def apply_msgfdb(in_file, msms_run_summary, modifications, num_mods):
""" Read output file of MS-GFDB and add child elements to msms_run_summary """
spectrum2element = {}
enzyme_list = [ArgC,LysC,Trypsin,LysCP,Chymotrypsin,TrypChymo,TrypsinP,PepsinA,
CNBr,V8E,AspN,Formic_acid,AspNambic,V8DE]
semi_list = []
sample_enzyme = msms_run_summary.find('sample_enzyme')
for f in read_msgfdb(in_file):
spectrum = '%(name)s.%(scan)05i.%(scan)05i.%(charge)i' % \
{'name': remove_file_extention(f['#SpecFile']),
'scan': f['Scan#'], 'charge': f['Charge']}
enzyme_list, semi_list = what_enzyme(enzyme_list, semi_list, f['Peptide'])
peptide_prev_aa = f['Peptide'][0]
if peptide_prev_aa == '_':
peptide_prev_aa = '-'
peptide_middle = f['Peptide'][2:-2]
peptide_next_aa = f['Peptide'][-1]
if peptide_next_aa == '_':
peptide_next_aa = '-'
if ' ' in f['Protein']:
protein_name, protein_descr = f['Protein'].split(' ', 1)
else:
protein_name = f['Protein']
protein_descr = ''
precursor_neutral_mass = f['Precursor'] * f['Charge'] - f['Charge'] * H_plus
if spectrum not in spectrum2element:
spectrum_query = SubElement(msms_run_summary, 'spectrum_query')
spectrum2element[spectrum] = spectrum_query
spectrum_query.append(Element('search_result'))
spectrum_query.set('spectrum', spectrum)
spectrum_query.set('start_scan', str(f['Scan#']))
spectrum_query.set('end_scan', str(f['Scan#']))
spectrum_query.set('assumed_charge', str(f['Charge']))
spectrum_query.set('precursor_neutral_mass', str(precursor_neutral_mass))
spectrum_query = spectrum2element[spectrum]
search_result = spectrum_query.find('search_result')
search_hit = SubElement(search_result, 'search_hit')
search_hit.set('peptide', "".join(aa for aa in peptide_middle if aa.isalpha()))
search_hit.set('peptide_prev_aa', peptide_prev_aa)
search_hit.set('peptide_next_aa', peptide_next_aa)
search_hit.set('protein', protein_name)
search_hit.set('protein_descr', protein_descr)
modification_instances = sum((find_modifications(mod, peptide_middle) for mod in modifications), [])
calc_neutral_pep_mass = modified_peptide_mass(modification_instances, peptide_middle, num_mods)
if modification_instances:
modification_info = SubElement(search_hit, 'modification_info')
for mass, mass_diff, aa_number, is_opt in modification_instances:
maam = SubElement(modification_info, 'mod_aminoacid_mass')
maam.set('position', str(aa_number))
maam.set('mass', str(mass))
search_hit.set('calc_neutral_pep_mass', str(calc_neutral_pep_mass))
search_hit.set('massdiff', str(precursor_neutral_mass - calc_neutral_pep_mass))
for field in score_fields:
if field in f:
SubElement(search_hit, 'search_score', name=field, value=f[field])
# sample_enzyme.set('fidelity',flag)
if enzyme_list == []:
if semi_list == []:
sample_enzyme.set('name','NoEnzyme')
sample_enzyme.set('fidelity','nonspecific')
else:
sample_enzyme.set('fidelity','semispecific')
enzyme = re.split("\|",enzyme2name[re.search(r'<(\w+)>',semi_list[0][0]).group(1)])
else:
sample_enzyme.set('fidelity','specific')
enzyme = re.split("\|",enzyme2name[re.search(r'<(\w+)>',enzyme_list[0][0]).group(1)])
if not(enzyme_list == [] and semi_list == []):
sample_enzyme.set('name',enzyme[0])
specificity = SubElement(sample_enzyme, 'specificity')
specificity.set('cut',enzyme[1])
if enzyme[2]:
specificity.set('no_cut',enzyme[2])
specificity.set('sense',enzyme[3])
def apply_msgfdb(in_file, msms_run_summary, modifications, num_mods):
""" Read output file of MS-GFDB and add child elements to msms_run_summary """
spectrum2element = {}
enzyme_list = [
ArgC, LysC, Trypsin, LysCP, Chymotrypsin, TrypChymo, TrypsinP, PepsinA,
CNBr, V8E, AspN, Formic_acid, AspNambic, V8DE
]
semi_list = []
sample_enzyme = msms_run_summary.find('sample_enzyme')
for f in read_msgfdb(in_file):
spectrum = '%(name)s.%(scan)05i.%(scan)05i.%(charge)i' % \
{'name': remove_file_extention(f['#SpecFile']),
'scan': f['Scan#'], 'charge': f['Charge']}
enzyme_list, semi_list = what_enzyme(enzyme_list, semi_list,
f['Peptide'])
peptide_prev_aa = f['Peptide'][0]
if peptide_prev_aa == '_':
peptide_prev_aa = '-'
peptide_middle = f['Peptide'][2:-2]
peptide_next_aa = f['Peptide'][-1]
if peptide_next_aa == '_':
peptide_next_aa = '-'
if ' ' in f['Protein']:
protein_name, protein_descr = f['Protein'].split(' ', 1)
else:
protein_name = f['Protein']
protein_descr = ''
precursor_neutral_mass = f['Precursor'] * f['Charge'] - f[
'Charge'] * H_plus
if spectrum not in spectrum2element:
spectrum_query = SubElement(msms_run_summary, 'spectrum_query')
spectrum2element[spectrum] = spectrum_query
spectrum_query.append(Element('search_result'))
spectrum_query.set('spectrum', spectrum)
spectrum_query.set('start_scan', str(f['Scan#']))
spectrum_query.set('end_scan', str(f['Scan#']))
spectrum_query.set('assumed_charge', str(f['Charge']))
spectrum_query.set('precursor_neutral_mass',
str(precursor_neutral_mass))
spectrum_query = spectrum2element[spectrum]
search_result = spectrum_query.find('search_result')
search_hit = SubElement(search_result, 'search_hit')
search_hit.set('peptide',
"".join(aa for aa in peptide_middle if aa.isalpha()))
search_hit.set('peptide_prev_aa', peptide_prev_aa)
search_hit.set('peptide_next_aa', peptide_next_aa)
search_hit.set('protein', protein_name)
search_hit.set('protein_descr', protein_descr)
modification_instances = sum((find_modifications(mod, peptide_middle)
for mod in modifications), [])
calc_neutral_pep_mass = modified_peptide_mass(modification_instances,
peptide_middle, num_mods)
if modification_instances:
modification_info = SubElement(search_hit, 'modification_info')
for mass, mass_diff, aa_number, is_opt in modification_instances:
maam = SubElement(modification_info, 'mod_aminoacid_mass')
maam.set('position', str(aa_number))
maam.set('mass', str(mass))
search_hit.set('calc_neutral_pep_mass', str(calc_neutral_pep_mass))
search_hit.set('massdiff',
str(precursor_neutral_mass - calc_neutral_pep_mass))
for field in score_fields:
if field in f:
SubElement(search_hit,
'search_score',
name=field,
value=f[field])
# sample_enzyme.set('fidelity',flag)
if enzyme_list == []:
if semi_list == []:
sample_enzyme.set('name', 'NoEnzyme')
sample_enzyme.set('fidelity', 'nonspecific')
else:
sample_enzyme.set('fidelity', 'semispecific')
enzyme = re.split(
"\|", enzyme2name[re.search(r'<(\w+)>',
semi_list[0][0]).group(1)])
else:
sample_enzyme.set('fidelity', 'specific')
enzyme = re.split(
"\|", enzyme2name[re.search(r'<(\w+)>',
enzyme_list[0][0]).group(1)])
if not (enzyme_list == [] and semi_list == []):
sample_enzyme.set('name', enzyme[0])
specificity = SubElement(sample_enzyme, 'specificity')
specificity.set('cut', enzyme[1])
if enzyme[2]:
specificity.set('no_cut', enzyme[2])
specificity.set('sense', enzyme[3])