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"""
Generate allele sequences for pan-class I models.
Additional dependency: biopython
"""
from __future__ import print_function
import sys
import argparse
import numpy
import pandas
import mhcnames
import Bio.SeqIO # pylint: disable=import-error
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def normalize_simple(s):
return mhcnames.normalize_allele_name(s)
def normalize_complex(s, disallowed=["MIC", "HFE"]):
if any(item in s for item in disallowed):
return None
try:
return normalize_simple(s)
except:
while s:
s = ":".join(s.split(":")[:-1])
try:
return normalize_simple(s)
except:
pass
return None
parser = argparse.ArgumentParser(usage=__doc__)
parser.add_argument(
"aligned_fasta",
help="Aligned sequences")
parser.add_argument(
"--recapitulate-sequences",
required=True,
help="CSV giving sequences to recapitulate")
parser.add_argument(
"--differentiate-alleles",
help="File listing alleles to differentiate using additional positions")
parser.add_argument(
"--out-csv",
help="Result file")
def run():
args = parser.parse_args(sys.argv[1:])
print(args)
allele_to_sequence = {}
reader = Bio.SeqIO.parse(args.aligned_fasta, "fasta")
for record in reader:
name = record.description.split()[1]
print(record.name, record.description)
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allele_to_sequence[name] = str(record.seq)
print("Read %d aligned sequences" % len(allele_to_sequence))
allele_sequences = pandas.Series(allele_to_sequence).to_frame()
allele_sequences.columns = ['aligned']
allele_sequences['aligned'] = allele_sequences['aligned'].str.replace(
"-", "X")
allele_sequences['normalized_allele'] = allele_sequences.index.map(normalize_complex)
allele_sequences = allele_sequences.set_index("normalized_allele", drop=True)
selected_positions = []
recapitulate_df = pandas.read_csv(args.recapitulate_sequences)
recapitulate_df["normalized_allele"] = recapitulate_df.allele.map(
normalize_complex)
recapitulate_df = (
recapitulate_df
.dropna()
.drop_duplicates("normalized_allele")
.set_index("normalized_allele", drop=True))
allele_sequences["recapitulate_target"] = recapitulate_df.iloc[:,-1]
print("Sequences in recapitulate CSV that are not in aligned fasta:")
print(recapitulate_df.index[
~recapitulate_df.index.isin(allele_sequences.index)
].tolist())
allele_sequences_with_target = allele_sequences.loc[
~allele_sequences.recapitulate_target.isnull()
]
position_identities = []
target_length = int(
allele_sequences_with_target.recapitulate_target.str.len().max())
for i in range(target_length):
series_i = allele_sequences_with_target.recapitulate_target.str.get(i)
row = []
full_length_sequence_length = int(
allele_sequences_with_target.aligned.str.len().max())
for k in range(full_length_sequence_length):
series_k = allele_sequences_with_target.aligned.str.get(k)
row.append((series_i == series_k).mean())
position_identities.append(row)
position_identities = pandas.DataFrame(numpy.array(position_identities))
selected_positions = position_identities.idxmax(1).tolist()
fractions = position_identities.max(1)
print("Selected positions: ", *selected_positions)
print("Lowest concordance fraction: %0.5f" % fractions.min())
assert fractions.min() > 0.99
allele_sequences["recapitulated"] = allele_sequences.aligned.map(
lambda s: "".join(s[p] for p in selected_positions))
allele_sequences_with_target = allele_sequences.loc[
~allele_sequences.recapitulate_target.isnull()
]
agreement = (
allele_sequences_with_target.recapitulated ==
allele_sequences_with_target.recapitulate_target).mean()
print("Overall agreement: %0.5f" % agreement)
assert agreement > 0.9
# Add additional positions
if args.differentiate_alleles:
differentiate_alleles = pandas.read_csv(
args.differentiate_alleles).iloc[:,0].values
print(
"Read %d alleles to differentiate:" % len(differentiate_alleles),
differentiate_alleles)
allele_sequences_to_differentiate = allele_sequences.loc[
allele_sequences.index.isin(differentiate_alleles)
]
print(allele_sequences_to_differentiate.shape)
additional_positions = []
for (_, sub_df) in allele_sequences_to_differentiate.groupby("recapitulated"):
if sub_df.aligned.nunique() > 1:
differing = pandas.DataFrame(
dict([(pos, chars) for (pos, chars) in
enumerate(zip(*sub_df.aligned.values)) if
any(c != chars[0] for c in chars) and "X" not in chars])).T
print(sub_df)
print(differing)
print()
additional_positions.extend(differing.index)
additional_positions = sorted(set(additional_positions))
print(
"Selected %d additional positions: " % len(additional_positions),
additional_positions)
extended_selected_positions = sorted(
set(selected_positions).union(set(additional_positions)))
print(
"Extended selected positions (%d)" % len(extended_selected_positions),
*extended_selected_positions)
allele_sequences["sequence"] = allele_sequences.aligned.map(
lambda s: "".join(s[p] for p in extended_selected_positions))
allele_sequences[["sequence"]].to_csv(args.out_csv, index=True)
print("Wrote: %s" % args.out_csv)
if __name__ == '__main__':
run()