开发者提供了一个脚本generate_ptm_coordinates.py，请问我要怎么运行？
import numpy as np
import pandas as pd
from ExonPTMapper import mapping
from ExonPTMapper import config as mapper_config
from ptm_pose import pose_config
from Bio import SeqIO
import codecs
import gzip
import os
import datetime
import pyliftover
from tqdm import tqdm
def check_constitutive(ptm, nonconstitutive_list):
"""
For a given list of ptms, check if any of the ptms are found in the list of nonconstitutive ptms, meaning that they have previously been found to be missing from isoforms in Ensembl
Parameters
----------
ptm : list
List of PTMs to check (each ptm should be in the form of "UniProtID_ResiduePosition" (e.g. "P12345-1_Y100"))
nonconstitutive_list : list
List of PTMs that have been found to be nonconstitutive (based on data from ptm_info object generated by ExonPTMapper)
"""
if ptm in nonconstitutive_list:
return False
else:
return True
def extract_ptm_position_of_primary_isoform(row):
"""
For a given row in the ptm_coordinates dataframe, extract the position of the PTM in the primary isoform (canonical if available, else first isoform in list)
"""
pass
def get_unique_ptm_info(ptm_coordinates):
"""
For a given row in the ptm_coordinates dataframe, isolate PTM entries corresponding to the canonical isoform ID, if available. If not, use the first isoform ID in the list. Further, indicate whether the entry corresponds to a canonical or alternative isoform.
Parameters
----------
ptm_coordinates : pd.DataFrame
DataFrame containing PTM coordinates, generated by ExonPTMapper package
Returns
-------
pd.DataFrame
DataFrame containing PTM coordinates with additional columns indicating the UniProtKB accession number, the isoform ID, the type of isoform (canonical or alternative), the residue of the PTM, the position of the PTM in the isoform, and any alternative entries that also contain the PTM
"""
accession_list = [] #list of UniProtKB/Swiss-Prot accessions
residue_list = []
position_list = []
isoform_list = []
isoform_type = []
for i,row in ptm_coordinates.iterrows():
ptm_entries = row['Source of PTM'].split(';')
residue = ptm_entries[0].split('_')[1][0]
#iterate through each PTM entry and extract any that are associated with a canonical isoform. This will usually only be one, but in rare cases a PTM may be associated with multiple genes
found_in_canonical = False
positions = []
accessions = []
isoform_entries = []
for ptm in ptm_entries:
if ptm.split('_')[0] in mapper_config.canonical_isoIDs.values(): #check if the uniprot isoform ID is a canonical isoform. if so, add the position to the list of positions
positions.append(ptm.split('_')[1][1:])
accessions.append(ptm.split('-')[0]) #uniprot accession number
isoform_entries.append(ptm.split('_')[0]) #uniprot isoform ID
found_in_canonical = True #indicate that there is a PTM found in the canonical isoform
#check if position in canonical was found. If so, join the positions into a single string. If not, use the position associated with the first listed isoform
if found_in_canonical:
positions = ';'.join(positions)
accessions = ';'.join(accessions)
isoform_entries = ';'.join(isoform_entries)
isoform_type.append('Canonical')
else:
positions = ptm_entries[0].split('_')[1][1:]
accessions = ptm_entries[0].split('-')[0]
isoform_entries = ptm_entries[0].split('_')[0]
isoform_type.append('Alternative')
accession_list.append(accessions)
residue_list.append(residue)
position_list.append(positions)
isoform_list.append(isoform_entries)
ptm_coordinates['UniProtKB Accession'] = accession_list
ptm_coordinates['Isoform ID'] = isoform_list
ptm_coordinates['Isoform Type'] = isoform_type
ptm_coordinates['Residue'] = residue_list
ptm_coordinates['PTM Position in Isoform'] = position_list
return ptm_coordinates
def convert_coordinates(ptm_coordinates, from_coord = 'hg38', to_coord = 'hg19'):
"""
Given the ptm_coordinates dataframe, convert the genomic location of the PTMs from one coordinate system to another (e.g. hg38 to hg19, hg19 to hg38, etc.)
Parameters
----------
ptm_coordinates : pd.DataFrame
DataFrame containing PTM coordinates
from_coord : str
Coordinate system to convert from (e.g. 'hg38', 'hg19', 'hg18')
to_coord : str
Coordinate system to convert to (e.g. 'hg38', 'hg19', 'hg18')
"""
# convert coordinates to hg19 and hg38
new_coords = []
liftover_object = pyliftover.LiftOver(f'{from_coord}',f'{to_coord}')
for i, row in tqdm(ptm_coordinates.iterrows(), total = ptm_coordinates.shape[0], desc = f'Converting from {from_coord} to {to_coord} coordinates'):
new_coords.append(mapping.convert_genomic_coordinates(row[f'Gene Location ({from_coord})'], row['Chromosome/scaffold name'], row['Strand'], from_type = f'{from_coord}', to_type = f'{to_coord}', liftover_object = liftover_object))
return new_coords
residue_dict = {'R': 'arginine', 'H':'histidine', 'K':'lysine', 'D':'aspartic acid', 'E':'glutamic acid', 'S': 'serine', 'T':'threonine', 'N':'asparagine', 'Q':'glutamine', 'C':'cysteine', 'U':'selenocystein', 'G':'glycine', 'P':'proline', 'A':'alanine', 'V':'valine', 'I':'isoleucine', 'L':'leucine', 'M':'methionine', 'F':'phenylalanine', 'Y':'tyrosine', 'W':'tryptophan'}
def convert_modification_shorthand(mod, res, mod_group_type = 'fine'):
if mod_group_type == 'fine':
if mod == 'p':
mod_name = f'Phospho{residue_dict[res]}'
elif mod == 'ub':
mod_name = 'Ubiquitination'
elif mod == 'sm':
mod_name = 'Sumoylation'
elif mod == 'ga':
if res == 'N':
mod_name = 'N-Glycosylation'
else: #if you want a more general classification, this and T can be either just O-GalNAc or O-glycosylation
mod_name = 'O-GalNAc ' + residue_dict[res].capitalize()
elif mod == 'gl':
mod_name = 'O-GlcNAc ' + residue_dict[res].capitalize()
elif mod == 'm1' or mod == 'me':
mod_name = 'Methylation'
elif mod == 'm2':
mod_name = 'Dimethylation'
elif mod == 'm3':
mod_name = 'Trimethylation'
elif mod == 'ac':
mod_name = 'Acetylation'
else:
raise ValueError("ERROR: don't recognize PTM type %s"%(type))
elif mod_group_type == 'coarse':
if mod == 'p':
mod_name = 'Phosphorylation'
elif mod == 'ub':
mod_name = 'Ubiquitination'
elif mod == 'sm':
mod_name = 'Sumoylation'
elif mod == 'ga' or mod == 'gl':
mod_name = 'Glycosylation'
elif mod == 'm1' or mod == 'me' or mod == 'm2' or mod == 'm3':
mod_name = 'Methylation'
elif mod == 'ac':
mod_name = 'Acetylation'
return mod_name
def process_PSP_df(file, compressed = False, organism = None, include_flank = False, include_domain = False, include_MS = False, include_LTP = False, mod_group_type = 'fine'):
"""
Process the PhosphositePlus file into a dataframe with the Uniprot ID, residue, position, and modification, and any extra columns that are requested
Parameters
----------
file : str
The file to read in
compressed : bool
If the file is compressed or not
organism : str
The organism to filter the data by
include_flank : bool
If True, include the flanking amino acids
include_domain : bool
If True, include the domain
include_MS : bool
If True, include the MS_LIT and MS_CST columns
include_LTP : bool
If True, include the LT column (low throughput)
mod_group_type : str
The type of modification grouping to use (fine or coarse)
"""
if compressed:
df = pd.read_csv(file, sep='\t', skiprows=3, compression='gzip')
else:
df = pd.read_csv(file, sep='\t', skiprows=3)
df['Residue'] = df['MOD_RSD'].apply(lambda x: x.split('-')[0][0])
df['Position'] = df['MOD_RSD'].apply(lambda x: int(x.split('-')[0][1:]))
df['Modification'] = df['MOD_RSD'].str.split('-').str[1]
df['Modification'] = df.apply(lambda x: convert_modification_shorthand(x['Modification'], x['Residue'], mod_group_type=mod_group_type), axis=1)
if organism is not None:
df = df[df['ORGANISM'] == organism]
extra_cols = []
if organism is not None:
df = df[df['ORGANISM'] == organism]
else:
extra_cols += ['ORGANISM']
if include_flank:
extra_cols += ['SITE_+/-7_AA']
if include_domain:
extra_cols += ['DOMAIN']
if include_MS:
extra_cols += ['MS_LIT']
extra_cols += ['MS_CST']
if include_LTP:
extra_cols += ['LT_LIT']
df = df[['ACC_ID', 'Residue', 'Position', 'Modification'] + extra_cols]
return df
def combine_PSP_dfs(phosphositeplus_dir, compressed = True, organism = None, include_flank = False, include_domain = False, include_MS = False, include_LTP = False, mod_group_type = 'fine'):
extension = '.gz' if compressed else ''
phospho = process_PSP_df(phosphositeplus_dir+'Phosphorylation_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS,include_LTP=include_LTP, mod_group_type=mod_group_type)
ubiq = process_PSP_df(phosphositeplus_dir+'Ubiquitination_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS, include_LTP=include_LTP, mod_group_type=mod_group_type)
sumo = process_PSP_df(phosphositeplus_dir+'Sumoylation_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS, include_LTP=include_LTP, mod_group_type=mod_group_type)
galnac = process_PSP_df(phosphositeplus_dir+'O-GalNAc_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS, include_LTP=include_LTP, mod_group_type=mod_group_type)
glcnac = process_PSP_df(phosphositeplus_dir+'O-GlcNAc_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS, include_LTP=include_LTP, mod_group_type=mod_group_type)
meth = process_PSP_df(phosphositeplus_dir+'Methylation_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS, include_LTP=include_LTP, mod_group_type=mod_group_type)
acetyl = process_PSP_df(phosphositeplus_dir+'Acetylation_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS, include_LTP=include_LTP, mod_group_type=mod_group_type)
df = pd.concat([phospho, ubiq, sumo, galnac, glcnac, meth, acetyl])
return df
def extract_num_studies(psp_dir, pscout_data = None):
psp_df = combine_PSP_dfs(psp_dir, compressed = True, organism = 'human', include_MS = True, include_LTP = True, mod_group_type = 'coarse')
#get canonical isoform IDs
canonical_ids = mapper_config.translator[mapper_config.translator['UniProt Isoform Type'] == 'Canonical'][['UniProtKB/Swiss-Prot ID', 'UniProtKB isoform ID']].set_index('UniProtKB isoform ID').squeeze().to_dict()
#couple rare cases where the canonical isoform is listed with its isoform ID, which conflicts with how I processed the data. convert these ideas to base uniprot ID
psp_df_isoids = psp_df[psp_df['ACC_ID'].isin(canonical_ids.keys())].copy()
psp_correctids = psp_df[~psp_df['ACC_ID'].isin(canonical_ids.keys())].copy()
psp_df_isoids['ACC_ID'] = psp_df_isoids['ACC_ID'].map(canonical_ids)
psp_df = pd.concat([psp_df_isoids, psp_correctids])
#groupby number of experiments by PTM site and modification, taking the max value (some rare cases where entries are basically identical but have different numbers of experiments)
psp_df['PTM'] = psp_df['ACC_ID'] + '_' + psp_df['Residue'] + psp_df['Position'].astype(str)
psp_df = psp_df.groupby(['PTM', 'Modification'], as_index = False)[['MS_LIT', 'MS_CST', 'LT_LIT']].max()
psp_df = psp_df.rename(columns = {'Modification': 'Modification Class'})
if pscout_data is not None:
pscout_experiments = pscout_data[pscout_data['Number of Experiments'] > 0]
pscout_experiments = pscout_experiments.dropna(subset = 'Modification Class')
psp_df = psp_df.merge(pscout_experiments[['PTM', 'Number of Experiments']], on = 'PTM', how = 'outer')
psp_df = psp_df.fillna(0)
psp_df['MS_LIT'] = psp_df['MS_LIT'] + psp_df['Number of Experiments']
psp_df = psp_df.drop(columns = 'Number of Experiments')
return psp_df
def append_num_studies(ptm_coordinates, psp_df):
#remove ms columns if present
ptm_coordinates = ptm_coordinates.drop(columns = [i for i in ['MS_LIT', 'MS_CST', 'LT_LIT'] if i in ptm_coordinates.columns])
#add PTM column to PTM coordinates, allowing for alternative isoform IDs
ptm_coordinates['PTM'] = ptm_coordinates.apply(lambda x: x['Isoform ID'] + '_'+ x['Residue'] + str(int(x['PTM Position in Isoform'])) if x['Isoform Type'] == 'Alternative' else x['UniProtKB Accession'] + '_'+ x['Residue'] + str(int(x['PTM Position in Isoform'])), axis = 1)
#combine psp MS info with coordinate data, check to make sure size doesn't change
original_shape = ptm_coordinates.shape[0]
ptm_coordinates = ptm_coordinates.merge(psp_df[['PTM', 'Modification Class', 'MS_LIT', 'MS_CST', 'LT_LIT']], on = ['PTM', 'Modification Class'], how = 'left')
if original_shape != ptm_coordinates.shape[0]:
raise ValueError('Size of dataframe changed after merging PhosphoSitePlus data. Please check for duplicates.')
#go through entries without info and make sure it is not due to isoform ID issues
missing_db_info = ptm_coordinates[ptm_coordinates[['MS_LIT', 'MS_CST', 'LT_LIT']].isna().all(axis = 1)]
ptm_coordinates = ptm_coordinates[~ptm_coordinates[['MS_LIT', 'MS_CST', 'LT_LIT']].isna().all(axis = 1)]
for i, row in missing_db_info.iterrows():
mod = row['Modification Class']
sources = [s for s in row['Source of PTM'].split(';') if row['Isoform ID'] not in s]
for s in sources:
tmp_data = psp_df[(psp_df['PTM'] == s) & (psp_df['Modification Class'] == mod)]
if tmp_data.shape[0] > 0:
tmp_data = tmp_data.squeeze()
missing_db_info.loc[i, 'MS_LIT'] = tmp_data['MS_LIT']
missing_db_info.loc[i, 'MS_CST'] = tmp_data['MS_CST']
missing_db_info.loc[i, 'LT_LIT'] = tmp_data['LT_LIT']
break
ptm_coordinates = pd.concat([ptm_coordinates, missing_db_info])
return ptm_coordinates
def construct_mod_conversion_dict():
"""
Reformat modification conversion dataframe to a dictionary for easy conversion between modification site and modification class
"""
modification_conversion = pose_config.modification_conversion[['Modification', 'Modification Class']].set_index('Modification')
modification_conversion = modification_conversion.replace('Dimethylation', 'Methylation')
modification_conversion = modification_conversion.replace('Trimethylation', 'Methylation')
modification_conversion = modification_conversion.squeeze().to_dict()
return modification_conversion
def extract_number_compendia(ps_data_file):
"""
Given a proteomescout data file, extract the number of different compendia that support a PTM site in a format that works with ptm_coordinates file. The following experiment IDs are used to check for database evidence:
1395: HPRD
1790: PhosphoSitePlus
1323: Phospho.ELM
1688,1803: UniProt
1344: O-GlycBase
1575: dbPTM
Parameters:
------------
ps_data_file: str
Path to proteomescout data file
Returns:
------------
ps_data: pd.DataFrame
DataFrame with PTM site, modification class, and number of compendia that support the PTM site
"""
#load proteomescout data
ps_data = pd.read_csv(ps_data_file, sep = '\t')
ps_data = ps_data[ps_data['species'] == 'homo sapiens']
ps_data['modifications'] = ps_data['modifications'].str.split(';')
ps_data['evidence'] = ps_data['evidence'].str.split(';')
ps_data = ps_data.explode(['modifications','evidence'])
#extract site numbers and modification types into unique columns, then convert modification to broad class to match ptm coordinates dataframe
ps_data['site'] = ps_data['modifications'].str.split('-').str[0]
ps_data['Modification'] = ps_data['modifications'].apply(lambda x: '-'.join(x.split('-')[1:]))
modification_conversion = construct_mod_conversion_dict()
ps_data['Modification Class'] = ps_data['Modification'].map(modification_conversion)
ps_data = ps_data.drop(columns = ['Modification'])
#split evidence into a list, then check to see if which entries correspond to database rather than literature study
ps_data['evidence'] = ps_data['evidence'].apply(lambda x: [i.strip(' ') for i in x.split(',')])
ps_data['database evidence'] = ps_data['evidence'].apply(lambda x: set(x).intersection({'1395', '1790', '1323','1688','1803','1344','1575'}))
ps_data['experimental evidence'] = ps_data['evidence'].apply(lambda x: list(set(x).difference({'1395', '1790', '1323','1688','1803','1344','1575'})))
#add specific compendia with site
compendia_dict = {'1395':'HPRD','1790':'PhosphoSitePlus', '1323':'Phospho.ELM', '1688':'UniProt', '1803':'UniProt', '1344':'O-GlycBase','1575':'dbPTM'}
ps_data['Compendia'] = ps_data['database evidence'].apply(lambda x: ['ProteomeScout'] + [compendia_dict[i] for i in x])
#separate all accessions into separate rows for merge
ps_data['accessions'] = ps_data['accessions'].str.split(';')
ps_data = ps_data.explode('accessions')
#convert isoform IDs from having . to a - for consistency
ps_data['accessions'] = ps_data['accessions'].str.replace('.','-').str.strip(' ')
#create PTM column
ps_data['PTM'] = ps_data['accessions'] + '_' + ps_data['site'].str.strip(' ')
ps_data = ps_data.dropna(subset = ['PTM'])
#some ptms may have multiple entries (due to multiple similar modification types), so combine them
ps_data = ps_data.groupby(['PTM', 'Modification Class'], as_index = False)[['Compendia', 'experimental evidence']].agg(sum)
ps_data['Number of Compendia'] = ps_data['Compendia'].apply(lambda x: len(set(x)))
ps_data['Compendia'] = ps_data['Compendia'].apply(lambda x: ';'.join(set(x)))
ps_data['Number of Experiments'] = ps_data['experimental evidence'].apply(lambda x: len(set(x)))
ps_data['experimental evidence'] = ps_data['experimental evidence'].apply(lambda x: ';'.join(set(x)))
return ps_data
def append_num_compendia(ptm_coordinates, pscout_data):
"""
Given a PTM coordinates dataframe and processed proteomescout data generated by `extract_num_compendia()`, append the number of compendia that support a PTM site to the PTM coordinates dataframe. Check all potential isoform IDs for compendia information.
Parameters:
------------
ptm_coordinates: pd.DataFrame
DataFrame with PTM site information
pscout_data: pd.DataFrame
DataFrame with PTM site, modification class, and number of compendia that support the PTM site
"""
#remove existing columns if present
ptm_coordinates = ptm_coordinates.drop(columns = [i for i in ['Compendia', 'Number of Compendia'] if i in ptm_coordinates.columns])
if 'PTM' not in ptm_coordinates.columns:
ptm_coordinates['PTM'] = ptm_coordinates.apply(lambda x: x['Isoform ID'] + '_'+ x['Residue'] + str(int(x['PTM Position in Isoform'])) if x['Isoform Type'] == 'Alternative' else x['UniProtKB Accession'] + '_'+ x['Residue'] + str(int(x['PTM Position in Isoform'])), axis = 1)
#merge the data
ptm_coordinates = ptm_coordinates.merge(pscout_data, on = ['Modification Class', 'PTM'], how = 'left')
#go through any missing entries and see if they can be filled in with other isoform IDs
missing_db_info = ptm_coordinates[ptm_coordinates['Number of Compendia'].isna()]
ptm_coordinates = ptm_coordinates[~ptm_coordinates['Compendia'].isna()]
for i, row in tqdm(missing_db_info.iterrows(), total = missing_db_info.shape[0], desc = 'Going through missing compendia data, making sure not due to isoform ID issues'):
mod = row['Modification Class']
sources = [s for s in row['Source of PTM'].split(';') if row['Isoform ID'] not in s]
for s in sources:
tmp_data = pscout_data[(pscout_data['PTM'] == s) & (pscout_data['Modification Class'] == mod)]
if tmp_data.shape[0] > 0:
tmp_data = tmp_data.squeeze()
missing_db_info.loc[i, 'Compendia'] = tmp_data['Compendia']
missing_db_info.loc[i, 'Number of Compendia'] = tmp_data['Number of Compendia']
break
ptm_coordinates = pd.concat([ptm_coordinates, missing_db_info])
#fill in remainder entries as PhosphoSitePlus
ptm_coordinates['Compendia'] = ptm_coordinates['Compendia'].fillna('PhosphoSitePlus')
ptm_coordinates['Number of Compendia'] = ptm_coordinates['Number of Compendia'].fillna(1)
return ptm_coordinates
def separate_by_modification(ptm_coordinates, mod_mapper):
#separate out modification class (important for functional analysis)
ptm_coordinates['Modification Class'] = ptm_coordinates['Modification Class'].str.split(';')
ptm_coordinates = ptm_coordinates.explode('Modification Class')
#reduce modification columns to only those relevant to modification class
mod_mapper = mod_mapper.squeeze()
mod_mapper = mod_mapper.str.split(';')
mod_mapper = mod_mapper.to_dict()
def filter_mods(x, mod_class):
"""
Go through modification list and only keep those that are relevant to the modification class (removes mismatch due to exploded dataframe)
"""
x_list = x.split(';')
return ';'.join([i for i in x_list if i in mod_mapper[mod_class]])
ptm_coordinates['Modification'] = ptm_coordinates.apply(lambda x: filter_mods(x['Modification'], x['Modification Class']), axis = 1)
return ptm_coordinates
def generate_ptm_coordinates(pscout_data_file, phosphositeplus_filepath, mod_mapper, remap_PTMs = False, output_dir = None, ptm_info = None):
mapper = mapping.PTM_mapper()
if remap_PTMs:
mapper.find_ptms_all(phosphositeplus_file = phosphositeplus_filepath)
mapper.mapPTMs_all()
#get coordinate info
if mapper.ptm_coordinates is None:
raise ValueError('No PTM coordinates found. Please set remap_PTMs to True to generate PTM coordinates. If you want to include PhosphoSitePlus data, please also include location of phosphositeplus data file in phosphositeplus_filepath argument.')
#copy ptm_coordinates file to be edited for easier use with PTM-POSE
ptm_coordinates = mapper.ptm_coordinates.copy()
ptm_coordinates = get_unique_ptm_info(ptm_coordinates)
# convert coordinates to hg19 and hg38, then from hg19 to hg18
ptm_coordinates['Gene Location (hg19)'] = convert_coordinates(ptm_coordinates, from_coord = 'hg38', to_coord = 'hg19')
ptm_coordinates['Gene Location (hg18)'] = convert_coordinates(ptm_coordinates, from_coord = 'hg19', to_coord = 'hg18')
#separate out PTMs that are found in UniProt accessions
ptm_coordinates['PTM Position in Isoform'] = ptm_coordinates['PTM Position in Isoform'].str.split(';')
ptm_coordinates['UniProtKB Accession'] = ptm_coordinates['UniProtKB Accession'].str.split(';')
ptm_coordinates['Isoform ID'] = ptm_coordinates['Isoform ID'].str.split(';')
ptm_coordinates = ptm_coordinates.explode(['UniProtKB Accession', 'Isoform ID', 'PTM Position in Isoform']).reset_index()
#for further analysis
ptm_coordinates['PTM'] = ptm_coordinates['Isoform ID'] + '_' + ptm_coordinates['Residue'] + ptm_coordinates['PTM Position in Isoform']
if ptm_info is None:
ptm_info = mapper.ptm_info.copy()
#add whether or not the PTM is constitutive
if 'PTM Conservation Score' not in ptm_info.columns:
raise Warning('No PTM conservation score found in ptm_info object. Will not add column indicating whether the PTM is considered to be constitutive or not.')
else:
#grab nonconstitutive list from ptm_info object
nonconstitutive_list = set(ptm_info[ptm_info['PTM Conservation Score'] != 1].index.values)
const_list = []
for i, row in tqdm(ptm_coordinates.iterrows(), total = ptm_coordinates.shape[0], desc = 'Checking for constitutive PTMs'):
const_list.append(check_constitutive(row['PTM'], nonconstitutive_list))
ptm_coordinates['Constitutive'] = const_list
#add flanking sequence information
ptm_coordinates['Canonical Flanking Sequence'] = ptm_coordinates['PTM'].apply(lambda x: ptm_info.loc[x, 'Flanking Sequence'] if x == x else np.nan)
#drop unnecessary columns and save
#ptm_coordinates = ptm_coordinates.drop(columns = ['PTM Position in Canonical Isoform', 'PTM'], axis = 1)
ptm_coordinates = ptm_coordinates[['Gene name', 'UniProtKB Accession', 'Isoform ID', 'Isoform Type', 'Residue', 'PTM Position in Isoform', 'Modification', 'Modification Class', 'Chromosome/scaffold name', 'Strand', 'Gene Location (hg38)', 'Gene Location (hg19)', 'Gene Location (hg18)', 'Constitutive', 'Canonical Flanking Sequence', 'Source of PTM']]
ptm_coordinates = ptm_coordinates.reset_index() #there will be some non-unique genomic coordinates, so reset index to ensure unique index values
#separate out modification class (important for functional analysis)
ptm_coordinates = separate_by_modification(ptm_coordinates, mod_mapper)
#add filtering criteria
## add number of compendia
pscout_data = extract_number_compendia(pscout_data_file)
ptm_coordinates = append_num_compendia(ptm_coordinates, pscout_data)
## number of MS experiments (from PhosphoSitePlus, fill in any remainder with ProteomeScout experiments)
psp_df = generate_ptm_coordinates.extract_num_studies(phosphositeplus_filepath, pscout_data=pscout_data)
ptm_coordinates = generate_ptm_coordinates.append_num_studies(ptm_coordinates, psp_df)
#do some final custom editing for outlier examples
missing_db_info = ptm_coordinates[ptm_coordinates[['MS_LIT', 'MS_CST', 'LT_LIT', 'Compendia']].isna().all(axis = 1)]
ptm_coordinates = ptm_coordinates[~ptm_coordinates[['MS_LIT', 'MS_CST', 'LT_LIT', 'Compendia']].isna().all(axis = 1)]
#custom fixes
ms_lit = {'Q13765_K108':1, 'Q13765_K100':2}
ms_cst = {'Q13765_K108':np.nan, 'Q13765_K100':np.nan}
lt_lit = {'Q13765_K108':np.nan, 'Q13765_K100':np.nan}
custom_compendia = {'Q13765_K108':'PhosphoSitePlus;ProteomeScout', 'Q13765_K100': 'PhosphoSitePlus;ProteomeScout'}
custom_num_compendia = {'Q13765_K108':2, 'Q13765_K100':2}
for i,row in missing_db_info.iterrows():
ptm = row['PTM']
missing_db_info.loc[i, 'MS_LIT'] = ms_lit[ptm]
missing_db_info.loc[i, 'MS_CST'] = ms_cst[ptm]
missing_db_info.loc[i, 'LT_LIT'] = lt_lit[ptm]
missing_db_info.loc[i, 'Compendia'] = custom_compendia[ptm]
missing_db_info.loc[i, 'Number of Compendia'] = custom_num_compendia[ptm]
ptm_coordinates = pd.concat([ptm_coordinates, missing_db_info])
ptm_coordinates[['Number of Compendia', 'Number of Experiments', 'MS_LIT', 'MS_CST', 'LT_LIT']] = ptm_coordinates[['Number of Compendia', 'Number of Experiments', 'MS_LIT', 'MS_CST', 'LT_LIT']].fillna(0).astype(int)
if output_dir is not None:
ptm_coordinates.to_csv(output_dir + 'ptm_coordinates.csv', index = False)
#write to text file indicating when the data was last updated
with open(output_dir + 'last_updated.txt', 'w') as f:
f.write(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
return ptm_coordinates
SWISS-PROT是一个精心整理的蛋白质序列数据库，提供蛋白质功能、结构等详细注释。本文介绍了其记录格式及关键字段，如ID、DE、OS和SQ等，并通过示例说明了如何理解这些数据。
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