecami_output_path, 'r')
s = file.read()
if s == "":
ecami_result_json = {"ecami_ec": "Unpredictable", "ecami_name": "", "ecami_reac": ""}
else:
s_split = s.split("\t")
s_double_split = s_split[2].split("|")
s_result_split = s_double_split[3].split(":")
ecami_ec = s_result_split[0]
alpha_info = mysql_dao.connect_result(ecami_ec)
ecami_result_json = {"ecami_ec": ecami_ec, "ecami_name": alpha_info[0], "ecami_reac": alpha_info[1]}
file.close()
result.update({'eCAMI': ecami_result_json})
