def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
codon_table=default_codon_table, alphabet=None,
complete_protein=False, anchor_len=10, max_score=10):
"""Build a codon alignment from protein alignment and corresponding nucleotides.
Arguments:
- pro_align - a protein MultipleSeqAlignment object
- nucl_align - an object returned by SeqIO.parse or SeqIO.index
or a collection of SeqRecord.
- alphabet - alphabet for the returned codon alignment
- corr_dict - a dict that maps protein id to nucleotide id
- complete_protein - whether the sequence begins with a start
codon
- frameshift - whether to apply frameshift detection
Return a CodonAlignment object
>>> from Bio.Alphabet import IUPAC
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> seq1 = SeqRecord(Seq('TCAGGGACTGCGAGAACCAAGCTACTGCTGCTGCTGGCTGCGCTCTGCGCCGCAGGTGGGGCGCTGGAG',
... alphabet=IUPAC.IUPACUnambiguousDNA()), id='pro1')
>>> seq2 = SeqRecord(Seq('TCAGGGACTTCGAGAACCAAGCGCTCCTGCTGCTGGCTGCGCTCGGCGCCGCAGGTGGAGCACTGGAG',
... alphabet=IUPAC.IUPACUnambiguousDNA()), id='pro2')
>>> pro1 = SeqRecord(Seq('SGTARTKLLLLLAALCAAGGALE', alphabet=IUPAC.protein),id='pro1')
>>> pro2 = SeqRecord(Seq('SGTSRTKRLLLLAALGAAGGALE', alphabet=IUPAC.protein),id='pro2')
>>> aln = MultipleSeqAlignment([pro1, pro2])
>>> codon_aln = build(aln, [seq1, seq2])
>>> print(codon_aln)
CodonAlphabet(Standard) CodonAlignment with 2 rows and 69 columns (23 codons)
TCAGGGACTGCGAGAACCAAGCTACTGCTGCTGCTGGCTGCGCTCTGCGCCGCAGGT...GAG pro1
TCAGGGACTTCGAGAACCAAGCG-CTCCTGCTGCTGGCTGCGCTCGGCGCCGCAGGT...GAG pro2
"""
# TODO
# add an option to allow the user to specify the returned object?
from Bio.Alphabet import ProteinAlphabet
from Bio.Align import MultipleSeqAlignment
# check the type of object of pro_align
if not isinstance(pro_align, MultipleSeqAlignment):
raise TypeError("the first argument should be a MultipleSeqAlignment "
"object")
# check the alphabet of pro_align
for pro in pro_align:
if not isinstance(_get_base_alphabet(pro.seq.alphabet), ProteinAlphabet):
raise TypeError("Alphabet Error!\nThe input alignment should be "
"a *PROTEIN* alignemnt, found %r" % pro.seq.alphabet)
if alphabet is None:
alphabet = _get_codon_alphabet(codon_table, gap_char=gap_char)
# check whether the number of seqs in pro_align and nucl_seqs is
# the same
pro_num = len(pro_align)
if corr_dict is None:
if nucl_seqs.__class__.__name__ == "generator":
# nucl_seqs will be a tuple if read by SeqIO.parse()
nucl_seqs = tuple(nucl_seqs)
nucl_num = len(nucl_seqs)
if pro_num > nucl_num:
raise ValueError("Higher Number of SeqRecords in Protein Alignment "
"({0}) than the Number of Nucleotide SeqRecords "
"({1}) are found!".format(pro_num, nucl_num))
# Determine the protein sequences and nucl sequences
# correspondence. If nucl_seqs is a list, tuple or read by
# SeqIO.parse(), we assume the order of sequences in pro_align
# and nucl_seqs are the same. If nucl_seqs is a dict or read by
# SeqIO.index(), we match seqs in pro_align and those in
# nucl_seq by their id.
if nucl_seqs.__class__.__name__ in ("_IndexedSeqFileDict", "dict"):
corr_method = 1
elif nucl_seqs.__class__.__name__ in ("list", "tuple"):
corr_method = 0
else:
raise TypeError("Nucl Sequences Error, Unknown type to assign "
"correspondence method")
else:
if not isinstance(corr_dict, dict):
raise TypeError("corr_dict should be a dict that corresponds "
"protein id to nucleotide id!")
if len(corr_dict) >= pro_num:
# read by SeqIO.parse()
if nucl_seqs.__class__.__name__ == "generator":
from Bio import SeqIO
nucl_seqs = SeqIO.to_dict(nucl_seqs)
elif nucl_seqs.__class__.__name__ in ("list", "tuple"):
nucl_seqs = dict((i.id, i) for i in nucl_seqs)
# nucl_seqs = {i.id: i for i in nucl_seqs}
elif nucl_seqs.__class__.__name__ in \
("_IndexedSeqFileDict", "dict"):
pass
else:
raise TypeError("Nucl Sequences Error, Unknown type of "
"Nucleotide Records!")
corr_method = 2
else:
raise RuntimeError("Number of items in corr_dict ({0}) is less "
"than number of protein records "
"({1})".format(len(corr_dict), pro_num))
# set up pro-nucl correspondence based on corr_method
# corr_method = 0, consecutive pairing
if corr_method == 0:
pro_nucl_pair = zip(pro_align, nucl_seqs)
# corr_method = 1, keyword pairing
elif corr_method == 1:
nucl_id = set(nucl_seqs.keys())
pro_id = set(i.id for i in pro_align)
# check if there is pro_id that does not have a nucleotide match
if pro_id - nucl_id:
diff = pro_id - nucl_id
raise ValueError("Protein Record {0} cannot find a nucleotide "
"sequence match, please check the "
"id".format(', '.join(diff)))
else:
pro_nucl_pair = []
for pro_rec in pro_align:
pro_nucl_pair.append((pro_rec, nucl_seqs[pro_rec.id]))
# corr_method = 2, dict pairing
elif corr_method == 2:
pro_nucl_pair = []
for pro_rec in pro_align:
try:
nucl_id = corr_dict[pro_rec.id]
except KeyError:
print("Protein record (%s) is not in corr_dict!" % pro_rec.id)
exit(1)
pro_nucl_pair.append((pro_rec, nucl_seqs[nucl_id]))
codon_aln = []
shift = False
for pair in pro_nucl_pair:
# Beware that the following span corresponds to an ungapped
# nucleotide sequence.
corr_span = _check_corr(pair[0], pair[1], gap_char=gap_char,
codon_table=codon_table,
complete_protein=complete_protein,
anchor_len=anchor_len)
if not corr_span:
raise ValueError("Protein Record {0} and Nucleotide Record {1} do"
" not match!".format(pair[0].id, pair[1].id))
else:
codon_rec = _get_codon_rec(pair[0], pair[1], corr_span,
alphabet=alphabet,
complete_protein=False,
codon_table=codon_table,
max_score=max_score)
codon_aln.append(codon_rec)
if corr_span[1] == 2:
shift = True
if shift:
return CodonAlignment(_align_shift_recs(codon_aln), alphabet=alphabet)
else:
return CodonAlignment(codon_aln, alphabet=alphabet)
def build(pro_align,
nucl_seqs,
corr_dict=None,
gap_char='-',
unknown='X',
codon_table=default_codon_table,
alphabet=None,
complete_protein=False,
anchor_len=10,
max_score=10):
"""Build a codon alignment from a protein alignment and
corresponding nucleotide sequences
Arguments:
- pro_align - a protein MultipleSeqAlignment object
- nucl_align - an object returned by SeqIO.parse or SeqIO.index
or a collection of SeqRecord.
- alphabet - alphabet for the returned codon alignment
- corr_dict - a dict that maps protein id to nucleotide id
- complete_protein - whether the sequence begins with a start
codon
- frameshift - whether to apply frameshift detection
Return a CodonAlignment object
>>> from Bio.Alphabet import IUPAC
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> seq1 = SeqRecord(Seq('TCAGGGACTGCGAGAACCAAGCTACTGCTGCTGCTGGCTGCGCTCTGCGCCGCAGGTGGGGCGCTGGAG',
... alphabet=IUPAC.IUPACUnambiguousDNA()), id='pro1')
>>> seq2 = SeqRecord(Seq('TCAGGGACTTCGAGAACCAAGCGCTCCTGCTGCTGGCTGCGCTCGGCGCCGCAGGTGGAGCACTGGAG',
... alphabet=IUPAC.IUPACUnambiguousDNA()), id='pro2')
>>> pro1 = SeqRecord(Seq('SGTARTKLLLLLAALCAAGGALE', alphabet=IUPAC.protein),id='pro1')
>>> pro2 = SeqRecord(Seq('SGTSRTKRLLLLAALGAAGGALE', alphabet=IUPAC.protein),id='pro2')
>>> aln = MultipleSeqAlignment([pro1, pro2])
>>> codon_aln = build(aln, [seq1, seq2])
>>> print(codon_aln)
CodonAlphabet(Standard) CodonAlignment with 2 rows and 69 columns (23 codons)
TCAGGGACTGCGAGAACCAAGCTACTGCTGCTGCTGGCTGCGCTCTGCGCCGCAGGT...GAG pro1
TCAGGGACTTCGAGAACCAAGCG-CTCCTGCTGCTGGCTGCGCTCGGCGCCGCAGGT...GAG pro2
"""
# TODO
# add an option to allow the user to specify the returned object?
from Bio.Alphabet import ProteinAlphabet
from Bio.Align import MultipleSeqAlignment
# check the type of object of pro_align
if not isinstance(pro_align, MultipleSeqAlignment):
raise TypeError("the first argument should be a MultipleSeqAlignment "
"object")
# check the alphabet of pro_align
for pro in pro_align:
if not isinstance(pro.seq.alphabet, ProteinAlphabet):
raise TypeError("Alphabet Error!\nThe input alignment should be "
"a *PROTEIN* alignment")
if alphabet is None:
alphabet = _get_codon_alphabet(codon_table, gap_char=gap_char)
# check whether the number of seqs in pro_align and nucl_seqs is
# the same
pro_num = len(pro_align)
if corr_dict is None:
if nucl_seqs.__class__.__name__ == "generator":
# nucl_seqs will be a tuple if read by SeqIO.parse()
nucl_seqs = tuple(nucl_seqs)
nucl_num = len(nucl_seqs)
if pro_num > nucl_num:
raise ValueError(
"Higher Number of SeqRecords in Protein Alignment "
"({0}) than the Number of Nucleotide SeqRecords "
"({1}) are found!".format(pro_num, nucl_num))
# Determine the protein sequences and nucl sequences
# correspondence. If nucl_seqs is a list, tuple or read by
# SeqIO.parse(), we assume the order of sequences in pro_align
# and nucl_seqs are the same. If nucl_seqs is a dict or read by
# SeqIO.index(), we match seqs in pro_align and those in
# nucl_seq by their id.
if nucl_seqs.__class__.__name__ in ("_IndexedSeqFileDict", "dict"):
corr_method = 1
elif nucl_seqs.__class__.__name__ in ("list", "tuple"):
corr_method = 0
else:
raise TypeError("Nucl Sequences Error, Unknown type to assign "
"correspondence method")
else:
if not isinstance(corr_dict, dict):
raise TypeError("corr_dict should be a dict that corresponds "
"protein id to nucleotide id!")
if len(corr_dict) >= pro_num:
# read by SeqIO.parse()
if nucl_seqs.__class__.__name__ == "generator":
from Bio import SeqIO
nucl_seqs = SeqIO.to_dict(nucl_seqs)
elif nucl_seqs.__class__.__name__ in ("list", "tuple"):
nucl_seqs = dict((i.id, i) for i in nucl_seqs)
# nucl_seqs = {i.id: i for i in nucl_seqs}
elif nucl_seqs.__class__.__name__ in \
("_IndexedSeqFileDict", "dict"):
pass
else:
raise TypeError("Nucl Sequences Error, Unknown type of "
"Nucleotide Records!")
corr_method = 2
else:
raise RuntimeError("Number of items in corr_dict ({0}) is less "
"than number of protein records "
"({1})".format(len(corr_dict), pro_num))
# set up pro-nucl correspondence based on corr_method
# corr_method = 0, consecutive pairing
if corr_method == 0:
pro_nucl_pair = izip(pro_align, nucl_seqs)
# corr_method = 1, keyword pairing
elif corr_method == 1:
nucl_id = set(nucl_seqs.keys())
pro_id = set([i.id for i in pro_align])
# check if there is pro_id that does not have a nucleotide match
if pro_id - nucl_id:
diff = pro_id - nucl_id
raise ValueError("Protein Record {0} cannot find a nucleotide "
"sequence match, please check the "
"id".format(', '.join(diff)))
else:
pro_nucl_pair = []
for pro_rec in pro_align:
pro_nucl_pair.append((pro_rec, nucl_seqs[pro_rec.id]))
# corr_method = 2, dict pairing
elif corr_method == 2:
pro_nucl_pair = []
for pro_rec in pro_align:
try:
nucl_id = corr_dict[pro_rec.id]
except KeyError:
print("Protein record (%s) is not in corr_dict!" % pro_rec.id)
exit(1)
pro_nucl_pair.append((pro_rec, nucl_seqs[nucl_id]))
codon_aln = []
shift = False
for pair in pro_nucl_pair:
# Beware that the following span corresponds to an ungapped
# nucleotide sequence.
corr_span = _check_corr(pair[0],
pair[1],
gap_char=gap_char,
codon_table=codon_table,
complete_protein=complete_protein,
anchor_len=anchor_len)
if not corr_span:
raise ValueError("Protein Record {0} and Nucleotide Record {1} do"
" not match!".format((pair[0].id, pair[1].id)))
else:
codon_rec = _get_codon_rec(pair[0],
pair[1],
corr_span,
alphabet=alphabet,
complete_protein=False,
codon_table=codon_table,
max_score=max_score)
codon_aln.append(codon_rec)
if corr_span[1] == 2:
shift = True
if shift:
return CodonAlignment(_align_shift_recs(codon_aln), alphabet=alphabet)
else:
return CodonAlignment(codon_aln, alphabet=alphabet)
def build(
pro_align,
nucl_seqs,
corr_dict=None,
gap_char="-",
unknown="X",
codon_table=default_codon_table,
alphabet=None,
complete_protein=False,
anchor_len=10,
max_score=10,
):
"""Build a codon alignment from protein alignment and corresponding nucleotides.
Arguments:
- pro_align - a protein MultipleSeqAlignment object
- nucl_seqs - an object returned by SeqIO.parse or SeqIO.index
or a collection of SeqRecord.
- alphabet - alphabet for the returned codon alignment
- corr_dict - a dict that maps protein id to nucleotide id
- complete_protein - whether the sequence begins with a start
codon
Return a CodonAlignment object.
The example below answers this Biostars question: https://www.biostars.org/p/89741/
>>> from Bio.Alphabet import generic_dna, generic_protein
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> from Bio.codonalign import build
>>> seq1 = SeqRecord(Seq('ATGTCTCGT', alphabet=generic_dna), id='pro1')
>>> seq2 = SeqRecord(Seq('ATGCGT', alphabet=generic_dna), id='pro2')
>>> pro1 = SeqRecord(Seq('MSR', alphabet=generic_protein), id='pro1')
>>> pro2 = SeqRecord(Seq('M-R', alphabet=generic_protein), id='pro2')
>>> aln = MultipleSeqAlignment([pro1, pro2])
>>> codon_aln = build(aln, [seq1, seq2])
>>> print(codon_aln)
CodonAlphabet(Standard) CodonAlignment with 2 rows and 9 columns (3 codons)
ATGTCTCGT pro1
ATG---CGT pro2
"""
# TODO
# add an option to allow the user to specify the returned object?
from Bio.Alphabet import ProteinAlphabet
from Bio.Align import MultipleSeqAlignment
# check the type of object of pro_align
if not isinstance(pro_align, MultipleSeqAlignment):
raise TypeError(
"the first argument should be a MultipleSeqAlignment object")
# check the alphabet of pro_align
for pro in pro_align:
if not isinstance(_get_base_alphabet(pro.seq.alphabet),
ProteinAlphabet):
raise TypeError("Alphabet Error!\nThe input alignment should be "
"a *PROTEIN* alignemnt, found %r" %
pro.seq.alphabet)
if alphabet is None:
alphabet = _get_codon_alphabet(codon_table, gap_char=gap_char)
# check whether the number of seqs in pro_align and nucl_seqs is
# the same
pro_num = len(pro_align)
if corr_dict is None:
if nucl_seqs.__class__.__name__ == "generator":
# nucl_seqs will be a tuple if read by SeqIO.parse()
nucl_seqs = tuple(nucl_seqs)
nucl_num = len(nucl_seqs)
if pro_num > nucl_num:
raise ValueError(
"Higher Number of SeqRecords in Protein Alignment "
"({0}) than the Number of Nucleotide SeqRecords "
"({1}) are found!".format(pro_num, nucl_num))
# Determine the protein sequences and nucl sequences
# correspondence. If nucl_seqs is a list, tuple or read by
# SeqIO.parse(), we assume the order of sequences in pro_align
# and nucl_seqs are the same. If nucl_seqs is a dict or read by
# SeqIO.index(), we match seqs in pro_align and those in
# nucl_seq by their id.
if nucl_seqs.__class__.__name__ in ("_IndexedSeqFileDict", "dict"):
corr_method = 1
elif nucl_seqs.__class__.__name__ in ("list", "tuple"):
corr_method = 0
else:
raise TypeError(
"Nucl Sequences Error, Unknown type to assign correspondence method"
)
else:
if not isinstance(corr_dict, dict):
raise TypeError("corr_dict should be a dict that corresponds "
"protein id to nucleotide id!")
if len(corr_dict) >= pro_num:
# read by SeqIO.parse()
if nucl_seqs.__class__.__name__ == "generator":
from Bio import SeqIO
nucl_seqs = SeqIO.to_dict(nucl_seqs)
elif nucl_seqs.__class__.__name__ in ("list", "tuple"):
nucl_seqs = {i.id: i for i in nucl_seqs}
elif nucl_seqs.__class__.__name__ in ("_IndexedSeqFileDict",
"dict"):
pass
else:
raise TypeError(
"Nucl Sequences Error, Unknown type of Nucleotide Records!"
)
corr_method = 2
else:
raise RuntimeError(
"Number of items in corr_dict ({0}) is less than number of protein "
"records ({1})".format(len(corr_dict), pro_num))
# set up pro-nucl correspondence based on corr_method
# corr_method = 0, consecutive pairing
if corr_method == 0:
pro_nucl_pair = zip(pro_align, nucl_seqs)
# corr_method = 1, keyword pairing
elif corr_method == 1:
nucl_id = set(nucl_seqs.keys())
pro_id = {i.id for i in pro_align}
# check if there is pro_id that does not have a nucleotide match
if pro_id - nucl_id:
diff = pro_id - nucl_id
raise ValueError("Protein Record {0} cannot find a nucleotide "
"sequence match, please check the "
"id".format(", ".join(diff)))
else:
pro_nucl_pair = []
for pro_rec in pro_align:
pro_nucl_pair.append((pro_rec, nucl_seqs[pro_rec.id]))
# corr_method = 2, dict pairing
elif corr_method == 2:
pro_nucl_pair = []
for pro_rec in pro_align:
try:
nucl_id = corr_dict[pro_rec.id]
except KeyError:
print("Protein record (%s) is not in corr_dict!" % pro_rec.id)
exit(1)
pro_nucl_pair.append((pro_rec, nucl_seqs[nucl_id]))
codon_aln = []
shift = False
for pair in pro_nucl_pair:
# Beware that the following span corresponds to an ungapped
# nucleotide sequence.
corr_span = _check_corr(
pair[0],
pair[1],
gap_char=gap_char,
codon_table=codon_table,
complete_protein=complete_protein,
anchor_len=anchor_len,
)
if not corr_span:
raise ValueError("Protein Record {0} and Nucleotide Record {1} do"
" not match!".format(pair[0].id, pair[1].id))
else:
codon_rec = _get_codon_rec(
pair[0],
pair[1],
corr_span,
alphabet=alphabet,
complete_protein=False,
codon_table=codon_table,
max_score=max_score,
)
codon_aln.append(codon_rec)
if corr_span[1] == 2:
shift = True
if shift:
return CodonAlignment(_align_shift_recs(codon_aln), alphabet=alphabet)
else:
return CodonAlignment(codon_aln, alphabet=alphabet)
