# 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 from Bio.Align import _aligners from Bio.Align import substitution_matrices from Bio.Seq import Seq, MutableSeq, reverse_complement, UndefinedSequenceError from Bio.SeqRecord import SeqRecord, _RestrictedDict # Import errors may occur here if a compiled aligners.c file # (_aligners.pyd or _aligners.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 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 replaced the older Alignment object defined in module Bio.Align.Generic but is not fully backwards compatible with it. 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 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 "%s %s" % (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 "%s %s" % (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.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.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.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 or 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 columns), a string (for a single characters) 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 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 >>> print(align1) Alignment with 3 rows and 4 columns ACGC Chicken ACGT Human ACGG Mouse >>> align1.sort(key = lambda record: GC(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 class PairwiseAlignment: """Represents a pairwise sequence alignment. Internally, the pairwise alignment is stored as the path through the traceback matrix, i.e. a tuple of pairs of indices corresponding to the vertices of the path in the traceback matrix. """ def __init__(self, target, query, path, score): """Initialize a new PairwiseAlignment object. Arguments: - target - The first sequence, as a plain string, without gaps. - query - The second sequence, as a plain string, without gaps. - path - The path through the traceback matrix, defining an alignment. - score - The alignment score. You would normally obtain a PairwiseAlignment object by iterating over a PairwiseAlignments object. """ self.target = target self.query = query self.score = score self.path = path def __eq__(self, other): return self.path == other.path def __ne__(self, other): return self.path != other.path def __lt__(self, other): return self.path < other.path def __le__(self, other): return self.path <= other.path def __gt__(self, other): return self.path > other.path def __ge__(self, other): return self.path >= other.path def __getitem__(self, key): """Return self[key]. Currently, this is implemented only for indices of the form self[:, :] which returns a copy of the PairwiseAlignment object, and self[:, i:] self[:, :j] self[:, i:j] which returns a new PairwiseAlignment object spanning the indicated columns. >>> from Bio.Align import PairwiseAligner >>> aligner = PairwiseAligner() >>> alignments = aligner.align("ACCGGTTT", "ACGGGTT") >>> alignment = alignments[0] >>> print(alignment) ACCGG-TTT ||-||-||- AC-GGGTT- >>> alignment[:, 1:] # doctest:+ELLIPSIS >>> print(alignment[:, 1:]) ACCGG-TTT |-||-||- AC-GGGTT- >>> print(alignment[:, 2:]) ACCGG-TTT -||-||- AC-GGGTT- >>> print(alignment[:, 3:]) ACCGG-TTT ||-||- ACGGGTT- >>> print(alignment[:, 3:-1]) ACCGG-TTT ||-|| ACGGGTT """ if isinstance(key, slice): if key.indices(len(self)) == (0, 2, 1): target = self.target query = self.query path = self.path score = self.score return PairwiseAlignment(target, query, path, score) raise NotImplementedError if isinstance(key, int): raise NotImplementedError if isinstance(key, tuple): try: row, col = key except ValueError: raise ValueError("only tuples of length 2 can be alignment indices") if isinstance(row, int): raise NotImplementedError if isinstance(row, slice): if row.indices(len(self)) != (0, 2, 1): raise NotImplementedError if isinstance(col, int): raise NotImplementedError if isinstance(col, slice): n, m = self.shape start_index, stop_index, step = col.indices(m) if step != 1: raise NotImplementedError path = [] index = 0 path_iterator = iter(self.path) starts = next(path_iterator) for ends in path_iterator: index += max(e - s for s, e in zip(starts, ends)) if start_index < index: offset = index - start_index point = tuple( e - offset if s < e else s for s, e in zip(starts, ends) ) path.append(point) break starts = ends while True: if stop_index <= index: offset = index - stop_index point = tuple( e - offset if s < e else s for s, e in zip(starts, ends) ) path.append(point) break path.append(ends) starts = ends ends = next(path_iterator) index += max(e - s for s, e in zip(starts, ends)) path = tuple(path) target = self.target query = self.query if path == self.path: score = self.score else: score = None return PairwiseAlignment(target, query, path, score) raise TypeError("second index must be an integer or slice") raise TypeError("first index must be an integer or slice") raise TypeError("alignment indices must be integers, slices, or tuples") def _convert_sequence_string(self, sequence): 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 self.format(format_spec) def format(self, fmt="", **kwargs): """Return the alignment as a string in the specified file format. Arguments: - fmt - File format. Acceptable values are "" : create a human-readable representation of the alignment (default); "BED": create a line representing the alignment in the Browser Extensible Data (BED) file format; "PSL": create a line representing the alignment in the Pattern Space Layout (PSL) file format as generated by BLAT; "SAM": create a line representing the alignment in the Sequence Alignment/Map (SAM) format. - 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'. """ if fmt == "": return self._format_pretty(**kwargs) elif fmt == "psl": return self._format_psl(**kwargs) elif fmt == "bed": return self._format_bed(**kwargs) elif fmt == "sam": return self._format_sam(**kwargs) else: raise ValueError("Unknown format %s" % fmt) def _format_pretty(self): seq1 = self._convert_sequence_string(self.target) if seq1 is None: return self._format_generalized() seq2 = self._convert_sequence_string(self.query) if seq2 is None: return self._format_generalized() n1 = len(seq1) n2 = len(seq2) aligned_seq1 = "" aligned_seq2 = "" pattern = "" path = self.path if path[0][1] > path[-1][1]: # mapped to reverse strand path = tuple((c1, n2 - c2) for (c1, c2) in path) seq2 = reverse_complement(seq2) end1, end2 = path[0] if end1 > 0 or end2 > 0: end = max(end1, end2) aligned_seq1 += " " * (end - end1) + seq1[:end1] aligned_seq2 += " " * (end - end2) + seq2[:end2] pattern += " " * end start1 = end1 start2 = end2 for end1, end2 in path[1:]: if end1 == start1: gap = end2 - start2 aligned_seq1 += "-" * gap aligned_seq2 += seq2[start2:end2] pattern += "-" * gap elif end2 == start2: gap = end1 - start1 aligned_seq1 += seq1[start1:end1] aligned_seq2 += "-" * gap pattern += "-" * gap else: s1 = seq1[start1:end1] s2 = seq2[start2:end2] aligned_seq1 += s1 aligned_seq2 += s2 for c1, c2 in zip(s1, s2): if c1 == c2: pattern += "|" else: pattern += "." start1 = end1 start2 = end2 aligned_seq1 += seq1[end1:] aligned_seq2 += seq2[end2:] return "%s\n%s\n%s\n" % (aligned_seq1, pattern, aligned_seq2) def _format_generalized(self): seq1 = self.target seq2 = self.query aligned_seq1 = [] aligned_seq2 = [] pattern = [] path = self.path end1, end2 = path[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 path[1:]: 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: for c1, c2 in zip(seq1[start1:end1], seq2[start2:end2]): 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 "%s\n%s\n%s\n" % (aligned_seq1, pattern, aligned_seq2) def _format_bed(self): query = self.query target = self.target # variable names follow those in the BED file format specification try: chrom = target.id except AttributeError: chrom = "target" try: name = query.id except AttributeError: name = "query" path = self.path if path[0][1] < path[-1][1]: # mapped to forward strand strand = "+" else: # mapped to reverse strand strand = "-" n2 = len(query) path = tuple((c1, n2 - c2) for (c1, c2) in path) score = self.score blockSizes = [] tStarts = [] tStart, qStart = path[0] for tEnd, qEnd in path[1:]: tCount = tEnd - tStart qCount = qEnd - qStart if tCount == 0: qStart = qEnd elif qCount == 0: tStart = tEnd else: assert tCount == qCount tStarts.append(tStart) blockSizes.append(tCount) tStart = tEnd qStart = qEnd chromStart = tStarts[0] chromEnd = tStarts[-1] + blockSizes[-1] blockStarts = [tStart - chromStart for tStart in tStarts] blockCount = len(blockSizes) blockSizes = ",".join(map(str, blockSizes)) + "," blockStarts = ",".join(map(str, blockStarts)) + "," thickStart = chromStart thickEnd = chromEnd itemRgb = "0" words = [ chrom, str(chromStart), str(chromEnd), name, str(score), strand, str(thickStart), str(thickEnd), itemRgb, str(blockCount), blockSizes, blockStarts, ] line = "\t".join(words) + "\n" return line def _format_psl(self, mask=False, wildcard="N"): path = self.path if not path: # alignment consists of gaps only return "" query = self.query target = self.target try: qName = query.id except AttributeError: qName = "query" try: query = query.seq except AttributeError: pass try: tName = target.id except AttributeError: tName = "target" try: target = target.seq except AttributeError: pass n1 = len(target) n2 = len(query) try: seq1 = bytes(target) except TypeError: # string seq1 = bytes(target, "ASCII") except UndefinedSequenceError: # sequence contents is unknown seq1 = None if path[0][1] < path[-1][1]: # mapped to forward strand strand = "+" seq2 = query else: # mapped to reverse strand strand = "-" seq2 = reverse_complement(query) path = tuple((c1, n2 - c2) for (c1, c2) in path) try: seq2 = bytes(seq2) except TypeError: # string seq2 = bytes(seq2, "ASCII") except UndefinedSequenceError: # sequence contents is unknown seq2 = None if wildcard is not None: if mask == "upper": wildcard = ord(wildcard.lower()) else: wildcard = ord(wildcard.upper()) # variable names follow those in the PSL file format specification matches = 0 misMatches = 0 repMatches = 0 nCount = 0 qNumInsert = 0 qBaseInsert = 0 tNumInsert = 0 tBaseInsert = 0 qSize = n2 tSize = n1 blockSizes = [] qStarts = [] tStarts = [] tStart, qStart = path[0] for tEnd, qEnd in path[1:]: tCount = tEnd - tStart qCount = qEnd - qStart if tCount == 0: if qStart > 0 and qEnd < qSize: qNumInsert += 1 qBaseInsert += qCount qStart = qEnd elif qCount == 0: if tStart > 0 and tEnd < tSize: tNumInsert += 1 tBaseInsert += tCount tStart = tEnd else: assert tCount == qCount tStarts.append(tStart) qStarts.append(qStart) blockSizes.append(tCount) if seq1 is None or seq2 is None: # contents of at least one sequence is unknown; # count all alignments as matches: matches += tCount else: s1 = seq1[tStart:tEnd] s2 = seq2[qStart:qEnd] if mask == "lower": for u1, u2, c1 in zip(s1.upper(), s2.upper(), s1): if u1 == wildcard or u2 == wildcard: nCount += 1 elif u1 == u2: if u1 == c1: matches += 1 else: repMatches += 1 else: misMatches += 1 elif mask == "upper": for u1, u2, c1 in zip(s1.lower(), s2.lower(), s1): if u1 == wildcard or u2 == wildcard: nCount += 1 elif u1 == u2: if u1 == c1: matches += 1 else: repMatches += 1 else: misMatches += 1 else: for u1, u2 in zip(s1.upper(), s2.upper()): if u1 == wildcard or u2 == wildcard: nCount += 1 elif u1 == u2: matches += 1 else: misMatches += 1 tStart = tEnd qStart = qEnd tStart = tStarts[0] # start of alignment in target qStart = qStarts[0] # start of alignment in query tEnd = tStarts[-1] + blockSizes[-1] # end of alignment in target qEnd = qStarts[-1] + blockSizes[-1] # end of alignment in query if strand == "-": qStart, qEnd = qSize - qEnd, qSize - qStart blockCount = len(blockSizes) blockSizes = ",".join(map(str, blockSizes)) + "," qStarts = ",".join(map(str, qStarts)) + "," tStarts = ",".join(map(str, tStarts)) + "," words = [ str(matches), str(misMatches), str(repMatches), str(nCount), str(qNumInsert), str(qBaseInsert), str(tNumInsert), str(tBaseInsert), strand, qName, str(qSize), str(qStart), str(qEnd), tName, str(tSize), str(tStart), str(tEnd), str(blockCount), blockSizes, qStarts, tStarts, ] line = "\t".join(words) + "\n" return line def _format_sam(self): query = self.query target = self.target try: qName = query.id except AttributeError: qName = "query" else: query = query.seq try: rName = target.id except AttributeError: rName = "target" else: target = target.seq n1 = len(target) n2 = len(query) pos = None qSize = n2 tSize = n1 cigar = [] path = self.path if path[0][1] < path[-1][1]: # mapped to forward strand flag = 0 seq = query else: # mapped to reverse strand flag = 16 seq = reverse_complement(query) path = tuple((c1, n2 - c2) for (c1, c2) in path) try: seq = bytes(seq) except TypeError: # string pass else: seq = str(seq, "ASCII") tStart, qStart = path[0] for tEnd, qEnd in path[1:]: tCount = tEnd - tStart qCount = qEnd - qStart if tCount == 0: length = qCount if pos is None or tEnd == tSize: operation = "S" else: operation = "I" qStart = qEnd elif qCount == 0: if tStart > 0 and tEnd < tSize: length = tCount operation = "D" else: operation = None tStart = tEnd else: assert tCount == qCount if pos is None: pos = tStart tStart = tEnd qStart = qEnd operation = "M" length = tCount if operation is not None: cigar.append(str(length) + operation) mapQ = 255 # not available rNext = "*" pNext = 0 tLen = 0 qual = "*" cigar = "".join(cigar) tag = "AS:i:%d" % int(round(self.score)) words = [ qName, str(flag), rName, str(pos + 1), # 1-based coordinates str(mapQ), cigar, rNext, str(pNext), str(tLen), seq, qual, tag, ] line = "\t".join(words) + "\n" return line def __str__(self): return self.format() def __len__(self): """Return the number of sequences in the alignment, which is always 2.""" return 2 @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) -GACCT-G -||--|-| CGA--TCG >>> len(alignment) 2 >>> alignment.shape (2, 8) >>> aligner.mode = "local" >>> alignments = aligner.align("GACCTG", "CGATCG") >>> alignment = alignments[0] >>> print(alignment) GACCT-G ||--|-| CGA--TCG >>> len(alignment) 2 >>> alignment.shape (2, 7) """ path = self.path if path[0][1] > path[-1][1]: # mapped to reverse strand n2 = len(self.query) path = tuple((c1, n2 - c2) for (c1, c2) in path) start = path[0] n = len(start) m = 0 for end in path[1:]: m += max(e - s for s, e in zip(start, end)) start = end 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) GAACT ||--| GA--T >>> alignment.aligned (((0, 2), (4, 5)), ((0, 2), (2, 3))) >>> alignment = alignments[1] >>> print(alignment) GAACT |-|-| G-A-T >>> alignment.aligned (((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]) AAAC-AAA |||--||| AAA-GAAA >>> alignments[0].aligned (((0, 3), (4, 7)), ((0, 3), (4, 7))) >>> print(alignments[1]) AAA-CAAA |||--||| AAAG-AAA >>> alignments[1].aligned (((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. """ segments1 = [] segments2 = [] path = self.path if path[0][1] < path[-1][1]: # mapped to forward strand i1, i2 = path[0] for node in path[1:]: j1, j2 = node if j1 > i1 and j2 > i2: segment1 = (i1, j1) segment2 = (i2, j2) segments1.append(segment1) segments2.append(segment2) i1, i2 = j1, j2 else: # mapped to reverse strand n2 = len(self.query) i1, i2 = path[0] i2 = n2 - i2 for node in path[1:]: j1, j2 = node j2 = n2 - j2 if j1 > i1 and j2 > i2: segment1 = (i1, j1) segment2 = (n2 - i2, n2 - j2) segments1.append(segment1) segments2.append(segment2) i1, i2 = j1, j2 return tuple(segments1), tuple(segments2) 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) AATAA ||.|| AAGAA >>> alignment.sort() >>> print(alignment) AAGAA ||.|| AATAA 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 >>> alignment.sort(key=GC) >>> print(alignment) AATAA ||.|| AAGAA You can reverse the sort order by passing `reverse=True`: >>> alignment.sort(key=GC, reverse=True) >>> print(alignment) AAGAA ||.|| AATAA The sequences are now sorted by decreasing GC content value. """ path = self.path sequences = self.target, self.query 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) sequences = [sequences[index] for index in indices] self.target, self.query = sequences path = tuple(tuple(row[index] for index in indices) for row in path) self.path = path 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) AAAAAAAACCCCCCCAAAAAAAAAAAGGGGGGAAAAAAAA |||||||-----------|||||| CCCCCCC-----------GGGGGG >>> sequence = "CCCCGGGG" >>> alignments2 = aligner.align(transcript, sequence) >>> len(alignments2) 1 >>> alignment2 = alignments2[0] >>> print(alignment2) CCCCCCCGGGGGG |||||||| CCCCGGGG >>> alignment = alignment1.map(alignment2) >>> print(alignment) AAAAAAAACCCCCCCAAAAAAAAAAAGGGGGGAAAAAAAA ||||-----------|||| CCCC-----------GGGG >>> 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' """ from numpy import array 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 path1 = alignment1.path path2 = alignment2.path n1 = len(alignment1.query) n2 = len(alignment2.query) if path1[0][1] < path1[-1][1]: # mapped to forward strand strand1 = "+" else: # mapped to reverse strand strand1 = "-" if path2[0][1] < path2[-1][1]: # mapped to forward strand strand2 = "+" else: # mapped to reverse strand strand2 = "-" path1 = array(path1) path2 = array(path2) if strand1 == "+": if strand2 == "-": # mapped to reverse strand path2[:, 1] = n2 - path2[:, 1] else: # mapped to reverse strand path1[:, 1] = n1 - path1[:, 1] path2[:, 0] = n1 - path2[::-1, 0] if strand2 == "+": path2[:, 1] = n2 - path2[::-1, 1] else: # mapped to reverse strand path2[:, 1] = path2[::-1, 1] path = [] tEnd, qEnd = sys.maxsize, sys.maxsize path1 = iter(path1) tStart1, qStart1 = sys.maxsize, sys.maxsize for tEnd1, qEnd1 in path1: if tStart1 < tEnd1 and qStart1 < qEnd1: break tStart1, qStart1 = tEnd1, qEnd1 tStart2, qStart2 = sys.maxsize, sys.maxsize for tEnd2, qEnd2 in path2: while qStart2 < qEnd2 and tStart2 < tEnd2: while True: if tStart2 < qStart1: if tEnd2 < qStart1: size = tEnd2 - tStart2 else: size = qStart1 - tStart2 break elif tStart2 < qEnd1: offset = tStart2 - qStart1 if tEnd2 > qEnd1: size = qEnd1 - tStart2 else: size = tEnd2 - tStart2 qStart = qStart2 tStart = tStart1 + offset if tStart > tEnd and qStart > qEnd: # adding a gap both in target and in query; # add gap to target first: path.append([tStart, qEnd]) qEnd = qStart2 + size tEnd = tStart + size path.append([tStart, qStart]) path.append([tEnd, qEnd]) break tStart1, qStart1 = sys.maxsize, sys.maxsize for tEnd1, qEnd1 in path1: if tStart1 < tEnd1 and qStart1 < qEnd1: break tStart1, qStart1 = tEnd1, qEnd1 else: size = qEnd2 - qStart2 break qStart2 += size tStart2 += size tStart2, qStart2 = tEnd2, qEnd2 if strand1 != strand2: path = tuple((c1, n2 - c2) for (c1, c2) in path) alignment = PairwiseAlignment(target, query, path, None) 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) ATACTTACCTGGCAGGGGAGATACCATGATCACGAAGGTGGTTTTCCCAGGGCGAGGCTTATCCATTGCACTCCGGATGTGCTGACCCCTGCGATTTCCCCAAATGTGGGAAACTCGACTGCATAATTTGTGGTAGTGGGGGACTGCGTTCGCGCTTTCCCCTG |||||||||||.||||||||..|||||||||||..|||||||..|||||||||||||||..|||||||||||.|||..|.|.|||||||||||||||||||||||||||||||||||||||.||||||||||||||||||||||||||||||||||.|||||.| ATACTTACCTGACAGGGGAGGCACCATGATCACACAGGTGGTCCTCCCAGGGCGAGGCTCTTCCATTGCACTGCGGGAGGGTTGACCCCTGCGATTTCCCCAAATGTGGGAAACTCGACTGTATAATTTGTGGTAGTGGGGGACTGCGTTCGCGCTATCCCCCG >>> 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. """ target = self.target try: target = target.seq except AttributeError: pass query = self.query try: query = query.seq except AttributeError: pass sequences = (str(target), str(query)) letters = set.union(*[set(sequence) for sequence in sequences]) letters = "".join(sorted(letters)) m = substitution_matrices.Array(letters, dims=2) n = len(sequences) for i1 in range(n): path1 = [p[i1] for p in self.path] sequence1 = sequences[i1] for i2 in range(i1 + 1, n): path2 = [p[i2] for p in self.path] sequence2 = sequences[i2] start1, start2 = sys.maxsize, sys.maxsize for end1, end2 in zip(path1, path2): if start1 < end1 and start2 < end2: # aligned segment1 = sequence1[start1:end1] segment2 = sequence2[start2:end2] for c1, c2 in zip(segment1, segment2): m[c1, c2] += 1.0 start1, start2 = end1, end2 return m class PairwiseAlignments: """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 an PairwiseAlignments object by calling aligner.align(seqA, seqB), where aligner is a PairwiseAligner object. """ self.seqA = seqA self.seqB = seqB self.score = score self.paths = paths self.index = -1 def __len__(self): return len(self.paths) def __getitem__(self, index): if index == self.index: return self.alignment if index < self.index: self.paths.reset() self.index = -1 while self.index < index: try: alignment = next(self) except StopIteration: raise IndexError("index out of range") from None return alignment def __iter__(self): self.paths.reset() self.index = -1 return self def __next__(self): path = next(self.paths) self.index += 1 alignment = PairwiseAlignment(self.seqA, self.seqB, path, self.score) self.alignment = alignment return alignment class PairwiseAligner(_aligners.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: TACCG -|-|| -A-CG Score = 3.0: TACCG -||-| -AC-G 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: TACCG |-|| A-CG Score = 3.0: TACCG ||-| AC-G 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: TACCG -||-| -AC-G Score = 6.0: TACCG -|-|| -A-CG 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: TACCG -|-|| -A-CG Score = 5.5: TACCG -||-| -AC-G 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) KEVLA -|||- -EVL- You can also set the value of attributes directly during construction of the PairwiseAligner object by providing them as keyword arguemnts: >>> 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: TACCG -||-| -AC-G Score = 6.0: TACCG -|-|| -A-CG """ def __init__(self, **kwargs): """Initialize a new PairwiseAligner with the keyword arguments as attributes. Loops over the keyword arguments and sets them as attributes on the object. """ super().__init__() for name, value in kwargs.items(): setattr(self, name, value) def __setattr__(self, key, value): if key not in dir(_aligners.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) _aligners.PairwiseAligner.__setattr__(self, key, value) def align(self, seqA, seqB, strand="+"): """Return the alignments of two sequences using PairwiseAligner.""" if isinstance(seqA, (Seq, MutableSeq)): sA = bytes(seqA) else: sA = seqA if strand == "+": sB = seqB else: # strand == "-": sB = reverse_complement(seqB) if isinstance(sB, (Seq, MutableSeq)): sB = bytes(sB) score, paths = _aligners.PairwiseAligner.align(self, sA, sB, strand) alignments = PairwiseAlignments(seqA, seqB, score, paths) return alignments def score(self, seqA, seqB, strand="+"): """Return the alignments score of two sequences using PairwiseAligner.""" if isinstance(seqA, (Seq, MutableSeq)): seqA = bytes(seqA) if strand == "-": seqB = reverse_complement(seqB) if isinstance(seqB, (Seq, MutableSeq)): seqB = bytes(seqB) return _aligners.PairwiseAligner.score(self, 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 if __name__ == "__main__": from Bio._utils import run_doctest run_doctest()