datanator/data_source/rna_halflife/doi_10_1186_gb_2012_13_4_r30.py
import pandas as pd
from datanator.util import rna_halflife_util
import datetime
import datanator.config.core
import datetime
from pymongo.collation import Collation, CollationStrength
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)
self.collation = Collation('en', strength=CollationStrength.SECONDARY)
self.max_entries = max_entries
self.verbose = verbose
def load_uniprot(self):
"""Load new loci into uniprot collection
"""
url = """https://static-content.springer.com/esm/art%3A10.1186%2Fgb-2012-13-4-r30/MediaObjects/13059_2011_2880_MOESM3_ESM.XLSX"""
names = ['ordered_locus_name']
df_10987 = self.make_df(url, 'Bc10987', names=names, usecols='A', skiprows=[0,1], nrows=6014)
df_14579 = self.make_df(url, 'Bc14579', names=names, usecols='A', skiprows=[0,1], nrows=5497)
self.fill_uniprot_with_df(df_10987, 'ordered_locus_name')
self.fill_uniprot_with_df(df_14579, 'ordered_locus_name')
def fill_rna_half_life(self, df, column, species, quantification_method='Illumina GA-II'):
"""Load df into rna_halflife collection
Args:
df (:obj:`pandas.DataFrame`): dataframe to be loaded
column (:obj:`str`): name of column to be used for half-life; this
dataset has three half-life values under different methods.
species (:obj:`list`): species name and ncbi_id.
quantification_method (:obj:`str`): quantification method.
'Illumina GA-II', 'RT-qPCR', or 'Roche 454'
"""
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))
oln = row['ordered_locus_name']
halflives = {}
oln = row['ordered_locus_name']
protein_annotation = row['protein_annotation']
if quantification_method == 'Illumina GA-II':
if str(row['half_life_ga_2']) == 'nan':
continue
else:
halflives['halflife'] = row['half_life_ga_2'] * 60
halflives['expression_reads_per_kb_per_mb'] = row['reads_per_kb_per_mb']
elif quantification_method == 'RT-qPCR':
if str(row['half_life_qpcr']) == 'nan':
continue
else:
halflives['halflife'] = row['half_life_qpcr'] * 60
else:
if str(row['half_life_454']) == 'nan':
continue
else:
halflives['halflife'] = row['half_life_454'] * 60
halflives['quantification_method'] = quantification_method
halflives['transcriptional_start_sites'] = row['transcriptional_start_sites']
halflives['transcriptional_end_sites'] = row['transcriptional_end_sites']
halflives['unit'] = 's'
if isinstance(row['operon'], float):
halflives['operon'] = None
else:
halflives['operon'] = row['operon'].split(',')
halflives['reference'] = [{'doi': '10.1186/gb-2012-13-4-r30', 'pubmed_id': '22537947'}]
halflives['growth_medium'] = 'Luria-Bertani (LB) broth (500 ml) at 30 degree celcius, 250 rpm.'
halflives['ordered_locus_name'] = oln
halflives['gene_start'] = row['gene_start']
halflives['gene_end'] = row['gene_end']
halflives['strand'] = row['strand']
halflives['cog'] = row['cog']
halflives['species'] = species[0]
halflives['ncbi_taxonomy_id'] = species[1]
gene_name, protein_name = self.uniprot_query_manager.get_gene_protein_name_by_oln(oln)
if gene_name is not None: # record exists in uniprot collection with gene_name
self.rna_hl_collection.update_one({'gene_name': gene_name},
{'$set': {'modified': datetime.datetime.utcnow()},
'$addToSet': {'halflives': halflives,
'protein_synonyms': {'$each': [protein_annotation, protein_name]}}},
collation=self.collation, upsert=True)
elif (gene_name is None and protein_name is not None and
protein_name != 'Uncharacterized protein'): # record exists in uniprot collection with non-filler protein_name
self.rna_hl_collection.update_one({'protein_name': protein_name},
{'$set': {'modified': datetime.datetime.utcnow(),
'gene_name': gene_name},
'$addToSet': {'halflives': halflives,
'protein_synonyms': {'$each': [protein_annotation, protein_name]}}},
collation=self.collation, upsert=True)
else:
query = {'halflives.ordered_locus_name': oln}
doc = self.rna_hl_collection.find_one(filter=query, collation=self.collation)
if doc is not None:
self.rna_hl_collection.update_one({'halflives.ordered_locus_name': oln},
{'$set': {'modified': datetime.datetime.utcnow(),
'gene_name': gene_name},
'$addToSet': {'halflives': halflives,
'protein_synonyms': protein_annotation}},
collation=self.collation, upsert=True)
else:
doc = {'halflives': [halflives], 'modified': datetime.datetime.utcnow(),
'gene_name': gene_name, 'protein_name': protein_name}
self.rna_hl_collection.insert_one(doc)
def main():
src_db = 'datanator'
des_db = 'datanator'
rna_col = 'rna_halflife'
protein_col = 'uniprot'
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)
# src.load_uniprot()
url = 'https://static-content.springer.com/esm/art%3A10.1186%2Fgb-2012-13-4-r30/MediaObjects/13059_2011_2880_MOESM3_ESM.XLSX'
names = ['ordered_locus_name', 'half_life_ga_2', 'reads_per_kb_per_mb',
'transcriptional_start_sites', 'transcriptional_end_sites', 'operon',
'gene_start', 'gene_end', 'strand', 'gene_name', 'protein_annotation',
'cog', 'kegg', 'half_life_qpcr', 'half_life_454']
df = src.make_df(url, 'Bc10987', names=names, usecols='A:O', skiprows=[0,1], nrows=6014)
src.fill_rna_half_life(df, names, ['Bacillus cereus ATCC 10987', 222523])
src.fill_rna_half_life(df, names, ['Bacillus cereus ATCC 10987', 222523], quantification_method='RT-qPCR')
src.fill_rna_half_life(df, names, ['Bacillus cereus ATCC 10987', 222523], quantification_method='Roche 454')
df = src.make_df(url, 'Bc14579', names=names, usecols='A:O', skiprows=[0,1], nrows=5497)
src.fill_rna_half_life(df, names, ['Bacillus cereus ATCC 14579', 226900])
src.fill_rna_half_life(df, names, ['Bacillus cereus ATCC 14579', 226900], quantification_method='RT-qPCR')
src.fill_rna_half_life(df, names, ['Bacillus cereus ATCC 14579', 226900], quantification_method='Roche 454')
if __name__ == '__main__':
main()