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biopython/Bio/Align/__init__.py
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# Copyright 2000, 2004 by Brad Chapman.
# Revisions copyright 2010-2013, 2015-2018 by Peter Cock.
# All rights reserved.
#
# This file is part of the Biopython distribution and governed by your
# choice of the "Biopython License Agreement" or the "BSD 3-Clause License".
# Please see the LICENSE file that should have been included as part of this
# package.
"""Code for dealing with sequence alignments.
One of the most important things in this module is the MultipleSeqAlignment
class, used in the Bio.AlignIO module.
"""
import collections
import copy
import importlib
import numbers
import sys
import types
import warnings
from abc import ABC
from abc import abstractmethod
from itertools import zip_longest
from typing import Dict
try:
import numpy as np
except ImportError:
from Bio import MissingPythonDependencyError
raise MissingPythonDependencyError(
"Please install NumPy if you want to use Bio.Align. "
"See http://www.numpy.org/"
) from None
from Bio import BiopythonDeprecationWarning
from Bio.Align import _aligncore # type: ignore
from Bio.Align import _codonaligner # type: ignore
from Bio.Align import _pairwisealigner # type: ignore
from Bio.Align import substitution_matrices
from Bio.Data import CodonTable
from Bio.Seq import MutableSeq
from Bio.Seq import reverse_complement
from Bio.Seq import Seq
from Bio.Seq import translate
from Bio.Seq import UndefinedSequenceError
from Bio.SeqRecord import _RestrictedDict
from Bio.SeqRecord import SeqRecord
# Import errors may occur here if a compiled _pairwisealigner.c file or
# compiled _codonaligner.c file (_pairwisealigner.pyd or _pairwisealigner.so,
# or _codonaligner.pyd or _codonaligner.so) is missing or if the user is
# importing from within the Biopython source tree, see PR #2007:
# https://github.com/biopython/biopython/pull/2007
AlignmentCounts = collections.namedtuple(
"AlignmentCounts", ["gaps", "identities", "mismatches"]
)
class MultipleSeqAlignment:
"""Represents a classical multiple sequence alignment (MSA).
By this we mean a collection of sequences (usually shown as rows) which
are all the same length (usually with gap characters for insertions or
padding). The data can then be regarded as a matrix of letters, with well
defined columns.
You would typically create an MSA by loading an alignment file with the
AlignIO module:
>>> from Bio import AlignIO
>>> align = AlignIO.read("Clustalw/opuntia.aln", "clustal")
>>> print(align)
Alignment with 7 rows and 156 columns
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191
TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191
In some respects you can treat these objects as lists of SeqRecord objects,
each representing a row of the alignment. Iterating over an alignment gives
the SeqRecord object for each row:
>>> len(align)
7
>>> for record in align:
... print("%s %i" % (record.id, len(record)))
...
gi|6273285|gb|AF191659.1|AF191 156
gi|6273284|gb|AF191658.1|AF191 156
gi|6273287|gb|AF191661.1|AF191 156
gi|6273286|gb|AF191660.1|AF191 156
gi|6273290|gb|AF191664.1|AF191 156
gi|6273289|gb|AF191663.1|AF191 156
gi|6273291|gb|AF191665.1|AF191 156
You can also access individual rows as SeqRecord objects via their index:
>>> print(align[0].id)
gi|6273285|gb|AF191659.1|AF191
>>> print(align[-1].id)
gi|6273291|gb|AF191665.1|AF191
And extract columns as strings:
>>> print(align[:, 1])
AAAAAAA
Or, take just the first ten columns as a sub-alignment:
>>> print(align[:, :10])
Alignment with 7 rows and 10 columns
TATACATTAA gi|6273285|gb|AF191659.1|AF191
TATACATTAA gi|6273284|gb|AF191658.1|AF191
TATACATTAA gi|6273287|gb|AF191661.1|AF191
TATACATAAA gi|6273286|gb|AF191660.1|AF191
TATACATTAA gi|6273290|gb|AF191664.1|AF191
TATACATTAA gi|6273289|gb|AF191663.1|AF191
TATACATTAA gi|6273291|gb|AF191665.1|AF191
Combining this alignment slicing with alignment addition allows you to
remove a section of the alignment. For example, taking just the first
and last ten columns:
>>> print(align[:, :10] + align[:, -10:])
Alignment with 7 rows and 20 columns
TATACATTAAGTGTACCAGA gi|6273285|gb|AF191659.1|AF191
TATACATTAAGTGTACCAGA gi|6273284|gb|AF191658.1|AF191
TATACATTAAGTGTACCAGA gi|6273287|gb|AF191661.1|AF191
TATACATAAAGTGTACCAGA gi|6273286|gb|AF191660.1|AF191
TATACATTAAGTGTACCAGA gi|6273290|gb|AF191664.1|AF191
TATACATTAAGTATACCAGA gi|6273289|gb|AF191663.1|AF191
TATACATTAAGTGTACCAGA gi|6273291|gb|AF191665.1|AF191
Note - This object does NOT attempt to model the kind of alignments used
in next generation sequencing with multiple sequencing reads which are
much shorter than the alignment, and where there is usually a consensus or
reference sequence with special status.
"""
def __init__(
self, records, alphabet=None, annotations=None, column_annotations=None
):
"""Initialize a new MultipleSeqAlignment object.
Arguments:
- records - A list (or iterator) of SeqRecord objects, whose
sequences are all the same length. This may be an empty
list.
- alphabet - For backward compatibility only; its value should always
be None.
- annotations - Information about the whole alignment (dictionary).
- column_annotations - Per column annotation (restricted dictionary).
This holds Python sequences (lists, strings, tuples)
whose length matches the number of columns. A typical
use would be a secondary structure consensus string.
You would normally load a MSA from a file using Bio.AlignIO, but you
can do this from a list of SeqRecord objects too:
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> a = SeqRecord(Seq("AAAACGT"), id="Alpha")
>>> b = SeqRecord(Seq("AAA-CGT"), id="Beta")
>>> c = SeqRecord(Seq("AAAAGGT"), id="Gamma")
>>> align = MultipleSeqAlignment([a, b, c],
... annotations={"tool": "demo"},
... column_annotations={"stats": "CCCXCCC"})
>>> print(align)
Alignment with 3 rows and 7 columns
AAAACGT Alpha
AAA-CGT Beta
AAAAGGT Gamma
>>> align.annotations
{'tool': 'demo'}
>>> align.column_annotations
{'stats': 'CCCXCCC'}
"""
if alphabet is not None:
raise ValueError("The alphabet argument is no longer supported")
self._records = []
if records:
self.extend(records)
# Annotations about the whole alignment
if annotations is None:
annotations = {}
elif not isinstance(annotations, dict):
raise TypeError("annotations argument should be a dict")
self.annotations = annotations
# Annotations about each column of the alignment
if column_annotations is None:
column_annotations = {}
# Handle this via the property set function which will validate it
self.column_annotations = column_annotations
def _set_per_column_annotations(self, value):
if not isinstance(value, dict):
raise TypeError(
"The per-column-annotations should be a (restricted) dictionary."
)
# Turn this into a restricted-dictionary (and check the entries)
if len(self):
# Use the standard method to get the length
expected_length = self.get_alignment_length()
self._per_col_annotations = _RestrictedDict(length=expected_length)
self._per_col_annotations.update(value)
else:
# Bit of a problem case... number of columns is undefined
self._per_col_annotations = None
if value:
raise ValueError(
"Can't set per-column-annotations without an alignment"
)
def _get_per_column_annotations(self):
if self._per_col_annotations is None:
# This happens if empty at initialisation
if len(self):
# Use the standard method to get the length
expected_length = self.get_alignment_length()
else:
# Should this raise an exception? Compare SeqRecord behaviour...
expected_length = 0
self._per_col_annotations = _RestrictedDict(length=expected_length)
return self._per_col_annotations
column_annotations = property(
fget=_get_per_column_annotations,
fset=_set_per_column_annotations,
doc="""Dictionary of per-letter-annotation for the sequence.""",
)
def _str_line(self, record, length=50):
"""Return a truncated string representation of a SeqRecord (PRIVATE).
This is a PRIVATE function used by the __str__ method.
"""
if record.seq.__class__.__name__ == "CodonSeq":
if len(record.seq) <= length:
return f"{record.seq} {record.id}"
else:
return "%s...%s %s" % (
record.seq[: length - 3],
record.seq[-3:],
record.id,
)
else:
if len(record.seq) <= length:
return f"{record.seq} {record.id}"
else:
return "%s...%s %s" % (
record.seq[: length - 6],
record.seq[-3:],
record.id,
)
def __str__(self):
"""Return a multi-line string summary of the alignment.
This output is intended to be readable, but large alignments are
shown truncated. A maximum of 20 rows (sequences) and 50 columns
are shown, with the record identifiers. This should fit nicely on a
single screen. e.g.
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> a = SeqRecord(Seq("ACTGCTAGCTAG"), id="Alpha")
>>> b = SeqRecord(Seq("ACT-CTAGCTAG"), id="Beta")
>>> c = SeqRecord(Seq("ACTGCTAGATAG"), id="Gamma")
>>> align = MultipleSeqAlignment([a, b, c])
>>> print(align)
Alignment with 3 rows and 12 columns
ACTGCTAGCTAG Alpha
ACT-CTAGCTAG Beta
ACTGCTAGATAG Gamma
See also the alignment's format method.
"""
rows = len(self._records)
lines = [
"Alignment with %i rows and %i columns"
% (rows, self.get_alignment_length())
]
if rows <= 20:
lines.extend(self._str_line(rec) for rec in self._records)
else:
lines.extend(self._str_line(rec) for rec in self._records[:18])
lines.append("...")
lines.append(self._str_line(self._records[-1]))
return "\n".join(lines)
def __repr__(self):
"""Return a representation of the object for debugging.
The representation cannot be used with eval() to recreate the object,
which is usually possible with simple python objects. For example:
<Bio.Align.MultipleSeqAlignment instance (2 records of length 14)
at a3c184c>
The hex string is the memory address of the object, see help(id).
This provides a simple way to visually distinguish alignments of
the same size.
"""
# A doctest for __repr__ would be nice, but __class__ comes out differently
# if run via the __main__ trick.
return "<%s instance (%i records of length %i) at %x>" % (
self.__class__,
len(self._records),
self.get_alignment_length(),
id(self),
)
# This version is useful for doing eval(repr(alignment)),
# but it can be VERY long:
# return "%s(%r)" \
# % (self.__class__, self._records)
def __format__(self, format_spec):
"""Return the alignment as a string in the specified file format.
The format should be a lower case string supported as an output
format by Bio.AlignIO (such as "fasta", "clustal", "phylip",
"stockholm", etc), which is used to turn the alignment into a
string.
e.g.
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> a = SeqRecord(Seq("ACTGCTAGCTAG"), id="Alpha", description="")
>>> b = SeqRecord(Seq("ACT-CTAGCTAG"), id="Beta", description="")
>>> c = SeqRecord(Seq("ACTGCTAGATAG"), id="Gamma", description="")
>>> align = MultipleSeqAlignment([a, b, c])
>>> print(format(align, "fasta"))
>Alpha
ACTGCTAGCTAG
>Beta
ACT-CTAGCTAG
>Gamma
ACTGCTAGATAG
<BLANKLINE>
>>> print(format(align, "phylip"))
3 12
Alpha ACTGCTAGCT AG
Beta ACT-CTAGCT AG
Gamma ACTGCTAGAT AG
<BLANKLINE>
"""
if format_spec:
from io import StringIO
from Bio import AlignIO
handle = StringIO()
AlignIO.write([self], handle, format_spec)
return handle.getvalue()
else:
# Follow python convention and default to using __str__
return str(self)
def __iter__(self):
"""Iterate over alignment rows as SeqRecord objects.
e.g.
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> a = SeqRecord(Seq("ACTGCTAGCTAG"), id="Alpha")
>>> b = SeqRecord(Seq("ACT-CTAGCTAG"), id="Beta")
>>> c = SeqRecord(Seq("ACTGCTAGATAG"), id="Gamma")
>>> align = MultipleSeqAlignment([a, b, c])
>>> for record in align:
... print(record.id)
... print(record.seq)
...
Alpha
ACTGCTAGCTAG
Beta
ACT-CTAGCTAG
Gamma
ACTGCTAGATAG
"""
return iter(self._records)
def __len__(self):
"""Return the number of sequences in the alignment.
Use len(alignment) to get the number of sequences (i.e. the number of
rows), and alignment.get_alignment_length() to get the length of the
longest sequence (i.e. the number of columns).
This is easy to remember if you think of the alignment as being like a
list of SeqRecord objects.
"""
return len(self._records)
def get_alignment_length(self):
"""Return the maximum length of the alignment.
All objects in the alignment should (hopefully) have the same
length. This function will go through and find this length
by finding the maximum length of sequences in the alignment.
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> a = SeqRecord(Seq("ACTGCTAGCTAG"), id="Alpha")
>>> b = SeqRecord(Seq("ACT-CTAGCTAG"), id="Beta")
>>> c = SeqRecord(Seq("ACTGCTAGATAG"), id="Gamma")
>>> align = MultipleSeqAlignment([a, b, c])
>>> align.get_alignment_length()
12
If you want to know the number of sequences in the alignment,
use len(align) instead:
>>> len(align)
3
"""
max_length = 0
for record in self._records:
if len(record.seq) > max_length:
max_length = len(record.seq)
return max_length
def extend(self, records):
"""Add more SeqRecord objects to the alignment as rows.
They must all have the same length as the original alignment. For
example,
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> a = SeqRecord(Seq("AAAACGT"), id="Alpha")
>>> b = SeqRecord(Seq("AAA-CGT"), id="Beta")
>>> c = SeqRecord(Seq("AAAAGGT"), id="Gamma")
>>> d = SeqRecord(Seq("AAAACGT"), id="Delta")
>>> e = SeqRecord(Seq("AAA-GGT"), id="Epsilon")
First we create a small alignment (three rows):
>>> align = MultipleSeqAlignment([a, b, c])
>>> print(align)
Alignment with 3 rows and 7 columns
AAAACGT Alpha
AAA-CGT Beta
AAAAGGT Gamma
Now we can extend this alignment with another two rows:
>>> align.extend([d, e])
>>> print(align)
Alignment with 5 rows and 7 columns
AAAACGT Alpha
AAA-CGT Beta
AAAAGGT Gamma
AAAACGT Delta
AAA-GGT Epsilon
Because the alignment object allows iteration over the rows as
SeqRecords, you can use the extend method with a second alignment
(provided its sequences have the same length as the original alignment).
"""
if len(self):
# Use the standard method to get the length
expected_length = self.get_alignment_length()
else:
# Take the first record's length
records = iter(records) # records arg could be list or iterator
try:
rec = next(records)
except StopIteration:
# Special case, no records
return
expected_length = len(rec)
self._append(rec, expected_length)
# Can now setup the per-column-annotations as well, set to None
# while missing the length:
self.column_annotations = {}
# Now continue to the rest of the records as usual
for rec in records:
self._append(rec, expected_length)
def append(self, record):
"""Add one more SeqRecord object to the alignment as a new row.
This must have the same length as the original alignment (unless this is
the first record).
>>> from Bio import AlignIO
>>> align = AlignIO.read("Clustalw/opuntia.aln", "clustal")
>>> print(align)
Alignment with 7 rows and 156 columns
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191
TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191
>>> len(align)
7
We'll now construct a dummy record to append as an example:
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> dummy = SeqRecord(Seq("N"*156), id="dummy")
Now append this to the alignment,
>>> align.append(dummy)
>>> print(align)
Alignment with 8 rows and 156 columns
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191
TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191
TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191
TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191
NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN...NNN dummy
>>> len(align)
8
"""
if self._records:
self._append(record, self.get_alignment_length())
else:
self._append(record)
def _append(self, record, expected_length=None):
"""Validate and append a record (PRIVATE)."""
if not isinstance(record, SeqRecord):
raise TypeError("New sequence is not a SeqRecord object")
# Currently the get_alignment_length() call is expensive, so we need
# to avoid calling it repeatedly for __init__ and extend, hence this
# private _append method
if expected_length is not None and len(record) != expected_length:
# TODO - Use the following more helpful error, but update unit tests
# raise ValueError("New sequence is not of length %i"
# % self.get_alignment_length())
raise ValueError("Sequences must all be the same length")
self._records.append(record)
def __add__(self, other):
"""Combine two alignments with the same number of rows by adding them.
If you have two multiple sequence alignments (MSAs), there are two ways to think
about adding them - by row or by column. Using the extend method adds by row.
Using the addition operator adds by column. For example,
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> a1 = SeqRecord(Seq("AAAAC"), id="Alpha")
>>> b1 = SeqRecord(Seq("AAA-C"), id="Beta")
>>> c1 = SeqRecord(Seq("AAAAG"), id="Gamma")
>>> a2 = SeqRecord(Seq("GT"), id="Alpha")
>>> b2 = SeqRecord(Seq("GT"), id="Beta")
>>> c2 = SeqRecord(Seq("GT"), id="Gamma")
>>> left = MultipleSeqAlignment([a1, b1, c1],
... annotations={"tool": "demo", "name": "start"},
... column_annotations={"stats": "CCCXC"})
>>> right = MultipleSeqAlignment([a2, b2, c2],
... annotations={"tool": "demo", "name": "end"},
... column_annotations={"stats": "CC"})
Now, let's look at these two alignments:
>>> print(left)
Alignment with 3 rows and 5 columns
AAAAC Alpha
AAA-C Beta
AAAAG Gamma
>>> print(right)
Alignment with 3 rows and 2 columns
GT Alpha
GT Beta
GT Gamma
And add them:
>>> combined = left + right
>>> print(combined)
Alignment with 3 rows and 7 columns
AAAACGT Alpha
AAA-CGT Beta
AAAAGGT Gamma
For this to work, both alignments must have the same number of records (here
they both have 3 rows):
>>> len(left)
3
>>> len(right)
3
>>> len(combined)
3
The individual rows are SeqRecord objects, and these can be added together. Refer
to the SeqRecord documentation for details of how the annotation is handled. This
example is a special case in that both original alignments shared the same names,
meaning when the rows are added they also get the same name.
Any common annotations are preserved, but differing annotation is lost. This is
the same behaviour used in the SeqRecord annotations and is designed to prevent
accidental propagation of inappropriate values:
>>> combined.annotations
{'tool': 'demo'}
Similarly any common per-column-annotations are combined:
>>> combined.column_annotations
{'stats': 'CCCXCCC'}
"""
if not isinstance(other, MultipleSeqAlignment):
raise NotImplementedError
if len(self) != len(other):
raise ValueError(
"When adding two alignments they must have the same length"
" (i.e. same number of rows)"
)
merged = (left + right for left, right in zip(self, other))
# Take any common annotation:
annotations = {}
for k, v in self.annotations.items():
if k in other.annotations and other.annotations[k] == v:
annotations[k] = v
column_annotations = {}
for k, v in self.column_annotations.items():
if k in other.column_annotations:
column_annotations[k] = v + other.column_annotations[k]
return MultipleSeqAlignment(
merged, annotations=annotations, column_annotations=column_annotations
)
def __getitem__(self, index):
"""Access part of the alignment.
Depending on the indices, you can get a SeqRecord object
(representing a single row), a Seq object (for a single column),
a string (for a single character) or another alignment
(representing some part or all of the alignment).
align[r,c] gives a single character as a string
align[r] gives a row as a SeqRecord
align[r,:] gives a row as a SeqRecord
align[:,c] gives a column as a Seq
align[:] and align[:,:] give a copy of the alignment
Anything else gives a sub alignment, e.g.
align[0:2] or align[0:2,:] uses only row 0 and 1
align[:,1:3] uses only columns 1 and 2
align[0:2,1:3] uses only rows 0 & 1 and only cols 1 & 2
We'll use the following example alignment here for illustration:
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> a = SeqRecord(Seq("AAAACGT"), id="Alpha")
>>> b = SeqRecord(Seq("AAA-CGT"), id="Beta")
>>> c = SeqRecord(Seq("AAAAGGT"), id="Gamma")
>>> d = SeqRecord(Seq("AAAACGT"), id="Delta")
>>> e = SeqRecord(Seq("AAA-GGT"), id="Epsilon")
>>> align = MultipleSeqAlignment([a, b, c, d, e])
You can access a row of the alignment as a SeqRecord using an integer
index (think of the alignment as a list of SeqRecord objects here):
>>> first_record = align[0]
>>> print("%s %s" % (first_record.id, first_record.seq))
Alpha AAAACGT
>>> last_record = align[-1]
>>> print("%s %s" % (last_record.id, last_record.seq))
Epsilon AAA-GGT
You can also access use python's slice notation to create a sub-alignment
containing only some of the SeqRecord objects:
>>> sub_alignment = align[2:5]
>>> print(sub_alignment)
Alignment with 3 rows and 7 columns
AAAAGGT Gamma
AAAACGT Delta
AAA-GGT Epsilon
This includes support for a step, i.e. align[start:end:step], which
can be used to select every second sequence:
>>> sub_alignment = align[::2]
>>> print(sub_alignment)
Alignment with 3 rows and 7 columns
AAAACGT Alpha
AAAAGGT Gamma
AAA-GGT Epsilon
Or to get a copy of the alignment with the rows in reverse order:
>>> rev_alignment = align[::-1]
>>> print(rev_alignment)
Alignment with 5 rows and 7 columns
AAA-GGT Epsilon
AAAACGT Delta
AAAAGGT Gamma
AAA-CGT Beta
AAAACGT Alpha
You can also use two indices to specify both rows and columns. Using simple
integers gives you the entry as a single character string. e.g.
>>> align[3, 4]
'C'
This is equivalent to:
>>> align[3][4]
'C'
or:
>>> align[3].seq[4]
'C'
To get a single column (as a string) use this syntax:
>>> align[:, 4]
'CCGCG'
Or, to get part of a column,
>>> align[1:3, 4]
'CG'
However, in general you get a sub-alignment,
>>> print(align[1:5, 3:6])
Alignment with 4 rows and 3 columns
-CG Beta
AGG Gamma
ACG Delta
-GG Epsilon
This should all seem familiar to anyone who has used the NumPy
array or matrix objects.
"""
if isinstance(index, int):
# e.g. result = align[x]
# Return a SeqRecord
return self._records[index]
elif isinstance(index, slice):
# e.g. sub_align = align[i:j:k]
new = MultipleSeqAlignment(self._records[index])
if self.column_annotations and len(new) == len(self):
# All rows kept (although could have been reversed)
# Preserve the column annotations too,
for k, v in self.column_annotations.items():
new.column_annotations[k] = v
return new
elif len(index) != 2:
raise TypeError("Invalid index type.")
# Handle double indexing
row_index, col_index = index
if isinstance(row_index, int):
# e.g. row_or_part_row = align[6, 1:4], gives a SeqRecord
return self._records[row_index][col_index]
elif isinstance(col_index, int):
# e.g. col_or_part_col = align[1:5, 6], gives a string
return "".join(rec[col_index] for rec in self._records[row_index])
else:
# e.g. sub_align = align[1:4, 5:7], gives another alignment
new = MultipleSeqAlignment(
rec[col_index] for rec in self._records[row_index]
)
if self.column_annotations and len(new) == len(self):
# All rows kept (although could have been reversed)
# Preserve the column annotations too,
for k, v in self.column_annotations.items():
new.column_annotations[k] = v[col_index]
return new
def __delitem__(self, index):
"""Delete SeqRecord by index or multiple SeqRecords by slice."""
if not isinstance(index, int) and not isinstance(index, slice):
raise TypeError("Invalid index type.")
del self._records[index]
def sort(self, key=None, reverse=False):
"""Sort the rows (SeqRecord objects) of the alignment in place.
This sorts the rows alphabetically using the SeqRecord object id by
default. The sorting can be controlled by supplying a key function
which must map each SeqRecord to a sort value.
This is useful if you want to add two alignments which use the same
record identifiers, but in a different order. For example,
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> align1 = MultipleSeqAlignment([
... SeqRecord(Seq("ACGT"), id="Human"),
... SeqRecord(Seq("ACGG"), id="Mouse"),
... SeqRecord(Seq("ACGC"), id="Chicken"),
... ])
>>> align2 = MultipleSeqAlignment([
... SeqRecord(Seq("CGGT"), id="Mouse"),
... SeqRecord(Seq("CGTT"), id="Human"),
... SeqRecord(Seq("CGCT"), id="Chicken"),
... ])
If you simple try and add these without sorting, you get this:
>>> print(align1 + align2)
Alignment with 3 rows and 8 columns
ACGTCGGT <unknown id>
ACGGCGTT <unknown id>
ACGCCGCT Chicken
Consult the SeqRecord documentation which explains why you get a
default value when annotation like the identifier doesn't match up.
However, if we sort the alignments first, then add them we get the
desired result:
>>> align1.sort()
>>> align2.sort()
>>> print(align1 + align2)
Alignment with 3 rows and 8 columns
ACGCCGCT Chicken
ACGTCGTT Human
ACGGCGGT Mouse
As an example using a different sort order, you could sort on the
GC content of each sequence.
>>> from Bio.SeqUtils import gc_fraction
>>> print(align1)
Alignment with 3 rows and 4 columns
ACGC Chicken
ACGT Human
ACGG Mouse
>>> align1.sort(key = lambda record: gc_fraction(record.seq))
>>> print(align1)
Alignment with 3 rows and 4 columns
ACGT Human
ACGC Chicken
ACGG Mouse
There is also a reverse argument, so if you wanted to sort by ID
but backwards:
>>> align1.sort(reverse=True)
>>> print(align1)
Alignment with 3 rows and 4 columns
ACGG Mouse
ACGT Human
ACGC Chicken
"""
if key is None:
self._records.sort(key=lambda r: r.id, reverse=reverse)
else:
self._records.sort(key=key, reverse=reverse)
@property
def substitutions(self):
"""Return an Array with the number of substitutions of letters in the alignment.
As an example, consider a multiple sequence alignment of three DNA sequences:
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import MultipleSeqAlignment
>>> seq1 = SeqRecord(Seq("ACGT"), id="seq1")
>>> seq2 = SeqRecord(Seq("A--A"), id="seq2")
>>> seq3 = SeqRecord(Seq("ACGT"), id="seq3")
>>> seq4 = SeqRecord(Seq("TTTC"), id="seq4")
>>> alignment = MultipleSeqAlignment([seq1, seq2, seq3, seq4])
>>> print(alignment)
Alignment with 4 rows and 4 columns
ACGT seq1
A--A seq2
ACGT seq3
TTTC seq4
>>> m = alignment.substitutions
>>> print(m)
A C G T
A 3.0 0.5 0.0 2.5
C 0.5 1.0 0.0 2.0
G 0.0 0.0 1.0 1.0
T 2.5 2.0 1.0 1.0
<BLANKLINE>
Note that the matrix is symmetric, with counts divided equally on both
sides of the diagonal. For example, the total number of substitutions
between A and T in the alignment is 3.5 + 3.5 = 7.
Any weights associated with the sequences are taken into account when
calculating the substitution matrix. For example, given the following
multiple sequence alignment::
GTATC 0.5
AT--C 0.8
CTGTC 1.0
For the first column we have::
('A', 'G') : 0.5 * 0.8 = 0.4
('C', 'G') : 0.5 * 1.0 = 0.5
('A', 'C') : 0.8 * 1.0 = 0.8
"""
letters = set.union(*(set(record.seq) for record in self))
try:
letters.remove("-")
except KeyError:
pass
letters = "".join(sorted(letters))
m = substitution_matrices.Array(letters, dims=2)
for rec_num1, alignment1 in enumerate(self):
seq1 = alignment1.seq
weight1 = alignment1.annotations.get("weight", 1.0)
for rec_num2, alignment2 in enumerate(self):
if rec_num1 == rec_num2:
break
seq2 = alignment2.seq
weight2 = alignment2.annotations.get("weight", 1.0)
for residue1, residue2 in zip(seq1, seq2):
if residue1 == "-":
continue
if residue2 == "-":
continue
m[(residue1, residue2)] += weight1 * weight2
m += m.transpose()
m /= 2.0
return m
@property
def alignment(self):
"""Return an Alignment object based on the MultipleSeqAlignment object.
This makes a copy of each SeqRecord with a gap-less sequence. Any
future changes to the original records in the MultipleSeqAlignment will
not affect the new records in the Alignment.
"""
records = [copy.copy(record) for record in self._records]
if records:
lines = [bytes(record.seq) for record in records]
seqdata, coordinates = Alignment.parse_printed_alignment(lines)
for record, seqrow in zip(records, seqdata):
if record.letter_annotations:
indices = [i for i, c in enumerate(record.seq) if c != "-"]
letter_annotations = dict(record.letter_annotations)
record.letter_annotations.clear()
record.seq = Seq(seqrow)
for key, value in letter_annotations.items():
if isinstance(value, str):
value = "".join([value[i] for i in indices])
else: # list, tuple
cls = type(value)
value = cls(value[i] for i in indices)
letter_annotations[key] = value
record.letter_annotations = letter_annotations
else:
record.seq = Seq(seqrow)
alignment = Alignment(records, coordinates)
else:
alignment = Alignment([])
alignment.annotations = self.annotations
alignment.column_annotations = self.column_annotations
return alignment
class Alignment:
"""Represents a sequence alignment.
An Alignment object has a `.sequences` attribute storing the sequences
(Seq, MutableSeq, SeqRecord, or string objects) that were aligned, as well
as a `.coordinates` attribute storing the sequence coordinates defining the
alignment as a NumPy array.
Other commonly used attributes (which may or may not be present) are:
- annotations - A dictionary with annotations describing the
alignment;
- column_annotations - A dictionary with annotations describing each
column in the alignment;
- score - The alignment score.
"""
@classmethod
def infer_coordinates(cls, lines):
"""Infer the coordinates from a printed alignment (DEPRECATED).
This method is primarily employed in Biopython's alignment parsers,
though it may be useful for other purposes.
For an alignment consisting of N sequences, printed as N lines with
the same number of columns, where gaps are represented by dashes,
this method will calculate the sequence coordinates that define the
alignment. The coordinates are returned as a NumPy array of integers,
and can be used to create an Alignment object.
This is an example for the alignment of three sequences TAGGCATACGTG,
AACGTACGT, and ACGCATACTTG, with gaps in the second and third sequence:
>>> from Bio.Align import Alignment
>>> lines = ["TAGGCATACGTG",
... "AACG--TACGT-",
... "-ACGCATACTTG",
... ]
>>> sequences = [line.replace("-", "") for line in lines]
>>> sequences
['TAGGCATACGTG', 'AACGTACGT', 'ACGCATACTTG']
>>> coordinates = Alignment.infer_coordinates(lines)
>>> print(coordinates)
[[ 0 1 4 6 11 12]
[ 0 1 4 4 9 9]
[ 0 0 3 5 10 11]]
>>> alignment = Alignment(sequences, coordinates)
"""
warnings.warn(
"The method infer_coordinates is deprecated; please use the "
"method parse_printed_alignment instead. This method is much "
"faster than infer_coordinates, and returns both the sequences "
"after removal of the gaps and the coordinates.",
BiopythonDeprecationWarning,
)
lines = [line.encode() for line in lines]
seqdata, coordinates = cls.parse_printed_alignment(lines)
return coordinates
@classmethod
def parse_printed_alignment(cls, lines):
"""Infer the sequences and coordinates from a printed alignment.
This method is primarily employed in Biopython's alignment parsers,
though it may be useful for other purposes.
For an alignment consisting of N sequences, printed as N lines with
the same number of columns, where gaps are represented by dashes,
this method will calculate the sequence coordinates that define the
alignment. It returns the tuple (sequences, coordinates), where
sequences is the list of N sequences after removing the gaps, and
the coordinates is a 2D NumPy array of integers. Together, the
sequences and coordinates can be used to create an Alignment object.
This is an example for the alignment of three sequences TAGGCATACGTG,
AACGTACGT, and ACGCATACTTG, with gaps in the second and third sequence.
Note that the input sequences are bytes objects.
>>> from Bio.Align import Alignment
>>> from Bio.Seq import Seq
>>> lines = [b"TAGGCATACGTG",
... b"AACG--TACGT-",
... b"-ACGCATACTTG",
... ]
>>> sequences, coordinates = Alignment.parse_printed_alignment(lines)
>>> sequences
[b'TAGGCATACGTG', b'AACGTACGT', b'ACGCATACTTG']
>>> print(coordinates)
[[ 0 1 4 6 11 12]
[ 0 1 4 4 9 9]
[ 0 0 3 5 10 11]]
>>> sequences = [Seq(sequence) for sequence in sequences]
>>> sequences
[Seq('TAGGCATACGTG'), Seq('AACGTACGT'), Seq('ACGCATACTTG')]
>>> alignment = Alignment(sequences, coordinates)
>>> print(alignment)
0 TAGGCATACGTG 12
0 AACG--TACGT- 9
0 -ACGCATACTTG 11
<BLANKLINE>
"""
parser = _aligncore.PrintedAlignmentParser(b"\0")
sequences = []
for line in lines:
nbytes, sequence = parser.feed(line)
sequences.append(sequence)
shape = parser.shape
coordinates = np.empty(shape, np.int64)
parser.fill(coordinates)
return sequences, coordinates
def __init__(self, sequences, coordinates=None):
"""Initialize a new Alignment object.
Arguments:
- sequences - A list of the sequences (Seq, MutableSeq, SeqRecord,
or string objects) that were aligned.
- coordinates - The sequence coordinates that define the alignment.
If None (the default value), assume that the sequences
align to each other without any gaps.
"""
self.sequences = sequences
if coordinates is None:
try:
lengths = {len(sequence) for sequence in sequences}
except TypeError:
# this may happen if sequences contain a SeqRecord where
# the seq attribute is None, as neither the sequence nor
# its length are known.
pass
else:
if len(lengths) == 0:
coordinates = np.empty((0, 0), dtype=int)
elif len(lengths) == 1:
length = lengths.pop()
coordinates = np.array([[0, length]] * len(sequences))
else:
raise ValueError(
"sequences must have the same length if coordinates is None"
)
self.coordinates = coordinates
def __array__(self, dtype=None):
coordinates = self.coordinates.copy()
sequences = list(self.sequences)
steps = np.diff(self.coordinates, 1)
aligned = sum(steps != 0, 0) > 1
# True for steps in which at least two sequences align, False if a gap
for i, sequence in enumerate(sequences):
row = steps[i, aligned]
if (row >= 0).all():
pass
elif (row <= 0).all():
sequences[i] = reverse_complement(sequence)
coordinates[i, :] = len(sequence) - coordinates[i, :]
steps[i, :] = -steps[i, :]
else:
raise ValueError(f"Inconsistent steps in row {i}")
gaps = steps.max(0)
if not ((steps == gaps) | (steps <= 0)).all():
raise ValueError("Unequal step sizes in alignment")
n = len(steps)
m = sum(gaps)
data = np.empty((n, m), "S1")
for i in range(n):
sequence = sequences[i]
k = coordinates[i, 0]
m = 0
for step, gap in zip(steps[i], gaps):
if step > 0:
j = k + step
n = m + step
try:
subsequence = bytes(sequence[k:j])
except TypeError: # str
subsequence = bytes(sequence[k:j], "UTF8")
data[i, :].data.cast("B")[m:n] = subsequence
k = j
m = n
elif step < 0:
k += step
else: # step == 0
n = m + gap
data[i, m:n] = b"-"
m = n
if dtype is not None:
data = np.array(data, dtype)
return data
def __add__(self, other):
"""Combine two alignments by adding them row-wise.
For example,
>>> import numpy as np
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> from Bio.Align import Alignment
>>> a1 = SeqRecord(Seq("AAAAC"), id="Alpha")
>>> b1 = SeqRecord(Seq("AAAC"), id="Beta")
>>> c1 = SeqRecord(Seq("AAAAG"), id="Gamma")
>>> a2 = SeqRecord(Seq("GTT"), id="Alpha")
>>> b2 = SeqRecord(Seq("TT"), id="Beta")
>>> c2 = SeqRecord(Seq("GT"), id="Gamma")
>>> left = Alignment([a1, b1, c1],
... coordinates=np.array([[0, 3, 4, 5],
... [0, 3, 3, 4],
... [0, 3, 4, 5]]))
>>> left.annotations = {"tool": "demo", "name": "start"}
>>> left.column_annotations = {"stats": "CCCXC"}
>>> right = Alignment([a2, b2, c2],
... coordinates=np.array([[0, 1, 2, 3],
... [0, 0, 1, 2],
... [0, 1, 1, 2]]))
>>> right.annotations = {"tool": "demo", "name": "end"}
>>> right.column_annotations = {"stats": "CXC"}
Now, let's look at these two alignments:
>>> print(left)
Alpha 0 AAAAC 5
Beta 0 AAA-C 4
Gamma 0 AAAAG 5
<BLANKLINE>
>>> print(right)
Alpha 0 GTT 3
Beta 0 -TT 2
Gamma 0 G-T 2
<BLANKLINE>
And add them:
>>> combined = left + right
>>> print(combined)
Alpha 0 AAAACGTT 8
Beta 0 AAA-C-TT 6
Gamma 0 AAAAGG-T 7
<BLANKLINE>
For this to work, both alignments must have the same number of sequences
(here they both have 3 rows):
>>> len(left)
3
>>> len(right)
3
>>> len(combined)
3
The sequences are SeqRecord objects, and these can be added together. Refer
to the SeqRecord documentation for details of how the annotation is handled. This
example is a special case in that both original alignments shared the same names,
meaning when the rows are added they also get the same name.
Any common annotations are preserved, but differing annotation is lost. This is
the same behavior used in the SeqRecord annotations and is designed to prevent
accidental propagation of inappropriate values:
>>> combined.annotations
{'tool': 'demo'}
Similarly any common per-column-annotations are combined:
>>> combined.column_annotations
{'stats': 'CCCXCCXC'}
"""
if not isinstance(other, Alignment):
raise NotImplementedError
if len(self) != len(other):
raise ValueError(
"When adding two alignments they must have the same length"
" (i.e. same number of rows)"
)
starts1 = self.coordinates[:, 0]
ends1 = self.coordinates[:, -1]
sequences1 = self.sequences
starts2 = other.coordinates[:, 0]
ends2 = other.coordinates[:, -1]
sequences2 = other.sequences
sequences = []
for start1, end1, seq1, start2, end2, seq2 in zip(
starts1, ends1, sequences1, starts2, ends2, sequences2
):
sequence = seq1[start1:end1] + seq2[start2:end2]
sequences.append(sequence)
offset = starts2 - ends1 + starts1
coordinates1 = self.coordinates - starts1[:, None]
coordinates2 = other.coordinates - offset[:, None]
coordinates = np.append(coordinates1, coordinates2, axis=1)
alignment = Alignment(sequences, coordinates)
# Take any common annotation:
annotations = {}
try:
for k, v in self.annotations.items():
try:
if other.annotations[k] == v:
annotations[k] = v
except KeyError:
continue
except AttributeError:
pass
else:
alignment.annotations = annotations
column_annotations = {}
try:
for k, v in self.column_annotations.items():
try:
column_annotations[k] = v + other.column_annotations[k]
except KeyError:
continue
except AttributeError:
pass
else:
alignment.column_annotations = column_annotations
return alignment
@property
def frequencies(self):
"""Return the frequency of each letter in each column of the alignment.
Gaps are represented by a dash ("-") character.
For example,
>>> from Bio import Align
>>> aligner = Align.PairwiseAligner()
>>> aligner.mode = "global"
>>> alignments = aligner.align("GACCTG", "CGATCG")
>>> alignment = alignments[0]
>>> print(alignment)
target 0 -GACCT-G 6
0 -||--|-| 8
query 0 CGA--TCG 6
<BLANKLINE>
>>> alignment.frequencies
{'-': array([1., 0., 0., 1., 1., 0., 1., 0.]), 'G': array([0., 2., 0., 0., 0., 0., 0., 2.]), 'A': array([0., 0., 2., 0., 0., 0., 0., 0.]), 'C': array([1., 0., 0., 1., 1., 0., 1., 0.]), 'T': array([0., 0., 0., 0., 0., 2., 0., 0.])}
>>> aligner.mode = "local"
>>> alignments = aligner.align("GACCTG", "CGATCG")
>>> alignment = alignments[0]
>>> print(alignment)
target 0 GACCT-G 6
0 ||--|-| 7
query 1 GA--TCG 6
<BLANKLINE>
>>> alignment.frequencies
{'G': array([2., 0., 0., 0., 0., 0., 2.]), 'A': array([0., 2., 0., 0., 0., 0., 0.]), 'C': array([0., 0., 1., 1., 0., 1., 0.]), 'T': array([0., 0., 0., 0., 2., 0., 0.]), '-': array([0., 0., 1., 1., 0., 1., 0.])}
"""
coordinates = self.coordinates.copy()
sequences = list(self.sequences)
steps = np.diff(self.coordinates, 1)
aligned = sum(steps != 0, 0) > 1
# True for steps in which at least two sequences align, False if a gap
for i, sequence in enumerate(sequences):
row = steps[i, aligned]
if (row >= 0).all():
pass
elif (row <= 0).all():
sequences[i] = reverse_complement(sequence)
coordinates[i, :] = len(sequence) - coordinates[i, :]
steps[i, :] = -steps[i, :]
else:
raise ValueError(f"Inconsistent steps in row {i}")
gaps = steps.max(0)
if not ((steps == gaps) | (steps <= 0)).all():
raise ValueError("Unequal step sizes in alignment")
n = len(steps)
length = sum(gaps)
counts = {}
for i in range(n):
sequence = sequences[i]
try:
weight = sequence.annotations.get("weight", 1.0)
except AttributeError:
weight = 1.0
k = coordinates[i, 0]
m = 0
for step, gap in zip(steps[i], gaps):
if step > 0:
j = k + step
n = m + step
try:
subsequence = bytes(sequence[k:j])
except TypeError: # str
subsequence = bytes(sequence[k:j], "UTF8")
for index, letter in zip(range(m, n), subsequence):
character = chr(letter)
row = counts.get(character)
if row is None:
row = np.zeros(length)
counts[character] = row
row[index] += weight
k = j
m = n
elif step < 0:
k += step
else: # step == 0
n = m + gap
character = "-"
row = counts.get(character)
if row is None:
row = np.zeros(length)
counts[character] = row
row[m:n] += weight
m = n
return counts
@property
def target(self):
"""Return self.sequences[0] for a pairwise alignment."""
n = len(self.sequences)
if n > 2:
# also allow alignments in which only the target alignment is defined
raise ValueError(
"self.target is defined for pairwise alignments only (found alignment of %d sequences)"
% n
)
return self.sequences[0]
@target.setter
def target(self, value):
"""For a pairwise alignment, set self.sequences[0]."""
n = len(self.sequences)
if n != 2:
raise ValueError(
"self.target is defined for pairwise alignments only (found alignment of %d sequences)"
% n
)
self.sequences[0] = value
@property
def query(self):
"""Return self.sequences[1] for a pairwise alignment."""
n = len(self.sequences)
if n != 2:
raise ValueError(
"self.query is defined for pairwise alignments only (found alignment of %d sequences)"
% n
)
return self.sequences[1]
@query.setter
def query(self, value):
"""For a pairwise alignment, set self.sequences[1]."""
n = len(self.sequences)
if n != 2:
raise ValueError(
"self.query is defined for pairwise alignments only (found alignment of %d sequences)"
% n
)
self.sequences[1] = value
def __eq__(self, other):
"""Check if two Alignment objects specify the same alignment."""
for left, right in zip_longest(self.sequences, other.sequences):
try:
left = left.seq
except AttributeError:
pass
try:
right = right.seq
except AttributeError:
pass
if left != right:
return False
return np.array_equal(self.coordinates, other.coordinates)
def __ne__(self, other):
"""Check if two Alignment objects have different alignments."""
for left, right in zip_longest(self.sequences, other.sequences):
try:
left = left.seq
except AttributeError:
pass
try:
right = right.seq
except AttributeError:
pass
if left != right:
return True
return not np.array_equal(self.coordinates, other.coordinates)
def __lt__(self, other):
"""Check if self should come before other."""
for left, right in zip_longest(self.sequences, other.sequences):
try:
left = left.seq
except AttributeError:
pass
try:
right = right.seq
except AttributeError:
pass
if left < right:
return True
if left > right:
return False
for left, right in zip(
self.coordinates.transpose(), other.coordinates.transpose()
):
left, right = tuple(left), tuple(right)
if left < right:
return True
if left > right:
return False
return False
def __le__(self, other):
"""Check if self should come before or is equal to other."""
for left, right in zip_longest(self.sequences, other.sequences):
try:
left = left.seq
except AttributeError:
pass
try:
right = right.seq
except AttributeError:
pass
if left < right:
return True
if left > right:
return False
for left, right in zip(
self.coordinates.transpose(), other.coordinates.transpose()
):
left, right = tuple(left), tuple(right)
if left < right:
return True
if left > right:
return False
return True
def __gt__(self, other):
"""Check if self should come after other."""
for left, right in zip_longest(self.sequences, other.sequences):
try:
left = left.seq
except AttributeError:
pass
try:
right = right.seq
except AttributeError:
pass
if left < right:
return False
if left > right:
return True
for left, right in zip(
self.coordinates.transpose(), other.coordinates.transpose()
):
left, right = tuple(left), tuple(right)
if left > right:
return True
if left < right:
return False
return False
def __ge__(self, other):
"""Check if self should come after or is equal to other."""
for left, right in zip_longest(self.sequences, other.sequences):
try:
left = left.seq
except AttributeError:
pass
try:
right = right.seq
except AttributeError:
pass
if left < right:
return False
if left > right:
return True
for left, right in zip(
self.coordinates.transpose(), other.coordinates.transpose()
):
left, right = tuple(left), tuple(right)
if left > right:
return True
if left < right:
return False
return True
def _get_row(self, index):
"""Return self[index], where index is an integer (PRIVATE).
This method is called by __getitem__ for invocations of the form
self[row]
where row is an integer.
Return value is a string if the aligned sequences are string, Seq,
or SeqRecord objects, otherwise the return value is a list.
"""
steps = np.diff(self.coordinates, 1)
n = len(steps)
if index < 0:
index += n
if index < 0:
raise IndexError("row index out of range")
elif index >= n:
raise IndexError("row index out of range")
aligned = sum(steps != 0, 0) > 1
# True for steps in which at least two sequences align, False if a gap
coordinates = self.coordinates[index, :]
sequence = self.sequences[index]
for i in range(n):
aligned_steps = steps[i, aligned]
if sum(aligned_steps > 0) < sum(aligned_steps < 0):
steps[i, :] = -steps[i, :]
if i == index:
sequence = reverse_complement(sequence)
coordinates = len(sequence) - coordinates
gaps = steps.max(0)
try:
sequence = sequence.seq # SeqRecord confusion
except AttributeError:
pass
steps = steps[index]
k = coordinates[0]
if isinstance(sequence, (str, Seq)):
line = ""
for step, gap in zip(steps, gaps):
if step > 0:
j = k + step
line += str(sequence[k:j])
k = j
elif step < 0:
k += step
else: # step == 0
line += "-" * gap
else:
line = []
for step, gap in zip(steps, gaps):
if step > 0:
j = k + step
line.extend(sequence[k:j])
k = j
else:
line.extend([None] * gap)
return line
def _get_rows(self, key):
"""Return self[key], where key is a slice object (PRIVATE).
This method is called by __getitem__ for invocations of the form
self[rows]
where rows is a slice object. Return value is an Alignment object.
"""
sequences = self.sequences[key]
coordinates = self.coordinates[key].copy()
alignment = Alignment(sequences, coordinates)
if np.array_equal(self.coordinates, coordinates):
try:
alignment.score = self.score
except AttributeError:
pass
try:
alignment.column_annotations = self.column_annotations
except AttributeError:
pass
return alignment
def _get_row_col(self, j, col, steps, gaps, sequence):
"""Return the sequence contents at alignment column j (PRIVATE).
This method is called by __getitem__ for invocations of the form
self[row, col]
where both row and col are integers.
Return value is a string of length 1.
"""
indices = gaps.cumsum()
index = indices.searchsorted(col, side="right")
if steps[index]:
offset = col - indices[index]
j += sum(steps[: index + 1]) + offset
return sequence[j]
else:
return "-"
def _get_row_cols_slice(
self, coordinate, start_index, stop_index, steps, gaps, sequence
):
"""Return the alignment contents of one row and consecutive columns (PRIVATE).
This method is called by __getitem__ for invocations of the form
self[row, cols]
where row is an integer and cols is a slice object with step 1.
Return value is a string if the aligned sequences are string, Seq,
or SeqRecord objects, otherwise the return value is a list.
"""
indices = gaps.cumsum()
i = indices.searchsorted(start_index, side="right")
j = i + indices[i:].searchsorted(stop_index, side="right")
try:
sequence = sequence.seq # stupid SeqRecord
except AttributeError:
pass
if isinstance(sequence, (str, Seq)):
if i == j:
length = stop_index - start_index
if steps[i] == 0:
line = "-" * length
else:
start = coordinate[i] + start_index - indices[i - 1]
stop = start + length
line = str(sequence[start:stop])
else:
length = indices[i] - start_index
if steps[i] == 0:
line = "-" * length
else:
stop = coordinate[i + 1]
start = stop - length
line = str(sequence[start:stop])
i += 1
while i < j:
step = gaps[i]
if steps[i] == 0:
line += "-" * step
else:
start = coordinate[i]
stop = coordinate[i + 1]
line += str(sequence[start:stop])
i += 1
length = stop_index - indices[i - 1]
if length > 0:
if steps[i] == 0:
line += "-" * length
else:
start = coordinate[i]
stop = start + length
line += str(sequence[start:stop])
else:
if i == j:
length = stop_index - start_index
if steps[i] == 0:
line = [None] * length
else:
start = coordinate[i] + start_index - indices[i - 1]
stop = start + length
line = sequence[start:stop]
else:
length = indices[i] - start_index
if steps[i] == 0:
line = [None] * length
else:
stop = coordinate[i + 1]
start = stop - length
line = sequence[start:stop]
i += 1
while i < j:
step = gaps[i]
if steps[i] == 0:
line.extend([None] * step)
else:
start = coordinate[i]
stop = coordinate[i + 1]
line.extend(sequence[start:stop])
i += 1
length = stop_index - indices[i - 1]
if length > 0:
if steps[j] == 0:
line.extend([None] * length)
else:
start = coordinate[i]
stop = start + length
line.extend(sequence[start:stop])
return line
def _get_row_cols_iterable(self, coordinate, cols, gaps, sequence):
"""Return the alignment contents of one row and multiple columns (PRIVATE).
This method is called by __getitem__ for invocations of the form
self[row, cols]
where row is an integer and cols is an iterable of integers.
Return value is a string if the aligned sequences are string, Seq,
or SeqRecord objects, otherwise the return value is a list.
"""
try:
sequence = sequence.seq # stupid SeqRecord
except AttributeError:
pass
if isinstance(sequence, (str, Seq)):
line = ""
start = coordinate[0]
for end, gap in zip(coordinate[1:], gaps):
if start < end:
line += str(sequence[start:end])
else:
line += "-" * gap
start = end
try:
line = "".join(line[col] for col in cols)
except IndexError:
raise
except Exception:
raise TypeError(
"second index must be an integer, slice, or iterable of integers"
) from None
else:
line = []
start = coordinate[0]
for end, gap in zip(coordinate[1:], gaps):
if start < end:
line.extend(sequence[start:end])
else:
line.extend([None] * gap)
start = end
try:
line = [line[col] for col in cols]
except IndexError:
raise
except Exception:
raise TypeError(
"second index must be an integer, slice, or iterable of integers"
) from None
return line
def _get_rows_col(self, coordinates, col, steps, gaps, sequences):
"""Return the alignment contents of multiple rows and one column (PRIVATE).
This method is called by __getitem__ for invocations of the form
self[rows, col]
where rows is a slice object, and col is an integer.
Return value is a string.
"""
indices = gaps.cumsum()
j = indices.searchsorted(col, side="right")
offset = indices[j] - col
line = ""
for sequence, coordinate, step in zip(sequences, coordinates, steps):
if step[j] == 0:
line += "-"
else:
index = coordinate[j] + step[j] - offset
line += sequence[index]
return line
def _get_rows_cols_slice(
self, coordinates, row, start_index, stop_index, steps, gaps
):
"""Return a subalignment of multiple rows and consecutive columns (PRIVATE).
This method is called by __getitem__ for invocations of the form
self[rows, cols]
where rows is an arbitrary slice object, and cols is a slice object
with step 1, allowing the alignment sequences to be reused in the
subalignment. Return value is an Alignment object.
"""
rcs = np.any(coordinates != self.coordinates[row], axis=1)
indices = gaps.cumsum()
i = indices.searchsorted(start_index, side="right")
j = i + indices[i:].searchsorted(stop_index, side="left") + 1
offset = steps[:, i] - indices[i] + start_index
coordinates[:, i] += offset * (steps[:, i] > 0)
offset = indices[j - 1] - stop_index
coordinates[:, j] -= offset * (steps[:, j - 1] > 0)
coordinates = coordinates[:, i : j + 1]
sequences = self.sequences[row]
for coordinate, rc, sequence in zip(coordinates, rcs, sequences):
if rc:
# mapped to reverse strand
coordinate[:] = len(sequence) - coordinate[:]
alignment = Alignment(sequences, coordinates)
if np.array_equal(self.coordinates, coordinates):
try:
alignment.score = self.score
except AttributeError:
pass
try:
column_annotations = self.column_annotations
except AttributeError:
pass
else:
alignment.column_annotations = {}
for key, value in column_annotations.items():
value = value[start_index:stop_index]
try:
value = value.copy()
except AttributeError:
# immutable tuples like str, tuple
pass
alignment.column_annotations[key] = value
return alignment
def _get_rows_cols_iterable(self, coordinates, col, steps, gaps, sequences):
"""Return a subalignment of multiple rows and columns (PRIVATE).
This method is called by __getitem__ for invocations of the form
self[rows, cols]
where rows is a slice object and cols is an iterable of integers.
This method will create new sequences for use by the subalignment
object. Return value is an Alignment object.
"""
indices = tuple(col)
lines = []
for i, sequence in enumerate(sequences):
try:
s = sequence.seq # stupid SeqRecord
except AttributeError:
s = sequence
line = ""
k = coordinates[i, 0]
for step, gap in zip(steps[i], gaps):
if step:
j = k + step
line += str(s[k:j])
k = j
else:
line += "-" * gap
try:
line = "".join(line[index] for index in indices)
except IndexError:
raise
except Exception:
raise TypeError(
"second index must be an integer, slice, or iterable of integers"
) from None
line = line.encode()
lines.append(line)
seqdata, coordinates = self.parse_printed_alignment(lines)
for i, sequence in enumerate(sequences):
line = seqdata[i]
try:
s = sequence.seq
except AttributeError:
if isinstance(sequence, str): # str
sequence = line.decode()
else:
sequence = sequence.__class__(line) # Seq, MutableSeq
else: # SeqRecord
sequence = copy.deepcopy(sequence)
sequence.seq = s.__class__(line)
sequences[i] = sequence
alignment = Alignment(sequences, coordinates)
try:
column_annotations = self.column_annotations
except AttributeError:
pass
else:
alignment.column_annotations = {}
for key, value in column_annotations.items():
values = (value[index] for index in indices)
if isinstance(value, str):
value = "".join(values)
else:
value = value.__class__(values)
alignment.column_annotations[key] = value
return alignment
def __getitem__(self, key):
"""Return self[key].
Indices of the form
self[:, :]
return a copy of the Alignment object;
self[:, i:]
self[:, :j]
self[:, i:j]
self[:, iterable] (where iterable returns integers)
return a new Alignment object spanning the selected columns;
self[k, i]
self[k, i:]
self[k, :j]
self[k, i:j]
self[k, iterable] (where iterable returns integers)
self[k] (equivalent to self[k, :])
return a string with the aligned sequence (including gaps) for the
selected columns, where k = 0 represents the target and k = 1
represents the query sequence; and
self[:, i]
returns a string with the selected column in the alignment.
>>> from Bio.Align import PairwiseAligner
>>> aligner = PairwiseAligner()
>>> alignments = aligner.align("ACCGGTTT", "ACGGGTT")
>>> alignment = alignments[0]
>>> print(alignment)
target 0 ACCGG-TTT 8
0 ||-||-||- 9
query 0 AC-GGGTT- 7
<BLANKLINE>
>>> alignment[0, :]
'ACCGG-TTT'
>>> alignment[1, :]
'AC-GGGTT-'
>>> alignment[0]
'ACCGG-TTT'
>>> alignment[1]
'AC-GGGTT-'
>>> alignment[0, 1:-2]
'CCGG-T'
>>> alignment[1, 1:-2]
'C-GGGT'
>>> alignment[0, (1, 5, 2)]
'C-C'
>>> alignment[1, ::2]
'A-GT-'
>>> alignment[1, range(0, 9, 2)]
'A-GT-'
>>> alignment[:, 0]
'AA'
>>> alignment[:, 5]
'-G'
>>> alignment[:, 1:] # doctest:+ELLIPSIS
<Alignment object (2 rows x 8 columns) at 0x...>
>>> print(alignment[:, 1:])
target 1 CCGG-TTT 8
0 |-||-||- 8
query 1 C-GGGTT- 7
<BLANKLINE>
>>> print(alignment[:, 2:])
target 2 CGG-TTT 8
0 -||-||- 7
query 2 -GGGTT- 7
<BLANKLINE>
>>> print(alignment[:, 3:])
target 3 GG-TTT 8
0 ||-||- 6
query 2 GGGTT- 7
<BLANKLINE>
>>> print(alignment[:, 3:-1])
target 3 GG-TT 7
0 ||-|| 5
query 2 GGGTT 7
<BLANKLINE>
>>> print(alignment[:, ::2])
target 0 ACGTT 5
0 |-||- 5
query 0 A-GT- 3
<BLANKLINE>
>>> print(alignment[:, range(1, 9, 2)])
target 0 CG-T 3
0 ||-| 4
query 0 CGGT 4
<BLANKLINE>
>>> print(alignment[:, (2, 7, 3)])
target 0 CTG 3
0 -|| 3
query 0 -TG 2
<BLANKLINE>
"""
if isinstance(key, numbers.Integral):
return self._get_row(key)
if isinstance(key, slice):
return self._get_rows(key)
sequences = list(self.sequences)
coordinates = self.coordinates.copy()
steps = np.diff(coordinates, 1)
aligned = sum(steps != 0, 0) > 1
# True for steps in which at least two sequences align, False if a gap
for i, sequence in enumerate(sequences):
row = steps[i, aligned]
if (row >= 0).all():
pass
elif (row <= 0).all():
steps[i, :] = -steps[i, :]
coordinates[i, :] = len(sequence) - coordinates[i, :]
sequences[i] = reverse_complement(sequence)
try:
sequences[i].id = sequence.id
except AttributeError:
pass
else:
raise ValueError(f"Inconsistent steps in row {i}")
gaps = steps.max(0)
if not ((steps == gaps) | (steps <= 0)).all():
raise ValueError("Unequal step sizes in alignment")
m = sum(gaps)
if isinstance(key, tuple):
try:
row, col = key
except ValueError:
raise ValueError("only tuples of length 2 can be alignment indices")
else:
raise TypeError("alignment indices must be integers, slices, or tuples")
if isinstance(col, numbers.Integral):
if col < 0:
col += m
if col < 0 or col >= m:
raise IndexError(
"column index %d is out of bounds (%d columns)" % (col, m)
)
steps = steps[row]
if isinstance(row, numbers.Integral):
sequence = sequences[row]
if isinstance(col, numbers.Integral):
return self._get_row_col(
coordinates[row, 0], col, steps, gaps, sequence
)
coordinate = coordinates[row, :]
if isinstance(col, slice):
start_index, stop_index, step = col.indices(m)
if start_index < stop_index and step == 1:
return self._get_row_cols_slice(
coordinate, start_index, stop_index, steps, gaps, sequence
)
# make an iterable if step != 1
col = range(start_index, stop_index, step)
return self._get_row_cols_iterable(coordinate, col, gaps, sequence)
if isinstance(row, slice):
sequences = sequences[row]
coordinates = coordinates[row]
if isinstance(col, numbers.Integral):
return self._get_rows_col(coordinates, col, steps, gaps, sequences)
if isinstance(col, slice):
start_index, stop_index, step = col.indices(m)
if start_index < stop_index and step == 1:
return self._get_rows_cols_slice(
coordinates,
row,
start_index,
stop_index,
steps,
gaps,
)
# make an iterable if step != 1
col = range(start_index, stop_index, step)
# try if we can use col as an iterable
return self._get_rows_cols_iterable(
coordinates, col, steps, gaps, sequences
)
raise TypeError("first index must be an integer or slice")
def _convert_sequence_string(self, sequence):
"""Convert given sequence to string using the appropriate method (PRIVATE)."""
if isinstance(sequence, (bytes, bytearray)):
return sequence.decode()
if isinstance(sequence, str):
return sequence
if isinstance(sequence, Seq):
return str(sequence)
try: # check if target is a SeqRecord
sequence = sequence.seq
except AttributeError:
pass
else:
return str(sequence)
try:
view = memoryview(sequence)
except TypeError:
pass
else:
if view.format == "c":
return str(sequence)
return None
def __format__(self, format_spec):
"""Return the alignment as a string in the specified file format.
Wrapper for self.format().
"""
return self.format(format_spec)
def format(self, fmt="", *args, **kwargs):
"""Return the alignment as a string in the specified file format.
Arguments:
- fmt - File format. Acceptable values are an empty string to
create a human-readable representation of the alignment,
or any of the alignment file formats supported by
`Bio.Align` (some have not yet been implemented).
All other arguments are passed to the format-specific writer functions:
- mask - PSL format only. Specify if repeat regions in the target
sequence are masked and should be reported in the
`repMatches` field of the PSL file instead of in the
`matches` field. Acceptable values are
None : no masking (default);
"lower": masking by lower-case characters;
"upper": masking by upper-case characters.
- wildcard - PSL format only. Report alignments to the wildcard
character in the target or query sequence in the
`nCount` field of the PSL file instead of in the
`matches`, `misMatches`, or `repMatches` fields.
Default value is 'N'.
- md - SAM format only. If True, calculate the MD tag from
the alignment and include it in the output. If False
(default), do not include the MD tag in the output.
"""
if fmt == "":
return self._format_pretty()
module = _load(fmt)
if module.AlignmentIterator.mode == "b":
raise ValueError(f"{fmt} is a binary file format")
try:
writer = module.AlignmentWriter(None, *args, **kwargs)
except AttributeError:
raise ValueError(
f"Formatting alignments has not yet been implemented for the {fmt} format"
) from None
return writer.format_alignment(self)
def _format_pretty(self):
"""Return default string representation (PRIVATE).
Helper for self.format().
"""
n = len(self.sequences)
if n == 2:
write_pattern = True
else:
write_pattern = False
steps = np.diff(self.coordinates, 1)
aligned = sum(steps != 0, 0) > 1
# True for steps in which at least two sequences align, False if a gap
name_width = 10
names = []
seqs = []
indices = np.zeros(self.coordinates.shape, int)
for i, (seq, positions, row) in enumerate(
zip(self.sequences, self.coordinates, indices)
):
try:
name = seq.id
if name is None:
raise AttributeError
except AttributeError:
if n == 2:
if i == 0:
name = "target"
else:
name = "query"
else:
name = ""
else:
name = name[: name_width - 1]
name = name.ljust(name_width)
names.append(name)
try:
seq = seq.seq # SeqRecord confusion
except AttributeError:
pass
start = min(positions)
end = max(positions)
seq = seq[start:end]
aligned_steps = steps[i, aligned]
if len(aligned_steps) == 0:
aligned_steps = steps[i]
if sum(aligned_steps > 0) >= sum(aligned_steps < 0):
start = min(positions)
row[:] = positions - start
else:
steps[i, :] = -steps[i, :]
seq = reverse_complement(seq)
end = max(positions)
row[:] = end - positions
if isinstance(seq, str):
if not seq.isascii():
return self._format_unicode()
elif isinstance(seq, (Seq, MutableSeq)):
try:
seq = bytes(seq)
except UndefinedSequenceError:
s = bytearray(b"?" * (end - start))
for start, end in seq.defined_ranges:
s[start:end] = bytes(seq[start:end])
seq = s
seq = seq.decode()
else:
return self._format_generalized()
seqs.append(seq)
minstep = steps.min(0)
maxstep = steps.max(0)
steps = np.where(-minstep > maxstep, minstep, maxstep)
for i, row in enumerate(indices):
row_steps = np.diff(row)
row_aligned = (row_steps > 0) & aligned
row_steps = row_steps[row_aligned]
aligned_steps = steps[row_aligned]
if (row_steps == aligned_steps).all():
pass
elif (3 * row_steps == aligned_steps).all():
row[:] *= 3
seqs[i] = " ".join(seqs[i]) + " "
write_pattern = False
else:
raise ValueError("Inconsistent coordinates")
prefix_width = 10
position_width = 10
line_width = 80
lines = []
steps = indices[:, 1:] - indices[:, :-1]
minstep = steps.min(0)
maxstep = steps.max(0)
steps = np.where(-minstep > maxstep, minstep, maxstep)
for name, seq, positions, row in zip(names, seqs, self.coordinates, indices):
start = positions[0]
column = line_width
start_index = row[0]
for step, end, end_index in zip(steps, positions[1:], row[1:]):
if step < 0:
if prefix_width + position_width < column:
position_text = str(start)
offset = position_width - len(position_text) - 1
if offset < 0:
lines[-1] += " .." + position_text[-offset + 3 :]
else:
lines[-1] += " " + position_text
column = line_width
start = end
start_index = end_index
continue
elif end_index == start_index:
s = "-" * step
else:
s = seq[start_index:end_index]
while column + len(s) >= line_width:
rest = line_width - column
if rest > 0:
lines[-1] += s[:rest]
s = s[rest:]
if start != end:
if (end_index - start_index) == abs(end - start):
step = rest
else:
# protein to dna alignment;
# integer division, but round up:
step = -(rest // -3)
if start < end:
start += step
else:
start -= step
start_index += rest
line = name
position_text = str(start)
offset = position_width - len(position_text) - 1
if offset < 0:
line += " .." + position_text[-offset + 3 :]
else:
line += " " * offset + position_text
line += " "
lines.append(line)
column = name_width + position_width
lines[-1] += s
if start_index != end_index:
start_index = end_index
start = end
column += len(s)
if write_pattern is True:
dash = "-"
position = 0
m = len(lines) // 2
lines1 = lines[:m]
lines2 = lines[m:]
pattern_lines = []
for line1, line2 in zip(lines1, lines2):
aligned_seq1 = line1[name_width + position_width :]
aligned_seq2 = line2[name_width + position_width :]
pattern = ""
for c1, c2 in zip(aligned_seq1, aligned_seq2):
if c1 == c2:
if c1 == " ":
break
c = "|"
elif c1 == dash or c2 == dash:
c = "-"
else:
c = "."
pattern += c
pattern_line = " %9d %s" % (position, pattern)
pattern_lines.append(pattern_line)
position += len(pattern)
final_position_width = len(str(max(max(self.coordinates[:, -1]), position)))
if column + final_position_width <= line_width:
if prefix_width + position_width < column:
fmt = f" %{final_position_width}d"
lines1[-1] += fmt % self.coordinates[0, -1]
lines2[-1] += fmt % self.coordinates[1, -1]
pattern_lines[-1] += fmt % position
else:
name1, name2 = names
fmt = "%s%9d"
line = name1 + format(self.coordinates[0, -1], "9d")
lines1.append(line)
line = fmt % (" ", position)
pattern_lines.append(line)
line = fmt % (name2, self.coordinates[1, -1])
lines2.append(line)
lines.append("")
return "\n".join(
f"{line1}\n{pattern_line}\n{line2}\n"
for (line1, line2, pattern_line) in zip(lines1, lines2, pattern_lines)
)
else:
m = len(lines) // n
final_position_width = len(str(max(self.coordinates[:, -1])))
if column + final_position_width < line_width:
if prefix_width + position_width < column:
fmt = f" %{final_position_width}d"
for i in range(n):
lines[m - 1 + i * m] += fmt % self.coordinates[i, -1]
blocks = ["\n".join(lines[j::m]) + "\n" for j in range(m)]
else:
blocks = ["\n".join(lines[j::m]) + "\n" for j in range(m)]
lines = []
fmt = "%s%9d"
for i in range(n):
line = names[i] + format(self.coordinates[i, -1], "9d")
lines.append(line)
block = "\n".join(lines) + "\n"
blocks.append(block)
return "\n".join(blocks)
def _format_unicode(self):
"""Return default string representation (PRIVATE).
Helper for self.format().
"""
seqs = []
names = []
coordinates = self.coordinates.copy()
for seq, row in zip(self.sequences, coordinates):
seq = self._convert_sequence_string(seq)
if seq is None:
return self._format_generalized()
if row[0] > row[-1]: # mapped to reverse strand
row[:] = len(seq) - row[:]
seq = reverse_complement(seq)
seqs.append(seq)
try:
name = seq.id
except AttributeError:
if len(self.sequences) == 2:
if len(names) == 0:
name = "target"
else:
name = "query"
else:
name = ""
else:
name = name[:9]
name = name.ljust(10)
names.append(name)
steps = np.diff(coordinates, 1).max(0)
aligned_seqs = []
for row, seq in zip(coordinates, seqs):
aligned_seq = ""
start = row[0]
for step, end in zip(steps, row[1:]):
if end == start:
aligned_seq += "-" * step
else:
aligned_seq += seq[start:end]
start = end
aligned_seqs.append(aligned_seq)
if len(seqs) > 2:
return "\n".join(aligned_seqs) + "\n"
aligned_seq1, aligned_seq2 = aligned_seqs
pattern = ""
for c1, c2 in zip(aligned_seq1, aligned_seq2):
if c1 == c2:
c = "|"
elif c1 == "-" or c2 == "-":
c = "-"
else:
c = "."
pattern += c
return f"{aligned_seq1}\n{pattern}\n{aligned_seq2}\n"
def _format_generalized(self):
"""Return generalized string representation (PRIVATE).
Helper for self._format_pretty().
"""
seq1, seq2 = self.sequences
aligned_seq1 = []
aligned_seq2 = []
pattern = []
end1, end2 = self.coordinates[:, 0]
if end1 > 0 or end2 > 0:
if end1 <= end2:
for c2 in seq2[: end2 - end1]:
s2 = str(c2)
s1 = " " * len(s2)
aligned_seq1.append(s1)
aligned_seq2.append(s2)
pattern.append(s1)
else: # end1 > end2
for c1 in seq1[: end1 - end2]:
s1 = str(c1)
s2 = " " * len(s1)
aligned_seq1.append(s1)
aligned_seq2.append(s2)
pattern.append(s2)
start1 = end1
start2 = end2
for end1, end2 in self.coordinates[:, 1:].transpose():
if end1 == start1:
for c2 in seq2[start2:end2]:
s2 = str(c2)
s1 = "-" * len(s2)
aligned_seq1.append(s1)
aligned_seq2.append(s2)
pattern.append(s1)
start2 = end2
elif end2 == start2:
for c1 in seq1[start1:end1]:
s1 = str(c1)
s2 = "-" * len(s1)
aligned_seq1.append(s1)
aligned_seq2.append(s2)
pattern.append(s2)
start1 = end1
else:
t1 = seq1[start1:end1]
t2 = seq2[start2:end2]
if len(t1) != len(t2):
raise ValueError("Unequal step sizes in alignment")
for c1, c2 in zip(t1, t2):
s1 = str(c1)
s2 = str(c2)
m1 = len(s1)
m2 = len(s2)
if c1 == c2:
p = "|"
else:
p = "."
if m1 < m2:
space = (m2 - m1) * " "
s1 += space
pattern.append(p * m1 + space)
elif m1 > m2:
space = (m1 - m2) * " "
s2 += space
pattern.append(p * m2 + space)
else:
pattern.append(p * m1)
aligned_seq1.append(s1)
aligned_seq2.append(s2)
start1 = end1
start2 = end2
aligned_seq1 = " ".join(aligned_seq1)
aligned_seq2 = " ".join(aligned_seq2)
pattern = " ".join(pattern)
return f"{aligned_seq1}\n{pattern}\n{aligned_seq2}\n"
def __str__(self):
"""Return a human-readable string representation of the alignment.
For sequence alignments, each line has at most 80 columns.
The first 10 columns show the (possibly truncated) sequence name,
which may be the id attribute of a SeqRecord, or otherwise 'target'
or 'query' for pairwise alignments.
The next 10 columns show the sequence coordinate, using zero-based
counting as usual in Python.
The remaining 60 columns shown the sequence, using dashes to represent
gaps.
At the end of the alignment, the end coordinates are shown on the right
of the sequence, again in zero-based coordinates.
Pairwise alignments have an additional line between the two sequences
showing whether the sequences match ('|') or mismatch ('.'), or if
there is a gap ('-').
The coordinates shown for this line are the column indices, which can
be useful when extracting a subalignment.
For example,
>>> from Bio.Align import PairwiseAligner
>>> aligner = PairwiseAligner()
>>> seqA = "TTAACCCCATTTG"
>>> seqB = "AAGCCCCTTT"
>>> seqC = "AAAGGGGCTT"
>>> alignments = aligner.align(seqA, seqB)
>>> len(alignments)
1
>>> alignment = alignments[0]
>>> print(alignment)
target 0 TTAA-CCCCATTTG 13
0 --||-||||-|||- 14
query 0 --AAGCCCC-TTT- 10
<BLANKLINE>
Note that seqC is the reverse complement of seqB. Aligning it to the
reverse strand gives the same alignment, but the query coordinates are
switched:
>>> alignments = aligner.align(seqA, seqC, strand="-")
>>> len(alignments)
1
>>> alignment = alignments[0]
>>> print(alignment)
target 0 TTAA-CCCCATTTG 13
0 --||-||||-|||- 14
query 10 --AAGCCCC-TTT- 0
<BLANKLINE>
"""
return self.format()
def __repr__(self):
"""Return a representation of the alignment, including its shape.
The representation cannot be used with eval() to recreate the object,
which is usually possible with simple python objects. For example:
<Alignment object (2 rows x 14 columns) at 0x10403d850>
The hex string is the memory address of the object and can be used to
distinguish different Alignment objects. See help(id) for more
information.
>>> import numpy as np
>>> from Bio.Align import Alignment
>>> alignment = Alignment(("ACCGT", "ACGT"),
... coordinates = np.array([[0, 2, 3, 5],
... [0, 2, 2, 4],
... ]))
>>> print(alignment)
target 0 ACCGT 5
0 ||-|| 5
query 0 AC-GT 4
<BLANKLINE>
>>> alignment # doctest:+ELLIPSIS
<Alignment object (2 rows x 5 columns) at 0x...>
"""
if self.coordinates is None:
return "<%s object at 0x%x>" % (
self.__class__.__name__,
id(self),
)
n, m = self.shape
return "<%s object (%i rows x %i columns) at 0x%x>" % (
self.__class__.__name__,
n,
m,
id(self),
)
def __len__(self):
"""Return the number of sequences in the alignment."""
return len(self.sequences)
@property
def length(self):
"""Return the alignment length, i.e. the number of columns when printed..
The alignment length is the number of columns in the alignment when it
is printed, and is equal to the sum of the number of matches, number of
mismatches, and the total length of gaps in the target and query.
Sequence sections beyond the aligned segment are not included in the
number of columns.
For example,
>>> from Bio import Align
>>> aligner = Align.PairwiseAligner()
>>> aligner.mode = "global"
>>> alignments = aligner.align("GACCTG", "CGATCG")
>>> alignment = alignments[0]
>>> print(alignment)
target 0 -GACCT-G 6
0 -||--|-| 8
query 0 CGA--TCG 6
<BLANKLINE>
>>> alignment.length
8
>>> aligner.mode = "local"
>>> alignments = aligner.align("GACCTG", "CGATCG")
>>> alignment = alignments[0]
>>> print(alignment)
target 0 GACCT-G 6
0 ||--|-| 7
query 1 GA--TCG 6
<BLANKLINE>
>>> len(alignment)
2
>>> alignment.length
7
"""
n = len(self.coordinates)
if n == 0: # no sequences
return 0
steps = np.diff(self.coordinates, 1)
aligned = sum(steps != 0, 0) > 1
# True for steps in which at least two sequences align, False if a gap
for i in range(n):
row = steps[i, aligned]
if (row >= 0).all():
pass
elif (row <= 0).all():
steps[i, :] = -steps[i, :]
else:
raise ValueError(f"Inconsistent steps in row {i}")
gaps = steps.max(0)
if not ((steps == gaps) | (steps <= 0)).all():
raise ValueError("Unequal step sizes in alignment")
return int(sum(gaps))
@property
def shape(self):
"""Return the shape of the alignment as a tuple of two integer values.
The first integer value is the number of sequences in the alignment as
returned by len(alignment), which is always 2 for pairwise alignments.
The second integer value is the number of columns in the alignment when
it is printed, and is equal to the sum of the number of matches, number
of mismatches, and the total length of gaps in the target and query.
Sequence sections beyond the aligned segment are not included in the
number of columns.
For example,
>>> from Bio import Align
>>> aligner = Align.PairwiseAligner()
>>> aligner.mode = "global"
>>> alignments = aligner.align("GACCTG", "CGATCG")
>>> alignment = alignments[0]
>>> print(alignment)
target 0 -GACCT-G 6
0 -||--|-| 8
query 0 CGA--TCG 6
<BLANKLINE>
>>> len(alignment)
2
>>> alignment.shape
(2, 8)
>>> aligner.mode = "local"
>>> alignments = aligner.align("GACCTG", "CGATCG")
>>> alignment = alignments[0]
>>> print(alignment)
target 0 GACCT-G 6
0 ||--|-| 7
query 1 GA--TCG 6
<BLANKLINE>
>>> len(alignment)
2
>>> alignment.shape
(2, 7)
"""
n = len(self.coordinates)
m = self.length
return (n, m)
@property
def aligned(self):
"""Return the indices of subsequences aligned to each other.
This property returns the start and end indices of subsequences
in the target and query sequence that were aligned to each other.
If the alignment between target (t) and query (q) consists of N
chunks, you get two tuples of length N:
(((t_start1, t_end1), (t_start2, t_end2), ..., (t_startN, t_endN)),
((q_start1, q_end1), (q_start2, q_end2), ..., (q_startN, q_endN)))
For example,
>>> from Bio import Align
>>> aligner = Align.PairwiseAligner()
>>> alignments = aligner.align("GAACT", "GAT")
>>> alignment = alignments[0]
>>> print(alignment)
target 0 GAACT 5
0 ||--| 5
query 0 GA--T 3
<BLANKLINE>
>>> alignment.aligned
array([[[0, 2],
[4, 5]],
<BLANKLINE>
[[0, 2],
[2, 3]]])
>>> alignment = alignments[1]
>>> print(alignment)
target 0 GAACT 5
0 |-|-| 5
query 0 G-A-T 3
<BLANKLINE>
>>> alignment.aligned
array([[[0, 1],
[2, 3],
[4, 5]],
<BLANKLINE>
[[0, 1],
[1, 2],
[2, 3]]])
Note that different alignments may have the same subsequences
aligned to each other. In particular, this may occur if alignments
differ from each other in terms of their gap placement only:
>>> aligner.mismatch_score = -10
>>> alignments = aligner.align("AAACAAA", "AAAGAAA")
>>> len(alignments)
2
>>> print(alignments[0])
target 0 AAAC-AAA 7
0 |||--||| 8
query 0 AAA-GAAA 7
<BLANKLINE>
>>> alignments[0].aligned
array([[[0, 3],
[4, 7]],
<BLANKLINE>
[[0, 3],
[4, 7]]])
>>> print(alignments[1])
target 0 AAA-CAAA 7
0 |||--||| 8
query 0 AAAG-AAA 7
<BLANKLINE>
>>> alignments[1].aligned
array([[[0, 3],
[4, 7]],
<BLANKLINE>
[[0, 3],
[4, 7]]])
The property can be used to identify alignments that are identical
to each other in terms of their aligned sequences.
"""
if len(self.sequences) > 2:
raise NotImplementedError(
"aligned is currently implemented for pairwise alignments only"
)
coordinates = self.coordinates.copy()
steps = np.diff(coordinates, 1)
aligned = sum(steps != 0, 0) > 1
# True for steps in which at least two sequences align, False if a gap
for i, sequence in enumerate(self.sequences):
row = steps[i, aligned]
if (row >= 0).all():
pass
elif (row <= 0).all():
steps[i, :] = -steps[i, :]
coordinates[i, :] = len(sequence) - coordinates[i, :]
else:
raise ValueError(f"Inconsistent steps in row {i}")
coordinates = coordinates.transpose()
steps = np.diff(coordinates, axis=0)
steps = abs(steps).min(1)
indices = np.flatnonzero(steps)
starts = coordinates[indices, :]
ends = coordinates[indices + 1, :]
segments = np.stack([starts, ends], axis=0).transpose()
steps = np.diff(self.coordinates, 1)
for i, sequence in enumerate(self.sequences):
row = steps[i, aligned]
if (row >= 0).all():
pass
elif (row <= 0).all(): # mapped to reverse strand
segments[i, :] = len(sequence) - segments[i, :]
else:
raise ValueError(f"Inconsistent steps in row {i}")
return segments
@property
def indices(self):
"""Return the sequence index of each lettter in the alignment.
This property returns a 2D NumPy array with the sequence index of each
letter in the alignment. Gaps are indicated by -1. The array has the
same number of rows and columns as the alignment, as given by
`self.shape`.
For example,
>>> from Bio import Align
>>> aligner = Align.PairwiseAligner()
>>> aligner.mode = "local"
>>> alignments = aligner.align("GAACTGG", "AATG")
>>> alignment = alignments[0]
>>> print(alignment)
target 1 AACTG 6
0 ||-|| 5
query 0 AA-TG 4
<BLANKLINE>
>>> alignment.indices
array([[ 1, 2, 3, 4, 5],
[ 0, 1, -1, 2, 3]])
>>> alignment = alignments[1]
>>> print(alignment)
target 1 AACTGG 7
0 ||-|-| 6
query 0 AA-T-G 4
<BLANKLINE>
>>> alignment.indices
array([[ 1, 2, 3, 4, 5, 6],
[ 0, 1, -1, 2, -1, 3]])
>>> alignments = aligner.align("GAACTGG", "CATT", strand="-")
>>> alignment = alignments[0]
>>> print(alignment)
target 1 AACTG 6
0 ||-|| 5
query 4 AA-TG 0
<BLANKLINE>
>>> alignment.indices
array([[ 1, 2, 3, 4, 5],
[ 3, 2, -1, 1, 0]])
>>> alignment = alignments[1]
>>> print(alignment)
target 1 AACTGG 7
0 ||-|-| 6
query 4 AA-T-G 0
<BLANKLINE>
>>> alignment.indices
array([[ 1, 2, 3, 4, 5, 6],
[ 3, 2, -1, 1, -1, 0]])
"""
a = -np.ones(self.shape, int)
n, m = self.coordinates.shape
steps = np.diff(self.coordinates, 1)
aligned = sum(steps != 0, 0) > 1
# True for steps in which at least two sequences align, False if a gap
steps = steps[:, aligned]
rcs = np.zeros(n, bool)
for i, row in enumerate(steps):
if (row >= 0).all():
rcs[i] = False
elif (row <= 0).all():
rcs[i] = True
else:
raise ValueError(f"Inconsistent steps in row {i}")
i = 0
j = 0
ends = self.coordinates[:, 0]
for k in range(1, m):
starts = ends
ends = self.coordinates[:, k]
for row, start, end, rc in zip(a, starts, ends, rcs):
if rc == False and start < end: # noqa: E712
j = i + end - start
row[i:j] = range(start, end)
elif rc == True and start > end: # noqa: E712
j = i + start - end
row[i:j] = range(start - 1, end - 1, -1)
i = j
return a
@property
def inverse_indices(self):
"""Return the alignment column index for each letter in each sequence.
This property returns a list of 1D NumPy arrays; the number of arrays
is equal to the number of aligned sequences, and the length of each
array is equal to the length of the corresponding sequence. For each
letter in each sequence, the array contains the corresponding column
index in the alignment. Letters not included in the alignment are
indicated by -1.
For example,
>>> from Bio import Align
>>> aligner = Align.PairwiseAligner()
>>> aligner.mode = "local"
>>> alignments = aligner.align("GAACTGG", "AATG")
>>> alignment = alignments[0]
>>> print(alignment)
target 1 AACTG 6
0 ||-|| 5
query 0 AA-TG 4
<BLANKLINE>
>>> alignment.inverse_indices
[array([-1, 0, 1, 2, 3, 4, -1]), array([0, 1, 3, 4])]
>>> alignment = alignments[1]
>>> print(alignment)
target 1 AACTGG 7
0 ||-|-| 6
query 0 AA-T-G 4
<BLANKLINE>
>>> alignment.inverse_indices
[array([-1, 0, 1, 2, 3, 4, 5]), array([0, 1, 3, 5])]
>>> alignments = aligner.align("GAACTGG", "CATT", strand="-")
>>> alignment = alignments[0]
>>> print(alignment)
target 1 AACTG 6
0 ||-|| 5
query 4 AA-TG 0
<BLANKLINE>
>>> alignment.inverse_indices
[array([-1, 0, 1, 2, 3, 4, -1]), array([4, 3, 1, 0])]
>>> alignment = alignments[1]
>>> print(alignment)
target 1 AACTGG 7
0 ||-|-| 6
query 4 AA-T-G 0
<BLANKLINE>
>>> alignment.inverse_indices
[array([-1, 0, 1, 2, 3, 4, 5]), array([5, 3, 1, 0])]
"""
a = [-np.ones(len(sequence), int) for sequence in self.sequences]
n, m = self.coordinates.shape
steps = np.diff(self.coordinates, 1)
aligned = sum(steps != 0, 0) > 1
# True for steps in which at least two sequences align, False if a gap
steps = steps[:, aligned]
rcs = np.zeros(n, bool)
for i, row in enumerate(steps):
if (row >= 0).all():
rcs[i] = False
elif (row <= 0).all():
rcs[i] = True
else:
raise ValueError(f"Inconsistent steps in row {i}")
i = 0
j = 0
for k in range(m - 1):
starts = self.coordinates[:, k]
ends = self.coordinates[:, k + 1]
for row, start, end, rc in zip(a, starts, ends, rcs):
if rc == False and start < end: # noqa: E712
j = i + end - start
row[start:end] = range(i, j)
elif rc == True and start > end: # noqa: E712
j = i + start - end
if end > 0:
row[start - 1 : end - 1 : -1] = range(i, j)
elif start > 0:
row[start - 1 :: -1] = range(i, j)
i = j
return a
def sort(self, key=None, reverse=False):
"""Sort the sequences of the alignment in place.
By default, this sorts the sequences alphabetically using their id
attribute if available, or by their sequence contents otherwise.
For example,
>>> from Bio.Align import PairwiseAligner
>>> aligner = PairwiseAligner()
>>> aligner.gap_score = -1
>>> alignments = aligner.align("AATAA", "AAGAA")
>>> len(alignments)
1
>>> alignment = alignments[0]
>>> print(alignment)
target 0 AATAA 5
0 ||.|| 5
query 0 AAGAA 5
<BLANKLINE>
>>> alignment.sort()
>>> print(alignment)
target 0 AAGAA 5
0 ||.|| 5
query 0 AATAA 5
<BLANKLINE>
Alternatively, a key function can be supplied that maps each sequence
to a sort value. For example, you could sort on the GC content of each
sequence.
>>> from Bio.SeqUtils import gc_fraction
>>> alignment.sort(key=gc_fraction)
>>> print(alignment)
target 0 AATAA 5
0 ||.|| 5
query 0 AAGAA 5
<BLANKLINE>
You can reverse the sort order by passing `reverse=True`:
>>> alignment.sort(key=gc_fraction, reverse=True)
>>> print(alignment)
target 0 AAGAA 5
0 ||.|| 5
query 0 AATAA 5
<BLANKLINE>
The sequences are now sorted by decreasing GC content value.
"""
sequences = self.sequences
if key is None:
try:
values = [sequence.id for sequence in sequences]
except AttributeError:
values = sequences
else:
values = [key(sequence) for sequence in sequences]
indices = sorted(range(len(sequences)), key=values.__getitem__, reverse=reverse)
self.sequences = [sequences[index] for index in indices]
self.coordinates = self.coordinates.take(indices, 0)
def map(self, alignment):
r"""Map the alignment to self.target and return the resulting alignment.
Here, self.query and alignment.target are the same sequence.
A typical example is where self is the pairwise alignment between a
chromosome and a transcript, the argument is the pairwise alignment
between the transcript and a sequence (e.g., as obtained by RNA-seq),
and we want to find the alignment of the sequence to the chromosome:
>>> from Bio import Align
>>> aligner = Align.PairwiseAligner()
>>> aligner.mode = 'local'
>>> aligner.open_gap_score = -1
>>> aligner.extend_gap_score = 0
>>> chromosome = "AAAAAAAACCCCCCCAAAAAAAAAAAGGGGGGAAAAAAAA"
>>> transcript = "CCCCCCCGGGGGG"
>>> alignments1 = aligner.align(chromosome, transcript)
>>> len(alignments1)
1
>>> alignment1 = alignments1[0]
>>> print(alignment1)
target 8 CCCCCCCAAAAAAAAAAAGGGGGG 32
0 |||||||-----------|||||| 24
query 0 CCCCCCC-----------GGGGGG 13
<BLANKLINE>
>>> sequence = "CCCCGGGG"
>>> alignments2 = aligner.align(transcript, sequence)
>>> len(alignments2)
1
>>> alignment2 = alignments2[0]
>>> print(alignment2)
target 3 CCCCGGGG 11
0 |||||||| 8
query 0 CCCCGGGG 8
<BLANKLINE>
>>> alignment = alignment1.map(alignment2)
>>> print(alignment)
target 11 CCCCAAAAAAAAAAAGGGG 30
0 ||||-----------|||| 19
query 0 CCCC-----------GGGG 8
<BLANKLINE>
>>> format(alignment, "psl")
'8\t0\t0\t0\t0\t0\t1\t11\t+\tquery\t8\t0\t8\ttarget\t40\t11\t30\t2\t4,4,\t0,4,\t11,26,\n'
Mapping the alignment does not depend on the sequence contents. If we
delete the sequence contents, the same alignment is found in PSL format
(though we obviously lose the ability to print the sequence alignment):
>>> alignment1.target = Seq(None, len(alignment1.target))
>>> alignment1.query = Seq(None, len(alignment1.query))
>>> alignment2.target = Seq(None, len(alignment2.target))
>>> alignment2.query = Seq(None, len(alignment2.query))
>>> alignment = alignment1.map(alignment2)
>>> format(alignment, "psl")
'8\t0\t0\t0\t0\t0\t1\t11\t+\tquery\t8\t0\t8\ttarget\t40\t11\t30\t2\t4,4,\t0,4,\t11,26,\n'
The map method can also be used to lift over an alignment between
different genome assemblies. In this case, self is a DNA alignment
between two genome assemblies, and the argument is an alignment of a
transcript against one of the genome assemblies:
>>> np.set_printoptions(threshold=5) # print 5 array elements per row
>>> chain = Align.read("Blat/panTro5ToPanTro6.over.chain", "chain")
>>> chain.sequences[0].id
'chr1'
>>> len(chain.sequences[0].seq)
228573443
>>> chain.sequences[1].id
'chr1'
>>> len(chain.sequences[1].seq)
224244399
>>> print(chain.coordinates)
[[122250000 122250400 122250400 ... 122909818 122909819 122909835]
[111776384 111776784 111776785 ... 112019962 112019962 112019978]]
showing that the range 122250000:122909835 of chr1 on chimpanzee genome
assembly panTro5 aligns to range 111776384:112019978 of chr1 of
chimpanzee genome assembly panTro6.
>>> alignment = Align.read("Blat/est.panTro5.psl", "psl")
>>> alignment.sequences[0].id
'chr1'
>>> len(alignment.sequences[0].seq)
228573443
>>> alignment.sequences[1].id
'DC525629'
>>> len(alignment.sequences[1].seq)
407
>>> print(alignment.coordinates)
[[122835789 122835847 122840993 122841145 122907212 122907314]
[ 32 90 90 242 242 344]]
This shows that nucleotide range 32:344 of expressed sequence tag
DC525629 aligns to range 122835789:122907314 of chr1 of chimpanzee
genome assembly panTro5.
Note that the target sequence chain.sequences[0].seq and the target
sequence alignment.sequences[0] have the same length:
>>> len(chain.sequences[0].seq) == len(alignment.sequences[0].seq)
True
We swap the target and query of the chain such that the query of the
chain corresponds to the target of alignment:
>>> chain = chain[::-1]
>>> chain.sequences[0].id
'chr1'
>>> len(chain.sequences[0].seq)
224244399
>>> chain.sequences[1].id
'chr1'
>>> len(chain.sequences[1].seq)
228573443
>>> print(chain.coordinates)
[[111776384 111776784 111776785 ... 112019962 112019962 112019978]
[122250000 122250400 122250400 ... 122909818 122909819 122909835]]
Now we can get the coordinates of DC525629 against chimpanzee genome
assembly panTro6 by calling map on the chain, with alignment as the
argument:
>>> lifted_alignment = chain.map(alignment)
>>> lifted_alignment.sequences[0].id
'chr1'
>>> len(lifted_alignment.sequences[0].seq)
224244399
>>> lifted_alignment.sequences[1].id
'DC525629'
>>> len(lifted_alignment.sequences[1].seq)
407
>>> print(lifted_alignment.coordinates)
[[111982717 111982775 111987921 111988073 112009200 112009302]
[ 32 90 90 242 242 344]]
This shows that nucleotide range 32:344 of expressed sequence tag
DC525629 aligns to range 111982717:112009302 of chr1 of chimpanzee
genome assembly panTro6. Note that the genome span of DC525629 on
chimpanzee genome assembly panTro5 is 122907314 - 122835789 = 71525 bp,
while on panTro6 the genome span is 112009302 - 111982717 = 26585 bp.
"""
alignment1, alignment2 = self, alignment
if len(alignment1.query) != len(alignment2.target):
raise ValueError(
"length of alignment1 query sequence (%d) != length of alignment2 target sequence (%d)"
% (len(alignment1.query), len(alignment2.target))
)
target = alignment1.target
query = alignment2.query
coordinates1 = alignment1.coordinates
coordinates2 = alignment2.coordinates
n1 = len(alignment1.query)
n2 = len(alignment2.query)
steps1 = np.diff(coordinates1, 1)
row = np.prod(np.sign(steps1), 0)
if (row >= 0).all():
strand1 = "+"
elif (row <= 0).all():
strand1 = "-"
else:
raise ValueError("Inconsistent steps in the first alignment")
steps2 = np.diff(coordinates2, 1)
row = np.prod(np.sign(steps2), 0)
if (row >= 0).all():
strand2 = "+"
elif (row <= 0).all():
strand2 = "-"
else:
raise ValueError("Inconsistent steps in the second alignment")
if strand1 == "+":
if strand2 == "-": # mapped to reverse strand
coordinates2 = coordinates2.copy()
coordinates2[1, :] = n2 - coordinates2[1, :]
else: # mapped to reverse strand
coordinates1 = coordinates1.copy()
coordinates1[1, :] = n1 - coordinates1[1, :]
coordinates2 = coordinates2.copy()
coordinates2[0, :] = n1 - coordinates2[0, ::-1]
if strand2 == "+":
coordinates2[1, :] = n2 - coordinates2[1, ::-1]
else: # mapped to reverse strand
coordinates2[1, :] = coordinates2[1, ::-1]
steps1 = np.diff(coordinates1, 1)
gaps1 = steps1.max(0)
if not ((steps1 == gaps1) | (steps1 <= 0)).all():
raise ValueError("Unequal step sizes in first alignment")
steps2 = np.diff(coordinates2, 1)
gaps2 = steps2.max(0)
if not ((steps2 == gaps2) | (steps2 <= 0)).all():
raise ValueError("Unequal step sizes in second alignment")
path = []
tEnd, qEnd = sys.maxsize, sys.maxsize
coordinates1 = iter(coordinates1.transpose())
tStart1, qStart1 = sys.maxsize, sys.maxsize
for tEnd1, qEnd1 in coordinates1:
if tStart1 < tEnd1 and qStart1 < qEnd1:
break
tStart1, qStart1 = tEnd1, qEnd1
tStart2, qStart2 = sys.maxsize, sys.maxsize
for tEnd2, qEnd2 in coordinates2.transpose():
while qStart2 < qEnd2 and tStart2 < tEnd2:
while True:
if tStart2 < qStart1:
if tEnd2 < qStart1:
size = tEnd2 - tStart2
else:
size = qStart1 - tStart2
break
if tStart2 < qEnd1:
offset = tStart2 - qStart1
if tEnd2 < qEnd1:
size = tEnd2 - tStart2
else:
size = qEnd1 - tStart2
qStart = qStart2
tStart = tStart1 + offset
if tStart != tEnd or qStart != qEnd:
if tStart > tEnd and qStart > qEnd:
# adding a gap both in target and in query;
# add gap to target first:
path.append([tStart, qEnd])
path.append([tStart, qStart])
qEnd = qStart2 + size
tEnd = tStart + size
path.append([tEnd, qEnd])
break
tStart1, qStart1 = tEnd1, qEnd1
for tEnd1, qEnd1 in coordinates1:
if tStart1 < tEnd1 and qStart1 < qEnd1:
break
tStart1, qStart1 = tEnd1, qEnd1
else:
size = qEnd2 - qStart2
break
qStart2 += size
tStart2 += size
tStart2, qStart2 = tEnd2, qEnd2
coordinates = np.array(path).transpose()
if strand1 != strand2:
coordinates[1, :] = n2 - coordinates[1, :]
sequences = [target, query]
alignment = Alignment(sequences, coordinates)
return alignment
def mapall(self, alignments):
"""Map each of the alignments to self, and return the mapped alignment."""
factor = None
alignments = list(alignments)
for alignment in alignments:
steps = np.diff(alignment.coordinates, 1)
aligned = sum(steps != 0, 0) > 1
steps = steps[:, aligned]
step1, step2 = steps.sum(1)
if step1 == step2:
step = 1 # nucleotide-nucleotide or protein-protein alignment
elif step1 == -step2:
step = 1 # nucleotide-nucleotide alignment on reverse strand
elif step2 == 3 * step1:
step = 3 # protein-nucleotide alignment
else:
raise ValueError(f"unexpected steps {step1}, {step2}")
if factor is None:
factor = step
elif factor != step:
raise ValueError("inconsistent step sizes in alignments")
steps = abs(self.coordinates[:, 1:] - self.coordinates[:, :-1]).max(0).clip(0)
coordinates = np.empty((2, len(steps) + 1), int)
coordinates[0, 0] = 0
coordinates[0, 1:] = factor * np.cumsum(steps)
sequences = [Seq(None, length=coordinates[0, -1]), None]
for i, alignment in enumerate(alignments):
coordinates[1, :] = factor * self.coordinates[i, :]
sequences[1] = Seq(None, length=coordinates[1, -1])
alignment1 = Alignment(sequences, coordinates)
coordinates2 = alignment.coordinates.copy()
coordinates2[0, :] *= factor
sequences2 = [sequences[1], alignment.sequences[1]]
alignment2 = Alignment(sequences2, coordinates2)
alignments[i] = alignment1.map(alignment2)
coordinates = [[] for i in range(len(alignments))]
done = False
while done is False:
done = True
position = min(
alignment.coordinates[0, 0]
for alignment in alignments
if alignment.coordinates.size
)
for i, alignment in enumerate(alignments):
if alignment.coordinates.size == 0:
coordinates[i].append(coordinates[i][-1])
elif alignment.coordinates[0, 0] == position:
coordinates[i].append(alignment.coordinates[1, 0])
alignment.coordinates = alignment.coordinates[:, 1:]
if alignment.coordinates.any():
done = False
elif alignment.coordinates[0, 0] > position:
if len(coordinates[i]):
if alignment.coordinates[1, 0] > coordinates[i][-1]:
step = position - previous # noqa: F821
else:
step = 0
coordinates[i].append(coordinates[i][-1] + step)
else:
coordinates[i].append(alignment.coordinates[1, 0])
else:
raise Exception
previous = position
sequences = [alignment.sequences[1] for alignment in alignments]
coordinates = np.array(coordinates)
alignment = Alignment(sequences, coordinates)
return alignment
@property
def substitutions(self):
"""Return an Array with the number of substitutions of letters in the alignment.
As an example, consider a sequence alignment of two RNA sequences:
>>> from Bio.Align import PairwiseAligner
>>> target = "ATACTTACCTGGCAGGGGAGATACCATGATCACGAAGGTGGTTTTCCCAGGGCGAGGCTTATCCATTGCACTCCGGATGTGCTGACCCCTGCGATTTCCCCAAATGTGGGAAACTCGACTGCATAATTTGTGGTAGTGGGGGACTGCGTTCGCGCTTTCCCCTG" # human spliceosomal small nuclear RNA U1
>>> query = "ATACTTACCTGACAGGGGAGGCACCATGATCACACAGGTGGTCCTCCCAGGGCGAGGCTCTTCCATTGCACTGCGGGAGGGTTGACCCCTGCGATTTCCCCAAATGTGGGAAACTCGACTGTATAATTTGTGGTAGTGGGGGACTGCGTTCGCGCTATCCCCCG" # sea lamprey spliceosomal small RNA U1
>>> aligner = PairwiseAligner()
>>> aligner.gap_score = -10
>>> alignments = aligner.align(target, query)
>>> len(alignments)
1
>>> alignment = alignments[0]
>>> print(alignment)
target 0 ATACTTACCTGGCAGGGGAGATACCATGATCACGAAGGTGGTTTTCCCAGGGCGAGGCTT
0 |||||||||||.||||||||..|||||||||||..|||||||..|||||||||||||||.
query 0 ATACTTACCTGACAGGGGAGGCACCATGATCACACAGGTGGTCCTCCCAGGGCGAGGCTC
<BLANKLINE>
target 60 ATCCATTGCACTCCGGATGTGCTGACCCCTGCGATTTCCCCAAATGTGGGAAACTCGACT
60 .|||||||||||.|||..|.|.||||||||||||||||||||||||||||||||||||||
query 60 TTCCATTGCACTGCGGGAGGGTTGACCCCTGCGATTTCCCCAAATGTGGGAAACTCGACT
<BLANKLINE>
target 120 GCATAATTTGTGGTAGTGGGGGACTGCGTTCGCGCTTTCCCCTG 164
120 |.||||||||||||||||||||||||||||||||||.|||||.| 164
query 120 GTATAATTTGTGGTAGTGGGGGACTGCGTTCGCGCTATCCCCCG 164
<BLANKLINE>
>>> m = alignment.substitutions
>>> print(m)
A C G T
A 28.0 1.0 2.0 1.0
C 0.0 39.0 1.0 2.0
G 2.0 0.0 45.0 0.0
T 2.0 5.0 1.0 35.0
<BLANKLINE>
Note that the matrix is not symmetric: rows correspond to the target
sequence, and columns to the query sequence. For example, the number
of T's in the target sequence that are aligned to a C in the query
sequence is
>>> m['T', 'C']
5.0
and the number of C's in the query sequence tat are aligned to a T in
the query sequence is
>>> m['C', 'T']
2.0
For some applications (for example, to define a scoring matrix from
the substitution matrix), a symmetric matrix may be preferred, which
can be calculated as follows:
>>> m += m.transpose()
>>> m /= 2.0
>>> print(m)
A C G T
A 28.0 0.5 2.0 1.5
C 0.5 39.0 0.5 3.5
G 2.0 0.5 45.0 0.5
T 1.5 3.5 0.5 35.0
<BLANKLINE>
The matrix is now symmetric, with counts divided equally on both sides
of the diagonal:
>>> m['C', 'T']
3.5
>>> m['T', 'C']
3.5
The total number of substitutions between T's and C's in the alignment
is 3.5 + 3.5 = 7.
"""
coordinates = self.coordinates.copy()
sequences = list(self.sequences)
steps = np.diff(self.coordinates, 1)
aligned = sum(steps != 0, 0) > 1
# True for steps in which at least two sequences align, False if a gap
for i, sequence in enumerate(sequences):
row = steps[i, aligned]
if (row >= 0).all():
pass
elif (row <= 0).all():
sequences[i] = reverse_complement(sequence)
coordinates[i, :] = len(sequence) - coordinates[i, :]
else:
raise ValueError(f"Inconsistent steps in row {i}")
letters = set()
for sequence in sequences:
try:
s = set(sequence)
except UndefinedSequenceError:
try:
sequence = sequence.seq # SeqRecord confusion
except AttributeError:
pass
for start, end in sequence.defined_ranges:
s = set(sequence[start:end])
letters.update(s)
else:
letters.update(s)
letters = "".join(sorted(letters))
m = substitution_matrices.Array(letters, dims=2)
n = len(sequences)
for i1 in range(n):
sequence1 = sequences[i1]
coordinates1 = coordinates[i1, :]
for i2 in range(i1 + 1, n):
sequence2 = sequences[i2]
coordinates2 = coordinates[i2, :]
start1, start2 = sys.maxsize, sys.maxsize
for end1, end2 in zip(coordinates1, coordinates2):
if start1 < end1 and start2 < end2: # aligned
segment1 = sequence1[start1:end1]
segment2 = sequence2[start2:end2]
if len(segment1) != len(segment2):
raise ValueError("Unequal step sizes in alignment")
for c1, c2 in zip(segment1, segment2):
m[c1, c2] += 1.0
start1, start2 = end1, end2
return m
def counts(self):
"""Return number of identities, mismatches, and gaps of a pairwise alignment.
>>> aligner = PairwiseAligner(mode='global', match_score=2, mismatch_score=-1)
>>> for alignment in aligner.align("TACCG", "ACG"):
... print("Score = %.1f:" % alignment.score)
... c = alignment.counts() # namedtuple
... print(f"{c.gaps} gaps, {c.identities} identities, {c.mismatches} mismatches")
... print(alignment)
...
Score = 6.0:
2 gaps, 3 identities, 0 mismatches
target 0 TACCG 5
0 -||-| 5
query 0 -AC-G 3
<BLANKLINE>
Score = 6.0:
2 gaps, 3 identities, 0 mismatches
target 0 TACCG 5
0 -|-|| 5
query 0 -A-CG 3
<BLANKLINE>
This classifies each pair of letters in a pairwise alignment into gaps,
perfect matches, or mismatches. It has been defined as a method (not a
property) so that it may in future take optional argument(s) allowing
the behavior to be customized. These three values are returned as a
namedtuple. This is calculated for all the pairs of sequences in the
alignment.
"""
gaps = identities = mismatches = 0
for i, seq1 in enumerate(self):
for j, seq2 in enumerate(self):
if i == j:
# Don't count seq1 vs seq2 and seq2 vs seq1
break
for a, b in zip(seq1, seq2):
if a == "-" or b == "-":
gaps += 1
elif a == b:
identities += 1
else:
mismatches += 1
return AlignmentCounts(gaps, identities, mismatches)
def reverse_complement(self):
"""Reverse-complement the alignment and return it.
>>> sequences = ["ATCG", "AAG", "ATC"]
>>> coordinates = np.array([[0, 2, 3, 4], [0, 2, 2, 3], [0, 2, 3, 3]])
>>> alignment = Alignment(sequences, coordinates)
>>> print(alignment)
0 ATCG 4
0 AA-G 3
0 ATC- 3
<BLANKLINE>
>>> rc_alignment = alignment.reverse_complement()
>>> print(rc_alignment)
0 CGAT 4
0 C-TT 3
0 -GAT 3
<BLANKLINE>
The attribute `column_annotations`, if present, is associated with the
reverse-complemented alignment, with its values in reverse order.
>>> alignment.column_annotations = {"score": [3, 2, 2, 2]}
>>> rc_alignment = alignment.reverse_complement()
>>> print(rc_alignment.column_annotations)
{'score': [2, 2, 2, 3]}
"""
sequences = [reverse_complement(sequence) for sequence in self.sequences]
coordinates = np.array(
[
len(sequence) - row[::-1]
for sequence, row in zip(sequences, self.coordinates)
]
)
alignment = Alignment(sequences, coordinates)
try:
column_annotations = self.column_annotations
except AttributeError:
pass
else:
alignment.column_annotations = {}
for key, value in column_annotations.items():
if isinstance(value, np.ndarray):
value = value[::-1].copy()
else:
value = value[::-1]
alignment.column_annotations[key] = value
return alignment
class AlignmentsAbstractBaseClass(ABC):
"""Abstract base class for sequence alignments.
Most users will not need to use this class. It is used internally as a base
class for the list-like Alignments class, and for the AlignmentIterator
class in Bio.Align.interfaces, which itself is the abstract base class for
the alignment parsers in Bio/Align/.
"""
def __iter__(self):
"""Iterate over the alignments as Alignment objects.
This method SHOULD NOT be overridden by any subclass.
"""
self.rewind()
return self
@abstractmethod
def __next__(self):
"""Return the next alignment."""
@abstractmethod
def rewind(self):
"""Rewind the iterator to let it loop over the alignments from the beginning."""
@abstractmethod
def __len__(self):
"""Return the number of alignments."""
class Alignments(AlignmentsAbstractBaseClass, list): # noqa: D101
def __init__(self, alignments=()): # noqa: D107
super().__init__(alignments)
self._index = -1
def __next__(self):
index = self._index + 1
try:
item = self[index]
except IndexError:
raise StopIteration
self._index = index
return item
def rewind(self): # noqa: D102
self._index = -1
def __len__(self):
return list.__len__(self)
class PairwiseAlignments(AlignmentsAbstractBaseClass):
"""Implements an iterator over pairwise alignments returned by the aligner.
This class also supports indexing, which is fast for increasing indices,
but may be slow for random access of a large number of alignments.
Note that pairwise aligners can return an astronomical number of alignments,
even for relatively short sequences, if they align poorly to each other. We
therefore recommend to first check the number of alignments, accessible as
len(alignments), which can be calculated quickly even if the number of
alignments is very large.
"""
def __init__(self, seqA, seqB, score, paths):
"""Initialize a new PairwiseAlignments object.
Arguments:
- seqA - The first sequence, as a plain string, without gaps.
- seqB - The second sequence, as a plain string, without gaps.
- score - The alignment score.
- paths - An iterator over the paths in the traceback matrix;
each path defines one alignment.
You would normally obtain a PairwiseAlignments object by calling
aligner.align(seqA, seqB), where aligner is a PairwiseAligner object
or a CodonAligner object.
"""
self.sequences = [seqA, seqB]
self.score = score
self._paths = paths
self._index = -1
def __len__(self):
return len(self._paths)
def __getitem__(self, index):
if not isinstance(index, int):
raise TypeError(f"index must be an integer, not {index.__class__.__name__}")
if index < 0:
index += len(self._paths)
if index == self._index:
return self._alignment
if index < self._index:
self._paths.reset()
self._index = -1
while True:
try:
alignment = next(self)
except StopIteration:
raise IndexError("index out of range") from None
if self._index == index:
break
return alignment
def __next__(self):
path = next(self._paths)
self._index += 1
coordinates = np.array(path)
alignment = Alignment(self.sequences, coordinates)
alignment.score = self.score
self._alignment = alignment
return alignment
def rewind(self): # noqa: D102
self._paths.reset()
self._index = -1
class PairwiseAligner(_pairwisealigner.PairwiseAligner):
"""Performs pairwise sequence alignment using dynamic programming.
This provides functions to get global and local alignments between two
sequences. A global alignment finds the best concordance between all
characters in two sequences. A local alignment finds just the
subsequences that align the best.
To perform a pairwise sequence alignment, first create a PairwiseAligner
object. This object stores the match and mismatch scores, as well as the
gap scores. Typically, match scores are positive, while mismatch scores
and gap scores are negative or zero. By default, the match score is 1,
and the mismatch and gap scores are zero. Based on the values of the gap
scores, a PairwiseAligner object automatically chooses the appropriate
alignment algorithm (the Needleman-Wunsch, Smith-Waterman, Gotoh, or
Waterman-Smith-Beyer global or local alignment algorithm).
Calling the "score" method on the aligner with two sequences as arguments
will calculate the alignment score between the two sequences.
Calling the "align" method on the aligner with two sequences as arguments
will return a generator yielding the alignments between the two
sequences.
Some examples:
>>> from Bio import Align
>>> aligner = Align.PairwiseAligner()
>>> alignments = aligner.align("TACCG", "ACG")
>>> for alignment in sorted(alignments):
... print("Score = %.1f:" % alignment.score)
... print(alignment)
...
Score = 3.0:
target 0 TACCG 5
0 -|-|| 5
query 0 -A-CG 3
<BLANKLINE>
Score = 3.0:
target 0 TACCG 5
0 -||-| 5
query 0 -AC-G 3
<BLANKLINE>
Specify the aligner mode as local to generate local alignments:
>>> aligner.mode = 'local'
>>> alignments = aligner.align("TACCG", "ACG")
>>> for alignment in sorted(alignments):
... print("Score = %.1f:" % alignment.score)
... print(alignment)
...
Score = 3.0:
target 1 ACCG 5
0 |-|| 4
query 0 A-CG 3
<BLANKLINE>
Score = 3.0:
target 1 ACCG 5
0 ||-| 4
query 0 AC-G 3
<BLANKLINE>
Do a global alignment. Identical characters are given 2 points,
1 point is deducted for each non-identical character.
>>> aligner.mode = 'global'
>>> aligner.match_score = 2
>>> aligner.mismatch_score = -1
>>> for alignment in aligner.align("TACCG", "ACG"):
... print("Score = %.1f:" % alignment.score)
... print(alignment)
...
Score = 6.0:
target 0 TACCG 5
0 -||-| 5
query 0 -AC-G 3
<BLANKLINE>
Score = 6.0:
target 0 TACCG 5
0 -|-|| 5
query 0 -A-CG 3
<BLANKLINE>
Same as above, except now 0.5 points are deducted when opening a
gap, and 0.1 points are deducted when extending it.
>>> aligner.open_gap_score = -0.5
>>> aligner.extend_gap_score = -0.1
>>> aligner.target_end_gap_score = 0.0
>>> aligner.query_end_gap_score = 0.0
>>> for alignment in aligner.align("TACCG", "ACG"):
... print("Score = %.1f:" % alignment.score)
... print(alignment)
...
Score = 5.5:
target 0 TACCG 5
0 -|-|| 5
query 0 -A-CG 3
<BLANKLINE>
Score = 5.5:
target 0 TACCG 5
0 -||-| 5
query 0 -AC-G 3
<BLANKLINE>
The alignment function can also use known matrices already included in
Biopython:
>>> from Bio.Align import substitution_matrices
>>> aligner = Align.PairwiseAligner()
>>> aligner.substitution_matrix = substitution_matrices.load("BLOSUM62")
>>> alignments = aligner.align("KEVLA", "EVL")
>>> alignments = list(alignments)
>>> print("Number of alignments: %d" % len(alignments))
Number of alignments: 1
>>> alignment = alignments[0]
>>> print("Score = %.1f" % alignment.score)
Score = 13.0
>>> print(alignment)
target 0 KEVLA 5
0 -|||- 5
query 0 -EVL- 3
<BLANKLINE>
You can also set the value of attributes directly during construction
of the PairwiseAligner object by providing them as keyword arguments:
>>> aligner = Align.PairwiseAligner(mode='global', match_score=2, mismatch_score=-1)
>>> for alignment in aligner.align("TACCG", "ACG"):
... print("Score = %.1f:" % alignment.score)
... print(alignment)
...
Score = 6.0:
target 0 TACCG 5
0 -||-| 5
query 0 -AC-G 3
<BLANKLINE>
Score = 6.0:
target 0 TACCG 5
0 -|-|| 5
query 0 -A-CG 3
<BLANKLINE>
"""
def __init__(self, scoring=None, **kwargs):
"""Initialize a PairwiseAligner as specified by the keyword arguments.
If scoring is None, use the default scoring scheme match = 1.0,
mismatch = 0.0, gap score = 0.0
If scoring is "blastn", "megablast", or "blastp", use the default
substitution matrix and gap scores for BLASTN, MEGABLAST, or BLASTP,
respectively.
Loops over the remaining keyword arguments and sets them as attributes
on the object.
"""
super().__init__()
if scoring is None:
# use default values:
# match = 1.0
# mismatch = 0.0
# gap_score = 0.0
pass
elif scoring == "blastn":
self.substitution_matrix = substitution_matrices.load("BLASTN")
self.open_gap_score = -7.0
self.extend_gap_score = -2.0
elif scoring == "megablast":
self.substitution_matrix = substitution_matrices.load("MEGABLAST")
self.open_gap_score = -2.5
self.extend_gap_score = -2.5
elif scoring == "blastp":
self.substitution_matrix = substitution_matrices.load("BLASTP")
self.open_gap_score = -12.0
self.extend_gap_score = -1.0
else:
raise ValueError("Unknown scoring scheme '%s'" % scoring)
for name, value in kwargs.items():
setattr(self, name, value)
def __setattr__(self, key, value):
if key not in dir(_pairwisealigner.PairwiseAligner):
# To prevent confusion, don't allow users to create new attributes.
# On CPython, __slots__ can be used for this, but currently
# __slots__ does not behave the same way on PyPy at least.
raise AttributeError("'PairwiseAligner' object has no attribute '%s'" % key)
_pairwisealigner.PairwiseAligner.__setattr__(self, key, value)
def align(self, seqA, seqB, strand="+"):
"""Return the alignments of two sequences using PairwiseAligner."""
if isinstance(seqA, (Seq, MutableSeq, SeqRecord)):
sA = bytes(seqA)
else:
sA = seqA
if strand == "+":
sB = seqB
else: # strand == "-":
sB = reverse_complement(seqB)
if isinstance(seqB, (Seq, MutableSeq, SeqRecord)):
sB = bytes(sB)
score, paths = super().align(sA, sB, strand)
alignments = PairwiseAlignments(seqA, seqB, score, paths)
return alignments
def score(self, seqA, seqB, strand="+"):
"""Return the alignment score of two sequences using PairwiseAligner."""
if isinstance(seqA, (Seq, MutableSeq, SeqRecord)):
seqA = bytes(seqA)
if strand == "-":
seqB = reverse_complement(seqB)
if isinstance(seqB, (Seq, MutableSeq, SeqRecord)):
seqB = bytes(seqB)
return super().score(seqA, seqB, strand)
def __getstate__(self):
state = {
"wildcard": self.wildcard,
"target_internal_open_gap_score": self.target_internal_open_gap_score,
"target_internal_extend_gap_score": self.target_internal_extend_gap_score,
"target_left_open_gap_score": self.target_left_open_gap_score,
"target_left_extend_gap_score": self.target_left_extend_gap_score,
"target_right_open_gap_score": self.target_right_open_gap_score,
"target_right_extend_gap_score": self.target_right_extend_gap_score,
"query_internal_open_gap_score": self.query_internal_open_gap_score,
"query_internal_extend_gap_score": self.query_internal_extend_gap_score,
"query_left_open_gap_score": self.query_left_open_gap_score,
"query_left_extend_gap_score": self.query_left_extend_gap_score,
"query_right_open_gap_score": self.query_right_open_gap_score,
"query_right_extend_gap_score": self.query_right_extend_gap_score,
"mode": self.mode,
}
if self.substitution_matrix is None:
state["match_score"] = self.match_score
state["mismatch_score"] = self.mismatch_score
else:
state["substitution_matrix"] = self.substitution_matrix
return state
def __setstate__(self, state):
self.wildcard = state["wildcard"]
self.target_internal_open_gap_score = state["target_internal_open_gap_score"]
self.target_internal_extend_gap_score = state[
"target_internal_extend_gap_score"
]
self.target_left_open_gap_score = state["target_left_open_gap_score"]
self.target_left_extend_gap_score = state["target_left_extend_gap_score"]
self.target_right_open_gap_score = state["target_right_open_gap_score"]
self.target_right_extend_gap_score = state["target_right_extend_gap_score"]
self.query_internal_open_gap_score = state["query_internal_open_gap_score"]
self.query_internal_extend_gap_score = state["query_internal_extend_gap_score"]
self.query_left_open_gap_score = state["query_left_open_gap_score"]
self.query_left_extend_gap_score = state["query_left_extend_gap_score"]
self.query_right_open_gap_score = state["query_right_open_gap_score"]
self.query_right_extend_gap_score = state["query_right_extend_gap_score"]
self.mode = state["mode"]
substitution_matrix = state.get("substitution_matrix")
if substitution_matrix is None:
self.match_score = state["match_score"]
self.mismatch_score = state["mismatch_score"]
else:
self.substitution_matrix = substitution_matrix
class CodonAligner(_codonaligner.CodonAligner):
"""Aligns a nucleotide sequence to an amino acid sequence.
This class implements a dynamic programming algorithm to align a nucleotide
sequence to an amino acid sequence.
"""
def __init__(self, codon_table=None, anchor_len=10):
"""Initialize a CodonAligner for a specific genetic code.
Arguments:
- codon_table - a CodonTable object representing the genetic code.
If codon_table is None, the standard genetic code is used.
"""
super().__init__()
if codon_table is None:
codon_table = CodonTable.generic_by_id[1]
elif not isinstance(codon_table, CodonTable.CodonTable):
raise TypeError("Input table is not a CodonTable object")
self.codon_table = codon_table
def score(self, seqA, seqB):
"""Return the alignment score of a protein sequence and nucleotide sequence.
Arguments:
- seqA - the protein sequence of amino acids (plain string, Seq,
MutableSeq, or SeqRecord).
- seqB - the nucleotide sequence (plain string, Seq, MutableSeq, or
SeqRecord); both DNA and RNA sequences are accepted.
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> aligner = CodonAligner()
>>> dna = SeqRecord(Seq('ATGTCTCGT'), id='dna')
>>> pro = SeqRecord(Seq('MSR'), id='pro')
>>> score = aligner.score(pro, dna)
>>> print(score)
3.0
>>> rna = SeqRecord(Seq('AUGUCUCGU'), id='rna')
>>> score = aligner.score(pro, rna)
>>> print(score)
3.0
This is an example with a frame shift in the DNA sequence:
>>> dna = "ATGCTGGGCTCGAACGAGTCCGTGTATGCCCTAAGCTGAGCCCGTCG"
>>> pro = "MLGSNESRVCPKLSPS"
>>> len(pro)
16
>>> aligner.frameshift_score = -3.0
>>> score = aligner.score(pro, dna)
>>> print(score)
13.0
In the following example, the position of the frame shift is ambiguous:
>>> dna = 'TTTAAAAAAAAAAATTT'
>>> pro = 'FKKKKF'
>>> len(pro)
6
>>> aligner.frameshift_score = -1.0
>>> alignments = aligner.align(pro, dna)
>>> print(alignments.score)
5.0
>>> len(alignments)
3
>>> print(next(alignments))
target 0 F K K K 4
query 0 TTTAAAAAAAAA 12
<BLANKLINE>
target 4 K F 6
query 11 AAATTT 17
<BLANKLINE>
>>> print(next(alignments))
target 0 F K K 3
query 0 TTTAAAAAA 9
<BLANKLINE>
target 3 K K F 6
query 8 AAAAAATTT 17
<BLANKLINE>
>>> print(next(alignments))
target 0 F K 2
query 0 TTTAAA 6
<BLANKLINE>
target 2 K K K F 6
query 5 AAAAAAAAATTT 17
<BLANKLINE>
>>> print(next(alignments))
Traceback (most recent call last):
...
StopIteration
"""
codon_table = self.codon_table
if isinstance(seqA, (Seq, MutableSeq, SeqRecord)):
sA = bytes(seqA)
elif isinstance(seqA, str):
sA = seqA.encode()
else:
raise ValueError(
"seqA must be a string, Seq, MutableSeq, or SeqRecord object"
)
seqB0 = seqB[: 3 * (len(seqB) // 3)]
seqB1 = seqB[1 : 1 + 3 * ((len(seqB) - 1) // 3)]
seqB2 = seqB[2 : 2 + 3 * ((len(seqB) - 2) // 3)]
if isinstance(seqB, (Seq, MutableSeq, SeqRecord)):
sB0 = seqB0.translate(codon_table)
sB1 = seqB1.translate(codon_table)
sB2 = seqB2.translate(codon_table)
sB0 = bytes(sB0)
sB1 = bytes(sB1)
sB2 = bytes(sB2)
elif isinstance(seqA, str):
sB0 = translate(seqB0, codon_table)
sB1 = translate(seqB1, codon_table)
sB2 = translate(seqB2, codon_table)
sB0 = sB0.encode()
sB1 = sB1.encode()
sB2 = sB2.encode()
else:
raise ValueError(
"seqB must be a string, Seq, MutableSeq, or SeqRecord object"
)
return super().score(sA, sB0, sB1, sB2)
def align(self, seqA, seqB):
"""Align a nucleotide sequence to its corresponding protein sequence.
Arguments:
- seqA - the protein sequence of amino acids (plain string, Seq,
MutableSeq, or SeqRecord).
- seqB - the nucleotide sequence (plain string, Seq, MutableSeq, or
SeqRecord); both DNA and RNA sequences are accepted.
Returns an iterator of Alignment objects.
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> aligner = CodonAligner()
>>> dna = SeqRecord(Seq('ATGTCTCGT'), id='dna')
>>> pro = SeqRecord(Seq('MSR'), id='pro')
>>> alignments = aligner.align(pro, dna)
>>> alignment = alignments[0]
>>> print(alignment)
pro 0 M S R 3
dna 0 ATGTCTCGT 9
<BLANKLINE>
>>> rna = SeqRecord(Seq('AUGUCUCGU'), id='rna')
>>> alignments = aligner.align(pro, rna)
>>> alignment = alignments[0]
>>> print(alignment)
pro 0 M S R 3
rna 0 AUGUCUCGU 9
<BLANKLINE>
This is an example with a frame shift in the DNA sequence:
>>> dna = "ATGCTGGGCTCGAACGAGTCCGTGTATGCCCTAAGCTGAGCCCGTCG"
>>> pro = "MLGSNESRVCPKLSPS"
>>> alignments = aligner.align(pro, dna)
>>> alignment = alignments[0]
>>> print(alignment)
target 0 M L G S N E S 7
query 0 ATGCTGGGCTCGAACGAGTCC 21
<BLANKLINE>
target 7 R V C P K L S P S 16
query 20 CGTGTATGCCCTAAGCTGAGCCCGTCG 47
<BLANKLINE>
"""
codon_table = self.codon_table
if isinstance(seqA, (Seq, MutableSeq, SeqRecord)):
sA = bytes(seqA)
elif isinstance(seqA, str):
sA = seqA.encode()
else:
raise ValueError(
"seqA must be a string, Seq, MutableSeq, or SeqRecord object"
)
seqB0 = seqB[: 3 * (len(seqB) // 3)]
seqB1 = seqB[1 : 1 + 3 * ((len(seqB) - 1) // 3)]
seqB2 = seqB[2 : 2 + 3 * ((len(seqB) - 2) // 3)]
if isinstance(seqB, (Seq, MutableSeq, SeqRecord)):
sB0 = seqB0.translate(codon_table)
sB1 = seqB1.translate(codon_table)
sB2 = seqB2.translate(codon_table)
sB0 = bytes(sB0)
sB1 = bytes(sB1)
sB2 = bytes(sB2)
elif isinstance(seqA, str):
sB0 = translate(seqB0, codon_table)
sB1 = translate(seqB1, codon_table)
sB2 = translate(seqB2, codon_table)
sB0 = sB0.encode()
sB1 = sB1.encode()
sB2 = sB2.encode()
else:
raise ValueError(
"seqB must be a string, Seq, MutableSeq, or SeqRecord object"
)
score, paths = super().align(sA, sB0, sB1, sB2)
alignments = PairwiseAlignments(seqA, seqB, score, paths)
return alignments
# alignment.column_annotations = {"consensus": consensus}
# fmt: off
formats = (
"a2m", # A2M files created by align2model or hmmscore
"bed", # BED (Browser Extensible Data) files
"bigbed", # bigBed format
"bigmaf", # MAF file saved as a bigBed file
"bigpsl", # PSL file saved as a bigBed file
"chain", # UCSC chain file format
"clustal", # clustal output from CLUSTAL W and other tools.
"emboss", # emboss output from EMBOSS tools such as needle, water
"exonerate", # Exonerate pairwise alignment output
"fasta", # FASTA format with gaps represented by dashes
"hhr", # hhr files generated by HHsearch, HHblits in HH-suite
"maf", # MAF (Multiple Alignment Format) format.
"mauve", # xmfa output from Mauve/ProgressiveMauve
"msf", # MSF format produced by GCG PileUp and LocalPileUp
"nexus", # Nexus file format
"phylip", # Alignment format for input files for PHYLIP tools
"psl", # Pattern Space Layout (PSL) format generated by Blat
"sam", # Sequence Alignment/Map (SAM) format
"stockholm", # Stockholm file format used by PFAM and others
"tabular", # Tabular output from BLAST or FASTA
)
# fmt: on
_modules: Dict[str, types.ModuleType] = {}
def _load(fmt: str) -> types.ModuleType:
fmt = fmt.lower()
try:
return _modules[fmt]
except KeyError:
pass
if fmt not in formats:
raise ValueError("Unknown file format %s" % fmt)
module = importlib.import_module(f"Bio.Align.{fmt}")
_modules[fmt] = module
return module
def write(alignments, target, fmt, *args, **kwargs):
"""Write alignments to a file.
Arguments:
- alignments - An Alignments object, an iterator of Alignment objects, or
a single Alignment.
- target - File or file-like object to write to, or filename as string.
- fmt - String describing the file format (case-insensitive).
Note if providing a file or file-like object, your code should close the
target after calling this function, or call .flush(), to ensure the data
gets flushed to disk.
Returns the number of alignments written (as an integer).
"""
if isinstance(alignments, Alignment):
alignments = [alignments]
module = _load(fmt)
try:
writer = module.AlignmentWriter
except AttributeError:
raise ValueError(
f"File writing has not yet been implemented for the {fmt} format"
)
return writer(target, *args, **kwargs).write(alignments)
def parse(source, fmt):
"""Parse an alignment file and return an iterator over alignments.
Arguments:
- source - File or file-like object to read from, or filename as string.
- fmt - String describing the file format (case-insensitive).
Typical usage, opening a file to read in, and looping over the alignments:
>>> from Bio import Align
>>> filename = "Exonerate/exn_22_m_ner_cigar.exn"
>>> for alignment in Align.parse(filename, "exonerate"):
... print("Number of sequences in alignment", len(alignment))
... print("Alignment score:", alignment.score)
Number of sequences in alignment 2
Alignment score: 6150.0
Number of sequences in alignment 2
Alignment score: 502.0
Number of sequences in alignment 2
Alignment score: 440.0
For lazy-loading file formats such as bigMaf, for which the file contents
is read on demand only, ensure that the file remains open while extracting
alignment data.
You can use the Bio.Align.read(...) function when the file contains only
one alignment.
"""
module = _load(fmt)
alignments = module.AlignmentIterator(source)
return alignments
def read(handle, fmt):
"""Parse a file containing one alignment, and return it.
Arguments:
- source - File or file-like object to read from, or filename as string.
- fmt - String describing the file format (case-insensitive).
This function is for use parsing alignment files containing exactly one
alignment. For example, reading a Clustal file:
>>> from Bio import Align
>>> alignment = Align.read("Clustalw/opuntia.aln", "clustal")
>>> print("Alignment shape:", alignment.shape)
Alignment shape: (7, 156)
>>> for sequence in alignment.sequences:
... print(sequence.id, len(sequence))
gi|6273285|gb|AF191659.1|AF191 146
gi|6273284|gb|AF191658.1|AF191 148
gi|6273287|gb|AF191661.1|AF191 146
gi|6273286|gb|AF191660.1|AF191 146
gi|6273290|gb|AF191664.1|AF191 150
gi|6273289|gb|AF191663.1|AF191 150
gi|6273291|gb|AF191665.1|AF191 156
If the file contains no records, or more than one record, an exception is
raised. For example:
>>> from Bio import Align
>>> filename = "Exonerate/exn_22_m_ner_cigar.exn"
>>> alignment = Align.read(filename, "exonerate")
Traceback (most recent call last):
...
ValueError: More than one alignment found in file
Use the Bio.Align.parse function if you want to read a file containing
more than one alignment.
"""
alignments = parse(handle, fmt)
try:
alignment = next(alignments)
except StopIteration:
raise ValueError("No alignments found in file") from None
try:
next(alignments)
raise ValueError("More than one alignment found in file")
except StopIteration:
pass
return alignment
if __name__ == "__main__":
from Bio._utils import run_doctest
run_doctest()
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