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config.py
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config.py
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from dataclasses import dataclass
#input_spectrum_file_train = "Hela/spectrums.mgf"
#input_feature_file_train = "Hela/features.csv.identified.test.nodup"
#Deepnovo_result_filename = 'Hela/features.csv.identified.test.nodup.deepnovo_denovo'
input_spectrum_file_train = "Converters/feature_files/spectrum.mgf"
input_feature_file_train = "Converters/training_features/train.csv"
input_feature_file_val = "Converters/training_features/valid.csv"
input_feature_file_test = "Converters/training_features/test.csv"
output_file_test = "Converters/training_features/test_score.csv"
output_file_modify = "Converters/training_features/test_modify.csv"
mass_H = 1.0078
mass_H2O = 18.0106
mass_NH3 = 17.0265
mass_N_terminus = 1.0078
mass_C_terminus = 17.0027
mass_CO = 27.9949
mass_AA = {'_PAD': 0.0,
'_GO': mass_N_terminus-mass_H,
'_EOS': mass_C_terminus+mass_H,
'A': 71.03711, # 0
'R': 156.10111, # 1
'N': 114.04293, # 2
'N(Deamidation)': 115.02695,
'D': 115.02694, # 3
#~ 'C(Carbamidomethylation)': 103.00919, # 4
'C(Carbamidomethylation)': 160.03065, # C(+57.02)
#~ 'C(Carbamidomethylation)': 161.01919, # C(+58.01) # orbi
'E': 129.04259, # 5
'Q': 128.05858, # 6
'Q(Deamidation)': 129.0426,
'G': 57.02146, # 7
'H': 137.05891, # 8
'I': 113.08406, # 9
'L': 113.08406, # 10
'K': 128.09496, # 11
'M': 131.04049, # 12
'M(Oxidation)': 147.0354,
'F': 147.06841, # 13
'P': 97.05276, # 14
'S': 87.03203, # 15
'T': 101.04768, # 16
'W': 186.07931, # 17
'Y': 163.06333, # 18
'V': 99.06841, # 19
}
amino_acid_number = {
'A': 0,
'R': 1,
'N': 2,
'N(Deamidation)':20,
'D': 3,
'C': 4,
'C(Carbamidomethylation)':21,
'E': 5,
'Q': 6,
'Q(Deamidation)': 22,
'G': 7,
'H': 8,
'I': 9,
'L': 10,
'K': 11,
'M': 12,
'M(Oxidation)':23,
'F': 13,
'P': 14,
'S': 15,
'T': 16,
'W': 17,
'Y': 18,
'V': 19,
}
col_feature_id = "spec_group_id"
col_precursor_mz = "m/z"
col_precursor_charge = "z"
col_rt_mean = "rt_mean"
col_raw_sequence = "seq"
col_scan_list = "scans"
col_feature_area = "feature area"
col_predicted_seq = "novor_seq"
MZ_MAX = 3000.0
MAX_LEN = 50 #if args.search_denovo else 30
train_epochs = 12
splits = 5
lr = 0.01
momentum = 0.9
@dataclass
class DDAFeature:
feature_id: str
mz: float
z: int
rt_mean: float
peptide: list
scan: str
mass: float
# feature_area: str
predicted_seq: list
@dataclass
class DenovoData:
original_dda_feature: DDAFeature
mz_list: list
intensity_list: list
vocab_reverse = ['A',
'R',
'N',
'N(Deamidation)',
'D',
#~ 'C',
'C(Carbamidomethylation)',
'E',
'Q',
'Q(Deamidation)',
'G',
'H',
'I',
'L',
'K',
'M',
'M(Oxidation)',
'F',
'P',
'S',
'T',
'W',
'Y',
'V',
]
vocab = dict([(x, y) for (y, x) in enumerate(vocab_reverse)])
vocab_size = len(vocab_reverse)
mass_ID = [mass_AA[vocab_reverse[x]] for x in range(vocab_size)]
ppm = 10
mass_tol = 300
search_iterations = 10
step_size = 0.0