def run(self):
sqlrunner = SQL(database=self.database)
out = sqlrunner.custom_sql(statement=self.sql_query)
out.to_csv(os.path.join(self.root, "temp", "query.txt"), header=True, index=False, sep=",")
def grna_display_runner(self):
display_grna = DisplayGuideRNA(database_list=self.availible_databases, cas9_list=self.availible_cas9)
if display_grna.exec_():
holder = PandasModel(pd.DataFrame({'': []}))
self.display_candidates.setModel(holder)
self.display_backup.setModel(holder)
self.display_dropped.setModel(holder)
self.display_offtargets.setModel(holder)
self.database_querried = False
self.statusBar().showMessage("Preparing ...")
self.main_progressbar_value += 1
self.main_progressbar.setValue(self.main_progressbar_value)
user_options = display_grna.out()
if "temp" not in os.listdir(self.root):
tempdir = os.path.join(self.root, "temp")
os.mkdir(os.path.join(tempdir))
else:
tempdir = os.path.join(self.root, "temp")
database = str(user_options['organism']).replace(" ", "_")
mismatch = user_options['max_mismatch']
max_grna = user_options['max_grna_count']
max_primer_len = user_options['max_primer_len']
user_cas9 = user_options['cas9']
user_pam_tolerance = user_options['pam_tolerance']
user_fiveprime = user_options['nucleotides_5']
user_threeprime = user_options['nucleotides_3']
gene_mask_dictionary = {
'genes': [items.replace("_", "").lower() if "_" in items else items.lower() for items in
user_options['genes']],
'masks': [items.replace("_", "").lower() if "_" in items else items.lower() for items in
user_options['masks']]
}
sqlrunner = SQL(database=database)
headers = sqlrunner.custom_sql("SELECT header FROM genes").to_dict('list')
gene_check = [True if gene in headers['header'] else False for gene in gene_mask_dictionary['genes']]
mask_check = [True if gene in headers['header'] else False for gene in gene_mask_dictionary['masks']]
for idx, val in enumerate(gene_check):
if not val:
db = database.replace("_", " ")
QtWidgets.QMessageBox.about(self, "Error",
f"{gene_mask_dictionary['genes'][idx]} was not found in {db}")
self.main_progressbar_value = 0
self.main_progressbar.setValue(self.main_progressbar_value)
return None
for idx, val in enumerate(mask_check):
if not val:
db = database.replace("_", " ")
QtWidgets.QMessageBox.about(self, "Error",
f"{gene_mask_dictionary['masks'][idx]} was not found in {db}")
self.main_progressbar_value = 0
self.main_progressbar.setValue(self.main_progressbar_value)
return None
if mismatch == "":
QtWidgets.QMessageBox.about(self, "Error",
"First search guide RNA's")
self.main_progressbar_value = 0
self.main_progressbar.setValue(self.main_progressbar_value)
return None
# Strand is r for reverse
worker = CrisprInterference_worker(database=database,
mismatch=mismatch,
strand='r',
max_grna=max_grna,
genes_masks=gene_mask_dictionary,
max_primer_size=max_primer_len,
cas9_organism=user_cas9,
pam_tolerance=user_pam_tolerance,
fiveprime_nucleotides=user_fiveprime,
threeprime_nucleotides=user_threeprime)
self.threadingPool.start(worker)
while self.threadingPool.activeThreadCount() == 1:
self.statusBar().showMessage("Gathering guide RNA's...")
QtWidgets.QApplication.processEvents()
if self.main_progressbar_value < 90:
self.main_progressbar_value += 1
self.main_progressbar.setValue(self.main_progressbar_value)
time.sleep(0.8)
if self.threadingPool.waitForDone():
self.statusBar().showMessage("Gathering data ...")
self.candidate_gRNA_df = pd.read_csv(
filepath_or_buffer=os.path.join(self.root, "temp", "candidates.txt"),
sep=",")
self.backup_gRNA_df = pd.read_csv(filepath_or_buffer=os.path.join(self.root, "temp", "backup.txt"),
sep=",")
self.dropped_gRNA_df = pd.read_csv(filepath_or_buffer=os.path.join(self.root, "temp", "dropped.txt"),
sep=",")
self.offtarget_df = pd.read_csv(filepath_or_buffer=os.path.join(self.root, "temp", "offtargets.txt"),
sep=",")
cand_model, backup_model, dropped_model, offtargets_model = map(PandasModel, [self.candidate_gRNA_df,
self.backup_gRNA_df,
self.dropped_gRNA_df,
self.offtarget_df])
while self.main_progressbar_value < 100:
self.main_progressbar_value += 1
self.statusBar().showMessage("Formatting for display...")
self.main_progressbar.setValue(self.main_progressbar_value)
time.sleep(0.01)
self.display_candidates.setModel(cand_model)
self.display_backup.setModel(backup_model)
self.display_dropped.setModel(dropped_model)
self.display_offtargets.setModel(offtargets_model)
self.database_querried = True
self.main_progressbar_value = 0
self.main_progressbar.setValue(self.main_progressbar_value)
self.statusBar().showMessage("Ready")
hits = [genes for genes in self.candidate_gRNA_df['genes']]
missed = list(set(gene_mask_dictionary['genes']) - set(hits))
EOSpopup(missed_genes=missed).exec_()
shutil.rmtree(tempdir)
def run(self):
sqlrunner = SQL(database=os.path.join(self.root, "databases", self.database))
gRNA_db = sqlrunner.get_global_gRNA(mismatch=str(self.mismatch))
# This is a rate limiting step
if bool(self.gene_mask_dict['genes']):
query_data = self.get_targeted_data(dataframe=gRNA_db, gene_mask_dict=self.gene_mask_dict)
else:
query_data = gRNA_db
multifasta = sqlrunner.get_gene_multifasta()
gRNA_runner = RefineCripri(grna_dataframe=query_data,
strand=self.strand,
fasta_dataframe=multifasta,
cas9=self.cas9_organism,
offtarget_ids=sqlrunner.custom_sql("SELECT name, strand FROM global_offtarget"))
candidates, backup, dropped = gRNA_runner.cripr_interference()
candidates, backup, dropped = map(self.utils.annotate_dataframe,
[candidates, backup, dropped])
offtargets = sqlrunner.get_offtargets_by_mismatch(mismatch=self.mismatch)
offtargets.dropna(subset=['annotation'], inplace=True)
offtargets = offtargets[offtargets['strand'] != '+']
offtargets['annotation'] = offtargets['annotation'].apply(
lambda x: x.replace("_", "") if isinstance(x, str) else x)
offtargets = offtargets.query("gene != annotation")
offtargets.reset_index(drop=True, inplace=True)
offtarget_ids = list(set(offtargets['name']))
candidates_has_offtargets = self.list_comparison(list1=candidates['names'], list2=offtarget_ids)
backup_has_offtargets = self.list_comparison(list1=backup['names'], list2=offtarget_ids)
if candidates_has_offtargets:
candidate_off_ids = list(set(candidates['names']) & set(offtarget_ids))
candidates_offtargets = self.grab_offtargets(query=candidates,
offtargets=offtargets,
offtarget_ids=offtarget_ids)
candidates = self.negate_pam_mismatch(grna_dataframe=candidates,
offtarget_dataframe=candidates_offtargets,
target_ids=candidate_off_ids)
candidates, dropped = self.move_grna_by_offtargets(grna_dataframe=candidates,
dropped_dataframe=dropped,
offtarget_dataframe=candidates_offtargets,
masks=self.gene_mask_dict['masks'])
candidates_offtargets = pd.DataFrame(candidates_offtargets)
else:
candidates_offtargets = dict.fromkeys(offtargets, [])
candidates_offtargets = pd.DataFrame(candidates_offtargets)
if backup_has_offtargets:
backup_off_ids = list(set(backup['names']) & set(offtarget_ids))
backup_offtargets = self.grab_offtargets(query=backup, offtargets=offtargets, offtarget_ids=offtarget_ids)
backup = self.negate_pam_mismatch(grna_dataframe=backup,
offtarget_dataframe=backup_offtargets,
target_ids=backup_off_ids)
backup, dropped = self.move_grna_by_offtargets(grna_dataframe=backup,
dropped_dataframe=dropped,
offtarget_dataframe=backup_offtargets,
masks=self.gene_mask_dict['masks'])
backup_offtargets = pd.DataFrame(backup_offtargets)
else:
backup_offtargets = dict.fromkeys(offtargets, [])
backup_offtargets = pd.DataFrame(backup_offtargets)
## add ranking to pam, move between dataframes if ranking is f****d
candidates, backup = self.scan_maxmismatches(candidates=candidates, backup=backup)
candidates, backup = self.force_max_grna_in_candidates(candidates=candidates, backup=backup,
max_grna=self.max_grna)
candidates = self.force_ag_base(dataframe=candidates, max_primer_size=self.max_primer_size)
backup = self.force_ag_base(dataframe=backup, max_primer_size=self.max_primer_size)
candidates, backup, dropped = map(self.calculate_primer_len, [candidates, backup, dropped])
candidates, backup, dropped = map(self.calculate_gc_content, [candidates, backup, dropped])
candidates = self.design_primers(dataframe=candidates, cas9=self.cas9_organism,
fiveprime=self.fiveprime, threeprime=self.threeprime)
backup = self.design_primers(dataframe=backup, cas9=self.cas9_organism,
fiveprime=self.fiveprime, threeprime=self.threeprime)
candidates, backup, dropped = map(pd.DataFrame,
[candidates, backup, dropped])
offtarget_empty = [candidates_offtargets.empty, backup_offtargets.empty]
final_offtargets = pd.DataFrame()
if not all(offtarget_empty):
final_offtargets = candidates_offtargets
final_offtargets['from'] = "candidates"
backup_offtargets['from'] = "backup"
final_offtargets = final_offtargets.append(backup_offtargets, ignore_index=True)
else:
if not offtarget_empty[0]:
final_offtargets = candidates_offtargets
final_offtargets['from'] = "candidates"
if not offtarget_empty[1]:
final_offtargets = backup_offtargets
final_offtargets['from'] = "backup"
if final_offtargets.empty:
final_offtargets = pd.DataFrame(columns=offtargets.columns)
candidates.to_csv(os.path.join(self.root, "temp", "candidates.txt"), header=True, index=False, sep=",")
backup.to_csv(os.path.join(self.root, "temp", "backup.txt"), header=True, index=False, sep=",")
dropped.to_csv(os.path.join(self.root, "temp", "dropped.txt"), header=True, index=False, sep=",")
final_offtargets.to_csv(os.path.join(self.root, "temp", "offtargets.txt"), header=True, index=False, sep=",")