o RŀgZ@sUdZddlZddlZddlZddlZddlZddlZddlZddlm Z ddlm Z ddl m Z zddl ZWneyIddlmZeddwddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddl m!Z!ddl m"Z"e#dgdZ$GdddZ%GdddZ&Gddde Z'Gddde'e(Z)Gdd d e'Z*Gd!d"d"ej+Z+Gd#d$d$ej,Z,d%Z-iZ.e/e0ej1fe2d&<d'e0d(ej1fd)d*Z3d+d,Z4d-d.Z5d/d0Z6e7d1kr dd2l8m9Z9e9dSdS)3zCode for dealing with sequence alignments. One of the most important things in this module is the MultipleSeqAlignment class, used in the Bio.AlignIO module. N)ABC)abstractmethod) zip_longest)MissingPythonDependencyErrorzLPlease install NumPy if you want to use Bio.Align. See http://www.numpy.org/)BiopythonDeprecationWarning) _aligncore) _codonaligner)_pairwisealigner)substitution_matrices) CodonTable) MutableSeqreverse_complement)Seq) translate)UndefinedSequenceError)_RestrictedDict) SeqRecordAlignmentCounts)gaps identities mismatchesc@seZdZdZ d-ddZddZddZeeed d Zd.d d Z ddZ ddZ ddZ ddZ ddZddZddZddZd/ddZd d!Zd"d#Zd$d%Zd0d'd(Zed)d*Zed+d,ZdS)1MultipleSeqAlignmentaf 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. NcCs`|durtdg|_|r|||duri}n t|ts"td||_|dur+i}||_dS)afInitialize 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'} Nz,The alphabet argument is no longer supportedz%annotations argument should be a dict) ValueError_recordsextend isinstancedict TypeError annotationscolumn_annotations)selfrecordsalphabetrr r$F/var/www/html/myenv/lib/python3.10/site-packages/Bio/Align/__init__.py__init__s'   zMultipleSeqAlignment.__init__cCsTt|ts tdt|r|}t|d|_|j|dSd|_|r(tddS)Nz?The per-column-annotations should be a (restricted) dictionary.lengthz5Can't set per-column-annotations without an alignment) rrrlenget_alignment_lengthr_per_col_annotationsupdater)r!valueexpected_lengthr$r$r%_set_per_column_annotationss  z0MultipleSeqAlignment._set_per_column_annotationscCs2|jdurt|r|}nd}t|d|_|jS)Nrr')r+r)r*r)r!r.r$r$r%_get_per_column_annotationss   z0MultipleSeqAlignment._get_per_column_annotationsz5Dictionary of per-letter-annotation for the sequence.)fgetfsetdoc2cCs|jjjdkr+t|j|kr|jd|jSd|jd|d|jdd|jfSt|j|kr;|jd|jSd|jd|d|jdd|jfS)zReturn a truncated string representation of a SeqRecord (PRIVATE). This is a PRIVATE function used by the __str__ method. CodonSeq z %s...%s %sN)seq __class____name__r)id)r!recordr(r$r$r% _str_lines  zMultipleSeqAlignment._str_linecstj}d|fg}|dkr |fddjDn!|fddjddD|d|jd d |S) aqReturn a multi-line string summary of the alignment. This output is intended to be readable, but large alignments are shown truncated. A maximum of 20 rows (sequences) and 50 columns are shown, with the record identifiers. This should fit nicely on a single screen. e.g. >>> from Bio.Seq import Seq >>> from Bio.SeqRecord import SeqRecord >>> from Bio.Align import MultipleSeqAlignment >>> a = SeqRecord(Seq("ACTGCTAGCTAG"), id="Alpha") >>> b = SeqRecord(Seq("ACT-CTAGCTAG"), id="Beta") >>> c = SeqRecord(Seq("ACTGCTAGATAG"), id="Gamma") >>> align = MultipleSeqAlignment([a, b, c]) >>> print(align) Alignment with 3 rows and 12 columns ACTGCTAGCTAG Alpha ACT-CTAGCTAG Beta ACTGCTAGATAG Gamma See also the alignment's format method. z%Alignment with %i rows and %i columnsc3|]}|VqdSNr?.0recr!r$r% (z/MultipleSeqAlignment.__str__..c3rArBrCrDrGr$r%rH*rINz... )r)rr*rappendr?join)r!rowslinesr$rGr%__str__ s  "  zMultipleSeqAlignment.__str__cCs d|jt|j|t|fS)aReturn 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. z-<%s instance (%i records of length %i) at %x>)r;r)rr*r=rGr$r$r%__repr__/s zMultipleSeqAlignment.__repr__cCsB|rddlm}ddlm}|}||g|||St|S)a2Return 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 r)StringIO)AlignIO)iorSBiorTwritegetvaluestr)r! format_specrSrThandler$r$r% __format__Is  zMultipleSeqAlignment.__format__cC t|jS)aIterate 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 )iterrrGr$r$r%__iter__us zMultipleSeqAlignment.__iter__cCr])aReturn 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. )r)rrGr$r$r%__len__s zMultipleSeqAlignment.__len__cCs,d}|jD]}t|j|krt|j}q|S)a1Return 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 r)rr)r:)r! max_lengthr>r$r$r%r*s   z)MultipleSeqAlignment.get_alignment_lengthcCspt|r |}n"t|}zt|}Wn tyYdSwt|}|||i|_|D]}|||q-dS)a"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). N)r)r*r^next StopIteration_appendr )r!r"r.rFr$r$r%rs'    zMultipleSeqAlignment.extendcCs(|jr |||dS||dS)a7Add 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 N)rrdr*)r!r>r$r$r%rMs+zMultipleSeqAlignment.appendcCs>t|ts td|durt||krtd|j|dS)z'Validate and append a record (PRIVATE).z&New sequence is not a SeqRecord objectNz%Sequences must all be the same length)rrrr)rrrM)r!r>r.r$r$r%rd&s zMultipleSeqAlignment._appendcCst|tstt|t|krtdddt||D}i}|jD]\}}||jvr8|j||kr8|||<q$i}|jD]\}}||jvrR||j|||<q@t|||dS)a| 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'} TWhen adding two alignments they must have the same length (i.e. same number of rows)css|] \}}||VqdSrBr$)rEleftrightr$r$r%rHsz/MultipleSeqAlignment.__add__..)rr ) rrNotImplementedErrorr)rzipritemsr )r!othermergedrkvr r$r$r%__add__6s( I zMultipleSeqAlignment.__add__cst|tr |j|St|tr2t|j|}|jr0t|t|kr0|jD] \}}||j|<q&|St|dkr>> 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. Invalid index type.c3|]}|VqdSrBr$rD col_indexr$r%rHz3MultipleSeqAlignment.__getitem__..c3rsrBr$rDrtr$r%rHs ) rintrslicerr r)rjrrN)r!indexnewrmrn row_indexr$rtr% __getitem__s, o       z MultipleSeqAlignment.__getitem__cCs(t|tst|tstd|j|=dS)z:Delete SeqRecord by index or multiple SeqRecords by slice.rqN)rrwrxrr)r!ryr$r$r% __delitem__'s z MultipleSeqAlignment.__delitem__FcCs4|dur|jjdd|ddS|jj||ddS)a 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 NcSs|jSrBr=)rr$r$r%wsz+MultipleSeqAlignment.sort..keyreverse)rsort)r!rrr$r$r%r.sHzMultipleSeqAlignment.sortc Cstjdd|D}z|dWn tyYnwdt|}tj|dd}t|D]G\}}|j }|j dd}t|D]4\}}||krIn+|j } |j dd} t || D]\} } | dkraqX| dkrfqX|| | f|| 7<qXq?q-|| 7}|d }|S) aAReturn an Array with the number of substitutions of letters in the alignment. As an example, consider a multiple sequence alignment of three DNA sequences: >>> from Bio.Seq import Seq >>> from Bio.SeqRecord import SeqRecord >>> from Bio.Align import MultipleSeqAlignment >>> seq1 = SeqRecord(Seq("ACGT"), id="seq1") >>> seq2 = SeqRecord(Seq("A--A"), id="seq2") >>> seq3 = SeqRecord(Seq("ACGT"), id="seq3") >>> seq4 = SeqRecord(Seq("TTTC"), id="seq4") >>> alignment = MultipleSeqAlignment([seq1, seq2, seq3, seq4]) >>> print(alignment) Alignment with 4 rows and 4 columns ACGT seq1 A--A seq2 ACGT seq3 TTTC seq4 >>> m = alignment.substitutions >>> print(m) A C G T A 3.0 0.5 0.0 2.5 C 0.5 1.0 0.0 2.0 G 0.0 0.0 1.0 1.0 T 2.5 2.0 1.0 1.0 Note that the matrix is symmetric, with counts divided equally on both sides of the diagonal. For example, the total number of substitutions between A and T in the alignment is 3.5 + 3.5 = 7. Any weights associated with the sequences are taken into account when calculating the substitution matrix. For example, given the following multiple sequence alignment:: GTATC 0.5 AT--C 0.8 CTGTC 1.0 For the first column we have:: ('A', 'G') : 0.5 * 0.8 = 0.4 ('C', 'G') : 0.5 * 1.0 = 0.5 ('A', 'C') : 0.8 * 1.0 = 0.8 css|]}t|jVqdSrB)setr:rEr>r$r$r%rHrIz5MultipleSeqAlignment.substitutions..-rrrpdimsweight?g@)runionremoveKeyErrorrNsortedr Array enumerater:rgetri transpose) r!lettersmrec_num1 alignment1seq1weight1rec_num2 alignment2seq2weight2residue1residue2r$r$r% substitutions{s61  z"MultipleSeqAlignment.substitutionsc sdd|jD}|r{dd|D}t|\}}t||D]W\}}|jroddt|jD}t|j}|jt ||_| D])\} t t rWd fdd|Dnt} | fdd|D|| <qA||_qt ||_qt||} ntg} |j| _|j| _| S) aReturn an Alignment object based on the MultipleSeqAlignment object. This makes a copy of each SeqRecord with a gap-less sequence. Any future changes to the original records in the MultipleSeqAlignment will not affect the new records in the Alignment. cSsg|]}t|qSr$)copyrr$r$r% z2MultipleSeqAlignment.alignment..cSsg|]}t|jqSr$)bytesr:rr$r$r%rrcSsg|] \}}|dkr|qS)rr$)rEicr$r$r%rsrrcg|]}|qSr$r$rErr-r$r%rc3|]}|VqdSrBr$rrr$r%rHrvz1MultipleSeqAlignment.alignment..)r Alignmentparse_printed_alignmentriletter_annotationsrr:rclearrrjrrYrNtyperr ) r!r"rPseqdata coordinatesr>seqrowindicesrrcls alignmentr$rr%rs.       zMultipleSeqAlignment.alignment)NNN)r4rBNF)r< __module__ __qualname____doc__r&r/r0propertyr r?rQrRr\r_r`r*rrMrdror|r}rrrr$r$r$r%r?s<T ;  $,  < 0^ M Krc@seZdZdZeddZeddZd]ddZd]d d Zd d Z e d dZ e ddZ e j ddZ e ddZej ddZddZddZddZddZddZdd Zd!d"Zd#d$Zd%d&Zd'd(Zd)d*Zd+d,Zd-d.Zd/d0Zd1d2Zd3d4Zd5d6Zd^d8d9Z d:d;Z!dd?Z#d@dAZ$dBdCZ%dDdEZ&e dFdGZ'e dHdIZ(e dJdKZ)e dLdMZ*e dNdOZ+d_dQdRZ,dSdTZ-dUdVZ.e dWdXZ/dYdZZ0d[d\Z1dS)`raRepresents a sequence alignment. An Alignment object has a `.sequences` attribute storing the sequences (Seq, MutableSeq, SeqRecord, or string objects) that were aligned, as well as a `.coordinates` attribute storing the sequence coordinates defining the alignment as a NumPy array. Other commonly used attributes (which may or may not be present) are: - annotations - A dictionary with annotations describing the alignment; - column_annotations - A dictionary with annotations describing each column in the alignment; - score - The alignment score. cCs,tdtdd|D}||\}}|S)aInfer the coordinates from a printed alignment (DEPRECATED). This method is primarily employed in Biopython's alignment parsers, though it may be useful for other purposes. For an alignment consisting of N sequences, printed as N lines with the same number of columns, where gaps are represented by dashes, this method will calculate the sequence coordinates that define the alignment. The coordinates are returned as a NumPy array of integers, and can be used to create an Alignment object. This is an example for the alignment of three sequences TAGGCATACGTG, AACGTACGT, and ACGCATACTTG, with gaps in the second and third sequence: >>> from Bio.Align import Alignment >>> lines = ["TAGGCATACGTG", ... "AACG--TACGT-", ... "-ACGCATACTTG", ... ] >>> sequences = [line.replace("-", "") for line in lines] >>> sequences ['TAGGCATACGTG', 'AACGTACGT', 'ACGCATACTTG'] >>> coordinates = Alignment.infer_coordinates(lines) >>> print(coordinates) [[ 0 1 4 6 11 12] [ 0 1 4 4 9 9] [ 0 0 3 5 10 11]] >>> alignment = Alignment(sequences, coordinates) zThe method infer_coordinates is deprecated; please use the method parse_printed_alignment instead. This method is much faster than infer_coordinates, and returns both the sequences after removal of the gaps and the coordinates.cSsg|]}|qSr$)encode)rEliner$r$r%r!rz/Alignment.infer_coordinates..)warningswarnrr)rrPrrr$r$r%infer_coordinatesszAlignment.infer_coordinatesc CsVtd}g}|D]}||\}}||q |j}t|tj}||||fS)aInfer the sequences and coordinates from a printed alignment. This method is primarily employed in Biopython's alignment parsers, though it may be useful for other purposes. For an alignment consisting of N sequences, printed as N lines with the same number of columns, where gaps are represented by dashes, this method will calculate the sequence coordinates that define the alignment. It returns the tuple (sequences, coordinates), where sequences is the list of N sequences after removing the gaps, and the coordinates is a 2D NumPy array of integers. Together, the sequences and coordinates can be used to create an Alignment object. This is an example for the alignment of three sequences TAGGCATACGTG, AACGTACGT, and ACGCATACTTG, with gaps in the second and third sequence. Note that the input sequences are bytes objects. >>> from Bio.Align import Alignment >>> from Bio.Seq import Seq >>> lines = [b"TAGGCATACGTG", ... b"AACG--TACGT-", ... b"-ACGCATACTTG", ... ] >>> sequences, coordinates = Alignment.parse_printed_alignment(lines) >>> sequences [b'TAGGCATACGTG', b'AACGTACGT', b'ACGCATACTTG'] >>> print(coordinates) [[ 0 1 4 6 11 12] [ 0 1 4 4 9 9] [ 0 0 3 5 10 11]] >>> sequences = [Seq(sequence) for sequence in sequences] >>> sequences [Seq('TAGGCATACGTG'), Seq('AACGTACGT'), Seq('ACGCATACTTG')] >>> alignment = Alignment(sequences, coordinates) >>> print(alignment) 0 TAGGCATACGTG 12 0 AACG--TACGT- 9 0 -ACGCATACTTG 11 ) rPrintedAlignmentParserfeedrMshapenpemptyint64fill) rrPparser sequencesrnbytessequencerrr$r$r%r%s *  z!Alignment.parse_printed_alignmentNcCs||_|durCz dd|D}Wn tyYn*wt|dkr(tjdtd}nt|dkr?|}td|ggt|}ntd||_ dS) aInitialize 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. NcSsh|]}t|qSr$r)rErr$r$r% frz%Alignment.__init__..rrr)dtypez:sequences must have the same length if coordinates is None) rrr)rrrwpoparrayrr)r!rrlengthsr(r$r$r%r&Ys      zAlignment.__init__c Cs|j}t|j}t|jd}t|dkddk}t|D]E\}}|||f}|dkr/q|dkr]t |||<t |||ddf||ddf<||ddf ||ddf<qt d|| d} || k|dkBswt dt |} t| } t | | fd} t| D]o}||}||df} d} t||| D]Y\}}|dkr| |}| |} z t|| |}Wntyt|| |d}Ynw|| |ddfjd| | <|} | } q|dkr| |7} q| |} d| || | f<| } qq|durt| |} | S) NrrInconsistent steps in row Unequal step sizes in alignmentS1UTF8B-)rrlistrrdiffsumrallrr)rmaxrrangerirrdatacastr)r!rrrstepsalignedrrrowrnrrrmstepgapj subsequencer$r$r% __array__xsX      $         zAlignment.__array__c Cst|tstt|t|krtd|jdddf}|jdddf}|j}|jdddf}|jdddf}|j}g}t||||||D]\} } } } } }| | | || | }||qH|||}|j|dddf}|j|dddf}t j||dd}t||}i}z"|j D]\}}z |j ||kr|||<Wqt yYqwWn t yYnw||_ i}z |j D]\}}z ||j|||<Wqt yYqwWn t yY|Sw||_|S)a 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'} reNrrKraxis)rrrhr)rrrrirMrrrjrAttributeErrorr )r!rkstarts1ends1 sequences1starts2ends2 sequences2rstart1end1rstart2end2rroffset coordinates1 coordinates2rrrrmrnr r$r$r%rosj N        zAlignment.__add__c Cs|j}t|j}t|jd}t|dkddk}t|D]E\}}|||f}|dkr/q|dkr]t |||<t |||ddf||ddf<||ddf ||ddf<qt d|| d}||k|dkBswt dt |} t|} i} t | D]}||}z |jdd} Wn tyd} Ynw||df} d}t|||D]\}}|dkr | |}||} z t|| |}Wntyt|| |d}Ynwtt || |D]"\}}t|}| |}|durt| }|| |<||| 7<q|} | }q|dkr| |7} q||} d }| |}|dur0t| }|| |<||| | 7<| }qq| S) aLReturn 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.])} rrNrrrrrr)rrrrrrrrrrr)rrrrrrrirrchrzeros)r!rrrrrrrrrr(countsrrmrrrrrryletter characterr$r$r% frequencies'st      $              zAlignment.frequenciescCs(t|j}|dkrtd||jdS)z2Return self.sequences[0] for a pairwise alignment.rpUself.target is defined for pairwise alignments only (found alignment of %d sequences)rr)rrr!rr$r$r%targets  zAlignment.targetcC,t|j}|dkrtd|||jd<dS)z0For a pairwise alignment, set self.sequences[0].rprrNrr!r-rr$r$r%r cCs(t|j}|dkrtd||jdS)z2Return self.sequences[1] for a pairwise alignment.rpTself.query is defined for pairwise alignments only (found alignment of %d sequences)rrrr$r$r%querys  zAlignment.querycCr)z0For a pairwise alignment, set self.sequences[1].rpr rNrrr$r$r%r r c Csrt|j|jD])\}}z|j}Wn tyYnwz|j}Wn ty(Ynw||kr0dSqt|j|jS)z:Check if two Alignment objects specify the same alignment.Frrr:rr array_equalrr!rkrfrgr$r$r%__eq__s    zAlignment.__eq__c Cstt|j|jD])\}}z|j}Wn tyYnwz|j}Wn ty(Ynw||kr0dSqt|j|j S)z9Check if two Alignment objects have different alignments.Tr rr$r$r%__ne__s    zAlignment.__ne__c Cst|j|jD]0\}}z|j}Wn tyYnwz|j}Wn ty(Ynw||kr0dS||kr7dSqt|j|jD]\}}t|t|}}||krWdS||kr^dSqCdS)z'Check if self should come before other.TFrrr:rrirrtuplerr$r$r%__lt__4     zAlignment.__lt__c Cst|j|jD]0\}}z|j}Wn tyYnwz|j}Wn ty(Ynw||kr0dS||kr7dSqt|j|jD]\}}t|t|}}||krWdS||kr^dSqCdS)z6Check if self should come before or is equal to other.TFrrr$r$r%__le__rzAlignment.__le__c Cst|j|jD]0\}}z|j}Wn tyYnwz|j}Wn ty(Ynw||kr0dS||kr7dSqt|j|jD]\}}t|t|}}||krWdS||kr^dSqCdS)z&Check if self should come after other.FTrrr$r$r%__gt__rzAlignment.__gt__c Cst|j|jD]0\}}z|j}Wn tyYnwz|j}Wn ty(Ynw||kr0dS||kr7dSqt|j|jD]\}}t|t|}}||krWdS||kr^dSqCdS)z5Check if self should come after or is equal to other.FTrrr$r$r%__ge__rzAlignment.__ge__cCst|jd}t|}|dkr||7}|dkrtdn||kr$tdt|dkddk}|j|ddf}|j|}t|D]1}|||f}t|dkt|dkkrp||ddf ||ddf<||krpt|}t||}q?| d} z|j }Wn t yYnw||}|d} t |t tfrd} t|| D](\} } | dkr| | }| t || |7} |} q| dkr| | 7} q| d| 7} q| Sg} t|| D] \} } | dkr| | }| || ||} q| dg| q| S)aOReturn self[index], where index is an integer (PRIVATE). This method is called by __getitem__ for invocations of the form self[row] where row is an integer. Return value is a string if the aligned sequences are string, Seq, or SeqRecord objects, otherwise the return value is a list. rrzrow index out of rangeNrrr)rrrr) IndexErrorrrrrrr:rrrYrrir)r!ryrrrrrr aligned_stepsrrmrrrrr$r$r%_get_row0s^          zAlignment._get_rowcCsx|j|}|j|}t||}t|j|r:z|j|_Wn ty'Ynwz|j|_W|Sty9Y|Sw|S)zReturn self[key], where key is a slice object (PRIVATE). This method is called by __getitem__ for invocations of the form self[rows] where rows is a slice object. Return value is an Alignment object. ) rrrrrr scorerr )r!rrrrr$r$r% _get_rowsks"      zAlignment._get_rowsc CsR|}|j|dd}||r'|||}|t|d|d|7}||SdS)aReturn the sequence contents at alignment column j (PRIVATE). This method is called by __getitem__ for invocations of the form self[row, col] where both row and col are integers. Return value is a string of length 1. rgsideNrr)cumsum searchsortedr) r!rcolrrrrryrr$r$r% _get_row_cols  zAlignment._get_row_colcCs:|}|j|dd}|||dj|dd} z|j}Wn ty&Ynwt|ttfr|| kr\||} ||dkrBd| } | S|||||d} | | } t|| | } | S|||} ||dkrmd| } n||d} | | } t|| | } |d7}|| kr||}||dkr| d|7} n||} ||d} | t|| | 7} |d7}|| ks|||d} | dkr||dkr| d| 7} | S||} | | } | t|| | 7} | S|| kr||} ||dkrdg| } | S|||||d} | | } || | } | S|||} ||dkr#dg| } n||d} | | } || | } |d7}|| krl||}||dkrP| dg|n||} ||d} | || | |d7}|| ks<|||d} | dkr|| dkr| dg| | S||} | | } | || | | S)aReturn the alignment contents of one row and consecutive columns (PRIVATE). This method is called by __getitem__ for invocations of the form self[row, cols] where row is an integer and cols is a slice object with step 1. Return value is a string if the aligned sequences are string, Seq, or SeqRecord objects, otherwise the return value is a list. rgrNrrr)rr r:rrrYrr)r! coordinate start_index stop_indexrrrrrrr(rstartstoprr$r$r%_get_row_cols_slices    B>        ($            zAlignment._get_row_cols_slicecs`z|j}Wn tyYnwt|ttfrdd|d}t|dd|D]\}}||kr8t|||7nd|7|}q%zdfdd|DWStyXtyct ddwg|d}t|dd|D]\}}||kr |||n dg||}qsz fd d |DWStytyt ddw) aReturn 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. rrrrNrc3rrBr$rEr!rr$r%rH rvz3Alignment._get_row_cols_iterable..?second index must be an integer, slice, or iterable of integerscrr$r$r)r*r$r%rrz4Alignment._get_row_cols_iterable..) r:rrrYrrirNr Exceptionrr)r!r#colsrrr&endrr$r*r%_get_row_cols_iterablesX       z Alignment._get_row_cols_iterablecCsx|}|j|dd}|||}d} t|||D] \} } } | |dkr)| d7} q| || ||} | | | 7} q| S)aReturn 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. rgrrrrr)rr ri)r!rr!rrrrrrrrr#rryr$r$r% _get_rows_col$s    zAlignment._get_rows_colc Cstj||j|kdd}|}|j|dd} | || dj|ddd} |dd| f|| |} |dd| f| |dd| fdk7<|| d|} |dd| f| |dd| dfdk8<|dd| | df}|j|} t||| D]\} }}|rt|| dd| dd<qt| |}t |j|rz|j |_ Wn t yYnwz|j }Wn t yY|Swi|_ | D]\}}|||}z|}Wn t yYnw||j |<q|S)aReturn a subalignment of multiple rows and consecutive columns (PRIVATE). This method is called by __getitem__ for invocations of the form self[rows, cols] where rows is an arbitrary slice object, and cols is a slice object with step 1, allowing the alignment sequences to be reused in the subalignment. Return value is an Alignment object. rrrgrNrfr)ranyrrr rrir)rr rrr rjr)r!rrr$r%rrrcsrrrrrr#rcrrr rr-r$r$r%_get_rows_cols_slice:sJ ,0           zAlignment._get_rows_cols_slicec st|}g}t|D]m\}} z| j} Wn ty| } Ynwd||df} t|||D]\} } | rE| | }t| | |7|} q.d| 7q.zdfdd|DWntybtymt ddw | q | |\}}t|D]5\}} ||z| j} Wntyt | tr} n| } Yn wt| } | | _| ||<qt||}z|j}Wn tyY|Swi|_|D]"\}fdd|D}t trd|n||j|<q|S) atReturn 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. rrrrc3rrBr$rEryr*r$r%rHrvz4Alignment._get_rows_cols_iterable..r+Nc3rrBr$r5rr$r%rHrv)rrr:rrirYrNrr,rrrMrrdecoder;rdeepcopyrr rj)r!rr!rrrrrPrrsrmrrrrrr rvaluesr$)rr-r%_get_rows_cols_iterablekst                       z!Alignment._get_rows_cols_iterablec Cs t|tjr ||St|tr||St|j}|j }t |d}t |dkddk}t |D]V\}}|||f}|dkrCq2|dkr||ddf ||ddf<t|||ddf||ddf<t|||<z|j||_Wq2tyYq2wtd||d} || k|dkBstdt | } t|trz|\}} Wntytdwtdt| tjr| dkr| | 7} | dks| | krtd| | f||}t|tjr3||}t| tjr|||df| || |S||ddf} t| tr+| | \} }}| |kr%|dkr%|| | ||| |St| ||} || | | |St|tr||}||}t| tjrQ||| || |St| try| | \} }}| |krs|dkrs|||| ||| St| ||} ||| || |Std ) a| 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 rrNrrz0only tuples of length 2 can be alignment indicesz5alignment indices must be integers, slices, or tuplesz-column index %d is out of bounds (%d columns)z'first index must be an integer or slice) rnumbersIntegralrrxrrrrrrrrrrr)rr=rrrrrrr"rr(rr/r0r4r:)r!rrrrrrrrrrr!r#r$r%rr$r$r%r|s d         $                zAlignment.__getitem__cCst|ttfr |St|tr|St|trt|Sz|j}Wt|Sty,Ynwzt|}Wn t y=YdSw|j dkrGt|SdS)zHConvert given sequence to string using the appropriate method (PRIVATE).rN) rr bytearrayr6rYrr:r memoryviewrformat)r!rviewr$r$r%_convert_sequence_stringds*      z"Alignment._convert_sequence_stringcCs ||S)zkReturn the alignment as a string in the specified file format. Wrapper for self.format(). r?)r!rZr$r$r%r\{s zAlignment.__format__rrcOsz|dkr|St|}|jjdkrt|dz|jdg|Ri|}Wnty7td|ddw||S)aReturn 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. rrbz is a binary file formatNz;Formatting alignments has not yet been implemented for the  format)_format_pretty_loadAlignmentIteratormoderAlignmentWriterrformat_alignment)r!fmtargskwargsmodulewriterr$r$r%r?s    zAlignment.formatc5 st|j}|dkr d}nd}t|jd}t|dkddk}d}g}g}t|jjt}t t |j|j|D]\} \} } } z | j } | durGt Wnt ya|dkr]| dkrZd} nd } nd } Yn w| d|d} | |} || z| j} Wn t yYnwt| }t| }| ||} || |f}t|dkr|| }t|dkt|dkkrt| }| || dd<n|| ddf || ddf<t| } t| }|| | dd<t| tr| s|SnCt| ttfr&zt| } Wn)ty td ||}| jD]\}}t| |||||<q |} Ynw| } n|S|| q6|d}|d}t| |k||}t |D]E\} } t| }|dk|@}||}||}||k rjqJd ||k r| ddd 9<d !|| d || <d}qJt"dd}d}d}g|ddddf|ddddf}|d}|d}t| |k||}t |||j|D]\} } } } | d}|}| d}t || dd| ddD]\}}}|dkr-|||kr%t|}|t|d}|dkrdd|| d d7<n dd|7<|}|}|}q||kr7d|}n| ||}|t||kr||}|dkrd|d|7<||d}||kr||t#||krt|}n|d }||kr||7}n||8}||7}| } t|}|t|d}|dkr| d|| d d7} n| d||7} | d7} | ||}|t||ksFd|7<||kr|}|}|t|7}qq|durd}!d}"tdd}#d}$g}%t |#|$D]U\}&}'|&||d}(|'||d})d }*t |(|)D]'\}+},|+|,kr5|+dkr2nd}-n|+|!ks?|,|!krBd}-nd}-|*|-7}*q"d|"|*f}.|%|.|"t|*7}"qtttt|jdddf|"}/||/|kr|||krd|/d}0|#d|0|jd7<|$d|0|jd7<|%d|0|"7<n3|\}1}2d}0|1t$|jdd} |#| |0d|"f} |%| |0|2|jdf} |$| d d!d d!t |#|$|%DSt|ttt|jdddf}/||/|kr<|||kr/d|/d}0t%|D]} d| |0|j| df7<qfd"d#t%D}3n7fd$d#t%D}3gd}0t%|D]} || t$|j| dfd} | qPd!d}4|3|4d!|3S)%[Return default string representation (PRIVATE). Helper for self.format(). rpTFrr Nrr rr?r7z zInconsistent coordinatesPrKz ..r6rr8|.z %9d %sz %drrKrrKz%s%9d9dz rLcss,|]\}}}|d|d|dVqdSrLNr$)rEline1line2 pattern_liner$r$r%rHd s  z+Alignment._format_pretty..c$g|]}d|ddqSrZrNrErrPrr$r%rp $z,Alignment._format_pretty..cr^rZr_r`rar$r%rr rb)&r)rrrrrrrrwrrir=rljustrMr:minrrrrYisascii_format_unicoderr rrr=defined_rangesr6_format_generalizedwhererrNrabsr?r)5r!r write_patternrr name_widthnamesseqsrrr: positionsrnamer&r.rr8minstepmaxstep row_steps row_aligned prefix_widthposition_width line_widthcolumnr$r end_index position_textrrestrdashpositionlines1lines2 pattern_linesr[r\ aligned_seq1 aligned_seq2patternc1c2rr]final_position_widthrKname1name2blocksblockr$rar%rEs                   (  &  $             1        $           .   zAlignment._format_prettyc Csg}g}|j}t|j|D]f\}}||}|dur"|S|d|dkr>> 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 rBrGr$r$r%rQ s6zAlignment.__str__cCs@|jdurd|jjt|fS|j\}}d|jj||t|fS)aReturn 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 Nz<%s object at 0x%x>z*<%s object (%i rows x %i columns) at 0x%x>)rr;r<r=rr!rrr$r$r%rR9 s  zAlignment.__repr__cCr])z0Return the number of sequences in the alignment.)r)rrGr$r$r%r`` s zAlignment.__len__cCst|j}|dkr dSt|jd}t|dkddk}t|D]+}|||f}|dkr.q|dkrD||ddf ||ddf<qtd||d}||k|dkBs^tdt t|S)aReturn 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 rrNrr) r)rrrrrrrrrw)r!rrrrrrr$r$r%r(d s %       zAlignment.lengthcCst|j}|j}||fS)aReturn 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) )r)rr(rr$r$r%r s *zAlignment.shapec Cst|jdkr td|j}t|d}t|dkddk}t|jD]?\}}|||f}|dk r5q$|dk r]||ddf ||ddf<t|||ddf||ddf<q$t d|| }tj|dd}t | d}t|}||ddf}||dddf} tj|| gdd } t|jd}t|jD]0\}}|||f}|dk rq|dk rt|| |ddf| |ddf<qt d|| S)a 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. rpz=aligned is currently implemented for pairwise alignments onlyrrNrr)r)rrhrrrrrrrrrrjrd flatnonzerostack) r!rrrrrrrstartsendssegmentsr$r$r%r s>N     &    &zAlignment.alignedcCsnt|jt }|jj\}}t|jd}t|dkddk}|dd|f}t|t}t |D] \}}|dk r?d||<q0|dk rJd||<q0t d|d}d} |jdddf} t d|D]Q} | } |jdd| f} t || | |D]:\}} }}|dkr| |kr||| } t | |||| <qw|dkr| |kr|| |} t | d|dd||| <qw| }qc|S)aReturn 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]]) rrNFTrrK)ronesrrwrrrrboolrrrrri)r!arrrrr2rrrrrmrr&r.r3r$r$r%r; s88        zAlignment.indicescCsdd|jD}|jj\}}t|jd}t|dkddk}|dd|f}t|t}t|D] \}}|dk r?d||<q0|dk rJd||<q0t d|d}d} t |dD]o} |jdd| f} |jdd| df} t || | |D]O\}} }}|dkr| |kr||| } t || || |<qx|dkr| |kr|| |} |dkrt || || d|dd <qx| dkrt || || ddd <qx| }q[|S) aReturn 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])] cSsg|] }tt|t qSr$)rrr)rwrr$r$r%r sz-Alignment.inverse_indices..rrNFTrrK) rrrrrrrrrrrrri)r!rrrrrr2rrrrmrrr&r.r3r$r$r%inverse_indices s<5        zAlignment.inverse_indicesFcs|jdurz ddD}Wnty}Yn wfddD}ttt|j|d}fdd|D|_|j|d|_dS)aGSort the sequences of the alignment in place. By default, this sorts the sequences alphabetically using their id attribute if available, or by their sequence contents otherwise. For example, >>> from Bio.Align import PairwiseAligner >>> aligner = PairwiseAligner() >>> aligner.gap_score = -1 >>> alignments = aligner.align("AATAA", "AAGAA") >>> len(alignments) 1 >>> alignment = alignments[0] >>> print(alignment) target 0 AATAA 5 0 ||.|| 5 query 0 AAGAA 5 >>> alignment.sort() >>> print(alignment) target 0 AAGAA 5 0 ||.|| 5 query 0 AATAA 5 Alternatively, a key function can be supplied that maps each sequence to a sort value. For example, you could sort on the GC content of each sequence. >>> from Bio.SeqUtils import gc_fraction >>> alignment.sort(key=gc_fraction) >>> print(alignment) target 0 AATAA 5 0 ||.|| 5 query 0 AAGAA 5 You can reverse the sort order by passing `reverse=True`: >>> alignment.sort(key=gc_fraction, reverse=True) >>> print(alignment) target 0 AAGAA 5 0 ||.|| 5 query 0 AATAA 5 The sequences are now sorted by decreasing GC content value. NcSsg|]}|jqSr$r~rr$r$r%r sz"Alignment.sort..csg|]}|qSr$r$r)rr$r%r rrcrr$r$r5rr$r%r rr)rrrrr)r|rtake)r!rrr9rr$)rrr%r s1 zAlignment.sortc"CsZ||}}t|jt|jkrtdt|jt|jf|j}|j}|j}|j}t|j}t|j} t|d} tt| d} | dk rKd} n | dk rTd} ntdt|d} tt| d} | dk rpd}n | dk ryd}ntd| dkr|dkr| }| |dddf|dddf<nN| }||dddf|dddf<| }||dddd f|dddf<|dkr| |dddd f|dddf<n|dddd f|dddf<t|d} | d}| |k| dkB std t|d} | d}| |k| dkB std g}t j t j }}t|}t j t j }}|D]\}}||krD||krDn||}}q4t j t j }}|D]\}}||kr||kr ||kry||krt||}n||}nt||kr||}||kr||}n||}|}||}||ks||kr||kr||kr|||g|||g||}||}|||gn%||}}|D]\}}||kr||krn ||}}q||}nqe||7}||7}||kr||ksd||}}qVt|} | |kr"| | dddf| dddf<||g}!t|!| }|S) aMap 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. zUlength of alignment1 query sequence (%d) != length of alignment2 target sequence (%d)rr+rz)Inconsistent steps in the first alignmentz*Inconsistent steps in the second alignmentNrKz%Unequal step sizes in first alignmentz&Unequal step sizes in second alignment)r)r rrrrrprodsignrrrsysmaxsizer^rrMrr)"r!rrrrr rrn1n2steps1rstrand1steps2strand2gaps1gaps2pathtEndqEndtStart1qStart1tEnd1qEnd1tStart2qStart2tEnd2qEnd2sizerqStarttStartrrr$r$r%map# s           "$              !$   z Alignment.mapcCsRd}t|}|D]R}t|jd}t|dkddk}|dd|f}|d\}}||kr0d}n|| kr8d}n|d|krAd}n td|d||durR|}q||krZtdqt|jddddf|jddddfdd}t d t |dft } d| d <|t || dddf<t d| d d dg} t|D]J\} }||j| ddf| dddf<t d| d d | d<t| | } |j} | dddf|9<| d|jdg}t|| }| ||| <qddtt |D} d}|durd}tdd|D}t|D]~\} }|jjdkr(| | | | dq|jd |krP| | |jd|jddddf|_|jrNd}q|jd |krt | | r|jd| | dkrp||}nd}| | | | d|q| | |jdqt|}|dusdd|D} t| } t| | }|S)zDMap each of the alignments to self, and return the mapped alignment.Nrrr7zunexpected steps z, z%inconsistent step sizes in alignmentsrKrprrWr'rXcSsg|]}gqSr$r$rr$r$r%r; sz$Alignment.mapall..FTcss"|] }|jjr|jdVqdS)rN)rrrErr$r$r%rH? s z#Alignment.mapall..)rrcSsg|]}|jdqS)rrrr$r$r%rX r)rrrrrrrjrcliprr)rwrrrrrrrrrdrrMr1r,r)r! alignmentsfactorrrrstep1step2rrrrrrrrdoner}previousr$r$r%mapall s~  <"         zAlignment.mapallc CsB|j}t|j}t|jd}t|dkddk}t|D]6\}}|||f}|dkr/q|dkrNt |||<t |||ddf||ddf<qt d|t }|D];}zt |} Wn-t yz|j}Wn tywYnw|jD]\} } t || | } || q{YqZw|| qZdt|}tj|dd} t |} t| D]r}||}||ddf}t|d| D]\}||}||ddf}tjtj}}t||D]A\}}||kr||kr|||}|||}t |t |krt dt||D]\}}| ||fd 7<q||}}qqq| S) a- 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. rrNrrrrprrr)rrrrrrrrrrr)rrrr:rrgr,rNrr rrrrri)r!rrrrrrrrr8r&r.rri1 sequence1ri2 sequence2rrrrrsegment1segment2rrr$r$r%r] sd L     &           zAlignment.substitutionsc Csd}}}t|D]7\}}t|D].\}}||krn%t||D]\}} |dks-| dkr2|d7}q!|| kr;|d7}q!|d7}q!qq t|||S)aReturn number of identities, mismatches, and gaps of a pairwise alignment. >>> aligner = PairwiseAligner(mode='global', match_score=2, mismatch_score=-1) >>> for alignment in aligner.align("TACCG", "ACG"): ... print("Score = %.1f:" % alignment.score) ... c = alignment.counts() # namedtuple ... print(f"{c.gaps} gaps, {c.identities} identities, {c.mismatches} mismatches") ... print(alignment) ... Score = 6.0: 2 gaps, 3 identities, 0 mismatches target 0 TACCG 5 0 -||-| 5 query 0 -AC-G 3 Score = 6.0: 2 gaps, 3 identities, 0 mismatches target 0 TACCG 5 0 -|-|| 5 query 0 -A-CG 3 This classifies each pair of letters in a pairwise alignment into gaps, perfect matches, or mismatches. It has been defined as a method (not a property) so that it may in future take optional argument(s) allowing the behavior to be customized. These three values are returned as a namedtuple. This is calculated for all the pairs of sequences in the alignment. rrr)rrir) r!rrrrrrrrrCr$r$r%r s     zAlignment.countscCsdd|jD}tddt||jD}t||}z|j}Wn ty*Y|Swi|_|D] \}}t |tj rF|ddd }n|ddd}||j|<q2|S)aReverse-complement the alignment and return it. >>> sequences = ["ATCG", "AAG", "ATC"] >>> coordinates = np.array([[0, 2, 3, 4], [0, 2, 2, 3], [0, 2, 3, 3]]) >>> alignment = Alignment(sequences, coordinates) >>> print(alignment) 0 ATCG 4 0 AA-G 3 0 ATC- 3 >>> rc_alignment = alignment.reverse_complement() >>> print(rc_alignment) 0 CGAT 4 0 C-TT 3 0 -GAT 3 The attribute `column_annotations`, if present, is associated with the reverse-complemented alignment, with its values in reverse order. >>> alignment.column_annotations = {"score": [3, 2, 2, 2]} >>> rc_alignment = alignment.reverse_complement() >>> print(rc_alignment.column_annotations) {'score': [2, 2, 2, 3]} cSsg|]}t|qSr$r rr$r$r%r!rz0Alignment.reverse_complement..cSs&g|]\}}t||dddqSNrKr)rErrr$r$r%r#sNrK) rrrrirrr rrjrndarrayr)r!rrrr rr-r$r$r%rs(       zAlignment.reverse_complementrB)rrr)2r<rrr classmethodrrr&rrorrrsetterr rrrrrrrrr"r(r/r0r4r:r|rAr\r?rErfrhrQrRr`r(rrrrrrrrrrr$r$r$r%rs )  3 / X     ;\31C7 'T8M8' 7 - p U  T=vE | -rc@s<eZdZdZddZeddZeddZedd Zd S) AlignmentsAbstractBaseClassaFAbstract base class for sequence alignments. Most users will not need to use this class. It is used internally as a base class for the list-like Alignments class, and for the AlignmentIterator class in Bio.Align.interfaces, which itself is the abstract base class for the alignment parsers in Bio/Align/. cCs ||S)zyIterate over the alignments as Alignment objects. This method SHOULD NOT be overridden by any subclass. )rewindrGr$r$r%r_Asz$AlignmentsAbstractBaseClass.__iter__cCdS)zReturn the next alignment.Nr$rGr$r$r%__next__Iz$AlignmentsAbstractBaseClass.__next__cCr)zJRewind the iterator to let it loop over the alignments from the beginning.Nr$rGr$r$r%rMrz"AlignmentsAbstractBaseClass.rewindcCr)z Return the number of alignments.Nr$rGr$r$r%r`Qrz#AlignmentsAbstractBaseClass.__len__N) r<rrrr_rrrr`r$r$r$r%r8s  rcs6eZdZd fdd ZddZddZdd ZZS) Alignmentsr$cst|d|_dSr)superr&_index)r!rr;r$r%r&Ws  zAlignments.__init__cCs4|jd}z||}Wn tytw||_|SNr)rrrc)r!ryitemr$r$r%r[s   zAlignments.__next__cCs d|_dSr)rrGr$r$r%rd zAlignments.rewindcCs t|SrB)rr`rGr$r$r%r`grzAlignments.__len__)r$)r<rrr&rrr` __classcell__r$r$rr%rVs  rc@s8eZdZdZddZddZddZdd Zd d Zd S) PairwiseAlignmentsa<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. cCs ||g|_||_||_d|_dS)a5Initialize 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. rKN)rr_pathsr)r!seqAseqBrpathsr$r$r%r&xs  zPairwiseAlignments.__init__cCr]rB)r)rrGr$r$r%r`rzPairwiseAlignments.__len__cCst|tstd|jj|dkr|t|j7}||jkr!|jS||jkr.|j d|_ zt |}Wn t yAt ddw|j|krJ |Sq/)Nzindex must be an integer, not rrKTzindex out of range) rrwrr;r<r)rr _alignmentresetrbrcr)r!ryrr$r$r%r|s&        zPairwiseAlignments.__getitem__cCs@t|j}|jd7_t|}t|j|}|j|_||_|Sr) rbrrrrrrrr)r!rrrr$r$r%rs   zPairwiseAlignments.__next__cCs|jd|_dSr)rrrrGr$r$r%rs  zPairwiseAlignments.rewindN) r<rrrr&r`r|rrr$r$r$r%rks  rcsVeZdZdZdfdd ZddZdfdd Zdfd d Zd d ZddZ Z S)PairwiseAligneraVPerforms 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 Nc st|dur n9|dkrtd|_d|_d|_n(|dkr,td|_d|_d|_n|d kr=td |_d |_d |_ntd ||D] \}}t |||qGdS)aInitialize 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. NblastnBLASTNgg megablast MEGABLASTgblastpBLASTPg(gzUnknown scoring scheme '%s') rr&r loadsubstitution_matrixopen_gap_scoreextend_gap_scorerrjsetattr)r!scoringrMrpr-rr$r%r&@s&     zPairwiseAligner.__init__cCs.|ttjvr td|tj|||dS)Nz.'PairwiseAligner' object has no attribute '%s')dirr rr __setattr__)r!rr-r$r$r%rds zPairwiseAligner.__setattr__rc srt|tttfr t|}n|}|dkr|}nt|}t|tttfr&t|}t|||\}}t||||}|S)z=Return the alignments of two sequences using PairwiseAligner.r) rrr rrrralignr) r!rrstrandsAsBrrrrr$r%rls zPairwiseAligner.aligncsPt|tttfr t|}|dkrt|}t|tttfr t|}t|||S)zBReturn the alignment score of two sequences using PairwiseAligner.r)rrr rrrrr)r!rrrrr$r%r|szPairwiseAligner.scorecCsn|j|j|j|j|j|j|j|j|j|j |j |j |j |j d}|jdur0|j|d<|j|d<|S|j|d<|S)N)wildcardtarget_internal_open_gap_score target_internal_extend_gap_scoretarget_left_open_gap_scoretarget_left_extend_gap_scoretarget_right_open_gap_scoretarget_right_extend_gap_scorequery_internal_open_gap_scorequery_internal_extend_gap_scorequery_left_open_gap_scorequery_left_extend_gap_scorequery_right_open_gap_scorequery_right_extend_gap_scorerH match_scoremismatch_scorer)rrrrrrrrrrrrrrHrrr)r!stater$r$r% __getstate__s*    zPairwiseAligner.__getstate__cCs|d|_|d|_|d|_|d|_|d|_|d|_|d|_|d|_|d |_|d |_ |d |_ |d |_ |d |_ |d|_ |d}|dur[|d|_|d|_dS||_dS)NrrrrrrrrrrrrrrHrrr)rrrrrrrrrrrrrrHrrrr)r!rrr$r$r% __setstate__s*                zPairwiseAligner.__setstate__rB)r) r<rrrr&rrrrr rr$r$rr%rs$ rcs:eZdZdZd fdd ZfddZfdd ZZS) CodonAlignerzAligns 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. NrQcs<t|durtjd}n t|tjstd||_dS)zInitialize 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. Nrz&Input table is not a CodonTable object)rr&r generic_by_idrr codon_table)r!r  anchor_lenrr$r%r&s    zCodonAligner.__init__c s8|j}t|tttfrt|}nt|tr|}ntd|ddt |d}|dddt |dd}|dddt |dd}t|tttfrn| |}| |} | |} t|}t| } t| } n%t|trt ||}t ||} t ||} |}| } | } ntdt ||| | S)a Return the alignment score of a protein sequence and nucleotide sequence. Arguments: - seqA - the protein sequence of amino acids (plain string, Seq, MutableSeq, or SeqRecord). - seqB - the nucleotide sequence (plain string, Seq, MutableSeq, or SeqRecord); both DNA and RNA sequences are accepted. >>> from Bio.Seq import Seq >>> from Bio.SeqRecord import SeqRecord >>> aligner = CodonAligner() >>> dna = SeqRecord(Seq('ATGTCTCGT'), id='dna') >>> pro = SeqRecord(Seq('MSR'), id='pro') >>> score = aligner.score(pro, dna) >>> print(score) 3.0 >>> rna = SeqRecord(Seq('AUGUCUCGU'), id='rna') >>> score = aligner.score(pro, rna) >>> print(score) 3.0 This is an example with a frame shift in the DNA sequence: >>> dna = "ATGCTGGGCTCGAACGAGTCCGTGTATGCCCTAAGCTGAGCCCGTCG" >>> pro = "MLGSNESRVCPKLSPS" >>> len(pro) 16 >>> aligner.frameshift_score = -3.0 >>> score = aligner.score(pro, dna) >>> print(score) 13.0 In the following example, the position of the frame shift is ambiguous: >>> dna = 'TTTAAAAAAAAAAATTT' >>> pro = 'FKKKKF' >>> len(pro) 6 >>> aligner.frameshift_score = -1.0 >>> alignments = aligner.align(pro, dna) >>> print(alignments.score) 5.0 >>> len(alignments) 3 >>> print(next(alignments)) target 0 F K K K 4 query 0 TTTAAAAAAAAA 12 target 4 K F 6 query 11 AAATTT 17 >>> print(next(alignments)) target 0 F K K 3 query 0 TTTAAAAAA 9 target 3 K K F 6 query 8 AAAAAATTT 17 >>> print(next(alignments)) target 0 F K 2 query 0 TTTAAA 6 target 2 K K K F 6 query 5 AAAAAAAAATTT 17 >>> print(next(alignments)) Traceback (most recent call last): ... StopIteration ;seqA must be a string, Seq, MutableSeq, or SeqRecord objectNr7rrp;seqB must be a string, Seq, MutableSeq, or SeqRecord object) r rrr rrrYrrr)rrr) r!rrr rseqB0seqB1seqB2sB0sB1sB2rr$r%rs:H              zCodonAligner.scorecsN|j}t|tttfrt|}nt|tr|}ntd|ddt |d}|dddt |dd}|dddt |dd}t|tttfrn| |}| |} | |} t|}t| } t| } n%t|trt ||}t ||} t ||} |}| } | } ntdt ||| | \} } t ||| | } | S)aAlign 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 rNr7rrpr)r rrr rrrYrrr)rrrr)r!rrr rrrrrrrrrrrr$r%r5s>-              zCodonAligner.align)NrQ)r<rrrr&rrrr$r$rr%r s  hr )a2mbedbigbedbigmafbigpslchainclustalemboss exoneratefastahhrmafmauvemsfnexusphylippslsam stockholmtabular_modulesrKreturncCsV|}zt|WStyYnw|tvrtd|td|}|t|<|S)NzUnknown file format %sz Bio.Align.)lowerr*rformatsr importlib import_module)rKrNr$r$r%rFs   rFcOs^t|tr|g}t|}z|j}Wnty td|dw||g|Ri||S)a)Write alignments to a file. Arguments: - alignments - An Alignments object, an iterator of Alignment objects, or a single Alignment. - target - File or file-like object to write to, or filename as string. - fmt - String describing the file format (case-insensitive). Note if providing a file or file-like object, your code should close the target after calling this function, or call .flush(), to ensure the data gets flushed to disk. Returns the number of alignments written (as an integer). z2File writing has not yet been implemented for the rD)rrrFrIrrrW)rrrKrLrMrNrOr$r$r%rWs    rWcCst|}||}|S)aParse an alignment file and return an iterator over alignments. Arguments: - source - File or file-like object to read from, or filename as string. - fmt - String describing the file format (case-insensitive). Typical usage, opening a file to read in, and looping over the alignments: >>> from Bio import Align >>> filename = "Exonerate/exn_22_m_ner_cigar.exn" >>> for alignment in Align.parse(filename, "exonerate"): ... print("Number of sequences in alignment", len(alignment)) ... print("Alignment score:", alignment.score) Number of sequences in alignment 2 Alignment score: 6150.0 Number of sequences in alignment 2 Alignment score: 502.0 Number of sequences in alignment 2 Alignment score: 440.0 For lazy-loading file formats such as bigMaf, for which the file contents is read on demand only, ensure that the file remains open while extracting alignment data. You can use the Bio.Align.read(...) function when the file contains only one alignment. )rFrG)sourcerKrNrr$r$r%parses r1cCsVt||}zt|}Wn tytddwzt|tdty*Y|Sw)aDParse a file containing one alignment, and return it. Arguments: - source - File or file-like object to read from, or filename as string. - fmt - String describing the file format (case-insensitive). This function is for use parsing alignment files containing exactly one alignment. For example, reading a Clustal file: >>> from Bio import Align >>> alignment = Align.read("Clustalw/opuntia.aln", "clustal") >>> print("Alignment shape:", alignment.shape) Alignment shape: (7, 156) >>> for sequence in alignment.sequences: ... print(sequence.id, len(sequence)) gi|6273285|gb|AF191659.1|AF191 146 gi|6273284|gb|AF191658.1|AF191 148 gi|6273287|gb|AF191661.1|AF191 146 gi|6273286|gb|AF191660.1|AF191 146 gi|6273290|gb|AF191664.1|AF191 150 gi|6273289|gb|AF191663.1|AF191 150 gi|6273291|gb|AF191665.1|AF191 156 If the file contains no records, or more than one record, an exception is raised. For example: >>> from Bio import Align >>> filename = "Exonerate/exn_22_m_ner_cigar.exn" >>> alignment = Align.read(filename, "exonerate") Traceback (most recent call last): ... ValueError: More than one alignment found in file Use the Bio.Align.parse function if you want to read a file containing more than one alignment. zNo alignments found in fileNz%More than one alignment found in file)r1rbrcr)r[rKrrr$r$r%reads %    r2__main__) run_doctest):r collectionsrr.r;rtypesrabcrr itertoolsrnumpyr ImportErrorrVrr Bio.Alignrrr r Bio.Datar Bio.Seqr rrrr Bio.SeqRecordrr namedtuplerrrrrrrrr r-r*rrY ModuleType__annotations__rFrWr1r2r< Bio._utilsr4r$r$r$r%s                    3aD Q ! 2