def fr4_aa_mutation_rate(self):
if 'J' not in self.hits:
return None
j_subject_id = self.hits['J'].subject_id
if self.chain not in self.CHAINS:
return None
cdr3_ref_end = self._database.j_cdr3_end(j_subject_id,
self.CHAINS[self.chain])
if cdr3_ref_end is None:
return None
cdr3_query_end = self.hits['J'].query_position(
reference_position=cdr3_ref_end)
if cdr3_query_end is None:
return None
query = self.full_sequence[cdr3_query_end:self.hits['J'].query_end]
try:
query_aa = nt_to_aa(query)
except ValueError:
return None
ref = self._database.j[j_subject_id][cdr3_ref_end:self.hits['J'].
subject_end]
try:
ref_aa = nt_to_aa(ref)
except ValueError:
return None
if not ref_aa:
return None
return 100. * edit_distance(ref_aa, query_aa) / len(ref_aa)
def distances(sequences, band=0.2):
"""
Compute all pairwise edit distances and return a square matrix.
Entry [i,j] in the matrix is the edit distance between sequences[i]
and sequences[j].
"""
# Pre-compute distances between unique sequences
unique_sequences = list(set(sequences))
unique_distances = dict() # maps (seq1, seq2) tuples to edit distance
maxdiff = max((int(len(s) * band) for s in sequences),
default=0) # TODO double-check this
for i, s in enumerate(unique_sequences):
for j, t in enumerate(unique_sequences):
if i < j:
dist = min(maxdiff + 1, edit_distance(s, t, maxdiff=maxdiff))
unique_distances[(t, s)] = dist
unique_distances[(s, t)] = dist
# Fill the result matrix
m = np.zeros((len(sequences), len(sequences)), dtype=float)
for i, s in enumerate(sequences):
for j, t in enumerate(sequences):
if i < j:
d = 0 if s == t else unique_distances[(s, t)]
m[j, i] = m[i, j] = d
return m
def augment_group(table, v_shm_threshold=5, suffix='_mindiffrate'):
"""
Add columns to the given table that contain percentage difference of VDJ_nt, VDJ_aa, CDR3_nt,
CDR3_aa to the least mutated (in terms of V_SHM) sequence in this group.
"""
columns = ['CDR3_nt', 'CDR3_aa', 'VDJ_nt', 'VDJ_aa']
i = table.columns.get_loc('barcode') # insert before this column
for column in columns[::-1]:
table.insert(i, column + suffix, None)
# Find row whose V is least mutated
root = table.loc[table['V_SHM'].idxmin()]
import ipdb
ipdb.set_trace()
if root['V_SHM'] > v_shm_threshold:
return table
for column in columns:
root_seq = root[column]
table[column + suffix] = [
round(
edit_distance(root_seq, s, maxdiff=int(0.2 * len(root_seq))) /
len(root_seq) * 100., 1) for s in table[column]
]
return table
def should_discard(self, reference: Candidate, candidate: Candidate,
_same_gene: bool):
"""
Compare a candidate to a reference candidate and decide whether it should be discarded.
:param reference: The reference candidate. The decision this function makes is not about that one.
:param candidate: The candidate on which to decide.
:param dist: Edit distance between candidates
:return: False if the candidate should be kept. Otherwise, the candidate shoud be discarded
and a non-empty string with a reason describing why is returned.
"""
# When computing edit distance between the two sequences, ignore the
# bases in the 3' end that correspond to the CDR3
s_no_cdr3 = reference.sequence[:reference.cdr3_start]
t_no_cdr3 = candidate.sequence[:candidate.cdr3_start]
if len(s_no_cdr3) != len(t_no_cdr3):
t_prefix = t_no_cdr3[:len(s_no_cdr3)]
t_suffix = t_no_cdr3[-len(s_no_cdr3):]
dist_prefix = edit_distance(s_no_cdr3, t_prefix, 1)
dist_suffix = edit_distance(s_no_cdr3, t_suffix,
1) # TODO prefix and suffix?
dist_no_cdr3 = min(dist_prefix, dist_suffix)
else:
dist_no_cdr3 = edit_distance(s_no_cdr3, t_no_cdr3, 1)
if dist_no_cdr3 > 1:
# Cross-mapping is unlikely if the edit distance is larger than 1
return None
if not reference.is_database or not candidate.is_database:
# Cross-mapping can only occur if both sequences are in the database
return None
total_count = (reference.cluster_size + candidate.cluster_size)
if total_count == 0:
return False
ratio = candidate.cluster_size / total_count
if candidate.cluster_size_is_accurate and ratio < self._ratio:
# candidate is probably a cross-mapping artifact of the higher-expressed ref
return f'xmap_ratio={ratio:.4f},other={reference.name}'
return False
def merged(self, s, t):
"""
Merge two sequences if they overlap. If they should not be merged,
None is returned.
"""
# TODO copy-and-pasted from germlinefilter
#
# Check allele ratio. Somewhat similar to cross-mapping, but
# this uses sequence names to decide whether two genes can be
# alleles of each other and the ratio is between the CDR3s_exact
# values
if self._allele_ratio and is_same_gene(s.name, t.name):
for u, v in [(s, t), (t, s)]:
if v.unique_CDR3 == 0:
continue
ratio = u.unique_CDR3 / v.unique_CDR3
if ratio < self._allele_ratio:
# logger.info('Allele ratio %.4f too low for %r compared to %r',
# ratio, u.name, v.name)
return v
if self._cross_mapping_ratio:
# When checking for cross mapping, ignore overhanging bases in the 5' end.
# Example:
# ---ACTACGACTA...
# XXX|||||X||||
# ATTACTACTACTA...
if len(t.sequence) < len(s.sequence):
t, s = s, t # s is now the shorter sequence
t_seq = t.sequence[len(t.sequence) - len(s.sequence):]
s_seq = s.sequence
dist = edit_distance(s_seq, t_seq, 1)
if dist > 1:
return None
total_occ = (s.exact_occ + t.exact_occ)
if total_occ == 0:
return None
for u, v in [(s, t), (t, s)]:
ratio = u.exact_occ / total_occ
if ratio < self._cross_mapping_ratio:
# u is probably a cross-mapping artifact of the higher-expressed v
logger.info(
'%r is a cross-mapping artifact of %r (ratio %.4f)',
u.name, v.name, ratio)
return v
return None
def closest(self, sequence):
"""
Search for the whitelist sequence that is closest to the given sequence.
Return tuple (distance, name).
"""
if sequence in self._sequences:
return 0, self._sequences[sequence]
mindist = len(sequence)
distances = []
for seq, name in self._sequences.items():
ed = edit_distance(seq, sequence, maxdiff=mindist)
distances.append((ed, name))
if ed == 1:
# We know ed does not get smaller because the
# 'sequence in whitelist' check
# above covers that
return ed, name
mindist = min(mindist, ed)
distance, name = min(distances)
return distance, name
def main(args):
if args.database:
with dnaio.open(args.database) as fr:
database = list(fr)
logger.info('Read %d sequences from %r', len(database), args.database)
else:
database = None
column = {'V': 'V_nt', 'J': 'J_nt', 'D': 'D_region'}[args.gene]
other = 'V' if args.gene in ('D', 'J') else 'J'
other_gene = other + '_gene'
other_errors = other + '_errors'
table = read_table(args.table,
usecols=[
'count', 'V_gene', 'D_gene', 'J_gene', 'V_errors',
'J_errors', 'J_covered', column, 'CDR3_nt'
])
logger.info('Table with %s rows read', len(table))
if args.j_coverage is None and args.gene == 'J':
args.j_coverage = 90
if args.j_coverage:
table = table[table['J_covered'] >= args.j_coverage]
logger.info('Keeping %s rows that have J_covered >= %s', len(table),
args.j_coverage)
if args.perfect_matches:
table = table[table[other_errors] == 0]
logger.info('Keeping %s rows that have no %s mismatches', len(table),
other)
if args.merge is None:
args.merge = args.gene == 'D'
if args.min_count is None:
args.min_count = {
'J': 1,
'D': 10,
'V': 100
}[args.gene] # TODO J is fine, but are D and V?
if args.gene == 'D':
candidates = sequence_candidates(table,
column,
minimum_length=args.d_core_length,
core=args.d_core)
elif args.gene == 'J':
candidates = sequence_candidates(
table, column, minimum_length=MINIMUM_CANDIDATE_LENGTH)
else:
candidates = sequence_candidates(
table, column, minimum_length=MINIMUM_CANDIDATE_LENGTH)
candidates = list(candidates)
logger.info('Collected %s unique %s sequences', len(candidates), args.gene)
# Add whitelisted sequences
if database:
whitelist = make_whitelist(table, database, args.gene,
args.allele_ratio)
missing_whitelisted = set(whitelist) - set(c.sequence
for c in candidates)
for sequence in missing_whitelisted:
candidates.append(Candidate(None, sequence))
logger.info('Added %d whitelisted sequence%s',
len(missing_whitelisted),
's' if len(missing_whitelisted) != 1 else '')
candidates = list(discard_substring_occurrences(candidates))
logger.info(
'Removing candidate sequences that occur within others results in %s candidates',
len(candidates))
candidates = [
candidate for candidate in candidates if 'N' not in candidate.sequence
]
logger.info('Removing candidates containing "N" results in %s candidates',
len(candidates))
if args.merge:
logger.info('Merging overlapping sequences ...')
# Merge candidate sequences that overlap. If one candidate is longer than
# another, this is typically a sign that IgBLAST has not extended the
# alignment long enough.
merger = OverlappingSequenceMerger()
for candidate in candidates:
merger.add(candidate)
logger.info('After merging overlapping %s sequences, %s remain',
args.gene, len(merger))
candidates = list(merger)
del table
logger.info('%d candidates', len(candidates))
# Assign names etc.
if database:
for candidate in candidates:
distances = [(edit_distance(db.sequence, candidate.sequence), db)
for db in database]
candidate.db_distance, closest = min(distances, key=lambda x: x[0])
candidate.db_name = closest.name
if candidate.db_distance == 0:
candidate.name = closest.name
else:
# Exact db sequence not found, is there one that contains
# this candidate as a substring?
for db_record in database:
index = db_record.sequence.find(candidate.sequence)
if index == -1:
continue
if args.gene == 'D':
start = db_record.sequence.find(candidate.sequence)
prefix = db_record.sequence[:start]
suffix = db_record.sequence[start +
len(candidate.sequence):]
candidate.missing = '{}...{}'.format(prefix, suffix)
else:
# Replace this record with the full-length version
candidate.sequence = db_record.sequence
candidate.db_distance = 0
candidate.name = db_record.name
break
else:
candidate.name = unique_name(closest.name,
candidate.sequence)
else:
for candidate in candidates:
candidate.name = unique_name(args.gene, candidate.sequence)
logger.info('Counting occurrences ...')
if args.gene == 'D':
search_columns = ['VD_junction', 'D_region', 'DJ_junction']
elif args.gene == 'J':
search_columns = ['DJ_junction', 'J_nt']
else:
search_columns = ['genomic_sequence']
candidates = count_occurrences(candidates, args.table, search_columns,
other_gene, other_errors, args.merge,
args.perfect_matches)
# Filter by allele ratio
if args.allele_ratio or args.cross_mapping_ratio:
arm = AlleleRatioMerger(args.allele_ratio, args.cross_mapping_ratio)
arm.extend(candidates)
candidates = list(arm)
logger.info(
'After filtering by allele ratio and/or cross-mapping ratio, %d candidates remain',
len(candidates))
candidates = sorted(candidates, key=lambda c: c.name)
candidates = [
c for c in candidates
if c.exact_occ >= args.min_count or c.db_distance == 0
]
print_table(candidates, other_gene, missing=args.gene == 'D')
if args.fasta:
with open(args.fasta, 'w') as f:
for candidate in sorted(candidates, key=lambda r: r.name):
print('>{}\n{}'.format(candidate.name, candidate.sequence),
file=f)
logger.info('Wrote %d genes', len(candidates))
def _percent_identity(self):
# FIXME This is not quite how IgBLAST computes percent identity
if not self.nt_reference or not self.nt_sequence:
return None
dist = edit_distance(self.nt_reference, self.nt_sequence)
return 100. - 100. * dist / len(self.nt_reference)
def __call__(self, args):
"""
Discover new V genes. args is a tuple (gene, group)
gene -- name of the gene
assignments -- a pandas DataFrame with the assignments to the gene
"""
gene, assignments = args
self.set_random_seed(gene)
siblings = SiblingMerger()
for sibling in self._collect_siblings(gene, assignments):
siblings.add(sibling)
candidates = []
for sibling_info in siblings:
sibling = sibling_info.sequence
n_bases = sibling.count('N')
if n_bases > self.max_n_bases:
logger.debug('Sibling %s has too many N bases', sibling_info.name)
continue
# Sequence without the CDR3-covering part
sibling_no_cdr3 = sibling[:self._guess_cdr3_start(assignments)]
group_exact_v = assignments[assignments.V_no_CDR3 == sibling_no_cdr3]
groups = (
('window', sibling_info.group),
('exact', group_exact_v))
del sibling_no_cdr3
# self.cdr3_counts are CDR3 counts of all CDR3s in the entire table.
# We restrict this here to the counts for CDR3s belonging to
# clusters other than the current one.
other_cdr3_counts = self.cdr3_counts - Counter(s for s in sibling_info.group.CDR3_nt if s)
info = dict()
for key, group in groups:
cdr3_counts = Counter(s for s in group.CDR3_nt if s)
unique_cdr3 = len(cdr3_counts)
shared_cdr3_ratio = safe_divide(len(other_cdr3_counts & cdr3_counts), unique_cdr3)
unique_j = len(set(s for s in group.J_gene if s))
clonotypes = self.count_clonotypes(group)
unique_d = self.count_unique_d(group)
unique_barcodes = self.count_unique_barcodes(group)
count = len(group.index)
read_names = list(group.name)
info[key] = Groupinfo(count=count, unique_D=unique_d, unique_J=unique_j,
unique_CDR3=unique_cdr3, shared_CDR3_ratio=shared_cdr3_ratio,
clonotypes=clonotypes, read_names=read_names,
unique_barcodes=unique_barcodes)
if gene in self.database:
database_diff = edit_distance(sibling, self.database[gene])
database_changes = describe_nt_change(self.database[gene], sibling)
else:
database_diff = None
database_changes = None
# Build the Candidate
sequence_id = gene if database_diff == 0 else unique_name(gene, sibling)
chain = self._guess_chain(sibling_info.group)
cdr3_start = self._guess_cdr3_start(sibling_info.group)
ratio = safe_divide(info['exact'].count, info['exact'].unique_CDR3)
# Apply some very light filtering on non-database sequences
if database_diff > 0 and info['exact'].count < 2:
continue
candidate = Candidate(
name=sequence_id,
source=gene,
chain=chain,
cluster=sibling_info.name,
cluster_size=info['window'].count,
Js=info['window'].unique_J,
CDR3s=info['window'].unique_CDR3,
exact=info['exact'].count,
barcodes_exact=info['exact'].unique_barcodes,
Ds_exact=info['exact'].unique_D,
Js_exact=info['exact'].unique_J,
CDR3s_exact=info['exact'].unique_CDR3,
clonotypes=info['exact'].clonotypes,
CDR3_exact_ratio=ratio,
CDR3_shared_ratio=info['exact'].shared_CDR3_ratio,
N_bases=n_bases,
database_diff=database_diff,
database_changes=database_changes,
has_stop=has_stop(sibling),
CDR3_start=cdr3_start,
consensus=sibling,
read_names=info['exact'].read_names,
)
candidates.append(candidate)
return candidates
def linked(s, t):
return edit_distance(s, t, distance) <= distance
