# 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 sys import collections import copy import importlib import types import warnings import numbers from itertools import zip_longest from abc import ABC, abstractmethod 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.Align import _pairwisealigner # type: ignore from Bio.Align import _codonaligner # type: ignore from Bio.Align import substitution_matrices from Bio.Data import CodonTable from Bio.Seq import Seq, MutableSeq, reverse_complement, UndefinedSequenceError from Bio.Seq import translate from Bio.SeqRecord import SeqRecord, _RestrictedDict # 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: 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 >>> print(format(align, "phylip")) 3 12 Alpha ACTGCTAGCT AG Beta ACT-CTAGCT AG Gamma ACTGCTAGAT AG """ 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 ACGGCGTT 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 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 = [str(record.seq) for record in records] coordinates = Alignment.infer_coordinates(lines) for record in records: 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 = record.seq.replace("-", "") 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 = record.seq.replace("-", "") 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, skipped_columns=None): """Infer the 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. The coordinates are returned as a NumPy array of integers, and can be used to create an Alignment object. The argument skipped columns should be None (the default) or an empty list. If skipped_columns is a list, then the indices of any columns in the alignment with a gap in all lines are appended to skipped_columns. 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) >>> coordinates array([[ 0, 1, 4, 6, 11, 12], [ 0, 1, 4, 4, 9, 9], [ 0, 0, 3, 5, 10, 11]]) >>> alignment = Alignment(sequences, coordinates) """ n = len(lines) m = len(lines[0]) for line in lines: assert m == len(line) path = [] if m > 0: indices = [0] * n current_state = [None] * n for i in range(m): next_state = [line[i] != "-" for line in lines] if not any(next_state): # skip columns in which all rows have a gap if skipped_columns is not None: skipped_columns.append(i) elif next_state == current_state: step += 1 # noqa: F821 else: indices = [ index + step if state else index for index, state in zip(indices, current_state) ] path.append(indices) step = 1 current_state = next_state indices = [ index + step if state else index for index, state in zip(indices, current_state) ] path.append(indices) coordinates = np.array(path).transpose() return 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 >>> print(right) Alpha 0 GTT 3 Beta 0 -TT 2 Gamma 0 G-T 2 And add them: >>> combined = left + right >>> print(combined) Alpha 0 AAAACGTT 8 Beta 0 AAA-C-TT 6 Gamma 0 AAAAGG-T 7 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 >>> 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 >>> 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 lines.append(line) line = line.replace("-", "") s = s.__class__(line) try: sequence.seq # stupid SeqRecord except AttributeError: sequence = s else: sequence = copy.deepcopy(sequence) sequence.seq = s sequences[i] = sequence coordinates = self.infer_coordinates(lines) 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 >>> 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 >>> print(alignment[:, 1:]) target 1 CCGG-TTT 8 0 |-||-||- 8 query 1 C-GGGTT- 7 >>> print(alignment[:, 2:]) target 2 CGG-TTT 8 0 -||-||- 7 query 2 -GGGTT- 7 >>> print(alignment[:, 3:]) target 3 GG-TTT 8 0 ||-||- 6 query 2 GGGTT- 7 >>> print(alignment[:, 3:-1]) target 3 GG-TT 7 0 ||-|| 5 query 2 GGGTT 7 >>> print(alignment[:, ::2]) target 0 ACGTT 5 0 |-||- 5 query 0 A-GT- 3 >>> print(alignment[:, range(1, 9, 2)]) target 0 CG-T 3 0 ||-| 4 query 0 CGGT 4 >>> print(alignment[:, (2, 7, 3)]) target 0 CTG 3 0 -|| 3 query 0 -TG 2 """ 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 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 """ 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: 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 >>> alignment # doctest:+ELLIPSIS """ 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 >>> 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 >>> 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 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 >>> 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 >>> 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 >>> alignment.aligned array([[[0, 2], [4, 5]], [[0, 2], [2, 3]]]) >>> alignment = alignments[1] >>> print(alignment) target 0 GAACT 5 0 |-|-| 5 query 0 G-A-T 3 >>> alignment.aligned array([[[0, 1], [2, 3], [4, 5]], [[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 >>> alignments[0].aligned array([[[0, 3], [4, 7]], [[0, 3], [4, 7]]]) >>> print(alignments[1]) target 0 AAA-CAAA 7 0 |||--||| 8 query 0 AAAG-AAA 7 >>> alignments[1].aligned array([[[0, 3], [4, 7]], [[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 >>> 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 >>> 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 >>> 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 >>> 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 >>> 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 >>> 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 >>> 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 >>> 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 >>> alignment.sort() >>> print(alignment) target 0 AAGAA 5 0 ||.|| 5 query 0 AATAA 5 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 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 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 >>> 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 >>> alignment = alignment1.map(alignment2) >>> print(alignment) target 11 CCCCAAAAAAAAAAAGGGG 30 0 ||||-----------|||| 19 query 0 CCCC-----------GGGG 8 >>> 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 target 60 ATCCATTGCACTCCGGATGTGCTGACCCCTGCGATTTCCCCAAATGTGGGAAACTCGACT 60 .|||||||||||.|||..|.|.|||||||||||||||||||||||||||||||||||||| query 60 TTCCATTGCACTGCGGGAGGGTTGACCCCTGCGATTTCCCCAAATGTGGGAAACTCGACT target 120 GCATAATTTGTGGTAGTGGGGGACTGCGTTCGCGCTTTCCCCTG 164 120 |.||||||||||||||||||||||||||||||||||.|||||.| 164 query 120 GTATAATTTGTGGTAGTGGGGGACTGCGTTCGCGCTATCCCCCG 164 >>> 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 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 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 Score = 6.0: 2 gaps, 3 identities, 0 mismatches target 0 TACCG 5 0 -|-|| 5 query 0 -A-CG 3 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 >>> rc_alignment = alignment.reverse_complement() >>> print(rc_alignment) 0 CGAT 4 0 C-TT 3 0 -GAT 3 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. """ 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 __iter__(self): self.rewind() return self 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 Score = 3.0: target 0 TACCG 5 0 -||-| 5 query 0 -AC-G 3 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 Score = 3.0: target 1 ACCG 5 0 ||-| 4 query 0 AC-G 3 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 Score = 6.0: target 0 TACCG 5 0 -|-|| 5 query 0 -A-CG 3 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 Score = 5.5: target 0 TACCG 5 0 -||-| 5 query 0 -AC-G 3 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 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 Score = 6.0: target 0 TACCG 5 0 -|-|| 5 query 0 -A-CG 3 """ 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 target 4 K F 6 query 11 AAATTT 17 >>> print(next(alignments)) target 0 F K K 3 query 0 TTTAAAAAA 9 target 3 K K F 6 query 8 AAAAAATTT 17 >>> print(next(alignments)) target 0 F K 2 query 0 TTTAAA 6 target 2 K K K F 6 query 5 AAAAAAAAATTT 17 >>> 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 >>> 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 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 target 7 R V C P K L S P S 16 query 20 CGTGTATGCCCTAAGCTGAGCCCGTCG 47 """ 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 aligments: >>> 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()