datanator/data_source/rna_halflife/doi_10_1093_nar_gkt1150.py
import pandas as pd
from datanator.util import rna_halflife_util, file_util
import datetime
import datanator.config.core
import datetime
from pymongo.collation import Collation, CollationStrength
import tempfile
import shutil
class Halflife(rna_halflife_util.RnaHLUtil):
def __init__(self, cache_dir=None, server=None, src_db=None, protein_col=None,
authDB=None, readPreference=None, username=None, password=None,
verbose=None, max_entries=None, des_db=None, rna_col=None):
"""Init
Args:
cache_dir (:obj:`str`, optional): Cache directory for logs. Defaults to None.
server (:obj:`str`, optional): MongoDB server address. Defaults to None.
db (:obj:`str`, optional): Database where initial uniprot collection resides. Defaults to None.
collection_str (:obj:`str`, optional): name of collection. Defaults to None.
authDB (:obj:`str`, optional): MongoDB authentication database. Defaults to None.
readPreference (:obj:`str`, optional): MongoDB read preference. Defaults to None.
username (:obj:`str`, optional): MongoDB username. Defaults to None.
password (:obj:`str`, optional): MongoDB password. Defaults to None.
verbose (:obj:`bool`, optional): Wheter to display verbose messages. Defaults to None.
max_entries (:obj:`int`, optional): Number of records to be processed. Defaults to None.
uniprot_col_db (:obj:`int`, optional): Database to which new uniprot records will be inserted. Defaults to None.
"""
super().__init__(server=server, username=username, password=password, src_db=src_db,
des_db=des_db, protein_col=protein_col, rna_col=rna_col, authDB=authDB, readPreference=readPreference,
max_entries=max_entries, verbose=verbose, cache_dir=cache_dir)
self.collation = Collation('en', strength=CollationStrength.SECONDARY)
self.max_entries = max_entries
self.verbose = verbose
def fill_rna_half_life(self, df, species):
"""Load df into rna_halflife collection
Args:
df (:obj:`pandas.DataFrame`): dataframe to be loaded
species (:obj:`list`): species name and ncbi_id.
"""
row_count = len(df.index)
for i, row in df.iterrows():
if i == self.max_entries:
break
if i % 10 == 0 and self.verbose:
print("Processing locus {} out {}".format(i, row_count))
if row[2:].isnull().values.all():
continue
oln = row['oln']
symbol = row['gene_symbol']
a = row['a'] * 60
vc_a = row['vc_a']
b = row['b'] * 60
vc_b = row['vc_b']
c = row['c'] * 60
vc_c = row['vc_c']
d = row['d'] * 60
vc_d = row['vc_d']
dic_0 = {'halflife': a, 'variation_coefficient': vc_a, 'species': species[0], 'ncbi_taxonomy_id': species[1],
'unit': 's', 'reference': [{'doi': '10.1093/nar/gkt1150'}], 'growth_medium': 'M9 minimal medium supplemented with glucose',
'ordered_locus_name': oln, 'doubling_time': {'value': 6.9, 'unit': 'h'}}
dic_1 = {'halflife': b, 'variation_coefficient': vc_b, 'species': species[0], 'ncbi_taxonomy_id': species[1],
'unit': 's', 'reference': [{'doi': '10.1093/nar/gkt1150'}], 'growth_medium': 'M9 minimal medium supplemented with glucose',
'ordered_locus_name': oln, 'doubling_time': {'value': 3.5, 'unit': 'h'}}
dic_2 = {'halflife': c, 'variation_coefficient': vc_c, 'species': species[0], 'ncbi_taxonomy_id': species[1],
'unit': 's', 'reference': [{'doi': '10.1093/nar/gkt1150'}], 'growth_medium': 'M9 minimal medium supplemented with glucose',
'ordered_locus_name': oln, 'doubling_time': {'value': 2.3, 'unit': 'h'}}
dic_3 = {'halflife': d, 'variation_coefficient': vc_d, 'species': species[0], 'ncbi_taxonomy_id': species[1],
'unit': 's', 'reference': [{'doi': '10.1093/nar/gkt1150'}], 'growth_medium': 'M9 minimal medium supplemented with glucose',
'ordered_locus_name': oln, 'doubling_time': {'value': 1.7, 'unit': 'h'}}
halflives = [dic_0, dic_1, dic_2, dic_3]
# record is guaranteed to exist in uniprot because uniprot was filled prior to this operation
gene_name, protein_name = self.uniprot_query_manager.get_gene_protein_name_by_oln(oln, species=[83333, 511145])
if gene_name is None:
gene_name = symbol
self.rna_hl_collection.update_one({'gene_name': gene_name},
{'$addToSet': {'halflives': {'$each': halflives},
'protein_synonyms': protein_name}},
collation=self.collation, upsert=True)
def main():
src_db = 'datanator'
des_db = 'datanator'
rna_col = 'rna_halflife'
protein_col = 'uniprot'
cache_dir = tempfile.mkdtemp()
username = datanator.config.core.get_config()[
'datanator']['mongodb']['user']
password = datanator.config.core.get_config(
)['datanator']['mongodb']['password']
server = datanator.config.core.get_config(
)['datanator']['mongodb']['server']
src = Halflife(server=server, src_db=src_db,
protein_col=protein_col, authDB='admin', readPreference='nearest',
username=username, password=password, verbose=True, max_entries=float('inf'),
des_db=des_db, rna_col=rna_col, cache_dir=cache_dir)
url = 'https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/nar/42/4/10.1093_nar_gkt1150/1/gkt1150_Supplementary_Data.zip?Expires=1578928721&Signature=ADjsCSaceimzGs6aJ~uG7np88TzHNooAoBabdm-6utYVIZOEwRbzTdiBp~76vM4KEHz9Nir8GNrtA3AwHwGFm0bu~aorTG4xrOChS6UgfBQiUtgr8vfbDIUno1y1nxLGCKIfQrb2Gx-SVnigum2gjcveymK995zadSNZqN~w-vz-Ii0a6fH7kvKN8m9vLWf6fdo0NXSmgnkjj9KPCuS-bmK0y4ZH5Ex0Rl4qi5uCroYmDBNOhXY23pcalbpFwB1-07tA3~756gZN4Mo9uMeSVQKl5UsHzx5amB6WvSCXS8z756YoaaMCg0FQbUCcQ46fRGdHxcvPNcrPo5IMEGmi8g__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA'
df_s1 = src.make_df(url, 'TableS1', names=['oln', 'gene_symbol', 'a', 'vc_a', 'b', 'vc_b', 'c', 'vc_c', 'd', 'vc_d'], usecols='A,B,L:S',
skiprows=list(range(0, 7)), file_type='zip', file_name='nar-01935-a-2013-File011.xlsx')
# src.fill_uniprot_with_df(df_s1, 'oln', species=[83333, 511145])
src.fill_rna_half_life(df_s1, ['Escherichia coli str. K-12 substr. MG1655', 511145])
shutil.rmtree(cache_dir)
if __name__ == '__main__':
main()