def extract_features(seq, sec_str):
feature_dict = {}
seq_temp = seq
dp = sec_str
bp = rna_toolkit.dp_to_bp(dp)
feature_dict['ent_3'] = []
feature_dict['gc_perentage'] = []
feature_dict['ensemble_diversity'] = []
feature_dict['expected_accuracy'] = []
feature_dict['fe_per'] = []
if (rna_toolkit.is_pseudoknotted(bp) > 0) or (len(bp) != len(seq_temp)):
feature_dict['ent_3'].append(float('nan'))
feature_dict['gc_perentage'].append(float('nan'))
feature_dict['ensemble_diversity'].append(float('nan'))
feature_dict['expected_accuracy'].append(float('nan'))
feature_dict['fe_per'].append(float('nan'))
else:
dp_temp = rna_toolkit.bp_to_dp(bp)
a = RNA.fold_compound(seq_temp)
a.pf()
bp_prob = np.array(a.bpp())
prob_unbp_array = np.ones(len(seq_temp) + 1)
(s, mm) = a.mfe()
for ii in range(len(bp) + 1):
prob_unbp_array[ii] -= np.sum(bp_prob[ii, :])
prob_unbp_array[ii] -= np.sum(bp_prob[:, ii])
expected_accuracy = 0
gamma = 1
for ii in range(len(seq_temp)):
if bp[ii] == 0:
expected_accuracy += prob_unbp_array[ii + 1]
elif ii + 1 < bp[ii]:
expected_accuracy += 2 * gamma * bp_prob[ii + 1, bp[ii]]
expected_accuracy /= len(seq_temp)
ensemble_diversity = 0
dim_x, dim_y = bp_prob.shape
for bp_i in bp_prob[np.triu_indices(dim_x)]:
ensemble_diversity += 2 * bp_i * (1 - bp_i)
ensemble_diversity /= len(seq_temp)
pos_entropy = 0
for ubp_i in prob_unbp_array:
if ubp_i > 0 and ubp_i < 1:
pos_entropy -= (ubp_i * math.log(ubp_i) +
(1 - ubp_i) * math.log(1 - ubp_i))
pos_entropy /= len(seq_temp)
gc_perentage = 0
gc_perentage = (seq_temp.count('G') + seq_temp.count('C')) / float(
len(seq_temp))
bp_percentage = 0
bp_percentage = (dp_temp.count('(') + dp_temp.count(')')) / float(
len(seq_temp))
if rna_toolkit.entropy_max(len(seq_temp), 3) > 0:
ent_3 = rna_toolkit.entropy(seq_temp, 3) / rna_toolkit.entropy_max(
len(seq_temp), 3)
else:
ent_3 = float('nan')
fe_temp = a.eval_structure(dp_temp)
if mm != 0:
fe_per = abs(mm - fe_temp) / abs(mm)
else:
fe_per = float('nan')
feature_dict['ent_3'].append(ent_3)
feature_dict['gc_perentage'].append(gc_perentage)
feature_dict['ensemble_diversity'].append(ensemble_diversity)
feature_dict['expected_accuracy'].append(expected_accuracy)
feature_dict['fe_per'].append(fe_per)
df = pd.DataFrame(feature_dict)
print(df)
return df
def extract_features_pseudoknotted(seq, bp):
feature_dict = {}
seq_temp = seq
feature_dict['gc_perentage'] = float('nan')
feature_dict['ent_3'] = float('nan')
feature_dict['ent_4'] = float('nan')
feature_dict['ent_5'] = float('nan')
feature_dict['ent_6'] = float('nan')
feature_dict['ent_7'] = float('nan')
feature_dict['ent_8'] = float('nan')
feature_dict['bfe_per'] = float('nan')
feature_dict['kfe_per'] = float('nan')
if rna_toolkit.entropy_max(len(seq_temp), 3) > 0:
ent_3 = rna_toolkit.entropy(seq_temp, 3) / rna_toolkit.entropy_max(
len(seq_temp), 3)
if rna_toolkit.entropy_max(len(seq_temp), 4) > 0:
ent_4 = rna_toolkit.entropy(seq_temp, 4) / rna_toolkit.entropy_max(
len(seq_temp), 4)
if rna_toolkit.entropy_max(len(seq_temp), 5) > 0:
ent_5 = rna_toolkit.entropy(seq_temp, 5) / rna_toolkit.entropy_max(
len(seq_temp), 5)
if rna_toolkit.entropy_max(len(seq_temp), 6) > 0:
ent_6 = rna_toolkit.entropy(seq_temp, 6) / rna_toolkit.entropy_max(
len(seq_temp), 6)
if rna_toolkit.entropy_max(len(seq_temp), 7) > 0:
ent_7 = rna_toolkit.entropy(seq_temp, 7) / rna_toolkit.entropy_max(
len(seq_temp), 7)
if rna_toolkit.entropy_max(len(seq_temp), 8) > 0:
ent_8 = rna_toolkit.entropy(seq_temp, 8) / rna_toolkit.entropy_max(
len(seq_temp), 8)
gc_perentage = (seq_temp.count('G') + seq_temp.count('C')) / float(
len(seq_temp))
a = RNA.fold_compound(seq_temp)
a.pf()
(s, mfe) = a.mfe()
bfe, kfe = Peudo_Decom(bp, seq, 'a')
if mfe != 0:
bfe_per = abs(bfe - mfe) / abs(mfe)
feature_dict['bfe_per'] = bfe_per
if bfe != 0:
kfe_per = abs(bfe - kfe) / abs(bfe)
feature_dict['kfe_per'] = kfe_per
feature_dict['ent_3'] = ent_3
feature_dict['ent_4'] = ent_4
feature_dict['ent_5'] = ent_5
feature_dict['ent_6'] = ent_6
feature_dict['ent_7'] = ent_7
feature_dict['ent_8'] = ent_8
feature_dict['gc_perentage'] = gc_perentage
df = pd.DataFrame(feature_dict, index=[0])
return df