def handle(self, **options):
seq_starter = (LibrarySequencing.objects.select_related(
'source_stock', 'source_stock__intended_clone'))
# Categorize all sequences by blat results and intended clone
seqs = seq_starter.all()
seqs_blat = categorize_sequences_by_blat_results(seqs)
self.print_categories('ALL SEQUENCES', seqs_blat)
# Categorize sequences that have any SUP score
any_suppression = get_positives_any_worm('SUP', 2,
shows_any_suppression)
seqs_any_sup = seq_starter.filter(source_stock__in=any_suppression)
seqs_any_sup_blat = categorize_sequences_by_blat_results(seqs_any_sup)
self.print_categories('SEQUENCES CORRESPONDING TO ANY SUP '
'SCORES', seqs_any_sup_blat)
# Categorize sequences for SUP percentage-criteria positives
positives = get_positives_any_worm('SUP', 2,
passes_sup_secondary_percent)
seqs_pos = seq_starter.filter(source_stock__in=positives)
seqs_pos_blat = categorize_sequences_by_blat_results(seqs_pos)
self.print_categories(
'SEQUENCES CORRESPONDING TO SUP SECONDARY '
'PERCENT POSITIVES ONLY', seqs_pos_blat)
# Categorize sequences for SUP high-confidence positives
high_conf = get_positives_any_worm('SUP', 2,
passes_sup_secondary_stringent)
seqs_high = seq_starter.filter(source_stock__in=high_conf)
seqs_high_blat = categorize_sequences_by_blat_results(seqs_high)
self.print_categories(
'SEQUENCES CORRESPONDING TO SUP SECONDARY '
'STRINGENT POSITIVES ONLY', seqs_high_blat)
def handle(self, **options):
seq_starter = LibrarySequencing.objects.select_related("source_stock", "source_stock__intended_clone")
# Categorize all sequences by blat results and intended clone
seqs = seq_starter.all()
seqs_blat = categorize_sequences_by_blat_results(seqs)
self.print_categories("ALL SEQUENCES", seqs_blat)
# Categorize sequences that have any SUP score
any_suppression = get_positives_any_worm("SUP", 2, shows_any_suppression)
seqs_any_sup = seq_starter.filter(source_stock__in=any_suppression)
seqs_any_sup_blat = categorize_sequences_by_blat_results(seqs_any_sup)
self.print_categories("SEQUENCES CORRESPONDING TO ANY SUP " "SCORES", seqs_any_sup_blat)
# Categorize sequences for SUP percentage-criteria positives
positives = get_positives_any_worm("SUP", 2, passes_sup_secondary_percent)
seqs_pos = seq_starter.filter(source_stock__in=positives)
seqs_pos_blat = categorize_sequences_by_blat_results(seqs_pos)
self.print_categories("SEQUENCES CORRESPONDING TO SUP SECONDARY " "PERCENT POSITIVES ONLY", seqs_pos_blat)
# Categorize sequences for SUP high-confidence positives
high_conf = get_positives_any_worm("SUP", 2, passes_sup_secondary_stringent)
seqs_high = seq_starter.filter(source_stock__in=high_conf)
seqs_high_blat = categorize_sequences_by_blat_results(seqs_high)
self.print_categories("SEQUENCES CORRESPONDING TO SUP SECONDARY " "STRINGENT POSITIVES ONLY", seqs_high_blat)
def handle(self, **options):
positives = get_positives_any_worm('SUP', 2, criteria)
seqs = (LibrarySequencing.objects
.filter(source_stock__in=positives)
.select_related('source_stock',
'source_stock__intended_clone'))
categorized_seqs = categorize_sequences_by_blat_results(seqs)
stocks_to_resequence = []
for category, values in categorized_seqs.items():
if _should_be_redone(category):
stocks_to_resequence.extend(
[x.source_stock for x in values])
stocks_to_resequence = sorted(stocks_to_resequence,
key=lambda stock: stock.id)
assigned = assign_to_plates(
stocks_to_resequence, vertical=True,
empties_per_plate=options['empties_per_plate'])
rows = get_plate_assignment_rows(assigned)
# Write output
self.stdout.write('source_plate,source_well,'
'destination_plate,destination_well')
for row in rows:
destination_plate = get_destination_plate(
row[0], options['first_plate_number'])
destination_well = row[1]
source = row[2]
if hasattr(source, 'plate'):
source_plate = source.plate
else:
source_plate = None
if hasattr(source, 'well'):
source_well = source.well
else:
source_well = None
self.stdout.write('{},{},{},{}'.format(
source_plate, source_well,
destination_plate, destination_well))
def handle(self, **options):
if options['summary']:
summary_mode = True
else:
summary_mode = False
# Categorize sequences corresponding to high confidence positives
seqs = (LibrarySequencing.objects
.select_related('source_stock',
'source_stock__intended_clone'))
seqs_blat = categorize_sequences_by_blat_results(seqs)
# Get set of "verified" wells (for now, just those for which the top
# BLAT hit is an amplicon for the intended clone)
verified = set(x.source_stock for x in seqs_blat[1])
# Get all SUP worm strains
worms = WormStrain.objects.exclude(
restrictive_temperature__isnull=True)
verified_doublecheck = set()
num_interactions_by_well = 0
num_interactions_by_clone = 0
# For each worm, find the intersection of verified positives,
# and add to the list of interactions
w = {}
for worm in worms:
positives = worm.get_positives('SUP', 2,
passes_sup_secondary_stringent)
pos_verified = positives.intersection(verified)
verified_doublecheck.update(pos_verified)
num_interactions_by_well += len(pos_verified)
pos_verified_clones = set()
for well in pos_verified:
clone = well.intended_clone_id
if clone not in pos_verified:
pos_verified_clones.add(clone)
num_interactions_by_clone += len(pos_verified_clones)
w[worm] = pos_verified_clones
if summary_mode:
self.stdout.write('{} library wells both positive and verified\n'
'{} interactions by well\n'
'{} interactions by clone'
.format(len(verified_doublecheck),
num_interactions_by_well,
num_interactions_by_clone))
self.stdout.write('Breakdown by worm:')
for worm in sorted(w):
self.stdout.write('\t{}: {}'.format(worm.gene, len(w[worm])))
else:
for worm in sorted(w):
for clone in sorted(w[worm]):
self.stdout.write('{},{}'.format(worm.gene, clone))
