def increment(self, info):
# first do overall mute freqs
freq = utils.get_mutation_rate(self.germline_seqs, info)
self.mean_rates['all'].fill(freq)
# then per-region stuff
for region in utils.regions:
# per-region mean freqs
freq = utils.get_mutation_rate(self.germline_seqs, info, restrict_to_region=region)
self.mean_rates[region].fill(freq)
# per-gene per-position
if info[region + '_gene'] not in self.counts:
self.counts[info[region + '_gene']] = {}
# self.tmpcounts[info[region + '_gene']] = {}
mute_counts = self.counts[info[region + '_gene']] # temporary variable to avoid long dict access
# tmpmute_counts = self.tmpcounts[info[region + '_gene']] # temporary variable to avoid long dict access
germline_seq = info[region + '_gl_seq']
query_seq = info[region + '_qr_seq']
assert len(germline_seq) == len(query_seq)
for inuke in range(len(germline_seq)):
i_germline = inuke + int(info[region + '_5p_del']) # account for left-side deletions in the indexing
if germline_seq[inuke] in utils.ambiguous_bases or query_seq[inuke] in utils.ambiguous_bases:
continue
if i_germline not in mute_counts: # if we have not yet observed this position in a query sequence, initialize it
mute_counts[i_germline] = {n : 0 for n in utils.nukes + ['total', ]}
mute_counts[i_germline]['gl_nuke'] = germline_seq[inuke]
mute_counts[i_germline]['total'] += 1
mute_counts[i_germline][query_seq[inuke]] += 1
def increment(self, info):
self.mean_rates['all'].fill(utils.get_mutation_rate(info)) # mean freq over whole sequence (excluding insertions)
for region in utils.regions:
# first do mean freqs
regional_freq = utils.get_mutation_rate(info, restrict_to_region=region)
self.mean_rates[region].fill(regional_freq) # per-region mean freq
# then do per-gene per-position freqs
gene = info[region + '_gene']
if gene not in self.counts:
self.counts[gene] = {}
gcts = self.counts[gene] # shorthand name
germline_seq = info[region + '_gl_seq']
query_seq = info[region + '_qr_seq']
assert len(germline_seq) == len(query_seq)
for ipos in range(len(germline_seq)):
igl = ipos + int(info[region + '_5p_del']) # account for left-side deletions in the indexing
if germline_seq[ipos] in utils.ambiguous_bases or query_seq[ipos] in utils.ambiguous_bases: # skip if either germline or query sequence is ambiguous at this position
continue
if igl not in gcts: # if we have not yet observed this position in a query sequence, initialize it
gcts[igl] = {n : 0 for n in utils.nukes + ['total', ]}
gcts[igl]['gl_nuke'] = germline_seq[ipos]
gcts[igl]['total'] += 1
gcts[igl][query_seq[ipos]] += 1 # note that if <query_seq[ipos]> isn't among <utils.nukes>, this will toss a key error
def evaluate(self, true_line, inf_line):
for column in self.values:
if column in bool_columns:
if utils.are_alleles(true_line[column], inf_line[column]): # NOTE you have to change this above as well!
# if true_line[column] == inf_line[column]:
self.values[column]['right'] += 1
else:
self.values[column]['wrong'] += 1
else:
trueval, guessval = 0, 0
if column[2:] == '_insertion': # insertion length
trueval = len(true_line[column])
guessval = len(inf_line[column])
# elif '_content' in column:
# seq_to_use = inf_line[column[ : column.find('_', 3)]] # NOTE has to work for seq_content *and* vd_insertion_content, hence the 3
# for nuke in seq_to_use:
# self.counts[col][nuke] += 1
elif 'hamming_to_true_naive' in column:
trueval = 0 # NOTE this is a kind of weird way to do it, since diff ends up as really just the guessval, but it does the job
restrict_to_region = column[0].replace('h', '') # if fist char in <column> is not an 'h', restrict to that region
normalize = '_norm' in column
guessval = self.hamming_distance_to_true_naive(true_line, inf_line, inf_line['unique_id'], restrict_to_region=restrict_to_region, normalize=normalize)
else:
trueval = int(true_line[column])
guessval = int(inf_line[column])
diff = guessval - trueval
if diff not in self.values[column]:
self.values[column][diff] = 0
self.values[column][diff] += 1
for column in self.hists:
trueval = utils.get_mutation_rate(self.germlines, true_line)
guessval = utils.get_mutation_rate(self.germlines, inf_line)
self.hists[column].fill(guessval - trueval)
def evaluate(self, true_line, inf_line):
overall_mute_freq = utils.get_mutation_rate(true_line,
iseq=0) # true value
for column in self.values:
if column in bool_columns:
self.set_bool_column(
true_line, inf_line, column, overall_mute_freq
) # this also sets the fraction-correct-vs-mute-freq hists
else: # these should all be integer-valued
trueval, guessval = 0, 0
if column[2:] == '_insertion': # insertion length
trueval = len(true_line[column])
guessval = len(inf_line[column])
elif 'hamming_to_true_naive' in column:
restrict_to_region = column.replace(
'hamming_to_true_naive', '').replace('_', '')
trueval = 0
guessval = self.hamming_to_true_naive(
true_line,
inf_line,
restrict_to_region=restrict_to_region)
elif 'muted_bases' in column:
restrict_to_region = column.replace('muted_bases',
'').replace('_', '')
trueval = utils.get_n_muted(
true_line,
iseq=0,
restrict_to_region=restrict_to_region
) # when we're evaluating on multi-seq hmm output, we synthesize single-sequence lines for each sequence
guessval = utils.get_n_muted(
inf_line,
iseq=0,
restrict_to_region=restrict_to_region)
else:
trueval = int(true_line[column])
guessval = int(inf_line[column])
diff = guessval - trueval
if diff not in self.values[column]:
self.values[column][diff] = 0
self.values[column][diff] += 1
for region in utils.regions:
if region + '_per_gene_support' in inf_line:
self.set_per_gene_support(true_line, inf_line, region)
tmptrueval = utils.get_mutation_rate(
true_line, iseq=0, restrict_to_region=''
) # when we're evaluating on multi-seq hmm output, we synthesize single-sequence lines for each sequence
tmpguessval = utils.get_mutation_rate(inf_line,
iseq=0,
restrict_to_region='')
self.hists['mute_freqs'].fill(tmpguessval - tmptrueval)
def evaluate(self, true_line, inf_line, padfo=None):
#CHANGES FOR MIXCR
#overall_mute_freq = utils.get_mutation_rate(self.germlines, true_line) # true value
for column in self.values:
if self.only_correct_gene_fractions and column not in bool_columns:
continue
if column in bool_columns:
if utils.are_alleles(true_line[column], inf_line[column]): # NOTE you have to change this above as well!
self.values[column]['right'] += 1
self.hists[column + '_right_vs_mute_freq'].fill(overall_mute_freq) # NOTE this'll toss a KeyError if you add bool column that aren't [vdj]_gene
else:
self.values[column]['wrong'] += 1
self.hists[column + '_wrong_vs_mute_freq'].fill(overall_mute_freq)
else:
trueval, guessval = 0, 0
if column[2:] == '_insertion': # insertion length
trueval = len(true_line[column])
guessval = len(inf_line[column])
# elif '_content' in column:
# seq_to_use = inf_line[column[ : column.find('_', 3)]] # NOTE has to work for seq_content *and* vd_insertion_content, hence the 3
# for nuke in seq_to_use:
# self.counts[col][nuke] += 1
elif 'hamming_to_true_naive' in column:
trueval = 0 # NOTE this is a kind of weird way to do it, since diff ends up as really just the guessval, but it does the job
restrict_to_region = column[0].replace('h', '') # if fist char in <column> is not an 'h', restrict to that region
normalize = '_norm' in column
guessval = self.hamming_distance_to_true_naive(true_line, inf_line, inf_line['unique_id'], restrict_to_region=restrict_to_region, normalize=normalize, padfo=padfo)
else:
#CHANGES FOR MIXCR
return
#trueval = int(true_line[column])
#guessval = int(inf_line[column])
diff = guessval - trueval
if diff not in self.values[column]:
self.values[column][diff] = 0
self.values[column][diff] += 1
for column in self.hists:
if '_vs_mute_freq' in column: # fill these above
continue
if len(re.findall('[vdj]_', column)) == 1:
region = re.findall('[vdj]_', column)[0][0]
else:
region = ''
trueval = utils.get_mutation_rate(self.germlines, true_line, restrict_to_region=region)
guessval = utils.get_mutation_rate(self.germlines, inf_line, restrict_to_region=region)
self.hists[column].fill(guessval - trueval)
def check_tree_simulation(self, mean_total_height, regional_heights, chosen_tree, scaled_trees, regional_naive_seqs, mseqs, reco_event, debug=False):
assert reco_event.line is not None # make sure we already set it
# check the height for each region
mean_observed = {n : 0.0 for n in ['all'] + utils.regions}
for iseq in range(len(reco_event.final_seqs)):
mean_observed['all'] += reco_event.line['mut_freqs'][iseq]
for region in utils.regions: # NOTE for simulating, we mash the insertions in with the D, but this isn't accounted for here
rrate = utils.get_mutation_rate(reco_event.line, iseq=iseq, restrict_to_region=region)
mean_observed[region] += rrate
if debug:
print ' in out'
for rname in ['all'] + utils.regions:
mean_observed[rname] /= float(len(reco_event.final_seqs))
if rname == 'all':
input_height = mean_total_height
if self.args.mutation_multiplier is not None: # multiply the branch lengths by some factor
input_height *= self.args.mutation_multiplier
else:
input_height = regional_heights[rname]
self.validation_values['heights'][rname]['in'].append(input_height)
self.validation_values['heights'][rname]['out'].append(mean_observed[rname])
if debug:
print ' %4s %7.3f %7.3f' % (rname, input_height, mean_observed[rname])
treeutils.get_tree_difference_metrics('all', chosen_tree, reco_event.final_seqs, reco_event.line['naive_seq'])
def evaluate(self, true_line, inf_line, padfo=None):
overall_mute_freq = utils.get_mutation_rate(self.germlines, true_line) # true value
for column in self.values:
if self.only_correct_gene_fractions and column not in bool_columns:
continue
if column in bool_columns:
self.set_bool_column(true_line, inf_line, column, overall_mute_freq)
else:
trueval, guessval = 0, 0
if column[2:] == '_insertion': # insertion length
trueval = len(true_line[column])
guessval = len(inf_line[column])
# elif '_content' in column:
# seq_to_use = inf_line[column[ : column.find('_', 3)]] # NOTE has to work for seq_content *and* vd_insertion_content, hence the 3
# for nuke in seq_to_use:
# self.counts[col][nuke] += 1
elif 'hamming_to_true_naive' in column:
trueval = 0 # NOTE this is a kind of weird way to do it, since diff ends up as really just the guessval, but it does the job
restrict_to_region = column[0].replace('h', '') # if fist char in <column> is not an 'h', restrict to that region
normalize = '_norm' in column
guessval = self.hamming_distance_to_true_naive(true_line, inf_line, inf_line['unique_id'], restrict_to_region=restrict_to_region, normalize=normalize, padfo=padfo)
else:
trueval = int(true_line[column])
guessval = int(inf_line[column])
diff = guessval - trueval
if diff not in self.values[column]:
self.values[column][diff] = 0
self.values[column][diff] += 1
for region in utils.regions:
if region + '_per_gene_support' in inf_line:
self.set_per_gene_support(true_line, inf_line, region)
for column in self.hists:
if '_vs_mute_freq' in column or '_per_gene_support' in column: # fill these above
continue
if len(re.findall('[vdj]_', column)) == 1:
region = re.findall('[vdj]_', column)[0][0]
else:
region = ''
trueval = utils.get_mutation_rate(self.germlines, true_line, restrict_to_region=region)
guessval = utils.get_mutation_rate(self.germlines, inf_line, restrict_to_region=region)
self.hists[column].fill(guessval - trueval)
def choose_clonal_representatives(self, swfo, debug=False):
# NOTE do *not* modify <self.glfo> (in the future it would be nice to just modify <self.glfo>, but for now we need it to be super clear in partitiondriver what is happening to <self.glfo>)
# first remove non-full-length sequences
full_length_queries = [q for q in swfo['queries'] if swfo[q]['v_5p_del'] == 0 and swfo[q]['j_3p_del'] == 0]
print ' removing %d/%d sequences with v_5p or j_3p deletions' % (len(swfo['queries']) - len(full_length_queries), len(swfo['queries']))
if len(full_length_queries) == 0:
return None, None, None
# then cluster by full-length (v+d+j) naive sequence
clusters = utils.collapse_naive_seqs(swfo, queries=full_length_queries)
# then build <qr_seqs> from the v sequences corresponding to the least-j-mutated sequence in each of these clusters (skipping clusterings that are too mutated)
qr_seqs = {}
self.all_j_mutations = {}
for cluster in clusters:
clusterstr = ':'.join(cluster)
j_mutations = {q : utils.get_n_muted(swfo[q], iseq=0, restrict_to_region=self.other_region) for q in cluster}
best_query, smallest_j_mutations = sorted(j_mutations.items(), key=operator.itemgetter(1))[0] # take the sequence with the lowest j mutation for each cluster, if it doesn't have too many j mutations NOTE choose_cluster_representatives() in allelefinder is somewhat similar
if smallest_j_mutations < self.max_mutations['j']:
qr_seqs[best_query] = indelutils.get_qr_seqs_with_indels_reinstated(swfo[best_query], iseq=0)[self.region]
for query in cluster:
self.all_j_mutations[query] = j_mutations[query] # I don't think I can key by the cluster str, since here things correspond to the naive-seq-collapsed clusters, then we remove some of the clusters, and then cluster with vsearch
print ' collapsed %d input sequences into %d representatives from %d clones (removed %d clones with >= %d j mutations)' % (len(full_length_queries), len(qr_seqs), len(clusters), len(clusters) - len(qr_seqs), self.max_mutations['j'])
self.gene_info = {q : swfo[q][self.region + '_gene'] for q in qr_seqs} # assigned gene for the clonal representative from each cluster that we used (i.e. *not* from every sequence in the sample)
self.mfreqs = { # NOTE only includes cluster representatives, i.e. it's biased towards sequences with low overall mutation, and low j mutation
'v' : {q : utils.get_mutation_rate(swfo[q], iseq=0, restrict_to_region='v') for q in qr_seqs},
'j' : {q : utils.get_mutation_rate(swfo[q], iseq=0, restrict_to_region='j') for q in qr_seqs},
}
self.mean_mfreqs = {r : numpy.mean(self.mfreqs[r].values()) for r in self.mfreqs}
# assert self.region == 'v' # this won't work if our region is j, since it's too short; there's always/often a dip/gap between 0 mutations and the rest of the distribution
# self.mfreq_hists = {self.region : Hist(30, 0., 0.3)} # not reall sure whether it's better to use n_mutes or mfreq, but I already have mfreq
# for query in qr_seqs:
# for region in self.mfreq_hists:
# self.mfreq_hists[region].fill(self.mfreqs[region][query])
print ' mutation among all cluster representatives: v / j = %6.3f / %6.3f = %6.3f' % (self.mean_mfreqs['v'], self.mean_mfreqs['j'], self.mean_mfreqs['v'] / self.mean_mfreqs['j'])
assert self.region == 'v' # need to think about whether this should always be j, or if it should be self.other_region
j_mfreqs = [utils.get_mutation_rate(swfo[q], iseq=0, restrict_to_region='j') for q in qr_seqs]
threshold = numpy.mean(j_mfreqs) / 1.5 # v mut freq will be way off for any very different new alleles
return qr_seqs, threshold
def increment(self, info):
# first do overall mute freqs
freq = utils.get_mutation_rate(self.germline_seqs, info)
self.mean_rates['all'].fill(freq)
# then per-region stuff
for region in utils.regions:
# per-region mean freqs
freq = utils.get_mutation_rate(self.germline_seqs,
info,
restrict_to_region=region)
self.mean_rates[region].fill(freq)
# per-gene per-position
if info[region + '_gene'] not in self.counts:
self.counts[info[region + '_gene']] = {}
# self.tmpcounts[info[region + '_gene']] = {}
mute_counts = self.counts[
info[region +
'_gene']] # temporary variable to avoid long dict access
# tmpmute_counts = self.tmpcounts[info[region + '_gene']] # temporary variable to avoid long dict access
germline_seq = info[region + '_gl_seq']
query_seq = info[region + '_qr_seq']
assert len(germline_seq) == len(query_seq)
for inuke in range(len(germline_seq)):
i_germline = inuke + int(
info[region + '_5p_del']
) # account for left-side deletions in the indexing
if germline_seq[inuke] in utils.ambiguous_bases or query_seq[
inuke] in utils.ambiguous_bases:
continue
if i_germline not in mute_counts: # if we have not yet observed this position in a query sequence, initialize it
mute_counts[i_germline] = {
n: 0
for n in utils.nukes + [
'total',
]
}
mute_counts[i_germline]['gl_nuke'] = germline_seq[inuke]
mute_counts[i_germline]['total'] += 1
mute_counts[i_germline][query_seq[inuke]] += 1
def increment(self, info):
self.mean_rates['all'].fill(utils.get_mutation_rate(self.germline_seqs, info)) # mean freq over whole sequence (excluding insertions)
for region in utils.regions:
regional_freq, len_excluding_ambig = utils.get_mutation_rate(self.germline_seqs, info, restrict_to_region=region, return_len_excluding_ambig=True)
n_mutes = regional_freq * len_excluding_ambig # total number of mutations in the region (for tigger stuff)
if abs(n_mutes - int(n_mutes)) > 1e6:
raise Exception('n mutated %f not an integer' % n_mutes)
n_mutes = int(n_mutes)
self.mean_rates[region].fill(regional_freq) # per-region mean freq
# per-gene per-position freqs
gene = info[region + '_gene']
if gene not in self.counts:
self.counts[gene] = {}
gcounts = self.counts[gene] # temporary variable to avoid long dict access
germline_seq = info[region + '_gl_seq']
query_seq = info[region + '_qr_seq']
assert len(germline_seq) == len(query_seq)
for ipos in range(len(germline_seq)):
igl = ipos + int(info[region + '_5p_del']) # account for left-side deletions in the indexing
if germline_seq[ipos] in utils.ambiguous_bases or query_seq[ipos] in utils.ambiguous_bases:
continue
if igl not in gcounts: # if we have not yet observed this position in a query sequence, initialize it
gcounts[igl] = {n : 0 for n in utils.nukes + ['total', ]}
gcounts[igl]['gl_nuke'] = germline_seq[ipos]
gcounts[igl]['tigger'] = {}
gcounts[igl]['total'] += 1
gcounts[igl][query_seq[ipos]] += 1 # note that if <query_seq[ipos]> isn't among <utils.nukes>, this will toss a key error
if self.tigger:
if igl not in gcounts:
gcounts[igl]['tigger'] = {}
if utils.get_region(gene) == 'v':
if n_mutes not in gcounts[igl]['tigger']:
gcounts[igl]['tigger'][n_mutes] = {'muted' : 0, 'total' : 0}
gcounts[igl]['tigger'][n_mutes]['total'] += 1
if query_seq[ipos] != germline_seq[ipos]: # if this position is mutated
gcounts[igl]['tigger'][n_mutes]['muted'] += 1 # mark that we saw this germline position mutated once in a sequence with <n_mutes> regional mutation frequency
def add_partial_fail(self, true_line, line):
overall_mute_freq = utils.get_mutation_rate(self.germlines, true_line) # true value
for column in self.values:
if column in bool_columns:
if column in line and utils.are_alleles(true_line[column], line[column]): # NOTE you have to change this below as well!
self.values[column]['right'] += 1
self.hists[column + '_right_vs_mute_freq'].fill(overall_mute_freq) # NOTE this'll toss a KeyError if you add bool column that aren't [vdj]_gene
else:
self.values[column]['wrong'] += 1
self.hists[column + '_wrong_vs_mute_freq'].fill(overall_mute_freq)
else:
pass
def add_partial_fail(self, true_line, line):
overall_mute_freq = utils.get_mutation_rate(self.germlines, true_line) # true value
for column in self.values:
if column in bool_columns:
if column in line and utils.are_alleles(true_line[column], line[column]): # NOTE you have to change this below as well!
self.values[column]['right'] += 1
self.hists[column + '_right_vs_mute_freq'].fill(overall_mute_freq) # NOTE this'll toss a KeyError if you add bool column that aren't [vdj]_gene
else:
self.values[column]['wrong'] += 1
self.hists[column + '_wrong_vs_mute_freq'].fill(overall_mute_freq)
else:
pass
def add_partial_fail(self, true_line, line):
# NOTE does not fill all the hists ('cause it kind of can't, right?)
overall_mute_freq = utils.get_mutation_rate(self.germlines, true_line) # true value
for column in self.values:
if column in bool_columns:
if column in line:
self.set_bool_column(true_line, line, column, overall_mute_freq)
else:
pass
for region in utils.regions:
if region + '_per_gene_support' in inf_line:
self.set_per_gene_support(true_line, inf_line, region)
def evaluate(self, true_line, inf_line):
overall_mute_freq = utils.get_mutation_rate(true_line, iseq=0) # true value
for column in self.values:
if column in bool_columns:
self.set_bool_column(true_line, inf_line, column, overall_mute_freq) # this also sets the fraction-correct-vs-mute-freq hists
else: # these should all be integer-valued
trueval, guessval = 0, 0
if column[2:] == '_insertion': # insertion length
trueval = len(true_line[column])
guessval = len(inf_line[column])
elif 'hamming_to_true_naive' in column:
restrict_to_region = column.replace('hamming_to_true_naive', '').replace('_', '')
trueval = 0
guessval = self.hamming_to_true_naive(true_line, inf_line, restrict_to_region=restrict_to_region)
elif 'muted_bases' in column:
restrict_to_region = column.replace('muted_bases', '').replace('_', '')
trueval = utils.get_n_muted(true_line, iseq=0, restrict_to_region=restrict_to_region) # when we're evaluating on multi-seq hmm output, we synthesize single-sequence lines for each sequence
guessval = utils.get_n_muted(inf_line, iseq=0, restrict_to_region=restrict_to_region)
else:
trueval = int(true_line[column])
guessval = int(inf_line[column])
diff = guessval - trueval
if diff not in self.values[column]:
self.values[column][diff] = 0
self.values[column][diff] += 1
for region in utils.regions:
if region + '_per_gene_support' in inf_line:
self.set_per_gene_support(true_line, inf_line, region)
tmptrueval = utils.get_mutation_rate(true_line, iseq=0, restrict_to_region='') # when we're evaluating on multi-seq hmm output, we synthesize single-sequence lines for each sequence
tmpguessval = utils.get_mutation_rate(inf_line, iseq=0, restrict_to_region='')
self.hists['mute_freqs'].fill(tmpguessval - tmptrueval)
def add_partial_fail(self, true_line, line):
# NOTE does not fill all the hists ('cause it kind of can't, right?)
overall_mute_freq = utils.get_mutation_rate(true_line,
iseq=0) # true value
for column in self.values:
if column in bool_columns:
if column in line:
self.set_bool_column(true_line, line, column,
overall_mute_freq)
else:
pass
for region in utils.regions:
if region + '_per_gene_support' in inf_line:
self.set_per_gene_support(true_line, inf_line, region)
def increment(self, info):
self.mfreqer.increment(info)
for region in utils.regions:
regional_freq, len_excluding_ambig = utils.get_mutation_rate(info, restrict_to_region=region, return_len_excluding_ambig=True)
n_mutes = regional_freq * len_excluding_ambig # total number of mutations in the region (for allele finding stuff)
if abs(n_mutes - int(n_mutes)) > 1e6:
raise Exception('n mutated %f not an integer' % n_mutes)
n_mutes = int(n_mutes)
gene = info[region + '_gene']
if gene not in self.counts:
self.counts[gene] = {}
self.gene_obs_counts[gene] = 0
self.gene_obs_counts[gene] += 1
gcts = self.counts[gene] # shorthand name
germline_seq = info[region + '_gl_seq']
query_seq = info[region + '_qr_seq']
assert len(germline_seq) == len(query_seq)
for ipos in range(len(germline_seq)):
igl = ipos + int(info[region + '_5p_del']) # account for left-side deletions in the indexing
if germline_seq[ipos] in utils.ambiguous_bases or query_seq[ipos] in utils.ambiguous_bases: # skip if either germline or query sequence is ambiguous at this position
continue
if igl not in gcts: # if we have not yet observed this position in a query sequence, initialize it
gcts[igl] = {}
if igl not in gcts:
gcts[igl] = {}
if utils.get_region(gene) == 'v':
if n_mutes not in gcts[igl]:
gcts[igl][n_mutes] = {n : 0 for n in ['muted', 'total'] + utils.nukes}
gcts[igl][n_mutes]['total'] += 1
if query_seq[ipos] != germline_seq[ipos]: # if this position is mutated
gcts[igl][n_mutes]['muted'] += 1 # mark that we saw this germline position mutated once in a sequence with <n_mutes> regional mutation frequency
gcts[igl][n_mutes][query_seq[ipos]] += 1 # only used to work out what the snp'd base is if there's a new allele
