def fill_protein_parameters(protein_records):
label = 'Filling protein parameters: '
show_progress(label, 35, 0.0)
index = 1
for protein_record in protein_records:
protein_record.protein_parameters = ProteinParameters(protein_record.protein.sequence)
show_progress(label, 35, index / len(protein_records))
index += 1
print()
def save_proteins_to_csv(proteins, file_name):
label = 'Saving proteins to \'{0}\': '.format(file_name)
show_progress(label, 40, 0.0)
with open(file_name, 'w') as file:
file.write('id;name;sequence\n')
index = 1
for protein in proteins:
file.write(protein.id + ';' + protein.name + ';' + protein.sequence + '\n')
show_progress(label, 40, index / len(proteins))
index += 1
print()
def load_proteins_from_csv(file_name):
label = 'Loading proteins from \'{0}\': '.format(file_name)
show_progress(label, 40, 0.0)
data = genfromtxt(file_name, dtype=None, delimiter=';', names=True)
proteins = []
index = 1
for line in data:
proteins.append(Protein(id=b2str(line['id']), name=b2str(line['name']), sequence=b2str(line['sequence'])))
show_progress(label, 40, index / len(data))
index += 1
print()
return proteins
def save_protein_records_to_folder(protein_records, folder='results/'):
if not folder[-1] == '/':
folder += '/'
label = 'Saving protein records to \'{0}\': '.format(folder)
show_progress(label, 40, 0.0)
index = 1
for protein_record in protein_records:
with open(folder + protein_record.protein.id + '.txt', 'w') as file:
file.write(str(protein_record))
show_progress(label, 40, index / len(protein_records))
index += 1
print()
def fill_peptide_parameters(protein_records):
print('Filling peptide parameters:')
protein_index = 1
for protein_record in protein_records:
print('Processing protein record #{0} of {1}:'.format(protein_index, len(protein_records)))
stdout.flush()
# 1. process received peptide records first
label = '{0:>25}: '.format('Received peptides ({0})'.format(len(protein_record.received_peptide_records)))
show_progress(label, 40, 0.0)
peptide_index = 1
for peptide_record in protein_record.received_peptide_records:
peptide_record.peptide_parameters = PeptideParameters(peptide_record.peptide.sequence)
show_progress(label, 40, peptide_index / len(protein_record.received_peptide_records))
peptide_index += 1
print()
# 2. process then missed peptide records
if len(protein_record.missed_peptide_records) == 0:
protein_index += 1
continue
label = '{0:>25}: '.format('Missed peptides ({0})'.format(len(protein_record.missed_peptide_records)))
show_progress(label, 40, 0.0)
peptide_index = 1
for peptide_record in protein_record.missed_peptide_records:
peptide_record.peptide_parameters = PeptideParameters(peptide_record.peptide.sequence)
show_progress(label, 40, peptide_index / len(protein_record.missed_peptide_records))
peptide_index += 1
print()
protein_index += 1
print()
print('Filling peptide parameters: done.')
def fill_parameter_lists(protein_records):
total_received_peptides_number = 0
total_missed_peptides_number = 0
for protein_record in protein_records:
total_received_peptides_number += len(protein_record.received_peptide_records)
total_missed_peptides_number += len(protein_record.missed_peptide_records)
received_parameters = DataFrame(zeros((total_received_peptides_number, len(peptide_parameter_names)),
dtype=float64), columns=peptide_parameter_names)
missed_parameters = DataFrame(zeros((total_missed_peptides_number, len(peptide_parameter_names)),
dtype=float64), columns=peptide_parameter_names)
# fill received peptides parameters
label = 'Filling received peptides parameter lists: '
show_progress(label, 32, 0.0)
index = 1
for protein_record in protein_records:
for received_peptide_record in protein_record.received_peptide_records:
received_parameters['Sequence length'][index] = received_peptide_record.peptide_parameters.sequence_length
received_parameters['Aromaticity'][index] = received_peptide_record.peptide_parameters.aromaticity
received_parameters['Instability'][index] = received_peptide_record.peptide_parameters.instability
received_parameters['Isoelectric point'][index] = \
received_peptide_record.peptide_parameters.isoelectric_point
received_parameters['Molecular weight'][index] = received_peptide_record.peptide_parameters.molecular_weight
received_parameters['Kyte plot'][index] = received_peptide_record.peptide_parameters.kyte_plot
received_parameters['Aliphatic index'][index] = received_peptide_record.peptide_parameters.aliphatic_index
received_parameters['Boman index'][index] = received_peptide_record.peptide_parameters.boman_index
received_parameters['Hydrophobicity'][index] = received_peptide_record.peptide_parameters.hydrophobicity
for kidera_factor in received_peptide_record.peptide_parameters.kidera_factors:
received_parameters['Kidera factor: {0}'.format(kidera_factor['name'])][index] = kidera_factor['value']
show_progress(label, 32, index / total_received_peptides_number)
index += 1
print()
# fill missed peptides parameters
label = 'Filling missed peptides parameter lists: '
show_progress(label, 32, 0.0)
index = 1
for protein_record in protein_records:
for missed_peptide_record in protein_record.missed_peptide_records:
missed_parameters['Sequence length'][index] = missed_peptide_record.peptide_parameters.sequence_length
missed_parameters['Aromaticity'][index] = missed_peptide_record.peptide_parameters.aromaticity
missed_parameters['Instability'][index] = missed_peptide_record.peptide_parameters.instability
missed_parameters['Isoelectric point'][index] = missed_peptide_record.peptide_parameters.isoelectric_point
missed_parameters['Molecular weight'][index] = missed_peptide_record.peptide_parameters.molecular_weight
missed_parameters['Kyte plot'][index] = missed_peptide_record.peptide_parameters.kyte_plot
missed_parameters['Aliphatic index'][index] = missed_peptide_record.peptide_parameters.aliphatic_index
missed_parameters['Boman index'][index] = missed_peptide_record.peptide_parameters.boman_index
missed_parameters['Hydrophobicity'][index] = missed_peptide_record.peptide_parameters.hydrophobicity
for kidera_factor in missed_peptide_record.peptide_parameters.kidera_factors:
missed_parameters['Kidera factor: {0}'.format(kidera_factor['name'])][index] = kidera_factor['value']
show_progress(label, 32, index / total_missed_peptides_number)
index += 1
print()
return received_parameters, missed_parameters
def construct_protein_records(proteins, main_data):
label = 'Constructing protein records: '
show_progress(label, 40, 0.0)
protein_records = []
# 1. process all main data
index = 1
for line in main_data:
# 1.1. construct peptide and peptide match from current analysis
current_peptide = Peptide(sequence=b2str(line['sequence']))
current_peptide_match = PeptideMatch(analysis_name=b2str(line['filename']), score=line['score'],
reverse_score=line['reverseScore'],
percent_of_scored_peak_intensity=line['percent_scored_peak_intensity'],
total_intensity=line['totalIntensity'],
precursor_averagine_chi_squared=line['precursorAveragineChiSquared'],
retention_time_min=line['retentionTimeMin'],
chromatographic_peak_width_in_seconds=line['chromatographicPeakWidthSec'])
# 1.2. get protein id for current analysis
current_protein_id = b2str(line['accession_number'])
# 1.3. find protein with such id
protein = find_protein_with_id(proteins,
current_protein_id) # TODO: if such protein not exists, extract Protein object and add to proteins
# 1.4. find record with such protein
protein_record = find_protein_record_with_protein(protein_records, protein)
# 1.5. if record with such protein exists, add current match to received peptides
if protein_record is not None:
# 1.5.1. if such peptide was already received, add peptide match
peptide_record = find_peptide_record_with_peptide(protein_record.received_peptide_records, current_peptide)
if peptide_record is not None:
peptide_record.matches.append(current_peptide_match)
# 1.5.2. if such peptide was not received yet, add peptide record with this one peptide match
else:
current_peptide_record = PeptideRecord(current_peptide, [current_peptide_match])
protein_record.received_peptide_records.append(current_peptide_record)
# 1.6. if protein record with such protein not exists, create new protein record
else:
current_peptide_record = PeptideRecord(current_peptide, [current_peptide_match])
protein_record = ProteinRecord(protein, received_peptide_records=[current_peptide_record])
protein_records.append(protein_record)
show_progress(label, 40, index / len(main_data))
index += 1
print()
# 2. sort peptide records by length (starting from longest)
label = 'Filling received peptide records: '
show_progress(label, 35, 0.0)
index = 1
for protein_record in protein_records:
protein_record.received_peptide_records = sorted(protein_record.received_peptide_records, key=lambda peptide_record: len(peptide_record.peptide.sequence), reverse=True)
show_progress(label, 35, index / len(protein_records))
index += 1
print()
return protein_records
def construct_proteins(main_data):
proteins = []
label = 'Constructing proteins from main data: '
show_progress(label, 35, 0.0)
# 1. fill list with unique proteins
index = 1
for line in main_data:
# 1.1. construct protein from current line
current_protein = Protein(id=b2str(line['accession_number']), name=b2str(line['entry_name']))
# 1.2. add if not already exists in list
if current_protein not in proteins:
proteins.append(current_protein)
show_progress(label, 35, index / len(main_data))
index += 1
print()
return proteins
def fill_missed_peptide_records(protein_records):
label = 'Filling missed peptide records: '
show_progress(label, 40, 0.0)
index = 1
for protein_record in protein_records:
# 1. construct list of sequences of received peptides
received_sequences = [peptide_record.peptide.sequence for peptide_record in protein_record.received_peptide_records]
# 2. calculate list of missed sequence fragments
missed_sequences = cut_received_peptide_sequences(protein_record.protein.sequence, received_sequences)
missed_sequences = [trypsinolize_sequence(x) for x in missed_sequences]
# 3. construct peptide record for each fragment and store them in missed peptide records
protein_record.missed_peptide_records = []
for missed_sequences_list in missed_sequences:
for fragment in missed_sequences_list:
protein_record.missed_peptide_records.append(PeptideRecord(peptide=Peptide(sequence=fragment)))
show_progress(label, 40, index / len(protein_records))
index += 1
print()
def calculate_simple_statistics(parameters, per_peptide_correlations=None):
label = 'Calculating simple statistics: '
show_progress(label, 40, 0.0)
stats = {}
total_stats_length = len(parameters.columns)
if per_peptide_correlations is not None:
total_stats_length += len(per_peptide_correlations.columns)
index = 1
for parameter_name in parameters.columns:
stats[parameter_name] = calculate_simple_statistics_for_serie(parameters[parameter_name])
show_progress(label, 40, index / total_stats_length)
index += 1
if per_peptide_correlations is not None:
for parameter_name in per_peptide_correlations.columns:
stats[parameter_name] = calculate_simple_statistics_for_serie(per_peptide_correlations[parameter_name])
show_progress(label, 40, index / total_stats_length)
index += 1
print()
return stats
def fill_per_peptide_correlations(protein_records):
per_peptide_correlation_parameter_labels = ['{0} per peptide correlation (Pearson)'.format(name) for name in
per_peptide_correlation_parameter_names]
total_received_peptides_number = 0
total_missed_peptides_number = 0
for protein_record in protein_records:
total_received_peptides_number += len(protein_record.received_peptide_records)
total_missed_peptides_number += len(protein_record.missed_peptide_records)
total_received_pairs_number = total_received_peptides_number * (total_received_peptides_number - 1) // 2
received_per_peptide_correlations = DataFrame(zeros((total_received_pairs_number,
len(per_peptide_correlation_parameter_labels)),
dtype=float64),
columns=per_peptide_correlation_parameter_labels)
total_missed_pairs_number = total_missed_peptides_number * (total_missed_peptides_number - 1) // 2
missed_per_peptide_correlations = DataFrame(zeros((total_missed_pairs_number,
len(per_peptide_correlation_parameter_labels)),
dtype=float64),
columns=per_peptide_correlation_parameter_labels)
received_kidera_factors = DataFrame(zeros((len(kidera_factor_names), total_received_peptides_number),
dtype=float64))
missed_kidera_factors = DataFrame(zeros((len(kidera_factor_names), total_missed_peptides_number),
dtype=float64))
received_acid_percents = DataFrame(zeros((len('AGVMDYNSWLFIKPQCERTH'), total_received_peptides_number),
dtype=float64))
missed_acid_percents = DataFrame(zeros((len('AGVMDYNSWLFIKPQCERTH'), total_missed_peptides_number),
dtype=float64))
received_acid_compounds = DataFrame(zeros((len(amino_acid_group_names), total_received_peptides_number),
dtype=float64))
missed_acid_compounds = DataFrame(zeros((len(amino_acid_group_names), total_missed_peptides_number),
dtype=float64))
# received_charges = []
# missed_charges = []
received_hydrophobic_moments = DataFrame(zeros((len(hydrophobic_moments_names), total_received_peptides_number),
dtype=float64))
missed_hydrophobic_moments = DataFrame(zeros((len(hydrophobic_moments_names), total_missed_peptides_number),
dtype=float64))
secondary_structure_fraction_names = ['Helix', 'Turn', 'Sheet']
received_secondary_structure_fractions = DataFrame(
zeros((len(secondary_structure_fraction_names), total_received_peptides_number),
dtype=float64))
missed_secondary_structure_fractions = DataFrame(
zeros((len(secondary_structure_fraction_names), total_missed_peptides_number),
dtype=float64))
label = 'Filling received peptides array-like parameter lists: '
show_progress(label, 35, 0.0)
index = 1
for protein_record in protein_records:
for received_peptide_record in protein_record.received_peptide_records:
kidera_factor_index = 0
for kidera_factor in received_peptide_record.peptide_parameters.kidera_factors:
received_kidera_factors[index - 1][kidera_factor_index] = kidera_factor['value']
kidera_factor_index += 1
acid_index = 0
for acid in 'AGVMDYNSWLFIKPQCERTH':
received_acid_percents[index - 1][acid_index] = \
received_peptide_record.peptide_parameters.amino_acid_percents[acid]
acid_index += 1
group_index = 0
for group in received_peptide_record.peptide_parameters.amino_acids_composition:
received_acid_compounds[index - 1][group_index] = group['percent']
group_index += 1
# charges = []
# for charge in received_peptide_record.peptide_parameters.charges:
# charges.append(charge['charge'])
# received_charges.append(charges)
moment_index = 0
for moment in received_peptide_record.peptide_parameters.hydrophobic_moments:
if moment['name'] != 'Polygly-polypro helix':
received_hydrophobic_moments[index - 1][moment_index] = moment['moment']
group_index += 1
fraction_index = 0
for fraction in received_peptide_record.peptide_parameters.secondary_structure_fraction:
received_secondary_structure_fractions[index - 1][fraction_index] = fraction['value']
fraction_index += 1
show_progress(label, 35, index / total_received_peptides_number)
index += 1
print()
label = 'Filling missed peptides array-like parameter lists: '
show_progress(label, 35, 0.0)
index = 1
for protein_record in protein_records:
for missed_peptide_record in protein_record.missed_peptide_records:
kidera_factor_index = 0
for kidera_factor in missed_peptide_record.peptide_parameters.kidera_factors:
missed_kidera_factors[index - 1][kidera_factor_index] = kidera_factor['value']
kidera_factor_index += 1
acid_index = 0
for acid in 'AGVMDYNSWLFIKPQCERTH':
missed_acid_percents[index - 1][acid_index] = \
missed_peptide_record.peptide_parameters.amino_acid_percents[acid]
acid_index += 1
group_index = 0
for group in missed_peptide_record.peptide_parameters.amino_acids_composition:
missed_acid_compounds[index - 1][group_index] = group['percent']
group_index += 1
# charges = []
# for charge in missed_peptide_record.peptide_parameters.charges:
# charges.append(charge['charge'])
# missed_charges.append(charges)
#
moment_index = 0
for moment in missed_peptide_record.peptide_parameters.hydrophobic_moments:
if moment['name'] != 'Polygly-polypro helix':
missed_hydrophobic_moments[index - 1][moment_index] = moment['moment']
group_index += 1
fraction_index = 0
for fraction in missed_peptide_record.peptide_parameters.secondary_structure_fraction:
missed_secondary_structure_fractions[index - 1][fraction_index] = fraction['value']
fraction_index += 1
show_progress(label, 35, index / total_missed_peptides_number)
index += 1
print()
print('Calculating Kidera factors per peptide Pearson correlation (received peptides): ', end='')
received_per_peptide_correlations['Kidera factors per peptide correlation (Pearson)'] = \
convert_correlation_matrix_to_serie(received_kidera_factors.corr(method='pearson'), 'Kidera factors')
print('done')
print('Calculating Kidera factors per peptide Pearson correlation (missed peptides): ', end='')
missed_per_peptide_correlations['Kidera factors per peptide correlation (Pearson)'] = \
convert_correlation_matrix_to_serie(missed_kidera_factors.corr(method='pearson'), 'Kidera factors')
print('done')
print('Calculating amino acid percents per peptide Pearson correlation (received peptides): ', end='')
received_per_peptide_correlations['Amino acid percents per peptide correlation (Pearson)'] = \
convert_correlation_matrix_to_serie(received_acid_percents.corr(method='pearson'), 'Amino acid percents')
print('done')
print('Calculating amino acid percents per peptide Pearson correlation (missed peptides): ', end='')
missed_per_peptide_correlations['Amino acid percents per peptide correlation (Pearson)'] = \
convert_correlation_matrix_to_serie(missed_acid_percents.corr(method='pearson'), 'Amino acid percents')
print('done')
print('Calculating amino acid compositions per peptide Pearson correlation (received peptides): ', end='')
received_per_peptide_correlations['Amino acid compositions per peptide correlation (Pearson)'] = \
convert_correlation_matrix_to_serie(received_acid_compounds.corr(method='pearson'), 'Amino acid compositions')
print('done')
print('Calculating amino acid compositions per peptide Pearson correlation (missed peptides): ', end='')
missed_per_peptide_correlations['Amino acid compositions per peptide correlation (Pearson)'] = \
convert_correlation_matrix_to_serie(missed_acid_compounds.corr(method='pearson'), 'Amino acid compositions')
print('done')
#
# label = 'Calculating charges Kendall correlation (missed peptides): '
# show_progress(label, 40, 0.0)
# index = 1
# for first_charges in range(0, len(missed_charges)):
# for second_charges in range(first_charges + 1, len(missed_charges)):
# missed['Charges per peptide correlation (Kendall)'].append(
# statistics.kendalltau(missed_charges[first_charges], missed_charges[second_charges]).correlation)
# show_progress(label, 40, index / len(missed_charges))
# index += 1
# print()
print('Calculating hydrophobic moments per peptide Pearson correlation (received peptides): ', end='')
received_per_peptide_correlations['Hydrophobic moments per peptide correlation (Pearson)'] = \
convert_correlation_matrix_to_serie(received_hydrophobic_moments.corr(method='pearson'), 'Hydrophobic moments')
print('done')
print('Calculating hydrophobic moments per peptide Pearson correlation (missed peptides): ', end='')
missed_per_peptide_correlations['Hydrophobic moments per peptide correlation (Pearson)'] = \
convert_correlation_matrix_to_serie(missed_hydrophobic_moments.corr(method='pearson'), 'Hydrophobic moments')
print('done')
print('Calculating secondary structure fractions per peptide Pearson correlation (received peptides): ', end='')
received_per_peptide_correlations['Secondary structure fractions per peptide correlation (Pearson)'] = \
convert_correlation_matrix_to_serie(received_secondary_structure_fractions.corr(method='pearson'),
'Secondary structure fractions')
print('done')
print('Calculating secondary structure fractions per peptide Pearson correlation (missed peptides): ', end='')
missed_per_peptide_correlations['Secondary structure fractions per peptide correlation (Pearson)'] = \
convert_correlation_matrix_to_serie(missed_secondary_structure_fractions.corr(method='pearson'),
'Secondary structure fractions')
print('done')
return received_per_peptide_correlations, missed_per_peptide_correlations
