# Sequence alignments

Sequence alignments are a collection of two or more sequences that have been aligned to each other – usually with the insertion of gaps, and the addition of leading or trailing gaps – such that all the sequence strings are the same length.

Alignments may extend over the full length of each sequence, or may be limited to a subsection of each sequence. In Biopython, all sequence alignments are represented by an Alignment object, described in section Alignment objects. Alignment objects can be obtained by parsing the output of alignment software such as Clustal or BLAT (described in section Reading and writing alignments. or by using Biopython’s pairwise sequence aligner, which can align two sequences to each other (described in Chapter Pairwise sequence alignment).

See Chapter Multiple Sequence Alignment objects for a description of the older MultipleSeqAlignment class and the parsers in Bio.AlignIO that parse the output of sequence alignment software, generating MultipleSeqAlignment objects.

## Alignment objects

The Alignment class is defined in Bio.Align. Usually you would get an Alignment object by parsing the output of alignment programs (section Reading and writing alignments) or by running Biopython’s pairwise aligner (Chapter Pairwise sequence alignment). For the benefit of this section, however, we will create an Alignment object from scratch.

### Creating an Alignment object from sequences and coordinates

Suppose you have three sequences:

>>> seqA = "CCGGTTTTT"
>>> seqB = "AGTTTAA"
>>> seqC = "AGGTTT"
>>> sequences = [seqA, seqB, seqC]


To create an Alignment object, we also need the coordinates that define how the sequences are aligned to each other. We use a NumPy array for that:

>>> import numpy as np
>>> coordinates = np.array([[1, 3, 4, 7, 9], [0, 2, 2, 5, 5], [0, 2, 3, 6, 6]])


These coordinates define the alignment for the following sequence segments:

• SeqA[1:3], SeqB[0:2], and SeqC[0:2] are aligned to each other;

• SeqA[3:4] and SeqC[2:3] are aligned to each other, with a gap of one nucleotide in seqB;

• SeqA[4:7], SeqB[2:5], and SeqC[3:6] are aligned to each other;

• SeqA[7:9] is not aligned to seqB or seqC.

Note that the alignment does not include the first nucleotide of seqA and last two nucleotides of seqB.

Now we can create the Alignment object:

>>> from Bio.Align import Alignment
>>> alignment = Alignment(sequences, coordinates)
>>> alignment
<Alignment object (3 rows x 8 columns) at ...>


The alignment object has an attribute sequences pointing to the sequences included in this alignment:

>>> alignment.sequences
['CCGGTTTTT', 'AGTTTAA', 'AGGTTT']


and an attribute coordinates with the alignment coordinates:

>>> alignment.coordinates
array([[1, 3, 4, 7, 9],
[0, 2, 2, 5, 5],
[0, 2, 3, 6, 6]])


Print the Alignment object to show the alignment explicitly:

>>> print(alignment)
1 CGGTTTTT 9
0 AG-TTT-- 5
0 AGGTTT-- 6


with the starting and end coordinate for each sequence are shown to the left and right, respectively, of the alignment.

### Creating an Alignment object from aligned sequences

If you start out with the aligned sequences, with dashes representing gaps, then you can calculate the coordinates using the parse_printed_alignment class method. This method is primarily employed in Biopython’s alignment parsers (see Section Reading and writing alignments), but it may be useful for other purposes. For example, you can construct the Alignment object from aligned sequences as follows:

>>> lines = ["CGGTTTTT", "AG-TTT--", "AGGTTT--"]
>>> for line in lines:
...     print(line)
...
CGGTTTTT
AG-TTT--
AGGTTT--
>>> lines = [line.encode() for line in lines]  # convert to bytes
>>> lines
[b'CGGTTTTT', b'AG-TTT--', b'AGGTTT--']
>>> sequences, coordinates = Alignment.parse_printed_alignment(lines)
>>> sequences
[b'CGGTTTTT', b'AGTTT', b'AGGTTT']
>>> sequences = [sequence.decode() for sequence in sequences]
>>> sequences
['CGGTTTTT', 'AGTTT', 'AGGTTT']
>>> print(coordinates)
[[0 2 3 6 8]
[0 2 2 5 5]
[0 2 3 6 6]]


The initial G nucleotide of seqA and the final CC nucleotides of seqB were not included in the alignment and is therefore missing here. But this is easy to fix:

>>> from Bio.Seq import Seq
>>> sequences[0] = "C" + sequences[0]
>>> sequences[1] = sequences[1] + "AA"
>>> sequences
['CCGGTTTTT', 'AGTTTAA', 'AGGTTT']
>>> coordinates[0, :] += 1
>>> print(coordinates)
[[1 3 4 7 9]
[0 2 2 5 5]
[0 2 3 6 6]]


Now we can create the Alignment object:

>>> alignment = Alignment(sequences, coordinates)
>>> print(alignment)
1 CGGTTTTT 9
0 AG-TTT-- 5
0 AGGTTT-- 6


which identical to the Alignment object created above in section Creating an Alignment object from sequences and coordinates.

By default, the coordinates argument to the Alignment initializer is None, which assumes that there are no gaps in the alignment. All sequences in an ungapped alignment must have the same length. If the coordinates argument is None, then the initializer will fill in the coordinates attribute of the Alignment object for you:

>>> ungapped_alignment = Alignment(["ACGTACGT", "AAGTACGT", "ACGTACCT"])
>>> ungapped_alignment
<Alignment object (3 rows x 8 columns) at ...>
>>> print(ungapped_alignment.coordinates)
[[0 8]
[0 8]
[0 8]]
>>> print(ungapped_alignment)
0 ACGTACGT 8
0 AAGTACGT 8
0 ACGTACCT 8


### Common alignment attributes

The following attributes are commonly found on Alignment objects:

• sequences: This is a list of the sequences aligned to each other. Depending on how the alignment was created, the sequences can have the following types:

• plain Python string;

• Seq;

• MutableSeq;

• SeqRecord;

• bytes;

• bytearray;

• NumPy array with data type numpy.int32;

• any other object with a contiguous buffer of format "c", "B", "i", or "I";

• lists or tuples of objects defined in the alphabet attribute of the PairwiseAligner object that created the alignment (see section Generalized pairwise alignments).

For pairwise alignments (meaning an alignment of two sequences), the properties target and query are aliases for sequences[0] and sequences[1], respectively.

• coordinates: A NumPy array of integers storing the sequence indices defining how the sequences are aligned to each other;

• score: The alignment score, as found by the parser in the alignment file, or as calculated by the PairwiseAligner (see section Basic usage);

• annotations: A dictionary storing most other annotations associated with the alignment;

• column_annotations: A dictionary storing annotations that extend along the alignment and have the same length as the alignment, such as a consensus sequence (see section ClustalW for an example).

An Alignment object created by the parser in Bio.Align may have additional attributes, depending on the alignment file format from which the alignment was read.

## Slicing and indexing an alignment

Slices of the form alignment[k, i:j], where k is an integer and i and j are integers or are absent, return a string showing the aligned sequence (including gaps) for the target (if k=0) or the query (if k=1) that includes only the columns i through j in the printed alignment.

To illustrate this, in the following example the printed alignment has 8 columns:

>>> print(alignment)
1 CGGTTTTT 9
0 AG-TTT-- 5
0 AGGTTT-- 6

>>> alignment.length
8


To get the aligned sequence strings individually, use

>>> alignment[0]
'CGGTTTTT'
>>> alignment[1]
'AG-TTT--'
>>> alignment[2]
'AGGTTT--'
>>> alignment[0, :]
'CGGTTTTT'
>>> alignment[1, :]
'AG-TTT--'
>>> alignment[0, 1:-1]
'GGTTTT'
>>> alignment[1, 1:-1]
'G-TTT-'


Columns to be included can also be selected using an iterable over integers:

>>> alignment[0, (1, 2, 4)]
'GGT'
>>> alignment[1, range(0, 5, 2)]
'A-T'


To get the letter at position [i, j] of the printed alignment, use alignment[i, j]; this will return "-" if a gap is found at that position:

>>> alignment[0, 2]
'G'
>>> alignment[2, 6]
'-'


To get specific columns in the alignment, use

>>> alignment[:, 0]
'CAA'
>>> alignment[:, 1]
'GGG'
>>> alignment[:, 2]
'G-G'


Slices of the form alignment[i:j:k] return a new Alignment object including only sequences [i:j:k] of the alignment:

>>> alignment[1:]
<Alignment object (2 rows x 6 columns) at ...>
>>> print(alignment[1:])
target            0 AG-TTT 5
0 ||-||| 6
query             0 AGGTTT 6


Slices of the form alignment[:, i:j], where i and j are integers or are absent, return a new Alignment object that includes only the columns i through j in the printed alignment.

Extracting the first 4 columns for the example alignment above gives:

>>> alignment[:, :4]
<Alignment object (3 rows x 4 columns) at ...>
>>> print(alignment[:, :4])
1 CGGT 5
0 AG-T 3
0 AGGT 4


Similarly, extracting the last 6 columns gives:

>>> alignment[:, -6:]
<Alignment object (3 rows x 6 columns) at ...>
>>> print(alignment[:, -6:])
3 GTTTTT 9
2 -TTT-- 5
2 GTTT-- 6


The column index can also be an iterable of integers:

>>> print(alignment[:, (1, 3, 0)])
0 GTC 3
0 GTA 3
0 GTA 3


Calling alignment[:, :] returns a copy of the alignment.

## Getting information about the alignment

### Alignment shape

The number of aligned sequences is returned by len(alignment):

>>> len(alignment)
3


The alignment length is defined as the number of columns in the alignment as printed. This is equal to the sum of the number of matches, number of mismatches, and the total length of gaps in each sequence:

>>> alignment.length
8


The shape property returns a tuple consisting of the length of the alignment and the number of columns in the alignment as printed:

>>> alignment.shape
(3, 8)


### Comparing alignments

Two alignments are equal to each other (meaning that alignment1 == alignment2 evaluates to True) if each of the sequences in alignment1.sequences and alignment2.sequences are equal to each other, and alignment1.coordinates and alignment2.coordinates contain the same coordinates. If either of these conditions is not fulfilled, then alignment1 == alignment2 evaluates to False. Inequality of two alignments (e.g., alignment1 < alignment2) is established by first comparing alignment1.sequences and alignment2.sequences, and if they are equal, by comparing alignment1.coordinates to alignment2.coordinates.

### Finding the indices of aligned sequences

For pairwise alignments, the aligned property of an alignment 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,

>>> pairwise_alignment = alignment[:2, :]
>>> print(pairwise_alignment)
target            1 CGGTTTTT 9
0 .|-|||-- 8
query             0 AG-TTT-- 5

>>> print(pairwise_alignment.aligned)
[[[1 3]
[4 7]]

[[0 2]
[2 5]]]


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:

>>> pairwise_alignment1 = Alignment(["AAACAAA", "AAAGAAA"],
...                                 np.array([[0, 3, 4, 4, 7], [0, 3, 3, 4, 7]]))  # fmt: skip
...
>>> pairwise_alignment2 = Alignment(["AAACAAA", "AAAGAAA"],
...                                 np.array([[0, 3, 3, 4, 7], [0, 3, 4, 4, 7]]))  # fmt: skip
...
>>> print(pairwise_alignment1)
target            0 AAAC-AAA 7
0 |||--||| 8
query             0 AAA-GAAA 7

>>> print(pairwise_alignment2)
target            0 AAA-CAAA 7
0 |||--||| 8
query             0 AAAG-AAA 7

>>> pairwise_alignment1.aligned
array([[[0, 3],
[4, 7]],

[[0, 3],
[4, 7]]])
>>> pairwise_alignment2.aligned
array([[[0, 3],
[4, 7]],

[[0, 3],
[4, 7]]])


The property indices returns a 2D NumPy array with the sequence index of each letter in the alignment, with gaps indicated by -1:

>>> print(alignment)
1 CGGTTTTT 9
0 AG-TTT-- 5
0 AGGTTT-- 6

>>> alignment.indices
array([[ 1,  2,  3,  4,  5,  6,  7,  8],
[ 0,  1, -1,  2,  3,  4, -1, -1],
[ 0,  1,  2,  3,  4,  5, -1, -1]])


The property inverse_indices returns a list of 1D NumPy arrays, one for each of the aligned sequences, with the column index in the alignment for each letter in the sequence. Letters not included in the alignment are indicated by -1:

>>> alignment.sequences
['CCGGTTTTT', 'AGTTTAA', 'AGGTTT']
>>> alignment.inverse_indices
[array([-1,  0,  1,  2,  3,  4,  5,  6,  7]),
array([ 0,  1,  3,  4,  5, -1, -1]),
array([0, 1, 2, 3, 4, 5])]


### Counting identities, mismatches, and gaps

The counts method calculates the number of identities, mismatches, and gaps of a pairwise alignment. For an alignment of more than two sequences, the number of identities, mismatches, and gaps are calculated and summed for all pairs of sequences in the alignment. The three numbers are returned as an AlignmentCounts object, which is a namedtuple with fields gaps, identities, and mismatches. This method currently takes no arguments, but in the future will likely be modified to accept optional arguments allowing its behavior to be customized.

>>> print(pairwise_alignment)
target            1 CGGTTTTT 9
0 .|-|||-- 8
query             0 AG-TTT-- 5

>>> pairwise_alignment.counts()
AlignmentCounts(gaps=3, identities=4, mismatches=1)
>>> print(alignment)
1 CGGTTTTT 9
0 AG-TTT-- 5
0 AGGTTT-- 6

>>> alignment.counts()
AlignmentCounts(gaps=8, identities=14, mismatches=2)


### Letter frequencies

The frequencies method calculates how often each letter appears in each column of the alignment:

>>> alignment.frequencies
{'C': array([1., 0., 0., 0., 0., 0., 0., 0.]),
'G': array([0., 3., 2., 0., 0., 0., 0., 0.]),
'T': array([0., 0., 0., 3., 3., 3., 1., 1.]),
'A': array([2., 0., 0., 0., 0., 0., 0., 0.]),
'-': array([0., 0., 1., 0., 0., 0., 2., 2.])}


### Substitutions

Use the substitutions method to find the number of substitutions between each pair of nucleotides:

>>> m = alignment.substitutions
>>> print(m)
A   C   G   T
A 1.0 0.0 0.0 0.0
C 2.0 0.0 0.0 0.0
G 0.0 0.0 4.0 0.0
T 0.0 0.0 0.0 9.0


Note that the matrix is not symmetric: The counts for a row letter R and a column letter C is the number of times letter R in a sequence is replaced by letter C in a sequence appearing below it. For example, the number of C’s that are aligned to an A in a later sequence is

>>> m["C", "A"]
2.0


while the number of A’s that are aligned to a C in a later sequence is

>>> m["A", "C"]
0.0


To get a symmetric matrix, use

>>> m += m.transpose()
>>> m /= 2.0
>>> print(m)
A   C   G   T
A 1.0 1.0 0.0 0.0
C 1.0 0.0 0.0 0.0
G 0.0 0.0 4.0 0.0
T 0.0 0.0 0.0 9.0

>>> m["A", "C"]
1.0
>>> m["C", "A"]
1.0


The total number of substitutions between A’s and T’s in the alignment is 1.0 + 1.0 = 2.

### Alignments as arrays

Using NumPy, you can turn the alignment object into an array of letters. In particular, this may be useful for fast calculations on the alignment content.

>>> align_array = np.array(alignment)
>>> align_array.shape
(3, 8)
>>> align_array
array([[b'C', b'G', b'G', b'T', b'T', b'T', b'T', b'T'],
[b'A', b'G', b'-', b'T', b'T', b'T', b'-', b'-'],
[b'A', b'G', b'G', b'T', b'T', b'T', b'-', b'-']], dtype='|S1')


By default, this will give you an array of bytes characters (with data type dtype='|S1'). You can create an array of Unicode (Python string) characters by using dtype='U':

>>> align_array = np.array(alignment, dtype="U")

>>> align_array
array([['C', 'G', 'G', 'T', 'T', 'T', 'T', 'T'],
['A', 'G', '-', 'T', 'T', 'T', '-', '-'],
['A', 'G', 'G', 'T', 'T', 'T', '-', '-']], dtype='<U1')


(the printed dtype will be ‘<U1’ or ‘>U1’ depending on whether your system is little-endian or big-endian, respectively). Note that the alignment object and the NumPy array align_array are separate objects in memory - editing one will not update the other!

## Operations on an alignment

### Sorting an alignment

The sort method sorts the alignment sequences. By default, sorting is done based on the id attribute of each sequence if available, or the sequence contents otherwise.

>>> print(alignment)
1 CGGTTTTT 9
0 AG-TTT-- 5
0 AGGTTT-- 6

>>> alignment.sort()
>>> print(alignment)
0 AGGTTT-- 6
0 AG-TTT-- 5
1 CGGTTTTT 9


Alternatively, you can supply a key function to determine the sort order. For example, you can sort the sequences by increasing GC content:

>>> from Bio.SeqUtils import gc_fraction
>>> alignment.sort(key=gc_fraction)
>>> print(alignment)
0 AG-TTT-- 5
0 AGGTTT-- 6
1 CGGTTTTT 9


Note that the key function is applied to the full sequence (including the initial A and final GG nucleotides of seqB), not just to the aligned part.

The reverse argument lets you reverse the sort order to obtain the sequences in decreasing GC content:

>>> alignment.sort(key=gc_fraction, reverse=True)
>>> print(alignment)
1 CGGTTTTT 9
0 AGGTTT-- 6
0 AG-TTT-- 5


### Reverse-complementing the alignment

Reverse-complementing an alignment will take the reverse complement of each sequence, and recalculate the coordinates:

>>> alignment.sequences
['CCGGTTTTT', 'AGGTTT', 'AGTTTAA']
>>> rc_alignment = alignment.reverse_complement()
>>> print(rc_alignment.sequences)
['AAAAACCGG', 'AAACCT', 'TTAAACT']
>>> print(rc_alignment)
0 AAAAACCG 8
0 --AAACCT 6
2 --AAA-CT 7

>>> alignment[:, :4].sequences
['CCGGTTTTT', 'AGGTTT', 'AGTTTAA']
>>> print(alignment[:, :4])
1 CGGT 5
0 AGGT 4
0 AG-T 3

>>> rc_alignment = alignment[:, :4].reverse_complement()
>>> rc_alignment[:, :4].sequences
['AAAAACCGG', 'AAACCT', 'TTAAACT']
>>> print(rc_alignment[:, :4])
4 ACCG 8
2 ACCT 6
4 A-CT 7


Reverse-complementing an alignment preserves its column annotations (in reverse order), but discards all other annotations.

Alignments can be added together to form an extended alignment if they have the same number of rows. As an example, let’s first create two alignments:

>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> 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


Adding the two alignments will combine the two alignments row-wise:

>>> 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, which can be added together. Refer to Chapter Sequence annotation objects 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'}


### Mapping a pairwise sequence alignment

Suppose you have a pairwise alignment of a transcript to a chromosome:

>>> chromosome = "AAAAAAAACCCCCCCAAAAAAAAAAAGGGGGGAAAAAAAA"
>>> transcript = "CCCCCCCGGGGGG"
>>> sequences1 = [chromosome, transcript]
>>> coordinates1 = np.array([[8, 15, 26, 32], [0, 7, 7, 13]])
>>> alignment1 = Alignment(sequences1, coordinates1)
>>> print(alignment1)
target            8 CCCCCCCAAAAAAAAAAAGGGGGG 32
0 |||||||-----------|||||| 24
query             0 CCCCCCC-----------GGGGGG 13


and a pairwise alignment between the transcript and a sequence (e.g., obtained by RNA-seq):

>>> rnaseq = "CCCCGGGG"
>>> sequences2 = [transcript, rnaseq]
>>> coordinates2 = np.array([[3, 11], [0, 8]])
>>> alignment2 = Alignment(sequences2, coordinates2)
>>> print(alignment2)
target            3 CCCCGGGG 11
0 ||||||||  8
query             0 CCCCGGGG  8


Use the map method on alignment1, with alignment2 as argument, to find the alignment of the RNA-sequence to the genome:

>>> alignment3 = alignment1.map(alignment2)
>>> print(alignment3)
target           11 CCCCAAAAAAAAAAAGGGG 30
0 ||||-----------|||| 19
query             0 CCCC-----------GGGG  8

>>> print(alignment3.coordinates)
[[11 15 26 30]
[ 0  4  4  8]]
>>> format(alignment3, "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'


To be able to print the sequences, in this example we constructed alignment1 and alignment2 using sequences with a defined sequence contents. However, mapping the alignment does not depend on the sequence contents; only the coordinates of alignment1 and alignment2 are used to construct the coordinates for alignment3.

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:

>>> from Bio import Align
>>> chain.sequences[0].id
'chr1'
>>> len(chain.sequences[0].seq)
228573443
>>> chain.sequences[1].id
'chr1'
>>> len(chain.sequences[1].seq)
224244399
>>> import numpy as np
>>> np.set_printoptions(threshold=5)  # print 5 array elements per row
>>> 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. See section UCSC chain file format for more information about the chain file format.

>>> transcript = Align.read("Blat/est.panTro5.psl", "psl")
>>> transcript.sequences[0].id
'chr1'
>>> len(transcript.sequences[0].seq)
228573443
>>> transcript.sequences[1].id
'DC525629'
>>> len(transcript.sequences[1].seq)
407
>>> print(transcript.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 transcript.sequences[0] have the same length:

>>> len(chain.sequences[0].seq) == len(transcript.sequences[0].seq)
True


We swap the target and query of the chain such that the query of chain corresponds to the target of transcript:

>>> 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]]
>>> np.set_printoptions(threshold=1000)  # reset the print options


Now we can get the coordinates of DC525629 against chimpanzee genome assembly panTro6 by calling chain.map, with transcript as the argument:

>>> lifted_transcript = chain.map(transcript)
>>> lifted_transcript.sequences[0].id
'chr1'
>>> len(lifted_transcript.sequences[0].seq)
224244399
>>> lifted_transcript.sequences[1].id
'DC525629'
>>> len(lifted_transcript.sequences[1].seq)
407
>>> print(lifted_transcript.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.

### Mapping a multiple sequence alignment

Consider a multiple alignment of genomic sequences of chimpanzee, human, macaque, marmoset, mouse, and rat:

>>> from Bio import Align
>>> path = "Blat/panTro5.maf"
>>> for record in genome_alignment.sequences:
...     print(record.id, len(record.seq))
...
panTro5.chr1 228573443
hg19.chr1 249250621
rheMac8.chr1 225584828
calJac3.chr18 47448759
mm10.chr3 160039680
rn6.chr2 266435125
>>> print(genome_alignment.coordinates)
[[133922962 133922962 133922970 133922970 133922972 133922972 133922995
133922998 133923010]
[155784573 155784573 155784581 155784581 155784583 155784583 155784606
155784609 155784621]
[130383910 130383910 130383918 130383918 130383920 130383920 130383943
130383946 130383958]
[  9790455   9790455   9790463   9790463   9790465   9790465   9790488
9790491   9790503]
[ 88858039  88858036  88858028  88858026  88858024  88858020  88857997
88857997  88857985]
[188162970 188162967 188162959 188162959 188162957 188162953 188162930
188162930 188162918]]
>>> print(genome_alignment)
panTro5.c 133922962 ---ACTAGTTA--CA----GTAACAGAAAATAAAATTTAAATAGAAACTTAAAggcc
hg19.chr1 155784573 ---ACTAGTTA--CA----GTAACAGAAAATAAAATTTAAATAGAAACTTAAAggcc
rheMac8.c 130383910 ---ACTAGTTA--CA----GTAACAGAAAATAAAATTTAAATAGAAACTTAAAggcc
calJac3.c   9790455 ---ACTAGTTA--CA----GTAACAGAAAATAAAATTTAAATAGAAGCTTAAAggct
mm10.chr3  88858039 TATAATAATTGTATATGTCACAGAAAAAAATGAATTTTCAAT---GACTTAATAGCC
rn6.chr2  188162970 TACAATAATTG--TATGTCATAGAAAAAAATGAATTTTCAAT---AACTTAATAGCC

panTro5.c 133923010
hg19.chr1 155784621
rheMac8.c 130383958
calJac3.c   9790503
mm10.chr3  88857985
rn6.chr2  188162918


Suppose we want to replace the older versions of the genome assemblies (panTro5, hg19, rheMac8, calJac3, mm10, and rn6) by their current versions (panTro6, hg38, rheMac10, calJac4, mm39, and rn7). To do so, we need the pairwise alignment between the old and the new assembly version for each species. These are provided by UCSC as chain files, typically used for UCSC’s liftOver tool. The .chain files in the Tests/Align subdirectory in the Biopython source distribution were extracted from UCSC’s .chain files to only include the relevant genomic region. For example, to lift over panTro5 to panTro6, we use the file panTro5ToPanTro6.chain with the following contents:

chain 1198066 chr1 228573443 + 133919957 133932620 chr1 224244399 + 130607995 130620657 1
4990    0   2
1362    3   0
6308


To lift over the genome assembly for each species, we read in the corresponding .chain file:

>>> paths = [
...     "Blat/panTro5ToPanTro6.chain",
...     "Blat/hg19ToHg38.chain",
...     "Blat/rheMac8ToRheMac10.chain",
...     "Blat/calJac3ToCalJac4.chain",
...     "Blat/mm10ToMm39.chain",
...     "Blat/rn6ToRn7.chain",
... ]
>>> liftover_alignments = [Align.read(path, "chain") for path in paths]
>>> for liftover_alignment in liftover_alignments:
...     print(liftover_alignment.target.id, liftover_alignment.coordinates[0, :])
...
chr1 [133919957 133924947 133924947 133926309 133926312 133932620]
chr1 [155184381 156354347 156354348 157128497 157128497 157137496]
chr1 [130382477 130383872 130383872 130384222 130384222 130388520]
chr18 [9786631 9787941 9788508 9788508 9795062 9795065 9795737]
chr3 [66807541 74196805 74196831 94707528 94707528 94708176 94708178 94708718]
chr2 [188111581 188158351 188158351 188171225 188171225 188228261 188228261
188236997]


Note that the order of species is the same in liftover_alignments and genome_alignment.sequences. Now we can lift over the multiple sequence alignment to the new genome assembly versions:

>>> genome_alignment = genome_alignment.mapall(liftover_alignments)
>>> for record in genome_alignment.sequences:
...     print(record.id, len(record.seq))
...
chr1 224244399
chr1 248956422
chr1 223616942
chr18 47031477
chr3 159745316
chr2 249053267
>>> print(genome_alignment.coordinates)
[[130611000 130611000 130611008 130611008 130611010 130611010 130611033
130611036 130611048]
[155814782 155814782 155814790 155814790 155814792 155814792 155814815
155814818 155814830]
[ 95186253  95186253  95186245  95186245  95186243  95186243  95186220
95186217  95186205]
[  9758318   9758318   9758326   9758326   9758328   9758328   9758351
9758354   9758366]
[ 88765346  88765343  88765335  88765333  88765331  88765327  88765304
88765304  88765292]
[174256702 174256699 174256691 174256691 174256689 174256685 174256662
174256662 174256650]]


As the .chain files do not include the sequence contents, we cannot print the sequence alignment directly. Instead, we read in the genomic sequence separately (as a .2bit file, as it allows lazy loading; see section Sequence files as Dictionaries) for each species:

>>> from Bio import SeqIO
>>> names = ("panTro6", "hg38", "rheMac10", "calJac4", "mm39", "rn7")
>>> for i, name in enumerate(names):
...     filename = f"{name}.2bit"
...     genome = SeqIO.parse(filename, "twobit")
...     chromosome = genome_alignment.sequences[i].id
...     assert len(genome_alignment.sequences[i]) == len(genome[chromosome])
...     genome_alignment.sequences[i] = genome[chromosome]
...     genome_alignment.sequences[i].id = f"{name}.{chromosome}"
...
>>> print(genome_alignment)
panTro6.c 130611000 ---ACTAGTTA--CA----GTAACAGAAAATAAAATTTAAATAGAAACTTAAAggcc
hg38.chr1 155814782 ---ACTAGTTA--CA----GTAACAGAAAATAAAATTTAAATAGAAACTTAAAggcc
rheMac10.  95186253 ---ACTAGTTA--CA----GTAACAGAAAATAAAATTTAAATAGAAACTTAAAggcc
calJac4.c   9758318 ---ACTAGTTA--CA----GTAACAGAaaataaaatttaaatagaagcttaaaggct
mm39.chr3  88765346 TATAATAATTGTATATGTCACAGAAAAAAATGAATTTTCAAT---GACTTAATAGCC
rn7.chr2  174256702 TACAATAATTG--TATGTCATAGAAAAAAATGAATTTTCAAT---AACTTAATAGCC

panTro6.c 130611048
hg38.chr1 155814830
rheMac10.  95186205
calJac4.c   9758366
mm39.chr3  88765292
rn7.chr2  174256650


The mapall method can also be used to create a multiple alignment of codon sequences from a multiple sequence alignment of the corresponding amino acid sequences (see Section Generating a multiple sequence alignment of codon sequences for details).

## The Alignments class

The Alignments (plural) class inherits from AlignmentsAbstractBaseClass and from list, and can be used as a list to store Alignment objects. The behavior of Alignments objects is different from that of list objects in two important ways:

• An Alignments object is its own iterator, consistent with iterators returned by Bio.Align.parse (see section Reading alignments) or iterators returned by the pairwise aligner (see Section Pairwise sequence alignment). Calling iter on the iterator will always return the Alignments object itself. In contrast, calling iter on a list object creates a new iterator each time, allowing you to have multiple independent iterators for a given list.

In this example, alignment_iterator1 and alignment_iterator2 are obtained from a list and act independently of each other:

>>> alignment_list = [alignment1, alignment2, alignment3]
>>> alignment_iterator1 = iter(alignment_list)
>>> alignment_iterator2 = iter(alignment_list)
>>> next(alignment_iterator1)
<Alignment object (2 rows x 24 columns) at ...>
>>> next(alignment_iterator2)
<Alignment object (2 rows x 24 columns) at ...>
>>> next(alignment_iterator1)
<Alignment object (2 rows x 8 columns) at ...>
>>> next(alignment_iterator1)
<Alignment object (2 rows x 19 columns) at ...>
>>> next(alignment_iterator2)
<Alignment object (2 rows x 8 columns) at ...>
>>> next(alignment_iterator2)
<Alignment object (2 rows x 19 columns) at ...>


In contrast, alignment_iterator1 and alignment_iterator2 obtained by calling iter on an Alignments object are identical to each other:

>>> from Bio.Align import Alignments
>>> alignments = Alignments([alignment1, alignment2, alignment3])
>>> alignment_iterator1 = iter(alignments)
>>> alignment_iterator2 = iter(alignments)
>>> alignment_iterator1 is alignment_iterator2
True
>>> next(alignment_iterator1)
<Alignment object (2 rows x 24 columns) at ...>
>>> next(alignment_iterator2)
<Alignment object (2 rows x 8 columns) at ...>
>>> next(alignment_iterator1)
<Alignment object (2 rows x 19 columns) at ...>
>>> next(alignment_iterator2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration


Calling iter on an Alignments object resets the iterator to its first item, so you can loop over it again. You can also iterate over the alignments multiple times using a for-loop, which implicitly calls iter on the iterator:

>>> for item in alignments:
...     print(repr(item))
...
<Alignment object (2 rows x 24 columns) at ...>
<Alignment object (2 rows x 8 columns) at ...>
<Alignment object (2 rows x 19 columns) at ...>

>>> for item in alignments:
...     print(repr(item))
...
<Alignment object (2 rows x 24 columns) at ...>
<Alignment object (2 rows x 8 columns) at ...>
<Alignment object (2 rows x 19 columns) at ...>


This behavior is consistent with regular Python lists, and with iterators returned by Bio.Align.parse (see section Reading alignments) or by the pairwise aligner (see Section Pairwise sequence alignment).

• Metadata can be stored as attributes on an Alignments object, whereas a plain list does not accept attributes:

>>> alignment_list.score = 100
Traceback (most recent call last):
...
AttributeError: 'list' object has no attribute 'score'...
>>> alignments.score = 100
>>> alignments.score
100


Output from sequence alignment software such as Clustal can be parsed into Alignment objects by the Bio.Align.read and Bio.Align.parse functions. Their usage is analogous to the read and parse functions in Bio.SeqIO (see Section Parsing or Reading Sequences): The read function is used to read an output file containing a single alignment and returns an Alignment object, while the parse function returns an iterator to iterate over alignments stored in an output file containing one or more alignments. Section Alignment file formats describes the alignment formats that can be parsed in Bio.Align. Bio.Align also provides a write function that can write alignments in most of these formats.

Use Bio.Align.parse to parse a file of sequence alignments. For example, the file ucsc_mm9_chr10.maf contains 48 multiple sequence alignments in the MAF (Multiple Alignment Format) format (see section Multiple Alignment Format (MAF)):

>>> from Bio import Align
>>> alignments = Align.parse("MAF/ucsc_mm9_chr10.maf", "maf")
>>> alignments
<Bio.Align.maf.AlignmentIterator object at 0x...>


where "maf" is the file format. The alignments object returned by Bio.Align.parse may contain attributes that store metadata found in the file, such as the version number of the software that was used to create the alignments. The specific attributes stored for each file format are described in Section Alignment file formats. For MAF files, we can obtain the file format version and the scoring scheme that was used:

>>> alignments.metadata
{'MAF Version': '1', 'Scoring': 'autoMZ.v1'}


As alignment files can be very large, Align.parse returns an iterator over the alignments, so you won’t have to store all alignments in memory at the same time. You can iterate over these alignments and print out, for example, the number of aligned sequences in each alignment:

>>> for a in alignments:
...     print(len(a.sequences))
...
2
4
5
6
...
15
14
7
6


You can also call len on the alignments to obtain the number of alignments.

>>> len(alignments)
48


Depending on the file format, the number of alignments may be explicitly stored in the file (for example in the case of bigBed, bigPsl, and bigMaf files), or otherwise the number of alignments is counted by looping over them once (and returning the iterator to its original position). If the file is large, it may therefore take a considerable amount of time for len to return. However, as the number of alignments is cached, subsequent calls to len will return quickly.

If the number of alignments is not excessively large and will fit in memory, you can convert the alignments iterator to a list of alignments. To do so, you could call list on the alignments:

>>> alignment_list = list(alignments)
>>> len(alignment_list)
48
>>> alignment_list[27]
<Alignment object (3 rows x 91 columns) at 0x...>
>>> print(alignment_list[27])
mm9.chr10   3019377 CCCCAGCATTCTGGCAGACACAGTG-AAAAGAGACAGATGGTCACTAATAAAATCTGT-A
felCat3.s     46845 CCCAAGTGTTCTGATAGCTAATGTGAAAAAGAAGCATGTGCCCACCAGTAAGCTTTGTGG
canFam2.c  47545247 CCCAAGTGTTCTGATTGCCTCTGTGAAAAAGAAACATGGGCCCGCTAATAagatttgcaa

mm9.chr10   3019435 TAAATTAG-ATCTCAGAGGATGGATGGACCA  3019465
felCat3.s     46785 TGAACTAGAATCTCAGAGGATG---GGACTC    46757
canFam2.c  47545187 tgacctagaatctcagaggatg---ggactc 47545159


But this will lose the metadata information:

>>> alignment_list.metadata
Traceback (most recent call last):
...
AttributeError: 'list' object has no attribute 'metadata'


>>> type(alignments)
<class 'Bio.Align.maf.AlignmentIterator'>
>>> alignments = alignments[:]
>>> type(alignments)
<class 'Bio.Align.Alignments'>


This returns a Bio.Align.Alignments object, which can be used as a list, while keeping the metadata information:

>>> len(alignments)
48
>>> print(alignments[11])
mm9.chr10   3014742 AAGTTCCCTCCATAATTCCTTCCTCCCACCCCCACA 3014778
calJac1.C      6283 AAATGTA-----TGATCTCCCCATCCTGCCCTG---    6311
otoGar1.s    175262 AGATTTC-----TGATGCCCTCACCCCCTCCGTGCA  175231
loxAfr1.s      9317 AGGCTTA-----TG----CCACCCCCCACCCCCACA    9290

{'MAF Version': '1', 'Scoring': 'autoMZ.v1'}


### Writing alignments

To write alignments to a file, use

>>> from Bio import Align
>>> target = "myfile.txt"
>>> Align.write(alignments, target, "clustal")


where alignments is either a single alignment or a list of alignments, target is a file name or an open file-like object, and "clustal" is the file format to be used. As some file formats allow or require metadata to be stored with the alignments, you may want to use the Alignments (plural) class instead of a plain list of alignments (see Section The Alignments class), allowing you to store a metadata dictionary as an attribute on the alignments object:

>>> from Bio import Align
>>> alignments = Align.Alignments(alignments)
>>> metadata = {"Program": "Biopython", "Version": "1.81"}
>>> target = "myfile.txt"
>>> Align.write(alignments, target, "clustal")


### Printing alignments

For text (non-binary) formats, you can call Python’s built-in format function on an alignment to get a string showing the alignment in the requested format, or use Alignment objects in formatted (f-) strings. If called without an argument, the format function returns the string representation of the alignment:

>>> str(alignment)
'                  1 CGGTTTTT 9\n                  0 AGGTTT-- 6\n                  0 AG-TTT-- 5\n'
>>> format(alignment)
'                  1 CGGTTTTT 9\n                  0 AGGTTT-- 6\n                  0 AG-TTT-- 5\n'
>>> print(format(alignment))
1 CGGTTTTT 9
0 AGGTTT-- 6
0 AG-TTT-- 5


By specifying one of the formats shown in Section Alignment file formats, format will create a string showing the alignment in the requested format:

>>> format(alignment, "clustal")
'sequence_0                          CGGTTTTT\nsequence_1                          AGGTTT--\nsequence_2                          AG-TTT--\n\n\n'
>>> print(format(alignment, "clustal"))
sequence_0                          CGGTTTTT
sequence_1                          AGGTTT--
sequence_2                          AG-TTT--

>>> print(f"*** this is the alignment in Clustal format: ***\n{alignment:clustal}\n***")
*** this is the alignment in Clustal format: ***
sequence_0                          CGGTTTTT
sequence_1                          AGGTTT--
sequence_2                          AG-TTT--

***
>>> format(alignment, "maf")
'a\ns sequence_0 1 8 + 9 CGGTTTTT\ns sequence_1 0 6 + 6 AGGTTT--\ns sequence_2 0 5 + 7 AG-TTT--\n\n'
>>> print(format(alignment, "maf"))
a
s sequence_0 1 8 + 9 CGGTTTTT
s sequence_1 0 6 + 6 AGGTTT--
s sequence_2 0 5 + 7 AG-TTT--



As optional keyword arguments cannot be used with Python’s built-in format function or with formatted strings, the Alignment class has a format method with optional arguments to customize the alignment format, as described in the subsections below. For example, we can print the alignment in BED format (see section Browser Extensible Data (BED)) with a specific number of columns:

>>> print(pairwise_alignment)
target            1 CGGTTTTT 9
0 .|-|||-- 8
query             0 AG-TTT-- 5

>>> print(format(pairwise_alignment, "bed"))
target  1   7   query   0   +   1   7   0   2   2,3,    0,3,

>>> print(pairwise_alignment.format("bed"))
target  1   7   query   0   +   1   7   0   2   2,3,    0,3,

>>> print(pairwise_alignment.format("bed", bedN=3))
target  1   7

>>> print(pairwise_alignment.format("bed", bedN=6))
target  1   7   query   0   +


## Alignment file formats

The table below shows the alignment formats that can be parsed in Bio.Align. The format argument fmt used in Bio.Align functions to specify the file format is case-insensitive. Most of these file formats can also be written by Bio.Align, as shown in the table.

 File format fmt Description text / binary Supported by write Subsection a2m A2M text yes 1.7.11 bed Browser Extensible Data (BED) text yes 1.7.14 bigbed bigBed binary yes 1.7.15 bigmaf bigMaf binary yes 1.7.19 bigpsl bigPsl binary yes 1.7.17 chain UCSC chain file text yes 1.7.20 clustal ClustalW text yes 1.7.2 emboss EMBOSS text no 1.7.5  exonerate Exonerate text yes 1 .7.7 fasta Aligned FASTA text yes 1.7.1 hhr HH-suite output files text no 1.7.10 maf Multiple Alignment Format (MAF) text yes 1.7.18 mauve Mauve eXtended Multi-FastA (xmfa) format text yes 1.7.12 msf GCG Multiple Sequence Format (MSF) text no 1.7.6 nexus NEXUS text yes 1.7.8 phylip PHYLIP output files text yes 1.7.4 psl Pattern Space Layout (PSL) text yes 1.7.16 sam Sequence Al ignment/Map (SAM) text yes 1.7.13  stockholm Stockholm text yes 1 .7.3 tabular Tabular output from BLAST or FASTA text no 1.7.9

### Aligned FASTA

Files in the aligned FASTA format store exactly one (pairwise or multiple) sequence alignment, in which gaps in the alignment are represented by dashes (-). Use fmt="fasta" to read or write files in the aligned FASTA format. Note that this is different from output generated by William Pearson’s FASTA alignment program (parsing such output is described in section Tabular output from BLAST or FASTA instead).

The file probcons.fa in Biopython’s test suite stores one multiple alignment in the aligned FASTA format. The contents of this file is as follows:

>plas_horvu
D-VLLGANGGVLVFEPNDFSVKAGETITFKNNAGYPHNVVFDEDAVPSG-VD-VSKISQEEYLTAPGETFSVTLTV---PGTYGFYCEPHAGAGMVGKVTV
>plas_chlre
>plas_anava
>plas_proho
VQIKMGTDKYAPLYEPKALSISAGDTVEFVMNKVGPHNVIFDK--VPAG-ES-APALSNTKLRIAPGSFYSVTLGT---PGTYSFYCTPHRGAGMVGTITV
>azup_achcy
VHMLNKGKDGAMVFEPASLKVAPGDTVTFIPTDK-GHNVETIKGMIPDG-AE-A-------FKSKINENYKVTFTA---PGVYGVKCTPHYGMGMVGVVEV


>>> from Bio import Align
>>> alignment
<Alignment object (5 rows x 101 columns) at ...>


We can print the alignment to see its default representation:

>>> print(alignment)
plas_horv         0 D-VLLGANGGVLVFEPNDFSVKAGETITFKNNAGYPHNVVFDEDAVPSG-VD-VSKISQE
plas_proh         0 VQIKMGTDKYAPLYEPKALSISAGDTVEFVMNKVGPHNVIFDK--VPAG-ES-APALSNT
azup_achc         0 VHMLNKGKDGAMVFEPASLKVAPGDTVTFIPTDK-GHNVETIKGMIPDG-AE-A------

plas_horv        57 EYLTAPGETFSVTLTV---PGTYGFYCEPHAGAGMVGKVTV 95
plas_chlr        56 DYLNAPGETYSVKLTA---AGEYGYYCEPHQGAGMVGKIIV 94
plas_proh        56 KLRIAPGSFYSVTLGT---PGTYSFYCTPHRGAGMVGTITV 94
azup_achc        51 -FKSKINENYKVTFTA---PGVYGVKCTPHYGMGMVGVVEV 88


or we can print it in the aligned FASTA format:

>>> print(format(alignment, "fasta"))
>plas_horvu
D-VLLGANGGVLVFEPNDFSVKAGETITFKNNAGYPHNVVFDEDAVPSG-VD-VSKISQEEYLTAPGETFSVTLTV---PGTYGFYCEPHAGAGMVGKVTV
>plas_chlre
>plas_anava
>plas_proho
VQIKMGTDKYAPLYEPKALSISAGDTVEFVMNKVGPHNVIFDK--VPAG-ES-APALSNTKLRIAPGSFYSVTLGT---PGTYSFYCTPHRGAGMVGTITV
>azup_achcy
VHMLNKGKDGAMVFEPASLKVAPGDTVTFIPTDK-GHNVETIKGMIPDG-AE-A-------FKSKINENYKVTFTA---PGVYGVKCTPHYGMGMVGVVEV


or any other available format, for example Clustal (see section ClustalW):

>>> print(format(alignment, "clustal"))
plas_horvu                          D-VLLGANGGVLVFEPNDFSVKAGETITFKNNAGYPHNVVFDEDAVPSG-
plas_anava                          --VKLGSDKGLLVFEPAKLTIKPGDTVEFLNNKVPPHNVVFDAALNPAKS
plas_proho                          VQIKMGTDKYAPLYEPKALSISAGDTVEFVMNKVGPHNVIFDK--VPAG-
azup_achcy                          VHMLNKGKDGAMVFEPASLKVAPGDTVTFIPTDK-GHNVETIKGMIPDG-

plas_horvu                          VD-VSKISQEEYLTAPGETFSVTLTV---PGTYGFYCEPHAGAGMVGKVT
plas_proho                          ES-APALSNTKLRIAPGSFYSVTLGT---PGTYSFYCTPHRGAGMVGTIT
azup_achcy                          AE-A-------FKSKINENYKVTFTA---PGVYGVKCTPHYGMGMVGVVE

plas_horvu                          V
plas_chlre                          V
plas_anava                          V
plas_proho                          V
azup_achcy                          V



The sequences associated with the alignment are SeqRecord objects:

>>> alignment.sequences
[SeqRecord(seq=Seq('DVLLGANGGVLVFEPNDFSVKAGETITFKNNAGYPHNVVFDEDAVPSGVDVSKI...VTV'), id='plas_horvu', name='<unknown name>', description='', dbxrefs=[]), SeqRecord(seq=Seq('VKLGADSGALEFVPKTLTIKSGETVNFVNNAGFPHNIVFDEDAIPSGVNADAIS...IIV'), id='plas_chlre', name='<unknown name>', description='', dbxrefs=[]), SeqRecord(seq=Seq('VKLGSDKGLLVFEPAKLTIKPGDTVEFLNNKVPPHNVVFDAALNPAKSADLAKS...ITV'), id='plas_anava', name='<unknown name>', description='', dbxrefs=[]), SeqRecord(seq=Seq('VQIKMGTDKYAPLYEPKALSISAGDTVEFVMNKVGPHNVIFDKVPAGESAPALS...ITV'), id='plas_proho', name='<unknown name>', description='', dbxrefs=[]), SeqRecord(seq=Seq('VHMLNKGKDGAMVFEPASLKVAPGDTVTFIPTDKGHNVETIKGMIPDGAEAFKS...VEV'), id='azup_achcy', name='<unknown name>', description='', dbxrefs=[])]


Note that these sequences do not contain gaps (”-” characters), as the alignment information is stored in the coordinates attribute instead:

>>> print(alignment.coordinates)
[[ 0  1  1 33 34 42 44 48 48 50 50 51 58 73 73 95]
[ 0  0  0 32 33 41 43 47 47 49 49 50 57 72 72 94]
[ 0  0  0 32 33 41 43 47 48 50 51 52 59 74 77 99]
[ 0  1  2 34 35 43 43 47 47 49 49 50 57 72 72 94]
[ 0  1  2 34 34 42 44 48 48 50 50 51 51 66 66 88]]


Use Align.write to write this alignment to a file (here, we’ll use a StringIO object instead of a file):

>>> from io import StringIO
>>> stream = StringIO()
>>> Align.write(alignment, stream, "FASTA")
1
>>> print(stream.getvalue())
>plas_horvu
D-VLLGANGGVLVFEPNDFSVKAGETITFKNNAGYPHNVVFDEDAVPSG-VD-VSKISQEEYLTAPGETFSVTLTV---PGTYGFYCEPHAGAGMVGKVTV
>plas_chlre
>plas_anava
>plas_proho
VQIKMGTDKYAPLYEPKALSISAGDTVEFVMNKVGPHNVIFDK--VPAG-ES-APALSNTKLRIAPGSFYSVTLGT---PGTYSFYCTPHRGAGMVGTITV
>azup_achcy
VHMLNKGKDGAMVFEPASLKVAPGDTVTFIPTDK-GHNVETIKGMIPDG-AE-A-------FKSKINENYKVTFTA---PGVYGVKCTPHYGMGMVGVVEV


Note that Align.write returns the number of alignments written (1, in this case).

### ClustalW

Clustal is a set of multiple sequence alignment programs that are available both as standalone programs as as web servers. The file opuntia.aln (available online or in the Doc/examples subdirectory of the Biopython source code) is an output file generated by Clustal. Its first few lines are

CLUSTAL 2.1 multiple sequence alignment

gi|6273285|gb|AF191659.1|AF191      TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA
gi|6273284|gb|AF191658.1|AF191      TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA
gi|6273287|gb|AF191661.1|AF191      TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA
gi|6273286|gb|AF191660.1|AF191      TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA
gi|6273290|gb|AF191664.1|AF191      TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA
gi|6273289|gb|AF191663.1|AF191      TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA
gi|6273291|gb|AF191665.1|AF191      TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA
******* **** *************************************

...


To parse this file, use

>>> from Bio import Align
>>> alignments = Align.parse("opuntia.aln", "clustal")


The metadata attribute on alignments stores the information shown in the file header:

>>> alignments.metadata
{'Program': 'CLUSTAL', 'Version': '2.1'}


You can call next on the alignments to pull out the first (and only) alignment:

>>> alignment = next(alignments)
>>> print(alignment)
gi|627328         0 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAAT
gi|627328         0 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAAT
gi|627328         0 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAAT
gi|627328         0 TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAAT
gi|627329         0 TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAAT
gi|627328         0 TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAAT
gi|627329         0 TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAAT

gi|627328        60 CTAAATGATATACGATTCCACTATGTAAGGTCTTTGAATCATATCATAAAAGACAATGTA
gi|627328        60 CTAAATGATATACGATTCCACTATGTAAGGTCTTTGAATCATATCATAAAAGACAATGTA
gi|627328        60 CTAAATGATATACGATTCCACTATGTAAGGTCTTTGAATCATATCATAAAAGACAATGTA
gi|627328        60 CTAAATGATATACGATTCCACTA...


If you are not interested in the metadata, then it is more convenient to use the Align.read function, as anyway each Clustal file contains only one alignment:

>>> from Bio import Align


The consensus line below each alignment block in the Clustal output file contains an asterisk if the sequence is conserved at each position. This information is stored in the column_annotations attribute of the alignment:

>>> alignment.column_annotations
{'clustal_consensus': '******* **** **********************************...


Printing the alignment in clustal format will show the sequence alignment, but does not include the metadata:

>>> print(format(alignment, "clustal"))
gi|6273285|gb|AF191659.1|AF191      TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA
gi|6273284|gb|AF191658.1|AF191      TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA
gi|6273287|gb|AF191661.1|AF191      TATACATT...


Writing the alignments in clustal format will include both the metadata and the sequence alignment:

>>> from io import StringIO
>>> stream = StringIO()
>>> Align.write(alignments, stream, "clustal")
1
>>> print(stream.getvalue())
CLUSTAL 2.1 multiple sequence alignment

gi|6273285|gb|AF191659.1|AF191      TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA
gi|6273284|gb|AF191658.1|AF191      TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA
gi|6273287|gb|AF191661.1|AF191      TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA
gi|6273286|gb|AF191660.1|AF191      TATACATAAAAGAAG...


Use an Alignments (plural) object (see Section The Alignments class) if you are creating alignments by hand, and would like to include metadata information in the output.

### Stockholm

This is an example of a protein sequence alignment in the Stockholm file format used by PFAM:

# STOCKHOLM 1.0
#=GF ID   7kD_DNA_binding
#=GF AC   PF02294.20
#=GF DE   7kD DNA-binding domain
#=GF AU   Mian N;0000-0003-4284-4749
#=GF AU   Bateman A;0000-0002-6982-4660
#=GF SE   Pfam-B_8148 (release 5.2)
#=GF GA   25.00 25.00;
#=GF TC   26.60 46.20;
#=GF NC   23.20 19.20;
#=GF BM   hmmbuild HMM.ann SEED.ann
#=GF SM   hmmsearch -Z 57096847 -E 1000 --cpu 4 HMM pfamseq
#=GF TP   Domain
#=GF CL   CL0049
#=GF RN   [1]
#=GF RM   3130377
#=GF RT   Microsequence analysis of DNA-binding proteins 7a, 7b, and 7e
#=GF RT   from the archaebacterium Sulfolobus acidocaldarius.
#=GF RA   Choli T, Wittmann-Liebold B, Reinhardt R;
#=GF RL   J Biol Chem 1988;263:7087-7093.
#=GF DR   INTERPRO; IPR003212;
#=GF DR   SCOP; 1sso; fa;
#=GF DR   SO; 0000417; polypeptide_domain;
#=GF CC   This family contains members of the hyper-thermophilic
#=GF CC   archaebacterium  7kD DNA-binding/endoribonuclease P2 family.
#=GF CC   There are five 7kD DNA-binding proteins, 7a-7e, found as
#=GF CC   monomers in the cell. Protein 7e shows the  tightest DNA-binding
#=GF CC   ability.
#=GF SQ   3
#=GS DN7_METS5/4-61   AC A4YEA2.1
#=GS DN7A_SACS2/3-61  AC P61991.2
#=GS DN7A_SACS2/3-61  DR PDB; 1SSO A; 2-60;
#=GS DN7A_SACS2/3-61  DR PDB; 1JIC A; 2-60;
#=GS DN7A_SACS2/3-61  DR PDB; 2CVR A; 2-60;
#=GS DN7A_SACS2/3-61  DR PDB; 1B4O A; 2-60;
#=GS DN7E_SULAC/3-60  AC P13125.2
DN7_METS5/4-61              KIKFKYKGQDLEVDISKVKKVWKVGKMVSFTYDD.NGKTGRGAVSEKDAPKELLNMIGK
DN7A_SACS2/3-61             TVKFKYKGEEKQVDISKIKKVWRVGKMISFTYDEGGGKTGRGAVSEKDAPKELLQMLEK
#=GR DN7A_SACS2/3-61  SS    EEEEESSSSEEEEETTTEEEEEESSSSEEEEEE-SSSSEEEEEEETTTS-CHHHHHHTT
DN7E_SULAC/3-60             KVRFKYKGEEKEVDTSKIKKVWRVGKMVSFTYDD.NGKTGRGAVSEKDAPKELMDMLAR
#=GC SS_cons                EEEEESSSSEEEEETTTEEEEEESSSSEEEEEE-SSSSEEEEEEETTTS-CHHHHHHTT
#=GC seq_cons               KVKFKYKGEEKEVDISKIKKVWRVGKMVSFTYDD.NGKTGRGAVSEKDAPKELLsMLuK
//


This is the seed alignment for the 7kD_DNA_binding (PF02294.20) PFAM entry, downloaded from the InterPro website (https://www.ebi.ac.uk/interpro/). This version of the PFAM entry is also available in the Biopython source distribution as the file pfam2.seed.txt in the subdirectory Tests/Stockholm/. We can load this file as follows:

>>> from Bio import Align
>>> alignment
<Alignment object (3 rows x 59 columns) at ...>


We can print out a summary of the alignment:

>>> print(alignment)
DN7_METS5         0 KIKFKYKGQDLEVDISKVKKVWKVGKMVSFTYDD-NGKTGRGAVSEKDAPKELLNMIGK
DN7A_SACS         0 TVKFKYKGEEKQVDISKIKKVWRVGKMISFTYDEGGGKTGRGAVSEKDAPKELLQMLEK
DN7E_SULA         0 KVRFKYKGEEKEVDTSKIKKVWRVGKMVSFTYDD-NGKTGRGAVSEKDAPKELMDMLAR

DN7_METS5        58
DN7A_SACS        59
DN7E_SULA        58


You could also call Python’s built-in format function on the alignment object to show it in a particular file format (see section Printing alignments for details), for example in the Stockholm format to regenerate the file:

>>> print(format(alignment, "stockholm"))
# STOCKHOLM 1.0
#=GF ID   7kD_DNA_binding
#=GF AC   PF02294.20
#=GF DE   7kD DNA-binding domain
#=GF AU   Mian N;0000-0003-4284-4749
#=GF AU   Bateman A;0000-0002-6982-4660
#=GF SE   Pfam-B_8148 (release 5.2)
#=GF GA   25.00 25.00;
#=GF TC   26.60 46.20;
#=GF NC   23.20 19.20;
#=GF BM   hmmbuild HMM.ann SEED.ann
#=GF SM   hmmsearch -Z 57096847 -E 1000 --cpu 4 HMM pfamseq
#=GF TP   Domain
#=GF CL   CL0049
#=GF RN   [1]
#=GF RM   3130377
#=GF RT   Microsequence analysis of DNA-binding proteins 7a, 7b, and 7e from
#=GF RT   the archaebacterium Sulfolobus acidocaldarius.
#=GF RA   Choli T, Wittmann-Liebold B, Reinhardt R;
#=GF RL   J Biol Chem 1988;263:7087-7093.
#=GF DR   INTERPRO; IPR003212;
#=GF DR   SCOP; 1sso; fa;
#=GF DR   SO; 0000417; polypeptide_domain;
#=GF CC   This family contains members of the hyper-thermophilic
#=GF CC   archaebacterium  7kD DNA-binding/endoribonuclease P2 family. There
#=GF CC   are five 7kD DNA-binding proteins, 7a-7e, found as monomers in the
#=GF CC   cell. Protein 7e shows the  tightest DNA-binding ability.
#=GF SQ   3
#=GS DN7_METS5/4-61   AC A4YEA2.1
#=GS DN7A_SACS2/3-61  AC P61991.2
#=GS DN7A_SACS2/3-61  DR PDB; 1SSO A; 2-60;
#=GS DN7A_SACS2/3-61  DR PDB; 1JIC A; 2-60;
#=GS DN7A_SACS2/3-61  DR PDB; 2CVR A; 2-60;
#=GS DN7A_SACS2/3-61  DR PDB; 1B4O A; 2-60;
#=GS DN7E_SULAC/3-60  AC P13125.2
DN7_METS5/4-61                  KIKFKYKGQDLEVDISKVKKVWKVGKMVSFTYDD.NGKTGRGAVSEKDAPKELLNMIGK
DN7A_SACS2/3-61                 TVKFKYKGEEKQVDISKIKKVWRVGKMISFTYDEGGGKTGRGAVSEKDAPKELLQMLEK
#=GR DN7A_SACS2/3-61  SS        EEEEESSSSEEEEETTTEEEEEESSSSEEEEEE-SSSSEEEEEEETTTS-CHHHHHHTT
DN7E_SULAC/3-60                 KVRFKYKGEEKEVDTSKIKKVWRVGKMVSFTYDD.NGKTGRGAVSEKDAPKELMDMLAR
#=GC SS_cons                    EEEEESSSSEEEEETTTEEEEEESSSSEEEEEE-SSSSEEEEEEETTTS-CHHHHHHTT
#=GC seq_cons                   KVKFKYKGEEKEVDISKIKKVWRVGKMVSFTYDD.NGKTGRGAVSEKDAPKELLsMLuK
//


or alternatively as aligned FASTA (see section Aligned FASTA):

>>> print(format(alignment, "fasta"))
>DN7_METS5/4-61
KIKFKYKGQDLEVDISKVKKVWKVGKMVSFTYDD-NGKTGRGAVSEKDAPKELLNMIGK
>DN7A_SACS2/3-61
TVKFKYKGEEKQVDISKIKKVWRVGKMISFTYDEGGGKTGRGAVSEKDAPKELLQMLEK
>DN7E_SULAC/3-60
KVRFKYKGEEKEVDTSKIKKVWRVGKMVSFTYDD-NGKTGRGAVSEKDAPKELMDMLAR


or in the PHYLIP format (see section PHYLIP output files):

>>> print(format(alignment, "phylip"))
3 59
DN7_METS5/KIKFKYKGQDLEVDISKVKKVWKVGKMVSFTYDD-NGKTGRGAVSEKDAPKELLNMIGK
DN7A_SACS2TVKFKYKGEEKQVDISKIKKVWRVGKMISFTYDEGGGKTGRGAVSEKDAPKELLQMLEK
DN7E_SULACKVRFKYKGEEKEVDTSKIKKVWRVGKMVSFTYDD-NGKTGRGAVSEKDAPKELMDMLAR


General information of the alignment is stored under the annotations attribute of the Alignment object, for example

>>> alignment.annotations["identifier"]
'7kD_DNA_binding'
>>> alignment.annotations["clan"]
'CL0049'
>>> alignment.annotations["database references"]
[{'reference': 'INTERPRO; IPR003212;'}, {'reference': 'SCOP; 1sso; fa;'}, {'reference': 'SO; 0000417; polypeptide_domain;'}]


The individual sequences in this alignment are stored under alignment.sequences as SeqRecords, including any annotations associated with each sequence record:

>>> for record in alignment.sequences:
...     print("%s %s %s" % (record.id, record.annotations["accession"], record.dbxrefs))
...
DN7_METS5/4-61 A4YEA2.1 []
DN7A_SACS2/3-61 P61991.2 ['PDB; 1SSO A; 2-60;', 'PDB; 1JIC A; 2-60;', 'PDB; 2CVR A; 2-60;', 'PDB; 1B4O A; 2-60;']
DN7E_SULAC/3-60 P13125.2 []


The secondary structure of the second sequence (DN7A_SACS2/3-61) is stored in the letter_annotations attribute of the SeqRecord:

>>> alignment.sequences[0].letter_annotations
{}
>>> alignment.sequences[1].letter_annotations
{'secondary structure': 'EEEEESSSSEEEEETTTEEEEEESSSSEEEEEE-SSSSEEEEEEETTTS-CHHHHHHTT'}
>>> alignment.sequences[2].letter_annotations
{}


The consensus sequence and secondary structure are associated with the sequence alignment as a whole, and are therefore stored in the column_annotations attribute of the Alignment object:

>>> alignment.column_annotations
{'consensus secondary structure': 'EEEEESSSSEEEEETTTEEEEEESSSSEEEEEE-SSSSEEEEEEETTTS-CHHHHHHTT',
'consensus sequence': 'KVKFKYKGEEKEVDISKIKKVWRVGKMVSFTYDD.NGKTGRGAVSEKDAPKELLsMLuK'}


### PHYLIP output files

The PHYLIP format for sequence alignments is derived from the PHYLogeny Interference Package from Joe Felsenstein. Files in the PHYLIP format start with two numbers for the number of rows and columns in the printed alignment. The sequence alignment itself can be in sequential format or in interleaved format. An example of the former is the sequential.phy file (provided in Tests/Phylip/ in the Biopython source distribution):

 3 384
CYS1_DICDI   -----MKVIL LFVLAVFTVF VSS------- --------RG IPPEEQ---- --------SQ
FKNYYLNNKE AIFTDDLPVA DYLDDEFINS IPTAFDWRTR G-AVTPVKNQ GQCGSCWSFS
TTGNVEGQHF ISQNKLVSLS EQNLVDCDHE CMEYEGEEAC DEGCNGGLQP NAYNYIIKNG
GIQTESSYPY TAETGTQCNF NSANIGAKIS NFTMIP-KNE TVMAGYIVST GPLAIAADAV
E-WQFYIGGV F-DIPCN--P NSLDHGILIV GYSAKNTIFR KNMPYWIVKN SWGADWGEQG
YIYLRRGKNT CGVSNFVSTS II--
ALEU_HORVU   MAHARVLLLA LAVLATAAVA VASSSSFADS NPIRPVTDRA ASTLESAVLG ALGRTRHALR
FARFAVRYGK SYESAAEVRR RFRIFSESLE EVRSTN---- RKGLPYRLGI NRFSDMSWEE
FQATRL-GAA QTCSATLAGN HLMRDA--AA LPETKDWRED G-IVSPVKNQ AHCGSCWTFS
TTGALEAAYT QATGKNISLS EQQLVDCAGG FNNF------ --GCNGGLPS QAFEYIKYNG
GIDTEESYPY KGVNGV-CHY KAENAAVQVL DSVNITLNAE DELKNAVGLV RPVSVAFQVI
DGFRQYKSGV YTSDHCGTTP DDVNHAVLAV GYGVENGV-- ---PYWLIKN SWGADWGDNG
YFKMEMGKNM CAIATCASYP VVAA
CATH_HUMAN   ------MWAT LPLLCAGAWL LGV------- -PVCGAAELS VNSLEK---- --------FH
FKSWMSKHRK TY-STEEYHH RLQTFASNWR KINAHN---- NGNHTFKMAL NQFSDMSFAE
IKHKYLWSEP QNCSAT--KS NYLRGT--GP YPPSVDWRKK GNFVSPVKNQ GACGSCWTFS
TTGALESAIA IATGKMLSLA EQQLVDCAQD FNNY------ --GCQGGLPS QAFEYILYNK
GIMGEDTYPY QGKDGY-CKF QPGKAIGFVK DVANITIYDE EAMVEAVALY NPVSFAFEVT
QDFMMYRTGI YSSTSCHKTP DKVNHAVLAV GYGEKNGI-- ---PYWIVKN SWGPQWGMNG
YFLIERGKNM CGLAACASYP IPLV


In the sequential format, the complete alignment for one sequence is shown before proceeding to the next sequence. In the interleaved format, the alignments for different sequences are next to each other, for example in the file interlaced.phy (provided in Tests/Phylip/ in the Biopython source distribution):

 3 384
CYS1_DICDI   -----MKVIL LFVLAVFTVF VSS------- --------RG IPPEEQ---- --------SQ
ALEU_HORVU   MAHARVLLLA LAVLATAAVA VASSSSFADS NPIRPVTDRA ASTLESAVLG ALGRTRHALR
CATH_HUMAN   ------MWAT LPLLCAGAWL LGV------- -PVCGAAELS VNSLEK---- --------FH

FARFAVRYGK SYESAAEVRR RFRIFSESLE EVRSTN---- RKGLPYRLGI NRFSDMSWEE
FKSWMSKHRK TY-STEEYHH RLQTFASNWR KINAHN---- NGNHTFKMAL NQFSDMSFAE

FKNYYLNNKE AIFTDDLPVA DYLDDEFINS IPTAFDWRTR G-AVTPVKNQ GQCGSCWSFS
FQATRL-GAA QTCSATLAGN HLMRDA--AA LPETKDWRED G-IVSPVKNQ AHCGSCWTFS
IKHKYLWSEP QNCSAT--KS NYLRGT--GP YPPSVDWRKK GNFVSPVKNQ GACGSCWTFS

TTGNVEGQHF ISQNKLVSLS EQNLVDCDHE CMEYEGEEAC DEGCNGGLQP NAYNYIIKNG
TTGALEAAYT QATGKNISLS EQQLVDCAGG FNNF------ --GCNGGLPS QAFEYIKYNG
TTGALESAIA IATGKMLSLA EQQLVDCAQD FNNY------ --GCQGGLPS QAFEYILYNK

GIQTESSYPY TAETGTQCNF NSANIGAKIS NFTMIP-KNE TVMAGYIVST GPLAIAADAV
GIDTEESYPY KGVNGV-CHY KAENAAVQVL DSVNITLNAE DELKNAVGLV RPVSVAFQVI
GIMGEDTYPY QGKDGY-CKF QPGKAIGFVK DVANITIYDE EAMVEAVALY NPVSFAFEVT

E-WQFYIGGV F-DIPCN--P NSLDHGILIV GYSAKNTIFR KNMPYWIVKN SWGADWGEQG
DGFRQYKSGV YTSDHCGTTP DDVNHAVLAV GYGVENGV-- ---PYWLIKN SWGADWGDNG
QDFMMYRTGI YSSTSCHKTP DKVNHAVLAV GYGEKNGI-- ---PYWIVKN SWGPQWGMNG

YIYLRRGKNT CGVSNFVSTS II--
YFKMEMGKNM CAIATCASYP VVAA
YFLIERGKNM CGLAACASYP IPLV


The parser in Bio.Align detects from the file contents if it is in the sequential or in the interleaved format, and then parses it appropriately.

>>> from Bio import Align
>>> alignment
<Alignment object (3 rows x 384 columns) at ...>
>>> alignment2
<Alignment object (3 rows x 384 columns) at ...>
>>> alignment == alignment2
True


Here, two alignments are considered to be equal if they have the same sequence contents and the same alignment coordinates.

>>> alignment.shape
(3, 384)
>>> print(alignment)
CYS1_DICD         0 -----MKVILLFVLAVFTVFVSS---------------RGIPPEEQ------------SQ
CATH_HUMA         0 ------MWATLPLLCAGAWLLGV--------PVCGAAELSVNSLEK------------FH

ALEU_HORV        60 FARFAVRYGKSYESAAEVRRRFRIFSESLEEVRSTN----RKGLPYRLGINRFSDMSWEE
CATH_HUMA        34 FKSWMSKHRKTY-STEEYHHRLQTFASNWRKINAHN----NGNHTFKMALNQFSDMSFAE

ALEU_HORV       116 FQATRL-GAAQTCSATLAGNHLMRDA--AALPETKDWREDG-IVSPVKNQAHCGSCWTFS
CATH_HUMA        89 IKHKYLWSEPQNCSAT--KSNYLRGT--GPYPPSVDWRKKGNFVSPVKNQGACGSCWTFS

CYS1_DICD       146 TTGNVEGQHFISQNKLVSLSEQNLVDCDHECMEYEGEEACDEGCNGGLQPNAYNYIIKNG
ALEU_HORV       172 TTGALEAAYTQATGKNISLSEQQLVDCAGGFNNF--------GCNGGLPSQAFEYIKYNG
CATH_HUMA       145 TTGALESAIAIATGKMLSLAEQQLVDCAQDFNNY--------GCQGGLPSQAFEYILYNK

ALEU_HORV       224 GIDTEESYPYKGVNGV-CHYKAENAAVQVLDSVNITLNAEDELKNAVGLVRPVSVAFQVI
CATH_HUMA       197 GIMGEDTYPYQGKDGY-CKFQPGKAIGFVKDVANITIYDEEAMVEAVALYNPVSFAFEVT

CATH_HUMA       256 QDFMMYRTGIYSSTSCHKTPDKVNHAVLAVGYGEKNGI-----PYWIVKNSWGPQWGMNG

CYS1_DICD       321 YIYLRRGKNTCGVSNFVSTSII-- 343
ALEU_HORV       338 YFKMEMGKNMCAIATCASYPVVAA 362
CATH_HUMA       311 YFLIERGKNMCGLAACASYPIPLV 335


When outputting the alignment in PHYLIP format, Bio.Align writes each of the aligned sequences on one line:

>>> print(format(alignment, "phylip"))
3 384
CATH_HUMAN------MWATLPLLCAGAWLLGV--------PVCGAAELSVNSLEK------------FHFKSWMSKHRKTY-STEEYHHRLQTFASNWRKINAHN----NGNHTFKMALNQFSDMSFAEIKHKYLWSEPQNCSAT--KSNYLRGT--GPYPPSVDWRKKGNFVSPVKNQGACGSCWTFSTTGALESAIAIATGKMLSLAEQQLVDCAQDFNNY--------GCQGGLPSQAFEYILYNKGIMGEDTYPYQGKDGY-CKFQPGKAIGFVKDVANITIYDEEAMVEAVALYNPVSFAFEVTQDFMMYRTGIYSSTSCHKTPDKVNHAVLAVGYGEKNGI-----PYWIVKNSWGPQWGMNGYFLIERGKNMCGLAACASYPIPLV


We can write the alignment in PHYLIP format, parse the result, and confirm it is the same as the original alignment object:

>>> from io import StringIO
>>> stream = StringIO()
>>> Align.write(alignment, stream, "phylip")
1
>>> stream.seek(0)
0
>>> alignment == alignment3
True
>>> [record.id for record in alignment.sequences]
['CYS1_DICDI', 'ALEU_HORVU', 'CATH_HUMAN']
>>> [record.id for record in alignment3.sequences]
['CYS1_DICDI', 'ALEU_HORVU', 'CATH_HUMAN']


### EMBOSS

EMBOSS (European Molecular Biology Open Software Suite) is a set of open-source software tools for molecular biology and bioinformatics [Rice2000]. It includes software such as needle and water for pairwise sequence alignment. This is an example of output generated by the water program for Smith-Waterman local pairwise sequence alignment (available as water.txt in the Tests/Emboss directory of the Biopython distribution):

########################################
# Program:  water
# Rundate:  Wed Jan 16 17:23:19 2002
# Report_file: stdout
########################################
#=======================================
#
# Aligned_sequences: 2
# 1: IXI_234
# 2: IXI_235
# Matrix: EBLOSUM62
# Gap_penalty: 10.0
# Extend_penalty: 0.5
#
# Length: 131
# Identity:     112/131 (85.5%)
# Similarity:   112/131 (85.5%)
# Gaps:          19/131 (14.5%)
# Score: 591.5
#
#
#=======================================

IXI_234            1 TSPASIRPPAGPSSRPAMVSSRRTRPSPPGPRRPTGRPCCSAAPRRPQAT     50
|||||||||||||||         ||||||||||||||||||||||||||
IXI_235            1 TSPASIRPPAGPSSR---------RPSPPGPRRPTGRPCCSAAPRRPQAT     41

IXI_234           51 GGWKTCSGTCTTSTSTRHRGRSGWSARTTTAACLRASRKSMRAACSRSAG    100
||||||||||||||||||||||||          ||||||||||||||||
IXI_235           42 GGWKTCSGTCTTSTSTRHRGRSGW----------RASRKSMRAACSRSAG     81

IXI_234          101 SRPNRFAPTLMSSCITSTTGPPAWAGDRSHE    131
|||||||||||||||||||||||||||||||
IXI_235           82 SRPNRFAPTLMSSCITSTTGPPAWAGDRSHE    112

#---------------------------------------
#---------------------------------------


As this output file contains only one alignment, we can use Align.read to extract it directly. Here, instead we will use Align.parse so we can see the metadata of this water run:

>>> from Bio import Align
>>> alignments = Align.parse("water.txt", "emboss")


The metadata attribute of alignments stores the information shown in the header of the file, including the program used to generate the output, the date and time the program was run, the output file name, and the specific alignment file format that was used (assumed to be srspair by default):

>>> alignments.metadata
{'Align_format': 'srspair', 'Program': 'water', 'Rundate': 'Wed Jan 16 17:23:19 2002', 'Report_file': 'stdout'}


To pull out the alignment, we use

>>> alignment = next(alignments)
>>> alignment
<Alignment object (2 rows x 131 columns) at ...>
>>> alignment.shape
(2, 131)
>>> print(alignment)
IXI_234           0 TSPASIRPPAGPSSRPAMVSSRRTRPSPPGPRRPTGRPCCSAAPRRPQATGGWKTCSGTC
0 |||||||||||||||---------||||||||||||||||||||||||||||||||||||
IXI_235           0 TSPASIRPPAGPSSR---------RPSPPGPRRPTGRPCCSAAPRRPQATGGWKTCSGTC

IXI_234          60 TTSTSTRHRGRSGWSARTTTAACLRASRKSMRAACSRSAGSRPNRFAPTLMSSCITSTTG
60 ||||||||||||||----------||||||||||||||||||||||||||||||||||||
IXI_235          51 TTSTSTRHRGRSGW----------RASRKSMRAACSRSAGSRPNRFAPTLMSSCITSTTG

IXI_234         120 PPAWAGDRSHE 131
120 ||||||||||| 131
IXI_235         101 PPAWAGDRSHE 112

>>> print(alignment.coordinates)
[[  0  15  24  74  84 131]
[  0  15  15  65  65 112]]


We can use indices to extract specific parts of the alignment:

>>> alignment[0]
'TSPASIRPPAGPSSRPAMVSSRRTRPSPPGPRRPTGRPCCSAAPRRPQATGGWKTCSGTCTTSTSTRHRGRSGWSARTTTAACLRASRKSMRAACSRSAGSRPNRFAPTLMSSCITSTTGPPAWAGDRSHE'
>>> alignment[1]
'TSPASIRPPAGPSSR---------RPSPPGPRRPTGRPCCSAAPRRPQATGGWKTCSGTCTTSTSTRHRGRSGW----------RASRKSMRAACSRSAGSRPNRFAPTLMSSCITSTTGPPAWAGDRSHE'
>>> alignment[1, 10:30]
'GPSSR---------RPSPPG'


The annotations attribute of the alignment stores the information associated with this alignment specifically:

>>> alignment.annotations
{'Matrix': 'EBLOSUM62', 'Gap_penalty': 10.0, 'Extend_penalty': 0.5, 'Identity': 112, 'Similarity': 112, 'Gaps': 19, 'Score': 591.5}


The number of gaps, identities, and mismatches can also be obtained by calling the counts method on the alignment object:

>>> alignment.counts()
AlignmentCounts(gaps=19, identities=112, mismatches=0)


where AlignmentCounts is a namedtuple in the collections module in Python’s standard library.

The consensus line shown between the two sequences is stored in the column_annotations attribute:

>>> alignment.column_annotations
{'emboss_consensus': '|||||||||||||||         ||||||||||||||||||||||||||||||||||||||||||||||||||          |||||||||||||||||||||||||||||||||||||||||||||||'}


Use the format function (or the format method) to print the alignment in other formats, for example in the PHYLIP format (see section PHYLIP output files):

>>> print(format(alignment, "phylip"))
2 131
IXI_234   TSPASIRPPAGPSSRPAMVSSRRTRPSPPGPRRPTGRPCCSAAPRRPQATGGWKTCSGTCTTSTSTRHRGRSGWSARTTTAACLRASRKSMRAACSRSAGSRPNRFAPTLMSSCITSTTGPPAWAGDRSHE
IXI_235   TSPASIRPPAGPSSR---------RPSPPGPRRPTGRPCCSAAPRRPQATGGWKTCSGTCTTSTSTRHRGRSGW----------RASRKSMRAACSRSAGSRPNRFAPTLMSSCITSTTGPPAWAGDRSHE


We can use alignment.sequences to get the individual sequences. However, as this is a pairwise alignment, we can also use alignment.target and alignment.query to get the target and query sequences:

>>> alignment.target
SeqRecord(seq=Seq('TSPASIRPPAGPSSRPAMVSSRRTRPSPPGPRRPTGRPCCSAAPRRPQATGGWK...SHE'), id='IXI_234', name='<unknown name>', description='<unknown description>', dbxrefs=[])
>>> alignment.query
SeqRecord(seq=Seq('TSPASIRPPAGPSSRRPSPPGPRRPTGRPCCSAAPRRPQATGGWKTCSGTCTTS...SHE'), id='IXI_235', name='<unknown name>', description='<unknown description>', dbxrefs=[])


Currently, Biopython does not support writing sequence alignments in the output formats defined by EMBOSS.

### GCG Multiple Sequence Format (MSF)

The Multiple Sequence Format (MSF) was created to store multiple sequence alignments generated by the GCG (Genetics Computer Group) set of programs. The file W_prot.msf in the Tests/msf directory of the Biopython distribution is an example of a sequence alignment file in the MSF format This file shows an alignment of 11 protein sequences:

!!AA_MULTIPLE_ALIGNMENT

MSF: 99  Type: P  Oct 18, 2017  11:35  Check: 0 ..

Name: W*01:01:01:01    Len:    99  Check: 7236  Weight:  1.00
Name: W*01:01:01:02    Len:    99  Check: 7236  Weight:  1.00
Name: W*01:01:01:03    Len:    99  Check: 7236  Weight:  1.00
Name: W*01:01:01:04    Len:    99  Check: 7236  Weight:  1.00
Name: W*01:01:01:05    Len:    99  Check: 7236  Weight:  1.00
Name: W*01:01:01:06    Len:    99  Check: 7236  Weight:  1.00
Name: W*02:01          Len:    93  Check: 9483  Weight:  1.00
Name: W*03:01:01:01    Len:    93  Check: 9974  Weight:  1.00
Name: W*03:01:01:02    Len:    93  Check: 9974  Weight:  1.00
Name: W*04:01          Len:    93  Check: 9169  Weight:  1.00
Name: W*05:01          Len:    99  Check: 7331  Weight:  1.00
//

W*01:01:01:01  GLTPFNGYTA ATWTRTAVSS VGMNIPYHGA SYLVRNQELR SWTAADKAAQ
W*01:01:01:02  GLTPFNGYTA ATWTRTAVSS VGMNIPYHGA SYLVRNQELR SWTAADKAAQ
W*01:01:01:03  GLTPFNGYTA ATWTRTAVSS VGMNIPYHGA SYLVRNQELR SWTAADKAAQ
W*01:01:01:04  GLTPFNGYTA ATWTRTAVSS VGMNIPYHGA SYLVRNQELR SWTAADKAAQ
W*01:01:01:05  GLTPFNGYTA ATWTRTAVSS VGMNIPYHGA SYLVRNQELR SWTAADKAAQ
W*01:01:01:06  GLTPFNGYTA ATWTRTAVSS VGMNIPYHGA SYLVRNQELR SWTAADKAAQ
W*02:01  GLTPSNGYTA ATWTRTAASS VGMNIPYDGA SYLVRNQELR SWTAADKAAQ
W*03:01:01:01  GLTPSSGYTA ATWTRTAVSS VGMNIPYHGA SYLVRNQELR SWTAADKAAQ
W*03:01:01:02  GLTPSSGYTA ATWTRTAVSS VGMNIPYHGA SYLVRNQELR SWTAADKAAQ
W*04:01  GLTPSNGYTA ATWTRTAASS VGMNIPYDGA SYLVRNQELR SWTAADKAAQ
W*05:01  GLTPSSGYTA ATWTRTAVSS VGMNIPYHGA SYLVRNQELR SWTAADKAAQ

W*01:01:01:01  MPWRRNRQSC SKPTCREGGR SGSAKSLRMG RRGCSAQNPK DSHDPPPHL
W*01:01:01:02  MPWRRNRQSC SKPTCREGGR SGSAKSLRMG RRGCSAQNPK DSHDPPPHL
W*01:01:01:03  MPWRRNRQSC SKPTCREGGR SGSAKSLRMG RRGCSAQNPK DSHDPPPHL
W*01:01:01:04  MPWRRNRQSC SKPTCREGGR SGSAKSLRMG RRGCSAQNPK DSHDPPPHL
W*01:01:01:05  MPWRRNRQSC SKPTCREGGR SGSAKSLRMG RRGCSAQNPK DSHDPPPHL
W*01:01:01:06  MPWRRNRQSC SKPTCREGGR SGSAKSLRMG RRGCSAQNPK DSHDPPPHL
W*02:01  MPWRRNMQSC SKPTCREGGR SGSAKSLRMG RRRCTAQNPK RLT
W*03:01:01:01  MPWRRNRQSC SKPTCREGGR SGSAKSLRMG RRGCSAQNPK RLT
W*03:01:01:02  MPWRRNRQSC SKPTCREGGR SGSAKSLRMG RRGCSAQNPK RLT
W*04:01  MPWRRNMQSC SKPTCREGGR SGSAKSLRMG RRGCSAQNPK RLT
W*05:01  MPWRRNRQSC SKPTCREGGR SGSAKSLRMG RRGCSAQNPK DSHDPPPHL


To parse this file with Biopython, use

>>> from Bio import Align


The parser skips all lines up to and including the line starting with “MSF:”. The following lines (until the “//” demarcation) are read by the parser to verify the length of each sequence. The alignment section (after the “//” demarcation) is read by the parser and stored as an Alignment object:

>>> alignment
<Alignment object (11 rows x 99 columns) at ...>
>>> print(alignment)

W*01:01:0        60 SKPTCREGGRSGSAKSLRMGRRGCSAQNPKDSHDPPPHL 99
W*01:01:0        60 SKPTCREGGRSGSAKSLRMGRRGCSAQNPKDSHDPPPHL 99
W*01:01:0        60 SKPTCREGGRSGSAKSLRMGRRGCSAQNPKDSHDPPPHL 99
W*01:01:0        60 SKPTCREGGRSGSAKSLRMGRRGCSAQNPKDSHDPPPHL 99
W*01:01:0        60 SKPTCREGGRSGSAKSLRMGRRGCSAQNPKDSHDPPPHL 99
W*01:01:0        60 SKPTCREGGRSGSAKSLRMGRRGCSAQNPKDSHDPPPHL 99
W*02:01          60 SKPTCREGGRSGSAKSLRMGRRRCTAQNPKRLT------ 93
W*03:01:0        60 SKPTCREGGRSGSAKSLRMGRRGCSAQNPKRLT------ 93
W*03:01:0        60 SKPTCREGGRSGSAKSLRMGRRGCSAQNPKRLT------ 93
W*04:01          60 SKPTCREGGRSGSAKSLRMGRRGCSAQNPKRLT------ 93
W*05:01          60 SKPTCREGGRSGSAKSLRMGRRGCSAQNPKDSHDPPPHL 99


The sequences and their names are stored in the alignment.sequences attribute:

>>> len(alignment.sequences)
11
>>> alignment.sequences[0].id
'W*01:01:01:01'
>>> alignment.sequences[0].seq


The alignment coordinates are stored in the alignment.coordinates attribute:

>>> print(alignment.coordinates)
[[ 0 93 99]
[ 0 93 99]
[ 0 93 99]
[ 0 93 99]
[ 0 93 99]
[ 0 93 99]
[ 0 93 93]
[ 0 93 93]
[ 0 93 93]
[ 0 93 93]
[ 0 93 99]]


Currently, Biopython does not support writing sequence alignments in the MSF format.

### Exonerate

Exonerate is a generic program for pairwise sequence alignments [Slater2005]. The sequence alignments found by Exonerate can be output in a human-readable form, in the “cigar” (Compact Idiosyncratic Gapped Alignment Report) format, or in the “vulgar” (Verbose Useful Labelled Gapped Alignment Report) format. The user can request to include one or more of these formats in the output. The parser in Bio.Align can only parse alignments in the cigar or vulgar formats, and will not parse output that includes alignments in human-readable format.

The file exn_22_m_cdna2genome_vulgar.exn in the Biopython test suite is an example of an Exonerate output file showing the alignments in vulgar format:

Command line: [exonerate -m cdna2genome ../scer_cad1.fa /media/Waterloo/Downloads/genomes/scer_s288c/scer_s288c.fa --bestn 3 --showalignment no --showcigar no --showvulgar yes]
Hostname: [blackbriar]
vulgar: gi|296143771|ref|NM_001180731.1| 0 1230 + gi|330443520|ref|NC_001136.10| 1319275 1318045 - 6146 M 1 1 C 3 3 M 1226 1226
vulgar: gi|296143771|ref|NM_001180731.1| 1230 0 - gi|330443520|ref|NC_001136.10| 1318045 1319275 + 6146 M 129 129 C 3 3 M 1098 1098
vulgar: gi|296143771|ref|NM_001180731.1| 0 516 + gi|330443688|ref|NC_001145.3| 85010 667216 + 518 M 11 11 G 1 0 M 15 15 G 2 0 M 4 4 G 1 0 M 1 1 G 1 0 M 8 8 G 4 0 M 17 17 5 0 2 I 0 168904 3 0 2 M 4 4 G 0 1 M 8 8 G 2 0 M 3 3 G 1 0 M 33 33 G 0 2 M 7 7 G 0 1 M 102 102 5 0 2 I 0 96820 3 0 2 M 14 14 G 0 2 M 10 10 G 2 0 M 5 5 G 0 2 M 10 10 G 2 0 M 4 4 G 0 1 M 20 20 G 1 0 M 15 15 G 0 1 M 5 5 G 3 0 M 4 4 5 0 2 I 0 122114 3 0 2 M 20 20 G 0 5 M 6 6 5 0 2 I 0 193835 3 0 2 M 12 12 G 0 2 M 5 5 G 1 0 M 7 7 G 0 2 M 1 1 G 0 1 M 12 12 C 75 75 M 6 6 G 1 0 M 4 4 G 0 1 M 2 2 G 0 1 M 3 3 G 0 1 M 41 41
-- completed exonerate analysis


This file includes three alignments. To parse this file, use

>>> from Bio import Align
>>> alignments = Align.parse("exn_22_m_cdna2genome_vulgar.exn", "exonerate")


The dictionary alignments.metadata stores general information about these alignments, shown at the top of the output file:

>>> alignments.metadata
{'Program': 'exonerate',
'Hostname': 'blackbriar'}


Now we can iterate over the alignments. The first alignment, with alignment score 6146.0, has no gaps:

>>> alignment = next(alignments)
>>> alignment.score
6146.0
>>> print(alignment.coordinates)
[[1319275 1319274 1319271 1318045]
[      0       1       4    1230]]
>>> print(alignment)
gi|330443   1319275 ????????????????????????????????????????????????????????????
0 ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
gi|296143         0 ????????????????????????????????????????????????????????????
...
gi|330443   1318075 ?????????????????????????????? 1318045
1200 ||||||||||||||||||||||||||||||    1230
gi|296143      1200 ??????????????????????????????    1230


Note that the target (the first sequence) in the printed alignment is on the reverse strand while the query (the second sequence) is on the forward strand, with the target coordinate decreasing and the query coordinate increasing. Printing this alignment in exonerate format using Python’s built-in format function writes a vulgar line:

>>> print(format(alignment, "exonerate"))
vulgar: gi|296143771|ref|NM_001180731.1| 0 1230 + gi|330443520|ref|NC_001136.10| 1319275 1318045 - 6146 M 1 1 C 3 3 M 1226 1226


Using the format method allows us to request either a vulgar line (default) or a cigar line:

>>> print(alignment.format("exonerate", "vulgar"))
vulgar: gi|296143771|ref|NM_001180731.1| 0 1230 + gi|330443520|ref|NC_001136.10| 1319275 1318045 - 6146 M 1 1 C 3 3 M 1226 1226

>>> print(alignment.format("exonerate", "cigar"))
cigar: gi|296143771|ref|NM_001180731.1| 0 1230 + gi|330443520|ref|NC_001136.10| 1319275 1318045 - 6146 M 1 M 3 M 1226


The vulgar line contains information about the alignment (in the section M 1 1 C 3 3 M 1226) that is missing from the cigar line M 1 M 3 M 1226. The vulgar line specifies that the alignment starts with a single aligned nucleotides, followed by three aligned nucleotides that form a codon (C), followed by 1226 aligned nucleotides. In the cigar line, we see a single aligned nucleotide, followed by three aligned nucleotides, followed by 1226 aligned nucleotides; it does not specify that the three aligned nucleotides form a codon. This information from the vulgar line is stored in the operations attribute:

>>> alignment.operations
bytearray(b'MCM')


See the Exonerate documentation for the definition of other operation codes.

Similarly, the "vulgar" or "cigar" argument can be used when calling Bio.Align.write to write a file with vulgar or cigar alignment lines.

We can print the alignment in BED and PSL format:

>>> print(format(alignment, "bed"))
gi|330443520|ref|NC_001136.10|  1318045 1319275 gi|296143771|ref|NM_001180731.1| 6146   -   1318045 1319275 0   3   1226,3,1,   0,1226,1229,

>>> print(format(alignment, "psl"))
1230    0   0   0   0   0   0   0   -   gi|296143771|ref|NM_001180731.1|    1230    0   1230    gi|330443520|ref|NC_001136.10|  1319275 1318045 1319275 3   1226,3,1,   0,1226,1229,    1318045,1319271,1319274,


The SAM format parser defines its own (optional) operations attribute (section Sequence Alignment/Map (SAM)), which is not quite consistent with the operations attribute defined in the Exonerate format parser. As the operations attribute is optional, we delete it before printing the alignment in SAM format:

>>> del alignment.operations
>>> print(format(alignment, "sam"))
gi|296143771|ref|NM_001180731.1|    16  gi|330443520|ref|NC_001136.10|  1318046 255 1226M3M1M   *   0   0   *   *   AS:i:6146


The third alignment contains four long gaps:

>>> alignment = next(alignments)  # second alignment
>>> alignment = next(alignments)  # third alignment
>>> print(alignment)
gi|330443     85010 ???????????-???????????????--????-?-????????----????????????
0 |||||||||||-|||||||||||||||--||||-|-||||||||----||||||||||||
gi|296143         0 ????????????????????????????????????????????????????????????

gi|330443     85061 ????????????????????????????????????????????????????????????
60 |||||-------------------------------------------------------
gi|296143        60 ?????-------------------------------------------------------
...
gi|330443    666990 ????????????????????????????????????????????????????????????
582000 --------------------------------------------------||||||||||
gi|296143       346 --------------------------------------------------??????????

gi|330443    667050 ?????????-??????????????????????????????????????????????????
582060 ||--|||||-|||||||--|-|||||||||||||||||||||||||||||||||||||||
gi|296143       356 ??--?????????????--?-???????????????????????????????????????

gi|330443    667109 ??????????????????????????????????????????????????????-?????
582120 ||||||||||||||||||||||||||||||||||||||||||||||||||||||-||||-
gi|296143       411 ???????????????????????????????????????????????????????????-

gi|330443    667168 ???????????????????????????????????????????????? 667216
582180 ||-|||-||||||||||||||||||||||||||||||||||||||||| 582228
gi|296143       470 ??-???-?????????????????????????????????????????    516

>>> print(format(alignment, "exonerate"))
vulgar: gi|296143771|ref|NM_001180731.1| 0 516 + gi|330443688|ref|NC_001145.3|
85010 667216 + 518 M 11 11 G 1 0 M 15 15 G 2 0 M 4 4 G 1 0 M 1 1 G 1 0 M 8 8
G 4 0 M 17 17 5 0 2 I 0 168904 3 0 2 M 4 4 G 0 1 M 8 8 G 2 0 M 3 3 G 1 0
M 33 33 G 0 2 M 7 7 G 0 1 M 102 102 5 0 2 I 0 96820 3 0 2 M 14 14 G 0 2 M 10 10
G 2 0 M 5 5 G 0 2 M 10 10 G 2 0 M 4 4 G 0 1 M 20 20 G 1 0 M 15 15 G 0 1 M 5 5
G 3 0 M 4 4 5 0 2 I 0 122114 3 0 2 M 20 20 G 0 5 M 6 6 5 0 2 I 0 193835 3 0 2
M 12 12 G 0 2 M 5 5 G 1 0 M 7 7 G 0 2 M 1 1 G 0 1 M 12 12 C 75 75 M 6 6 G 1 0
M 4 4 G 0 1 M 2 2 G 0 1 M 3 3 G 0 1 M 41 41


### NEXUS

The NEXUS file format [Maddison1997] is used by several programs to store phylogenetic information. This is an example of a file in the NEXUS format (available as codonposset.nex in the Tests/Nexus subdirectory in the Biopython distribution):

#NEXUS
[MacClade 4.05 registered to Computational Biologist, University]

BEGIN DATA;
DIMENSIONS  NTAX=2 NCHAR=22;
FORMAT DATATYPE=DNA  MISSING=? GAP=- ;
MATRIX
[                           10        20 ]
[                           .         .  ]

Aegotheles         AAAAAGGCATTGTGGTGGGAAT   [22]
Aerodramus         ?????????TTGTGGTGGGAAT   [13]
;
END;

BEGIN CODONS;
CODONPOSSET * CodonPositions =
N: 1-10,
1: 11-22\3,
2: 12-22\3,
3: 13-22\3;
CODESET  * UNTITLED = Universal: all ;
END;


In general, files in the NEXUS format can be much more complex. Bio.Align relies heavily on NEXUS parser in Bio.Nexus (see Chapter Phylogenetics with Bio.Phylo) to extract Alignment objects from NEXUS files.

To read the alignment in this NEXUS file, use

>>> from Bio import Align
>>> print(alignment)
Aegothele         0 AAAAAGGCATTGTGGTGGGAAT 22
0 .........||||||||||||| 22
Aerodramu         0 ?????????TTGTGGTGGGAAT 22

>>> alignment.shape
(2, 22)


The sequences are stored under the sequences attribute:

>>> alignment.sequences[0].id
'Aegotheles'
>>> alignment.sequences[0].seq
Seq('AAAAAGGCATTGTGGTGGGAAT')
>>> alignment.sequences[0].annotations
{'molecule_type': 'DNA'}
>>> alignment.sequences[1].id
'Aerodramus'
>>> alignment.sequences[1].seq
Seq('?????????TTGTGGTGGGAAT')
>>> alignment.sequences[1].annotations
{'molecule_type': 'DNA'}


To print this alignment in the NEXUS format, use

>>> print(format(alignment, "nexus"))
#NEXUS
begin data;
dimensions ntax=2 nchar=22;
format datatype=dna missing=? gap=-;
matrix
Aegotheles AAAAAGGCATTGTGGTGGGAAT
Aerodramus ?????????TTGTGGTGGGAAT
;
end;


Similarly, you can use Align.write(alignment, "myfilename.nex", "nexus") to write the alignment in the NEXUS format to the file myfilename.nex.

### Tabular output from BLAST or FASTA

Alignment output in tabular output is generated by the FASTA aligner [Pearson1988] run with the -m 8CB or -m 8CC argument, or by BLAST [Altschul1990] run with the -outfmt 7 argument.

The file nucleotide_m8CC.txt in the Tests/Fasta subdirectory of the Biopython source distribution is an example of an output file generated by FASTA with the -m 8CC argument:

# fasta36 -m 8CC seq/mgstm1.nt seq/gst.nlib
# FASTA 36.3.8h May, 2020
# Query: pGT875  - 657 nt
# Database: seq/gst.nlib
# Fields: query id, subject id, % identity, alignment length, mismatches, gap opens, q. start, q. end, s. start, s. end, evalue, bit score, aln_code
# 12 hits found
pGT875  pGT875  100.00  657 0   0   1   657 38  694 4.6e-191    655.6   657M
pGT875  RABGLTR 79.10   646 135 0   1   646 34  679 1.6e-116    408.0   646M
pGT875  BTGST   59.56   413 167 21  176 594 228 655 1.9e-07 45.7    149M1D7M1I17M3D60M5I6M1I13M2I13M4I30M2I6M2D112M
pGT875  RABGSTB 66.93   127 42  8   159 289 157 287 3.2e-07 45.0    15M2I17M2D11M1I58M1I11M1D7M1D8M
pGT875  OCDHPR  91.30   23  2   1   266 289 2303    2325    0.012   29.7    17M1D6M
...
# FASTA processed 1 queries


To parse this file, use

>>> from Bio import Align
>>> alignments = Align.parse("nucleotide_m8CC.txt", "tabular")


Information shown in the file header is stored in the metadata attribute of alignments:

>>> alignments.metadata
{'Command line': 'fasta36 -m 8CC seq/mgstm1.nt seq/gst.nlib',
'Program': 'FASTA',
'Version': '36.3.8h May, 2020',
'Database': 'seq/gst.nlib'}


Extract a specific alignment by iterating over the alignments. As an example, let’s go to the fourth alignment:

>>> alignment = next(alignments)
>>> alignment = next(alignments)
>>> alignment = next(alignments)
>>> alignment = next(alignments)
>>> print(alignment)
RABGSTB         156 ??????????????????????????????????--????????????????????????
0 |||||||||||||||--|||||||||||||||||--|||||||||||-||||||||||||
pGT875          158 ???????????????--??????????????????????????????-????????????

RABGSTB         214 ??????????????????????????????????????????????????????????-?
60 ||||||||||||||||||||||||||||||||||||||||||||||-|||||||||||-|
pGT875          215 ??????????????????????????????????????????????-?????????????

RABGSTB         273 ??????-???????? 287
120 ||||||-|||||||| 135
pGT875          274 ??????????????? 289

>>> print(alignment.coordinates)
[[156 171 173 190 190 201 202 260 261 272 272 279 279 287]
[158 173 173 190 192 203 203 261 261 272 273 280 281 289]]
>>> alignment.aligned
array([[[156, 171],
[173, 190],
[190, 201],
[202, 260],
[261, 272],
[272, 279],
[279, 287]],

[[158, 173],
[173, 190],
[192, 203],
[203, 261],
[261, 272],
[273, 280],
[281, 289]]])


The sequence information of the target and query sequences is stored in the target and query attributes (as well as under alignment.sequences):

>>> alignment.target
SeqRecord(seq=Seq(None, length=287), id='RABGSTB', name='<unknown name>', description='<unknown description>', dbxrefs=[])
>>> alignment.query
SeqRecord(seq=Seq(None, length=657), id='pGT875', name='<unknown name>', description='<unknown description>', dbxrefs=[])


Information of the alignment is stored under the annotations attribute of the alignment:

>>> alignment.annotations
{'% identity': 66.93,
'mismatches': 42,
'gap opens': 8,
'evalue': 3.2e-07,
'bit score': 45.0}


BLAST in particular offers many options to customize tabular output by including or excluding specific columns; see the BLAST documentation for details. This information is stored in the dictionaries alignment.annotations, alignment.target.annotations, or alignment.query.annotations, as appropriate.

### HH-suite output files

Alignment files in the hhr format are generated by hhsearch or hhblits in HH-suite [Steinegger2019]. As an example, see the file 2uvo_hhblits.hhr in Biopython’s test suite:

Query         2UVO:A|PDBID|CHAIN|SEQUENCE
Match_columns 171
No_of_seqs    1560 out of 4005
Neff          8.3
Searched_HMMs 34
Date          Fri Feb 15 16:34:13 2019
Command       hhblits -i 2uvoAh.fasta -d /pdb70

No Hit                             Prob E-value P-value  Score    SS Cols Query HMM  Template HMM
1 2uvo_A Agglutinin isolectin 1; 100.0 3.7E-34 4.8E-38  210.3   0.0  171    1-171     1-171 (171)
2 2wga   ; lectin (agglutinin);   99.9 1.1E-30 1.4E-34  190.4   0.0  162    2-169     2-163 (164)
3 1ulk_A Lectin-C; chitin-bindin  99.8 5.2E-24 6.6E-28  148.2   0.0  120    1-124     2-121 (126)
...
31 4z8i_A BBTPGRP3, peptidoglycan  79.6    0.12 1.5E-05   36.1   0.0   37    1-37      9-54  (236)
32 1wga   ; lectin (agglutinin);   40.4     2.6 0.00029   25.9   0.0  106   54-163    11-116 (164)

No 1
>2uvo_A Agglutinin isolectin 1; carbohydrate-binding protein, hevein domain, chitin-binding, GERM agglutinin, chitin-binding protein; HET: NDG NAG GOL; 1.40A {Triticum aestivum} PDB: 1wgc_A* 2cwg_A* 2x3t_A* 4aml_A* 7wga_A 9wga_A 2wgc_A 1wgt_A 1k7t_A* 1k7v_A* 1k7u_A 2x52_A* 1t0w_A*
Probab=99.95  E-value=3.7e-34  Score=210.31  Aligned_cols=171  Identities=100%  Similarity=2.050  Sum_probs=166.9

Q 2UVO:A|PDBID|C    1 ERCGEQGSNMECPNNLCCSQYGYCGMGGDYCGKGCQNGACWTSKRCGSQAGGATCTNNQCCSQYGYCGFGAEYCGAGCQG   80 (171)
Q Consensus         1 ~~cg~~~~~~~c~~~~CCs~~g~CG~~~~~c~~~c~~~~c~~~~~Cg~~~~~~~c~~~~CCs~~g~CG~~~~~c~~~c~~   80 (171)
||||++.++..||++.|||+|+|||.+.+||+++||.+.|++..+|+++++.++|....|||.++||+.+.+||+.+||.
T Consensus         1 ~~cg~~~~~~~c~~~~CCS~~g~Cg~~~~~Cg~gC~~~~c~~~~~cg~~~~~~~c~~~~CCs~~g~Cg~~~~~c~~~c~~   80 (171)
T 2uvo_A            1 ERCGEQGSNMECPNNLCCSQYGYCGMGGDYCGKGCQNGACWTSKRCGSQAGGATCTNNQCCSQYGYCGFGAEYCGAGCQG   80 (171)
T ss_dssp             CBCBGGGTTBBCGGGCEECTTSBEEBSHHHHSTTCCBSSCSSCCBCBGGGTTBCCSTTCEECTTSBEEBSHHHHSTTCCB
T ss_pred             CCCCCCCCCcCCCCCCeeCCCCeECCCcccccCCccccccccccccCcccCCcccCCccccCCCceeCCCccccCCCccc
Confidence            79999999999999999999999999999999999999999999999999999999999999999999999999999999

Q 2UVO:A|PDBID|C   81 GPCRADIKCGSQAGGKLCPNNLCCSQWGFCGLGSEFCGGGCQSGACSTDKPCGKDAGGRVCTNNYCCSKWGSCGIGPGYC  160 (171)
Q Consensus        81 ~~~~~~~~Cg~~~~~~~c~~~~CCS~~G~CG~~~~~C~~~Cq~~~c~~~~~Cg~~~~~~~c~~~~CCS~~G~CG~~~~~C  160 (171)
+++++|+.|+...+++.||++.|||.|||||...+||+.+||+++|++|.+|++.+++++|..+.|||+++-||+...||
T Consensus        81 ~~~~~~~~cg~~~~~~~c~~~~CCs~~g~CG~~~~~C~~gCq~~~c~~~~~cg~~~~~~~c~~~~ccs~~g~Cg~~~~~C  160 (171)
T 2uvo_A           81 GPCRADIKCGSQAGGKLCPNNLCCSQWGFCGLGSEFCGGGCQSGACSTDKPCGKDAGGRVCTNNYCCSKWGSCGIGPGYC  160 (171)
T ss_dssp             SSCSSCCBCBGGGTTBCCGGGCEECTTSBEEBSHHHHSTTCCBSSCSSCCCCBTTTTTBCCSTTCEECTTSCEEBSHHHH
T ss_pred             ccccccccccccccCCCCCCCcccCCCCccCCCcccccCCCcCCccccccccccccccccCCCCCCcCCCCEecCchhhc
Confidence            99999999999988999999999999999999999999999999999999999999999999999999999999999999

Q 2UVO:A|PDBID|C  161 GAGCQSGGCDG  171 (171)
Q Consensus       161 ~~gCq~~~c~~  171 (171)
+++||++.|||
T Consensus       161 ~~~cq~~~~~~  171 (171)
T 2uvo_A          161 GAGCQSGGCDG  171 (171)
T ss_dssp             STTCCBSSCC-
T ss_pred             ccccccCCCCC
Confidence            99999999986

No 2
...

No 32
>1wga   ; lectin (agglutinin); NMR {}
Probab=40.43  E-value=2.6  Score=25.90  Aligned_cols=106  Identities=20%  Similarity=0.652  Sum_probs=54.7

Q 2UVO:A|PDBID|C   54 TCTNNQCCSQYGYCGFGAEYCGAGCQGGPCRADIKCGSQAGGKLCPNNLCCSQWGFCGLGSEFCGGGCQSGACSTDKPCG  133 (171)
Q Consensus        54 ~c~~~~CCs~~g~CG~~~~~c~~~c~~~~~~~~~~Cg~~~~~~~c~~~~CCS~~G~CG~~~~~C~~~Cq~~~c~~~~~Cg  133 (171)
.|....||.....|......|...|....|.....|...  ...|....||.....|......|...|....+.....|.
T Consensus        11 ~c~~~~cc~~~~~c~~~~~~c~~~c~~~~c~~~~~c~~~--~~~c~~~~cc~~~~~c~~~~~~c~~~c~~~~c~~~~~c~   88 (164)
T 1wga             11 XCXXXXCCXXXXXCXXXXXXCXXXCXXXXCXXXXXCXXX--XXXCXXXXCCXXXXXCXXXXXXCXXXCXXXXCXXXXXCX   88 (164)
T ss_pred             ccccccccccccccccccccccccccccccccccccccc--ccccccccccccccccccccccccccccccccccccccc
Confidence            344556666666666666566555543333223333321  234666677777777777766666655544332223333

Q 2UVO:A|PDBID|C  134 KDAGGRVCTNNYCCSKWGSCGIGPGYCGAG  163 (171)
Q Consensus       134 ~~~~~~~c~~~~CCS~~G~CG~~~~~C~~g  163 (171)
..  ...|....||.....|......|...
T Consensus        89 ~~--~~~c~~~~cc~~~~~c~~~~~~c~~~  116 (164)
T 1wga             89 XX--XXXCXXXXCCXXXXXCXXXXXXCXXX  116 (164)
T ss_pred             cc--cccccccccccccccccccccccccc
Confidence            22  23344455555555555555544433

Done!


The file contains three sections:

• A summary with one line for each of the alignments obtained;

• The alignments shown consecutively in detail.

To parse this file, use

>>> from Bio import Align
>>> alignments = Align.parse("2uvo_hhblits.hhr", "hhr")


Most of the header information is stored in the metadata attribute of alignments:

>>> alignments.metadata
{'Match_columns': 171,
'No_of_seqs': (1560, 4005),
'Neff': 8.3,
'Searched_HMMs': 34,
'Rundate': 'Fri Feb 15 16:34:13 2019',
'Command line': 'hhblits -i 2uvoAh.fasta -d /pdb70'}


except the query name, which is stored as an attribute:

>>> alignments.query_name
'2UVO:A|PDBID|CHAIN|SEQUENCE'


as it will reappear in each of the alignments.

Iterate over the alignments:

>>> for alignment in alignments:
...     print(alignment.target.id)
...
2uvo_A
2wga
1ulk_A
...
4z8i_A
1wga


Let’s look at the first alignment in more detail:

>>> alignments = iter(alignments)
>>> alignment = next(alignments)
>>> alignment
<Alignment object (2 rows x 171 columns) at ...>
>>> print(alignment)
2uvo_A            0 ERCGEQGSNMECPNNLCCSQYGYCGMGGDYCGKGCQNGACWTSKRCGSQAGGATCTNNQC
0 ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
2UVO:A|PD         0 ERCGEQGSNMECPNNLCCSQYGYCGMGGDYCGKGCQNGACWTSKRCGSQAGGATCTNNQC

60 ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

2uvo_A          120 CQSGACSTDKPCGKDAGGRVCTNNYCCSKWGSCGIGPGYCGAGCQSGGCDG 171
120 ||||||||||||||||||||||||||||||||||||||||||||||||||| 171
2UVO:A|PD       120 CQSGACSTDKPCGKDAGGRVCTNNYCCSKWGSCGIGPGYCGAGCQSGGCDG 171


The target and query sequences are stored in alignment.sequences. As these are pairwise alignments, we can also access them through alignment.target and alignment.query:

>>> alignment.target is alignment.sequences[0]
True
>>> alignment.query is alignment.sequences[1]
True


The ID of the query is set from the alignments.query_name (note that the query ID printed in the alignment in the hhr file is abbreviated):

>>> alignment.query.id
'2UVO:A|PDBID|CHAIN|SEQUENCE'


The ID of the target is taken from the sequence alignment block (the line starting with T 2uvo_A):

>>> alignment.target.id
'2uvo_A'


The sequence contents of the target and query are filled in from the information available in this alignment:

>>> alignment.target.seq
Seq('ERCGEQGSNMECPNNLCCSQYGYCGMGGDYCGKGCQNGACWTSKRCGSQAGGAT...CDG')
>>> alignment.query.seq
Seq('ERCGEQGSNMECPNNLCCSQYGYCGMGGDYCGKGCQNGACWTSKRCGSQAGGAT...CDG')


The sequence contents will be incomplete (a partially defined sequence; see Section Sequences with partially defined sequence contents) if the alignment does not extend over the full sequence.

The second line of this alignment block, starting with “>”, shows the name and description of the Hidden Markov Model from which the target sequence was taken. These are stored under the keys "hmm_name" and "hmm_description" in the alignment.target.annotations dictionary:

>>> alignment.target.annotations
{'hmm_name': '2uvo_A',
'hmm_description': 'Agglutinin isolectin 1; carbohydrate-binding protein, hevein domain, chitin-binding, GERM agglutinin, chitin-binding protein; HET: NDG NAG GOL; 1.40A {Triticum aestivum} PDB: 1wgc_A* 2cwg_A* 2x3t_A* 4aml_A* 7wga_A 9wga_A 2wgc_A 1wgt_A 1k7t_A* 1k7v_A* 1k7u_A 2x52_A* 1t0w_A*'}


The dictionary alignment.target.letter_annotations stores the target alignent consensus sequence, the secondary structure as predicted by PSIPRED, and the target secondary structure as determined by DSSP:

>>> alignment.target.letter_annotations
{'Consensus': '~~cg~~~~~~~c~~~~CCS~~g~Cg~~~~~Cg~gC~~~~c~~~~~cg~~~~~~~c~~~~CCs~~g~Cg~~~~~c~~~c~~~~~~~~~~cg~~~~~~~c~~~~CCs~~g~CG~~~~~C~~gCq~~~c~~~~~cg~~~~~~~c~~~~ccs~~g~Cg~~~~~C~~~cq~~~~~~',
'ss_pred': 'CCCCCCCCCcCCCCCCeeCCCCeECCCcccccCCccccccccccccCcccCCcccCCccccCCCceeCCCccccCCCcccccccccccccccccCCCCCCCcccCCCCccCCCcccccCCCcCCccccccccccccccccCCCCCCcCCCCEecCchhhcccccccCCCCC',
'ss_dssp': 'CBCBGGGTTBBCGGGCEECTTSBEEBSHHHHSTTCCBSSCSSCCBCBGGGTTBCCSTTCEECTTSBEEBSHHHHSTTCCBSSCSSCCBCBGGGTTBCCGGGCEECTTSBEEBSHHHHSTTCCBSSCSSCCCCBTTTTTBCCSTTCEECTTSCEEBSHHHHSTTCCBSSCC '}


In this example, for the query sequence only the consensus sequence is available:

>>> alignment.query.letter_annotations
{'Consensus': '~~cg~~~~~~~c~~~~CCs~~g~CG~~~~~c~~~c~~~~c~~~~~Cg~~~~~~~c~~~~CCs~~g~CG~~~~~c~~~c~~~~~~~~~~Cg~~~~~~~c~~~~CCS~~G~CG~~~~~C~~~Cq~~~c~~~~~Cg~~~~~~~c~~~~CCS~~G~CG~~~~~C~~gCq~~~c~~'}


The alignment.annotations dictionary stores information about the alignment shown on the third line of the alignment block:

>>> alignment.annotations
{'Probab': 99.95,
'E-value': 3.7e-34,
'Score': 210.31,
'Identities': 100.0,
'Similarity': 2.05,
'Sum_probs': 166.9}


Confidence values for the pairwise alignment are stored under the "Confidence" key in the alignment.column_annotations dictionary. This dictionary also stores the score for each column, shown between the query and the target section of each alignment block:

>>> alignment.column_annotations
{'column score': '||||++.++..||++.|||+|+|||.+.+||+++||.+.|++..+|+++++.++|....|||.++||+.+.+||+.+||.+++++|+.|+...+++.||++.|||.|||||...+||+.+||+++|++|.+|++.+++++|..+.|||+++-||+...||+++||++.|||',
'Confidence': '799999999999999999999999999999999999999999999999999999999999999999999999999999999999999999998899999999999999999999999999999999999999999999999999999999999999999999999999986'}


### A2M

A2M files are alignment files created by align2model or hmmscore in the SAM Sequence Alignment and Modeling Software System [Krogh1994], [Hughey1996]. An A2M file contains one multiple alignment. The A2M file format is similar to aligned FASTA (see section Aligned FASTA). However, to distinguish insertions from deletions, A2M uses both dashes and periods to represent gaps, and both upper and lower case characters in the aligned sequences. Matches are represented by upper case letters and deletions by dashes in alignment columns containing matches or deletions only. Insertions are represented by lower case letters, with gaps aligned to the insertion shown as periods. Header lines start with “>” followed by the name of the sequence, and optionally a description.

The file probcons.a2m in Biopython’s test suite is an example of an A2M file (see section Aligned FASTA for the same alignment in aligned FASTA format):

>plas_horvu
D.VLLGANGGVLVFEPNDFSVKAGETITFKNNAGYPHNVVFDEDAVPSG.VD.VSKISQEEYLTAPGETFSVTLTV...PGTYGFYCEPHAGAGMVGKVT
V
>plas_chlre
V
>plas_anava
V
>plas_proho
VqIKMGTDKYAPLYEPKALSISAGDTVEFVMNKVGPHNVIFDK--VPAG.ES.APALSNTKLRIAPGSFYSVTLGT...PGTYSFYCTPHRGAGMVGTIT
V
>azup_achcy
VhMLNKGKDGAMVFEPASLKVAPGDTVTFIPTDK-GHNVETIKGMIPDG.AE.A-------FKSKINENYKVTFTA...PGVYGVKCTPHYGMGMVGVVE
V


To parse this alignment, use

>>> from Bio import Align
>>> alignment
<Alignment object (5 rows x 101 columns) at ...>
>>> print(alignment)
plas_horv         0 D-VLLGANGGVLVFEPNDFSVKAGETITFKNNAGYPHNVVFDEDAVPSG-VD-VSKISQE
plas_proh         0 VQIKMGTDKYAPLYEPKALSISAGDTVEFVMNKVGPHNVIFDK--VPAG-ES-APALSNT
azup_achc         0 VHMLNKGKDGAMVFEPASLKVAPGDTVTFIPTDK-GHNVETIKGMIPDG-AE-A------

plas_horv        57 EYLTAPGETFSVTLTV---PGTYGFYCEPHAGAGMVGKVTV 95
plas_chlr        56 DYLNAPGETYSVKLTA---AGEYGYYCEPHQGAGMVGKIIV 94
plas_proh        56 KLRIAPGSFYSVTLGT---PGTYSFYCTPHRGAGMVGTITV 94
azup_achc        51 -FKSKINENYKVTFTA---PGVYGVKCTPHYGMGMVGVVEV 88


The parser analyzes the pattern of dashes, periods, and lower and upper case letters in the A2M file to determine if a column is an match/mismatch/deletion (”D”) or an insertion (”I”). This information is stored under the match key of the alignment.column_annotations dictionary:

>>> alignment.column_annotations
{'state': 'DIDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDIDDIDDDDDDDDDDDDDDDDDDDDDDDIIIDDDDDDDDDDDDDDDDDDDDDD'}


As the state information is stored in the alignment, we can print the alignment in the A2M format:

>>> print(format(alignment, "a2m"))
>plas_horvu
D.VLLGANGGVLVFEPNDFSVKAGETITFKNNAGYPHNVVFDEDAVPSG.VD.VSKISQEEYLTAPGETFSVTLTV...PGTYGFYCEPHAGAGMVGKVTV
>plas_chlre
>plas_anava
>plas_proho
VqIKMGTDKYAPLYEPKALSISAGDTVEFVMNKVGPHNVIFDK--VPAG.ES.APALSNTKLRIAPGSFYSVTLGT...PGTYSFYCTPHRGAGMVGTITV
>azup_achcy
VhMLNKGKDGAMVFEPASLKVAPGDTVTFIPTDK-GHNVETIKGMIPDG.AE.A-------FKSKINENYKVTFTA...PGVYGVKCTPHYGMGMVGVVEV


Similarly, the alignment can be written in the A2M format to an output file using Align.write (see section Writing alignments).

### Mauve eXtended Multi-FastA (xmfa) format

Mauve [Darling2004] is a software package for constructing multiple genome alignments. These alignments are stored in the eXtended Multi-FastA (xmfa) format. Depending on how exactly progressiveMauve (the aligner program in Mauve) was called, the xmfa format is slightly different.

If progressiveMauve is called with a single sequence input file, as in

progressiveMauve combined.fasta  --output=combined.xmfa ...


where combined.fasta contains the genome sequences:

>equCab1
GAAAAGGAAAGTACGGCCCGGCCACTCCGGGTGTGTGCTAGGAGGGCTTA
>mm9
GAAGAGGAAAAGTAGATCCCTGGCGTCCGGAGCTGGGACGT
>canFam2
CAAGCCCTGCGCGCTCAGCCGGAGTGTCCCGGGCCCTGCTTTCCTTTTC


then the output file combined.xmfa is as follows:

#FormatVersion Mauve1
#Sequence1File  combined.fa
#Sequence1Entry 1
#Sequence1Format    FastA
#Sequence2File  combined.fa
#Sequence2Entry 2
#Sequence2Format    FastA
#Sequence3File  combined.fa
#Sequence3Entry 3
#Sequence3Format    FastA
#BackboneFile   combined.xmfa.bbcols
> 1:2-49 - combined.fa
AAGCCCTCCTAGCACACACCCGGAGTGG-CCGGGCCGTACTTTCCTTTT
> 2:0-0 + combined.fa
-------------------------------------------------
> 3:2-48 + combined.fa
AAGCCCTGC--GCGCTCAGCCGGAGTGTCCCGGGCCCTGCTTTCCTTTT
=
> 1:1-1 + combined.fa
G
=
> 1:50-50 + combined.fa
A
=
> 2:1-41 + combined.fa
GAAGAGGAAAAGTAGATCCCTGGCGTCCGGAGCTGGGACGT
=
> 3:1-1 + combined.fa
C
=
> 3:49-49 + combined.fa
C
=


with numbers (1, 2, 3) referring to the input genome sequences for horse (equCab1), mouse (mm9), and dog (canFam2), respectively. This xmfa file consists of six alignment blocks, separated by = characters. Use Align.parse to extract these alignments:

>>> from Bio import Align
>>> alignments = Align.parse("combined.xmfa", "mauve")


The file header data are stored in the metadata attribute:

>>> alignments.metadata
{'FormatVersion': 'Mauve1',
'BackboneFile': 'combined.xmfa.bbcols',
'File': 'combined.fa'}


The identifiers attribute stores the sequence identifiers for the three sequences, which in this case is the three numbers:

>>> alignments.identifiers
['0', '1', '2']


These identifiers are used in the individual alignments:

>>> for alignment in alignments:
...     print([record.id for record in alignment.sequences])
...     print(alignment)
...     print("******")
...
['0', '1', '2']
0                49 AAGCCCTCCTAGCACACACCCGGAGTGG-CCGGGCCGTACTTTCCTTTT  1
1                 0 -------------------------------------------------  0
2                 1 AAGCCCTGC--GCGCTCAGCCGGAGTGTCCCGGGCCCTGCTTTCCTTTT 48

******
['0']
0                 0 G 1

******
['0']
0                49 A 50

******
['1']
1                 0 GAAGAGGAAAAGTAGATCCCTGGCGTCCGGAGCTGGGACGT 41

******
['2']
2                 0 C 1

******
['2']
2                48 C 49

******


Note that only the first block is a real alignment; the other blocks contain only a single sequence. By including these blocks, the xmfa file contains the full sequence that was provided in the combined.fa input file.

If progressiveMauve is called with a separate input file for each genome, as in

progressiveMauve equCab1.fa canFam2.fa mm9.fa --output=separate.xmfa ...


where each Fasta file contains the genome sequence for one species only, then the output file separate.xmfa is as follows:

#FormatVersion Mauve1
#Sequence1File  equCab1.fa
#Sequence1Format    FastA
#Sequence2File  canFam2.fa
#Sequence2Format    FastA
#Sequence3File  mm9.fa
#Sequence3Format    FastA
#BackboneFile   separate.xmfa.bbcols
> 1:1-50 - equCab1.fa
TAAGCCCTCCTAGCACACACCCGGAGTGGCC-GGGCCGTAC-TTTCCTTTTC
> 2:1-49 + canFam2.fa
CAAGCCCTGC--GCGCTCAGCCGGAGTGTCCCGGGCCCTGC-TTTCCTTTTC
> 3:1-19 - mm9.fa
---------------------------------GGATCTACTTTTCCTCTTC
=
> 3:20-41 + mm9.fa
CTGGCGTCCGGAGCTGGGACGT
=


The identifiers equCab1 for horse, mm9 for mouse, and canFam2 for dog are now shown explicitly in the output file. This xmfa file consists of two alignment blocks, separated by = characters. Use Align.parse to extract these alignments:

>>> from Bio import Align
>>> alignments = Align.parse("separate.xmfa", "mauve")


The file header data now does not include the input file name:

>>> alignments.metadata
{'FormatVersion': 'Mauve1',
'BackboneFile': 'separate.xmfa.bbcols'}


The identifiers attribute stores the sequence identifiers for the three sequences:

>>> alignments.identifiers
['equCab1.fa', 'canFam2.fa', 'mm9.fa']


These identifiers are used in the individual alignments:

>>> for alignment in alignments:
...     print([record.id for record in alignment.sequences])
...     print(alignment)
...     print("******")
...
['equCab1.fa', 'canFam2.fa', 'mm9.fa']
equCab1.f        50 TAAGCCCTCCTAGCACACACCCGGAGTGGCC-GGGCCGTAC-TTTCCTTTTC  0
canFam2.f         0 CAAGCCCTGC--GCGCTCAGCCGGAGTGTCCCGGGCCCTGC-TTTCCTTTTC 49
mm9.fa           19 ---------------------------------GGATCTACTTTTCCTCTTC  0

******
['mm9.fa']
mm9.fa           19 CTGGCGTCCGGAGCTGGGACGT 41

******


To output the alignments in Mauve format, use Align.write:

>>> from io import StringIO
>>> stream = StringIO()
>>> alignments = Align.parse("separate.xmfa", "mauve")
>>> Align.write(alignments, stream, "mauve")
2
>>> print(stream.getvalue())
#FormatVersion Mauve1
#Sequence1File  equCab1.fa
#Sequence1Format    FastA
#Sequence2File  canFam2.fa
#Sequence2Format    FastA
#Sequence3File  mm9.fa
#Sequence3Format    FastA
#BackboneFile   separate.xmfa.bbcols
> 1:1-50 - equCab1.fa
TAAGCCCTCCTAGCACACACCCGGAGTGGCC-GGGCCGTAC-TTTCCTTTTC
> 2:1-49 + canFam2.fa
CAAGCCCTGC--GCGCTCAGCCGGAGTGTCCCGGGCCCTGC-TTTCCTTTTC
> 3:1-19 - mm9.fa
---------------------------------GGATCTACTTTTCCTCTTC
=
> 3:20-41 + mm9.fa
CTGGCGTCCGGAGCTGGGACGT
=


Here, the writer makes use of the information stored in alignments.metadata and alignments.identifiers to create this format. If your alignments object does not have these attributes, you can provide them as keyword arguments to Align.write:

>>> stream = StringIO()
>>> alignments = Align.parse("separate.xmfa", "mauve")
>>> identifiers = alignments.identifiers
>>> alignments = list(alignments)  # this drops the attributes
Traceback (most recent call last):
...
AttributeError: 'list' object has no attribute 'metadata'
>>> alignments.identifiers
Traceback (most recent call last):
...
AttributeError: 'list' object has no attribute 'identifiers'
2
>>> print(stream.getvalue())
#FormatVersion Mauve1
#Sequence1File  equCab1.fa
#Sequence1Format    FastA
#Sequence2File  canFam2.fa
#Sequence2Format    FastA
#Sequence3File  mm9.fa
#Sequence3Format    FastA
#BackboneFile   separate.xmfa.bbcols
> 1:1-50 - equCab1.fa
TAAGCCCTCCTAGCACACACCCGGAGTGGCC-GGGCCGTAC-TTTCCTTTTC
> 2:1-49 + canFam2.fa
CAAGCCCTGC--GCGCTCAGCCGGAGTGTCCCGGGCCCTGC-TTTCCTTTTC
> 3:1-19 - mm9.fa
---------------------------------GGATCTACTTTTCCTCTTC
=
> 3:20-41 + mm9.fa
CTGGCGTCCGGAGCTGGGACGT
=


Python does not allow you to add these attributes to the alignments object directly, as in this example it was converted to a plain list. However, you can construct an Alignments object and add attributes to it (see Section The Alignments class):

>>> alignments = Align.Alignments(alignments)
>>> alignments.identifiers = identifiers
>>> stream = StringIO()
2
>>> print(stream.getvalue())
#FormatVersion Mauve1
#Sequence1File  equCab1.fa
#Sequence1Format    FastA
#Sequence2File  canFam2.fa
#Sequence2Format    FastA
#Sequence3File  mm9.fa
#Sequence3Format    FastA
#BackboneFile   separate.xmfa.bbcols
> 1:1-50 - equCab1.fa
TAAGCCCTCCTAGCACACACCCGGAGTGGCC-GGGCCGTAC-TTTCCTTTTC
> 2:1-49 + canFam2.fa
CAAGCCCTGC--GCGCTCAGCCGGAGTGTCCCGGGCCCTGC-TTTCCTTTTC
> 3:1-19 - mm9.fa
---------------------------------GGATCTACTTTTCCTCTTC
=
> 3:20-41 + mm9.fa
CTGGCGTCCGGAGCTGGGACGT
=


When printing a single alignment in Mauve format, use keyword arguments to provide the metadata and identifiers:

>>> alignment = alignments[0]
> 1:1-50 - equCab1.fa
TAAGCCCTCCTAGCACACACCCGGAGTGGCC-GGGCCGTAC-TTTCCTTTTC
> 2:1-49 + canFam2.fa
CAAGCCCTGC--GCGCTCAGCCGGAGTGTCCCGGGCCCTGC-TTTCCTTTTC
> 3:1-19 - mm9.fa
---------------------------------GGATCTACTTTTCCTCTTC
=


### Sequence Alignment/Map (SAM)

Files in the Sequence Alignment/Map (SAM) format [Li2009] store pairwise sequence alignments, usually of next-generation sequencing data against a reference genome. The file ex1.sam in Biopython’s test suite is an example of a minimal file in the SAM format. Its first few lines are as follows:

EAS56_57:6:190:289:82   69      chr1    100     0       *       =       100     0       CTCAAGGTTGTTGCAAGGGGGTCTATGTGAACAAA     <<<7<<<;<<<<<<<<8;;<7;4<;<;;;;;94<;     MF:i:192
EAS56_57:6:190:289:82   137     chr1    100     73      35M     =       100     0       AGGGGTGCAGAGCCGAGTCACGGGGTTGCCAGCAC     <<<<<<;<<<<<<<<<<;<<;<<<<;8<6;9;;2;     MF:i:64 Aq:i:0  NM:i:0  UQ:i:0  H0:i:1  H1:i:0
EAS51_64:3:190:727:308  99      chr1    103     99      35M     =       263     195     GGTGCAGAGCCGAGTCACGGGGTTGCCAGCACAGG     <<<<<<<<<<<<<<<<<<<<<<<<<<<::<<<844     MF:i:18 Aq:i:73 NM:i:0  UQ:i:0  H0:i:1  H1:i:0
...


To parse this file, use

>>> from Bio import Align
>>> alignments = Align.parse("ex1.sam", "sam")
>>> alignment = next(alignments)


The flag of the first line is 69. According to the SAM/BAM file format specification, lines for which the flag contains the bitwise flag 4 are unmapped. As 69 has the bit corresponding to this position set to True, this sequence is unmapped and was not aligned to the genome (in spite of the first line showing chr1). The target of this alignment (or the first item in alignment.sequences) is therefore None:

>>> alignment.flag
69
>>> bin(69)
'0b1000101'
>>> bin(4)
'0b100'
>>> if alignment.flag & 4:
...     print("unmapped")
... else:
...     print("mapped")
...
unmapped
>>> alignment.sequences
[None, SeqRecord(seq=Seq('CTCAAGGTTGTTGCAAGGGGGTCTATGTGAACAAA'), id='EAS56_57:6:190:289:82', name='<unknown name>', description='', dbxrefs=[])]
>>> alignment.target is None
True


The second line represents an alignment to chromosome 1:

>>> alignment = next(alignments)
>>> if alignment.flag & 4:
...     print("unmapped")
... else:
...     print("mapped")
...
mapped
>>> alignment.target
SeqRecord(seq=None, id='chr1', name='<unknown name>', description='', dbxrefs=[])


As this SAM file does not store the genome sequence information for each alignment, we cannot print the alignment. However, we can print the alignment information in SAM format or any other format (such as BED, see section Browser Extensible Data (BED)) that does not require the target sequence information:

>>> format(alignment, "sam")
'EAS56_57:6:190:289:82\t137\tchr1\t100\t73\t35M\t=\t100\t0\tAGGGGTGCAGAGCCGAGTCACGGGGTTGCCAGCAC\t<<<<<<;<<<<<<<<<<;<<;<<<<;8<6;9;;2;\tMF:i:64\tAq:i:0\tNM:i:0\tUQ:i:0\tH0:i:1\tH1:i:0\n'
>>> format(alignment, "bed")
'chr1\t99\t134\tEAS56_57:6:190:289:82\t0\t+\t99\t134\t0\t1\t35,\t0,\n'


However, we cannot print the alignment in PSL format (see section Pattern Space Layout (PSL)) as that would require knowing the size of the target sequence chr1:

>>> format(alignment, "psl")
Traceback (most recent call last):
...
TypeError: ...


If you know the size of the target sequences, you can set them by hand:

>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord
>>> target = SeqRecord(Seq(None, length=1575), id="chr1")
>>> alignment.target = target
>>> format(alignment, "psl")
'35\t0\t0\t0\t0\t0\t0\t0\t+\tEAS56_57:6:190:289:82\t35\t0\t35\tchr1\t1575\t99\t134\t1\t35,\t0,\t99,\n'


The file ex1_header.sam in Biopython’s test suite contains the same alignments, but now also includes a header. Its first few lines are as follows:

@HD\tVN:1.3\tSO:coordinate
@SQ\tSN:chr1\tLN:1575
@SQ\tSN:chr2\tLN:1584
EAS56_57:6:190:289:82   69      chr1    100     0       *       =       100     0       CTCAAGGTTGTTGCAAGGGGGTCTATGTGAACAAA     <<<7<<<;<<<<<<<<8;;<7;4<;<;;;;;94<;     MF:i:192
...


The header stores general information about the alignments, including the size of the target chromosomes. The target information is stored in the targets attribute of the alignments object:

>>> from Bio import Align
>>> len(alignments.targets)
2
>>> alignments.targets[0]
SeqRecord(seq=Seq(None, length=1575), id='chr1', name='<unknown name>', description='', dbxrefs=[])
>>> alignments.targets[1]
SeqRecord(seq=Seq(None, length=1584), id='chr2', name='<unknown name>', description='', dbxrefs=[])


Other information provided in the header is stored in the metadata attribute:

>>> alignments.metadata
{'HD': {'VN': '1.3', 'SO': 'coordinate'}}


With the target information, we can now also print the alignment in PSL format:

>>> alignment = next(alignments)  # the unmapped sequence; skip it
>>> alignment = next(alignments)
>>> format(alignment, "psl")
'35\t0\t0\t0\t0\t0\t0\t0\t+\tEAS56_57:6:190:289:82\t35\t0\t35\tchr1\t1575\t99\t134\t1\t35,\t0,\t99,\n'


We can now also print the alignment in human-readable form, but note that the target sequence contents is not available from this file:

>>> print(alignment)
chr1             99 ??????????????????????????????????? 134
0 ...................................  35
EAS56_57:         0 AGGGGTGCAGAGCCGAGTCACGGGGTTGCCAGCAC  35


Alignments in the file sam1.sam in the Biopython test suite contain an additional MD tag that shows how the query sequence differs from the target sequence:

@SQ     SN:1    LN:239940
@PG     ID:bwa  PN:bwa  VN:0.6.2-r126
HWI-1KL120:88:D0LRBACXX:1:1101:1780:2146        77      *       0       0       *       *       0       0       GATGGGAAACCCATGGCCGAGTGGGAAGAAACCAGCTGAGGTCACATCACCAGAGGAGGGAGAGTGTGGCCCCTGACTCAGTCCATCAGCTTGTGGAGCTG   @=?DDDDBFFFF7A;E?GGEGE8BB?FF?F>G@F=GIIDEIBCFF<FEFEC@EEEE2?8B8/=@((-;?@2<B9@##########################
...
HWI-1KL120:88:D0LRBACXX:1:1101:2852:2134        137     1       136186  25      101M    =       136186  0       TCACGGTGGCCTGTTGAGGCAGGGGCTCACGCTGACCTCTCTCGGCGTGGGAGGGGCCGGTGTGAGGCAAGGGCTCACGCTGACCTCTCTCGGCGTGGGAG   @C@FFFDFHGHHHJJJIJJJJIJJJGEDHHGGHGBGIIGIIAB@GEE=BDBBCCDD@D@B7@;@DDD?<A?DD728:>8()009>:>>C@>5??B######   XT:A:U  NM:i:5  SM:i:25 AM:i:0  X0:i:1  X1:i:0  XM:i:5  XO:i:0  XG:i:0  MD:Z:25G14G2C34A12A9


The parser reconstructs the local genome sequence from the MD tag, allowing us to see the target sequence explicitly when printing the alignment:

>>> from Bio import Align
>>> alignments = Align.parse("sam1.sam", "sam")
>>> for alignment in alignments:
...     if not alignment.flag & 4:  # Skip the unmapped lines
...         break
...
>>> alignment
<Alignment object (2 rows x 101 columns) at ...>
>>> print(alignment)
1            136185 TCACGGTGGCCTGTTGAGGCAGGGGGTCACGCTGACCTCTGTCCGCGTGGGAGGGGCCGG
0 |||||||||||||||||||||||||.||||||||||||||.||.||||||||||||||||
HWI-1KL12         0 TCACGGTGGCCTGTTGAGGCAGGGGCTCACGCTGACCTCTCTCGGCGTGGGAGGGGCCGG

1            136245 TGTGAGGCAAGGGCTCACACTGACCTCTCTCAGCGTGGGAG 136286
60 ||||||||||||||||||.||||||||||||.|||||||||    101
HWI-1KL12        60 TGTGAGGCAAGGGCTCACGCTGACCTCTCTCGGCGTGGGAG    101


SAM files may include additional information to distinguish simple sequence insertions and deletions from skipped regions of the genome (e.g. introns), hard and soft clipping, and padded sequence regions. As this information cannot be stored in the coordinates attribute of an Alignment object, and is stored in a dedicated operations attribute instead. Let’s use the third alignment in this SAM file as an example:

>>> from Bio import Align
>>> alignments = Align.parse("dna_rna.sam", "sam")
>>> alignment = next(alignments)
>>> alignment = next(alignments)
>>> alignment = next(alignments)
>>> print(format(alignment, "SAM"))
NR_111921.1 0   chr3    48663768    0   46M1827N82M3376N76M12H  *   0   0   CACGAGAGGAGCGGAGGCGAGGGGTGAACGCGGAGCACTCCAATCGCTCCCAACTAGAGGTCCACCCAGGACCCAGAGACCTGGATTTGAGGCTGCTGGGCGGCAGATGGAGCGATCAGAAGACCAGGAGACGGGAGCTGGAGTGCAGTGGCTGTTCACAAGCGTGAAAGCAAAGATTAAAAAATTTGTTTTTATATTAAAAAA    *   AS:i:1000   NM:i:0

>>> print(alignment.coordinates)
[[48663767 48663813 48665640 48665722 48669098 48669174]
[       0       46       46      128      128      204]]
>>> alignment.operations
bytearray(b'MNMNM')
>>> alignment.query.annotations["hard_clip_right"]
12


In this alignment, the cigar string 63M1062N75M468N43M defines 46 aligned nucleotides, an intron of 1827 nucleotides, 82 aligned nucleotides, an intron of 3376 nucleotides, 76 aligned nucleotides, and 12 hard-clipped nucleotides. These operations are shown in the operations attribute, except for hard-clipping, which is stored in alignment.query.annotations["hard_clip_right"] (or alignment.query.annotations["hard_clip_left"], if applicable) instead.

To write a SAM file with alignments created from scratch, use an Alignments (plural) object (see Section The Alignments class) to store the alignments as well as the metadata and targets:

>>> from io import StringIO
>>> import numpy as np

>>> from Bio import Align
>>> from Bio.Seq import Seq
>>> from Bio.SeqRecord import SeqRecord

>>> alignments = Align.Alignments()

>>> seq1 = Seq(None, length=10000)
>>> target1 = SeqRecord(seq1, id="chr1")
>>> seq2 = Seq(None, length=15000)
>>> target2 = SeqRecord(seq2, id="chr2")
>>> alignments.targets = [target1, target2]
>>> alignments.metadata = {"HD": {"VN": "1.3", "SO": "coordinate"}}

>>> seqA = Seq(None, length=20)
>>> sequences = [target1, queryA]
>>> coordinates = np.array([[4300, 4320], [0, 20]])
>>> alignment = Align.Alignment(sequences, coordinates)
>>> alignments.append(alignment)

>>> seqB = Seq(None, length=25)
>>> sequences = [target1, queryB]
>>> coordinates = np.array([[5900, 5925], [25, 0]])
>>> alignment = Align.Alignment(sequences, coordinates)
>>> alignments.append(alignment)

>>> seqC = Seq(None, length=40)
>>> sequences = [target2, queryC]
>>> coordinates = np.array([[12300, 12318], [0, 18]])
>>> alignment = Align.Alignment(sequences, coordinates)
>>> alignments.append(alignment)

>>> stream = StringIO()
>>> Align.write(alignments, stream, "sam")
3
>>> print(stream.getvalue())
@HD VN:1.3  SO:coordinate
@SQ SN:chr1 LN:10000
@SQ SN:chr2 LN:15000
readA   0   chr1    4301    255 20M *   0   0   *   *
readB   16  chr1    5901    255 25M *   0   0   *   *
readC   0   chr2    12301   255 18M22S  *   0   0   *       *


### Browser Extensible Data (BED)

BED (Browser Extensible Data) files are typically used to store the alignments of gene transcripts to the genome. See the description from UCSC for a full explanation of the BED format.

BED files have three required fields and nine optional fields. The file bed12.bed in subdirectory Tests/Blat is an example of a BED file with 12 fields:

chr22   1000    5000    mRNA1   960 +   1200    4900    255,0,0 2   567,488,    0,3512,
chr22   2000    6000    mRNA2   900 -   2300    5960    0,255,0 2   433,399,    0,3601,


To parse this file, use

>>> from Bio import Align
>>> alignments = Align.parse("bed12.bed", "bed")
>>> len(alignments)
2
>>> for alignment in alignments:
...     print(alignment.coordinates)
...
[[1000 1567 4512 5000]
[   0  567  567 1055]]
[[2000 2433 5601 6000]
[ 832  399  399    0]]


Note that the first sequence (”mRNA1”) was mapped to the forward strand, while the second sequence (”mRNA2”) was mapped to the reverse strand.

As a BED file does not store the length of each chromosome, the length of the target sequence is set to its maximum:

>>> alignment.target
SeqRecord(seq=Seq(None, length=9223372036854775807), id='chr22', name='<unknown name>', description='', dbxrefs=[])


The length of the query sequence can be inferred from its alignment information:

>>> alignment.query
SeqRecord(seq=Seq(None, length=832), id='mRNA2', name='<unknown name>', description='', dbxrefs=[])


The alignment score (field 5) and information stored in fields 7-9 (referred to as thickStart, thickEnd, and itemRgb in the BED format specification) are stored as attributes on the alignment object:

>>> alignment.score
900.0
>>> alignment.thickStart
2300
>>> alignment.thickEnd
5960
>>> alignment.itemRgb
'0,255,0'


To print an alignment in the BED format, you can use Python’s built-in format function:

>>> print(format(alignment, "bed"))
chr22   2000    6000    mRNA2   900 -   2300    5960    0,255,0 2   433,399,    0,3601,


or you can use the format method of the alignment object. This allows you to specify the number of fields to be written as the bedN keyword argument:

>>> print(alignment.format("bed"))
chr22   2000    6000    mRNA2   900 -   2300    5960    0,255,0 2   433,399,    0,3601,

>>> print(alignment.format("bed", 3))
chr22   2000    6000

>>> print(alignment.format("bed", 6))
chr22   2000    6000    mRNA2   900 -


The same keyword argument can be used with Align.write:

>>> Align.write(alignments, "mybed3file.bed", "bed", bedN=3)
2
>>> Align.write(alignments, "mybed6file.bed", "bed", bedN=6)
2
>>> Align.write(alignments, "mybed12file.bed", "bed")
2


### bigBed

The bigBed file format is an indexed binary version of a BED file Browser Extensible Data (BED). To create a bigBed file, you can either use the bedToBigBed program from UCSC () <https://genome.ucsc.edu/goldenPath/help/bigBed.html>__. or you can use Biopython for it by calling the Bio.Align.write function with fmt="bigbed". While the two methods should result in identical bigBed files, using bedToBigBed is much faster and may be more reliable, as it is the gold standard. As bigBed files come with a built-in index, it allows you to quickly search a specific genomic region.

As an example, let’s parse the bigBed file dna_rna.bb, available in the Tests/Blat subdirectory in the Biopython distribution:

>>> from Bio import Align
>>> alignments = Align.parse("dna_rna.bb", "bigbed")
>>> len(alignments)
4
>>> print(alignments.declaration)
table bed
"Browser Extensible Data"
(
string          chrom;          "Reference sequence chromosome or scaffold"
uint            chromStart;     "Start position in chromosome"
uint            chromEnd;       "End position in chromosome"
string          name;           "Name of item."
uint            score;          "Score (0-1000)"
char[1]         strand;         "+ or - for strand"
uint            thickStart;     "Start of where display should be thick (start codon)"
uint            thickEnd;       "End of where display should be thick (stop codon)"
uint            reserved;       "Used as itemRgb as of 2004-11-22"
int             blockCount;     "Number of blocks"
int[blockCount] blockSizes;     "Comma separated list of block sizes"
int[blockCount] chromStarts;    "Start positions relative to chromStart"
)


The declaration contains the specification of the columns, in AutoSql format, that was used to create the bigBed file. Target sequences (typically, the chromosomes against which the sequences were aligned) are stored in the targets attribute. In the bigBed format, only the identifier and the size of each target is stored. In this example, there is only a single chromosome:

>>> alignments.targets
[SeqRecord(seq=Seq(None, length=198295559), id='chr3', name='<unknown name>', description='<unknown description>', dbxrefs=[])]


Let’s look at the individual alignments. The alignment information is stored in the same way as for a BED file (see section Browser Extensible Data (BED)):

>>> alignment = next(alignments)
>>> alignment.target.id
'chr3'
>>> alignment.query.id
'NR_046654.1'
>>> alignment.coordinates
array([[42530895, 42530958, 42532020, 42532095, 42532563, 42532606],
[     181,      118,      118,       43,       43,        0]])
>>> alignment.thickStart
42530895
>>> alignment.thickEnd
42532606
>>> print(alignment)
chr3       42530895 ????????????????????????????????????????????????????????????
0 ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
NR_046654       181 ????????????????????????????????????????????????????????????

chr3       42530955 ????????????????????????????????????????????????????????????
60 |||---------------------------------------------------------
NR_046654       121 ???---------------------------------------------------------
...
chr3       42532515 ????????????????????????????????????????????????????????????
1620 ------------------------------------------------||||||||||||
NR_046654        43 ------------------------------------------------????????????

chr3       42532575 ??????????????????????????????? 42532606
1680 |||||||||||||||||||||||||||||||     1711
NR_046654        31 ???????????????????????????????        0


The default bigBed format does not store the sequence contents of the target and query. If these are available elsewhere (for example, a Fasta file), you can set alignment.target.seq and alignment.query.seq to show the sequence contents when printing the alignment, or to write the alignment in formats that require the sequence contents (such as Clustal, see section ClustalW). The test script test_Align_bigbed.py in the Tests subdirectory in the Biopython distribution gives some examples on how to do that.

Now let’s see how to search for a sequence region. These are the sequences stored in the bigBed file, printed in BED format (see section Browser Extensible Data (BED)):

>>> for alignment in alignments:
...     print(format(alignment, "bed"))
...
chr3    42530895    42532606    NR_046654.1 1000    -   42530895    42532606    0   3   63,75,43,   0,1125,1668,

chr3    42530895    42532606    NR_046654.1_modified    978 -   42530895    42532606    0   5   27,36,17,56,43, 0,27,1125,1144,1668,

chr3    48663767    48669174    NR_111921.1 1000    +   48663767    48669174    0   3   46,82,76,   0,1873,5331,

chr3    48663767    48669174    NR_111921.1_modified    972 +   48663767    48669174    0   5   28,17,76,6,76,  0,29,1873,1949,5331,


Use the search method on the alignments object to find regions on chr3 between positions 48000000 and 49000000. This method returns an iterator:

>>> selected_alignments = alignments.search("chr3", 48000000, 49000000)
>>> for alignment in selected_alignments:
...     print(alignment.query.id)
...
NR_111921.1
NR_111921.1_modified


The chromosome name may be None to include all chromosomes, and the start and end positions may be None to start searching from position 0 or to continue searching until the end of the chromosome, respectively.

Writing alignments in the bigBed format is as easy as calling Bio.Align.write:

>>> Align.write(alignments, "output.bb", "bigbed")


You can specify the number of BED fields to be included in the bigBed file. For example, to write a BED6 file, use

>>> Align.write(alignments, "output.bb", "bigbed", bedN=6)


Same as for bedToBigBed, you can include additional columns in the bigBed output. Suppose the file bedExample2.as (available in the Tests/Blat subdirectory of the Biopython distribution) stores the declaration of the included BED fields in AutoSql format. We can read this declaration as follows:

>>> from Bio.Align import bigbed
>>> with open("bedExample2.as") as stream:
...
>>> declaration = bigbed.AutoSQLTable.from_string(autosql_data)
>>> type(declaration)
<class 'Bio.Align.bigbed.AutoSQLTable'>
>>> print(declaration)
table hg18KGchr7
"UCSC Genes for chr7 with color plus GeneSymbol and SwissProtID"
(
string  chrom;         "Reference sequence chromosome or scaffold"
uint    chromStart;    "Start position of feature on chromosome"
uint    chromEnd;      "End position of feature on chromosome"
string  name;          "Name of gene"
uint    score;         "Score"
char[1] strand;        "+ or - for strand"
uint    thickStart;    "Coding region start"
uint    thickEnd;      "Coding region end"
uint    reserved;      "Green on + strand, Red on - strand"
string  geneSymbol;    "Gene Symbol"
string  spID;          "SWISS-PROT protein Accession number"
)


Now we can write a bigBed file with the 9 BED fields plus the additional fields geneSymbol and spID by calling

>>> Align.write(
...     alignments,
...     "output.bb",
...     "bigbed",
...     bedN=9,
...     declaration=declaration,
...     extraIndex=["name", "geneSymbol"],
... )


Here, we also requested to include additional indices on the name and geneSymbol in the bigBed file. Align.write expects to find the keys geneSymbol and spID in the alignment.annotations dictionary. Please refer to the test script test_Align_bigbed.py in the Tests subdirectory in the Biopython distribution for more examples of writing alignment files in the bigBed format.

Optional arguments are compress (default value is True), blockSize (default value is 256), and itemsPerSlot (default value is 512). See the documentation of UCSC’s bedToBigBed program for a description of these arguments. Searching a bigBed file can be faster by using compress=False and itemsPerSlot=1 when creating the bigBed file.

### Pattern Space Layout (PSL)

PSL (Pattern Space Layout) files are are generated by the BLAST-Like Alignment Tool BLAT [Kent2002]. Like BED files (see section Browser Extensible Data (BED)), PSL files are typically used to store alignments of transcripts to genomes. This is an example of a short BLAT file (available as dna_rna.psl in the Tests/Blat subdirectory of the Biopython distribution), with the standard PSL header consisting of 5 lines:

psLayout version 3

match   mis-    rep.    N's Q gap   Q gap   T gap   T gap   strand  Q           Q       Q       Q   T           T       T       T   block   blockSizes  qStarts  tStarts
match   match       count   bases   count   bases           name        size    start   end name        size    start   end count
---------------------------------------------------------------------------------------------------------------------------------------------------------------
165 0   39  0   0   0   2   5203    +   NR_111921.1 216 0   204 chr3    198295559   48663767    48669174    3   46,82,76,   0,46,128,   48663767,48665640,48669098,
175 0   6   0   0   0   2   1530    -   NR_046654.1 181 0   181 chr3    198295559   42530895    42532606    3   63,75,43,   0,63,138,   42530895,42532020,42532563,
162 2   39  0   1   2   3   5204    +   NR_111921.1_modified    220 3   208 chr3    198295559   48663767    48669174    5   28,17,76,6,76,  3,31,48,126,132,    48663767,48663796,48665640,48665716,48669098,
172 1   6   0   1   3   3   1532    -   NR_046654.1_modified    190 3   185 chr3    198295559   42530895    42532606    5   27,36,17,56,43, 5,35,71,88,144, 42530895,42530922,42532020,42532039,42532563,


To parse this file, use

>>> from Bio import Align
>>> alignments = Align.parse("dna_rna.psl", "psl")
{'psLayout version': '3'}


Iterate over the alignments to get one Alignment object for each line:

>>> for alignment in alignments:
...     print(alignment.target.id, alignment.query.id)
...
chr3 NR_046654.1
chr3 NR_046654.1_modified
chr3 NR_111921.1
chr3 NR_111921.1_modified


Let’s look at the last alignment in more detail. The first four columns in the PSL file show the number of matches, the number of mismatches, the number of nucleotides aligned to repeat regions, and the number of nucleotides aligned to N (unknown) characters. These values are stored as attributes to the Alignment object:

>>> alignment.matches
162
>>> alignment.misMatches
2
>>> alignment.repMatches
39
>>> alignment.nCount
0


As the sequence data of the target and query are not stored explicitly in the PSL file, the sequence content of alignment.target and alignment.query is undefined. However, their sequence lengths are known:

>>> alignment.target
SeqRecord(seq=Seq(None, length=198295559), id='chr3', ...)
>>> alignment.query
SeqRecord(seq=Seq(None, length=220), id='NR_111921.1_modified', ...)


We can print the alignment in BED or PSL format:

>>> print(format(alignment, "bed"))
chr3    48663767    48669174    NR_111921.1_modified    0   +   48663767    48669174    0   5   28,17,76,6,76,  0,29,1873,1949,5331,

>>> print(format(alignment, "psl"))
162 2   39  0   1   2   3   5204    +   NR_111921.1_modified    220 3   208 chr3    198295559   48663767    48669174    5   28,17,76,6,76,  3,31,48,126,132,    48663767,48663796,48665640,48665716,48669098,


Here, the number of matches, mismatches, repeat region matches, and matches to unknown nucleotides were taken from the corresponding attributes of the Alignment object. If these attributes are not available, for example if the alignment did not come from a PSL file, then these numbers are calculated using the sequence contents, if available. Repeat regions in the target sequence are indicated by masking the sequence as lower-case or upper-case characters, as defined by the following values for the mask keyword argument:

• False (default): Do not count matches to masked sequences separately;

• "lower": Count and report matches to lower-case characters as matches to repeat regions;

• "upper": Count and report matches to upper-case characters as matches to repeat regions;

The character used for unknown nucleotides is defined by the wildcard argument. For consistency with BLAT, the wildcard character is "N" by default. Use wildcard=None if you don’t want to count matches to any unknown nucleotides separately.

>>> import numpy
>>> from Bio import Align
>>> query = "GGTGGGGG"
>>> target = "AAAAAAAggggGGNGAAAAA"
>>> coordinates = numpy.array([[0, 7, 15, 20], [0, 0, 8, 8]])
>>> alignment = Align.Alignment([target, query], coordinates)
>>> print(alignment)
target            0 AAAAAAAggggGGNGAAAAA 20
0 -------....||.|----- 20
query             0 -------GGTGGGGG-----  8

>>> line = alignment.format("psl")
>>> print(line)
6   1   0   1   0   0   0   0   +   query   8   0   8   target   20   7   15   1   8,   0,   7,
>>> print(line)
3   1   3   1   0   0   0   0   +   query   8   0   8   target   20   7   15   1   8,   0,   7,
>>> line = alignment.format("psl", mask="lower", wildcard=None)
>>> print(line)
3   2   3   0   0   0   0   0   +   query   8   0   8   target   20   7   15   1   8,   0,   7,


The same arguments can be used when writing alignments to an output file in PSL format using Bio.Align.write. This function has an additional keyword header (True by default) specifying if the PSL header should be written.

In addition to the format method, you can use Python’s built-in format function:

>>> print(format(alignment, "psl"))
6   1   0   1   0   0   0   0   +   query   8   0   8   target   20   7   15   1   8,   0,   7,


allowing Alignment objects to be used in formatted (f-) strings in Python:

>>> line = f"The alignment in PSL format is '{alignment:psl}'."
>>> print(line)
The alignment in PSL format is '6   1   0   1   0   0   0   0   +   query   8   0   8   target   20   7   15   1   8,   0,   7,
'


Note that optional keyword arguments cannot be used with the format function or with formatted strings.

### bigPsl

A bigPsl file is a bigBed file with a BED12+13 format consisting of the 12 predefined BED fields and 13 custom fields defined in the AutoSql file bigPsl.as provided by UCSC, creating an indexed binary version of a PSL file (see section Pattern Space Layout (PSL)). To create a bigPsl file, you can either use the pslToBigPsl and bedToBigBed programs from UCSC. or you can use Biopython by calling the Bio.Align.write function with fmt="bigpsl". While the two methods should result in identical bigPsl files, the UCSC tools are much faster and may be more reliable, as it is the gold standard. As bigPsl files are bigBed files, they come with a built-in index, allowing you to quickly search a specific genomic region.

As an example, let’s parse the bigBed file dna_rna.psl.bb, available in the Tests/Blat subdirectory in the Biopython distribution. This file is the bigPsl equivalent of the bigBed file dna_rna.bb (see section bigBed) and of the PSL file dna_rna.psl (see section Pattern Space Layout (PSL)).

>>> from Bio import Align
>>> alignments = Align.parse("dna_rna.psl.bb", "bigpsl")
>>> len(alignments)
4
>>> print(alignments.declaration)
table bigPsl
"bigPsl pairwise alignment"
(
string          chrom;           "Reference sequence chromosome or scaffold"
uint            chromStart;      "Start position in chromosome"
uint            chromEnd;        "End position in chromosome"
string          name;            "Name or ID of item, ideally both human readable and unique"
uint            score;           "Score (0-1000)"
char[1]         strand;          "+ or - indicates whether the query aligns to the + or - strand on the reference"
uint            thickStart;      "Start of where display should be thick (start codon)"
uint            thickEnd;        "End of where display should be thick (stop codon)"
uint            reserved;        "RGB value (use R,G,B string in input file)"
int             blockCount;      "Number of blocks"
int[blockCount] blockSizes;      "Comma separated list of block sizes"
int[blockCount] chromStarts;     "Start positions relative to chromStart"
uint            oChromStart;     "Start position in other chromosome"
uint            oChromEnd;       "End position in other chromosome"
char[1]         oStrand;         "+ or -, - means that psl was reversed into BED-compatible coordinates"
uint            oChromSize;      "Size of other chromosome."
int[blockCount] oChromStarts;    "Start positions relative to oChromStart or from oChromStart+oChromSize depending on strand"
lstring         oSequence;       "Sequence on other chrom (or edit list, or empty)"
string          oCDS;            "CDS in NCBI format"
uint            chromSize;       "Size of target chromosome"
uint            match;           "Number of bases matched."
uint            misMatch;        "Number of bases that don't match"
uint            repMatch;        "Number of bases that match but are part of repeats"
uint            nCount;          "Number of 'N' bases"
uint            seqType;         "0=empty, 1=nucleotide, 2=amino_acid"
)


The declaration contains the specification of the columns as defined by the bigPsl.as AutoSql file from UCSC. Target sequences (typically, the chromosomes against which the sequences were aligned) are stored in the targets attribute. In the bigBed format, only the identifier and the size of each target is stored. In this example, there is only a single chromosome:

>>> alignments.targets
[SeqRecord(seq=Seq(None, length=198295559), id='chr3', name='<unknown name>', description='<unknown description>', dbxrefs=[])]


Iterating over the alignments gives one Alignment object for each line:

>>> for alignment in alignments:
...     print(alignment.target.id, alignment.query.id)
...
chr3 NR_046654.1
chr3 NR_046654.1_modified
chr3 NR_111921.1
chr3 NR_111921.1_modified


Let’s look at the individual alignments. The alignment information is stored in the same way as for the corresponding PSL file (see section Pattern Space Layout (PSL)):

>>> alignment.coordinates
array([[48663767, 48663795, 48663796, 48663813, 48665640, 48665716,
48665716, 48665722, 48669098, 48669174],
[       3,       31,       31,       48,       48,      124,
126,      132,      132,      208]])
>>> alignment.thickStart
48663767
>>> alignment.thickEnd
48669174
>>> alignment.matches
162
>>> alignment.misMatches
2
>>> alignment.repMatches
39
>>> alignment.nCount
0


We can print the alignment in BED or PSL format:

>>> print(format(alignment, "bed"))
chr3    48663767    48669174    NR_111921.1_modified    1000    +   48663767    48669174    0   5   28,17,76,6,76,  0,29,1873,1949,5331,

>>> print(format(alignment, "psl"))
162 2   39  0   1   2   3   5204    +   NR_111921.1_modified    220 3   208 chr3    198295559   48663767    48669174    5   28,17,76,6,76,  3,31,48,126,132,    48663767,48663796,48665640,48665716,48669098,


As a bigPsl file is a special case of a bigBed file, you can use the search method on the alignments object to find alignments to specific genomic regions. For example, we can look for regions on chr3 between positions 48000000 and 49000000:

>>> selected_alignments = alignments.search("chr3", 48000000, 49000000)
>>> for alignment in selected_alignments:
...     print(alignment.query.id)
...
NR_111921.1
NR_111921.1_modified


The chromosome name may be None to include all chromosomes, and the start and end positions may be None to start searching from position 0 or to continue searching until the end of the chromosome, respectively.

To write a bigPsl file with Biopython, use Bio.Align.write(alignments, "myfilename.bb", fmt="bigpsl"), where myfilename.bb is the name of the output bigPsl file. Alternatively, you can use a (binary) stream for output. Additional options are

• compress: If True (default), compress data using zlib; if False, do not compress data.

• extraIndex: List of strings with the names of extra columns to be indexed.

• cds: If True, look for a query feature of type CDS and write it in NCBI style in the PSL file (default: False).

• fa: If True, include the query sequence in the PSL file (default: False).

• mask: Specify if repeat regions in the target sequence are masked and should be reported in the repMatches field 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: Report alignments to the wildcard character (representing unknown nucleotides) in the target or query sequence in the nCount field instead of in the matches, misMatches, or repMatches fields. Default value is "N".

See section Pattern Space Layout (PSL) for an explanation on how the number of matches, mismatches, repeat region matches, and matches to unknown nucleotides are obtained.

Further optional arguments are blockSize (default value is 256), and itemsPerSlot (default value is 512). See the documentation of UCSC’s bedToBigBed program for a description of these arguments. Searching a bigPsl file can be faster by using compress=False and itemsPerSlot=1 when creating the bigPsl file.

### Multiple Alignment Format (MAF)

MAF (Multiple Alignment Format) files store a series of multiple sequence alignments in a human-readable format. MAF files are typically used to store alignment s of genomes to each other. The file ucsc_test.maf in the Tests/MAF subdirectory of the Biopython distribution is an example of a simple MAF file:

track name=euArc visibility=pack mafDot=off frames="multiz28wayFrames" speciesOrder="hg16 panTro1 baboon mm4 rn3" description="A sample alignment"
##maf version=1 scoring=tba.v8
# tba.v8 (((human chimp) baboon) (mouse rat))
# multiz.v7
# maf_project.v5 _tba_right.maf3 mouse _tba_C
# single_cov2.v4 single_cov2 /dev/stdin

a score=23262.0
s hg16.chr7    27578828 38 + 158545518 AAA-GGGAATGTTAACCAAATGA---ATTGTCTCTTACGGTG
s panTro1.chr6 28741140 38 + 161576975 AAA-GGGAATGTTAACCAAATGA---ATTGTCTCTTACGGTG
s baboon         116834 38 +   4622798 AAA-GGGAATGTTAACCAAATGA---GTTGTCTCTTATGGTG
s mm4.chr6     53215344 38 + 151104725 -AATGGGAATGTTAAGCAAACGA---ATTGTCTCTCAGTGTG
s rn3.chr4     81344243 40 + 187371129 -AA-GGGGATGCTAAGCCAATGAGTTGTTGTCTCTCAATGTG

a score=5062.0
s hg16.chr7    27699739 6 + 158545518 TAAAGA
s panTro1.chr6 28862317 6 + 161576975 TAAAGA
s baboon         241163 6 +   4622798 TAAAGA
s mm4.chr6     53303881 6 + 151104725 TAAAGA
s rn3.chr4     81444246 6 + 187371129 taagga

a score=6636.0
s hg16.chr7    27707221 13 + 158545518 gcagctgaaaaca
s panTro1.chr6 28869787 13 + 161576975 gcagctgaaaaca
s baboon         249182 13 +   4622798 gcagctgaaaaca
s mm4.chr6     53310102 13 + 151104725 ACAGCTGAAAATA


To parse this file, use

>>> from Bio import Align
>>> alignments = Align.parse("ucsc_test.maf", "maf")


Information shown in the file header (the track line and subsequent lines starting with “#”)) is stored in the metadata attribute of the alignments object:

>>> alignments.metadata
{'name': 'euArc',
'visibility': 'pack',
'mafDot': 'off',
'frames': 'multiz28wayFrames',
'speciesOrder': ['hg16', 'panTro1', 'baboon', 'mm4', 'rn3'],
'description': 'A sample alignment',
'MAF Version': '1',
'Scoring': 'tba.v8',
'Comments': ['tba.v8 (((human chimp) baboon) (mouse rat))',
'multiz.v7',
'maf_project.v5 _tba_right.maf3 mouse _tba_C',
'single_cov2.v4 single_cov2 /dev/stdin']}


By iterating over the alignments we obtain one Alignment object for each alignment block in the MAF file:

>>> alignment = next(alignments)
>>> alignment.score
23262.0
>>> {seq.id: len(seq) for seq in alignment.sequences}
{'hg16.chr7': 158545518,
'panTro1.chr6': 161576975,
'baboon': 4622798,
'mm4.chr6': 151104725,
'rn3.chr4': 187371129}
>>> print(alignment.coordinates)
[[27578828 27578829 27578831 27578831 27578850 27578850 27578866]
[28741140 28741141 28741143 28741143 28741162 28741162 28741178]
[  116834   116835   116837   116837   116856   116856   116872]
[53215344 53215344 53215346 53215347 53215366 53215366 53215382]
[81344243 81344243 81344245 81344245 81344264 81344267 81344283]]
>>> print(alignment)
hg16.chr7  27578828 AAA-GGGAATGTTAACCAAATGA---ATTGTCTCTTACGGTG 27578866
panTro1.c  28741140 AAA-GGGAATGTTAACCAAATGA---ATTGTCTCTTACGGTG 28741178
baboon       116834 AAA-GGGAATGTTAACCAAATGA---GTTGTCTCTTATGGTG   116872
mm4.chr6   53215344 -AATGGGAATGTTAAGCAAACGA---ATTGTCTCTCAGTGTG 53215382
rn3.chr4   81344243 -AA-GGGGATGCTAAGCCAATGAGTTGTTGTCTCTCAATGTG 81344283

>>> print(format(alignment, "phylip"))
5 42
hg16.chr7 AAA-GGGAATGTTAACCAAATGA---ATTGTCTCTTACGGTG
panTro1.chAAA-GGGAATGTTAACCAAATGA---ATTGTCTCTTACGGTG
baboon    AAA-GGGAATGTTAACCAAATGA---GTTGTCTCTTATGGTG
mm4.chr6  -AATGGGAATGTTAAGCAAACGA---ATTGTCTCTCAGTGTG
rn3.chr4  -AA-GGGGATGCTAAGCCAATGAGTTGTTGTCTCTCAATGTG


In addition to the “a” (alignment block) and “s” (sequence) lines, MAF files may contain “i” lines with information about the genome sequence before and after this block, “e” lines with information about empty parts of the alignment, and “q” lines showing the quality of each aligned base. This is an example of an alignment block including such lines:

a score=19159.000000
s mm9.chr10                         3014644 45 + 129993255 CCTGTACC---CTTTGGTGAGAATTTTTGTTTCAGTGTTAAAAGTTTG
s hg18.chr6                        15870786 46 - 170899992 CCTATACCTTTCTTTTATGAGAA-TTTTGTTTTAATCCTAAAC-TTTT
i hg18.chr6                        I 9085 C 0
s panTro2.chr6                     16389355 46 - 173908612 CCTATACCTTTCTTTTATGAGAA-TTTTGTTTTAATCCTAAAC-TTTT
q panTro2.chr6                                             99999999999999999999999-9999999999999999999-9999
i panTro2.chr6                     I 9106 C 0
s calJac1.Contig6394                   6182 46 +    133105 CCTATACCTTTCTTTCATGAGAA-TTTTGTTTGAATCCTAAAC-TTTT
i calJac1.Contig6394               N 0 C 0
s loxAfr1.scaffold_75566               1167 34 -     10574 ------------TTTGGTTAGAA-TTATGCTTTAATTCAAAAC-TTCC
q loxAfr1.scaffold_75566                                   ------------99999699899-9999999999999869998-9997
i loxAfr1.scaffold_75566           N 0 C 0
e tupBel1.scaffold_114895.1-498454   167376 4145 -    498454 I
e echTel1.scaffold_288249             87661 7564 +    100002 I
e otoGar1.scaffold_334.1-359464      181217 2931 -    359464 I
e ponAbe2.chr6                     16161448 8044 - 174210431 I


This is the 10th alignment block in the file ucsc_mm9_chr10.maf (available in the Tests/MAF subdirectory of the Biopython distribution):

>>> from Bio import Align
>>> alignments = Align.parse("ucsc_mm9_chr10.maf", "maf")
>>> for i in range(10):
...     alignment = next(alignments)
...
>>> alignment.score
19159.0
>>> print(alignment)
mm9.chr10   3014644 CCTGTACC---CTTTGGTGAGAATTTTTGTTTCAGTGTTAAAAGTTTG   3014689
hg18.chr6 155029206 CCTATACCTTTCTTTTATGAGAA-TTTTGTTTTAATCCTAAAC-TTTT 155029160
panTro2.c 157519257 CCTATACCTTTCTTTTATGAGAA-TTTTGTTTTAATCCTAAAC-TTTT 157519211
calJac1.C      6182 CCTATACCTTTCTTTCATGAGAA-TTTTGTTTGAATCCTAAAC-TTTT      6228
loxAfr1.s      9407 ------------TTTGGTTAGAA-TTATGCTTTAATTCAAAAC-TTCC      9373


The “i” lines show the relationship between the sequence in the current alignment block to the ones in the preceding and subsequent alignment block. This information is stored in the annotations attribute of the corresponding sequence:

>>> alignment.sequences[0].annotations
{}
>>> alignment.sequences[1].annotations
{'leftStatus': 'I', 'leftCount': 9085, 'rightStatus': 'C', 'rightCount': 0}


showing that there are 9085 bases inserted (”I”) between this block and the preceding one, while the block is contiguous (”C”) with the subsequent one. See the UCSC documentation for the full description of these fields and status characters.

The “q” lines show the sequence quality, which is stored under the “quality” dictionary key of theannotations attribute of the corresponding sequence:

>>> alignment.sequences[2].annotations["quality"]
'9999999999999999999999999999999999999999999999'
>>> alignment.sequences[4].annotations["quality"]
'9999969989999999999999998699989997'


The “e” lines show information about species with a contiguous sequence before and after this alignment bloack, but with no aligning nucleotides in this alignment block. This is stored under the “empty” key of the alignment.annotations dictionary:

>>> alignment.annotations["empty"]
[(SeqRecord(seq=Seq(None, length=498454), id='tupBel1.scaffold_114895.1-498454', name='', description='', dbxrefs=[]), (331078, 326933), 'I'),
(SeqRecord(seq=Seq(None, length=100002), id='echTel1.scaffold_288249', name='', description='', dbxrefs=[]), (87661, 95225), 'I'),
(SeqRecord(seq=Seq(None, length=359464), id='otoGar1.scaffold_334.1-359464', name='', description='', dbxrefs=[]), (178247, 175316), 'I'),
(SeqRecord(seq=Seq(None, length=174210431), id='ponAbe2.chr6', name='', description='', dbxrefs=[]), (158048983, 158040939), 'I')]


This shows for example that there were non-aligning bases inserted (”I”) from position 158040939 to 158048983 on the opposite strand of the ponAbe2.chr6 genomic sequence. Again, see the UCSC documentation for the full definition of “e” lines.

To print an alignment in MAF format, you can use Python’s built-in format function:

>>> print(format(alignment, "MAF"))
a score=19159.000000
s mm9.chr10                         3014644   45 + 129993255 CCTGTACC---CTTTGGTGAGAATTTTTGTTTCAGTGTTAAAAGTTTG
s hg18.chr6                        15870786   46 - 170899992 CCTATACCTTTCTTTTATGAGAA-TTTTGTTTTAATCCTAAAC-TTTT
i hg18.chr6                        I 9085 C 0
s panTro2.chr6                     16389355   46 - 173908612 CCTATACCTTTCTTTTATGAGAA-TTTTGTTTTAATCCTAAAC-TTTT
q panTro2.chr6                                               99999999999999999999999-9999999999999999999-9999
i panTro2.chr6                     I 9106 C 0
s calJac1.Contig6394                   6182   46 +    133105 CCTATACCTTTCTTTCATGAGAA-TTTTGTTTGAATCCTAAAC-TTTT
i calJac1.Contig6394               N 0 C 0
s loxAfr1.scaffold_75566               1167   34 -     10574 ------------TTTGGTTAGAA-TTATGCTTTAATTCAAAAC-TTCC
q loxAfr1.scaffold_75566                                     ------------99999699899-9999999999999869998-9997
i loxAfr1.scaffold_75566           N 0 C 0
e tupBel1.scaffold_114895.1-498454   167376 4145 -    498454 I
e echTel1.scaffold_288249             87661 7564 +    100002 I
e otoGar1.scaffold_334.1-359464      181217 2931 -    359464 I
e ponAbe2.chr6                     16161448 8044 - 174210431 I



To write a complete MAF file, use Bio.Align.write(alignments, "myfilename.maf", fmt="maf"), where myfilename.maf is the name of the output MAF file. Alternatively, you can use a (text) stream for output. File header information will be taken from the metadata attribute of the alignments object. If you are creating the alignments from scratch, you can use the Alignments (plural) class to create a list-like alignments object (see Section The Alignments class) and give it a metadata attribute.

### bigMaf

A bigMaf file is a bigBed file with a BED3+1 format consisting of the 3 required BED fields plus a custom field that stores a MAF alignment block as a string, creating an indexed binary version of a MAF file (see section Multiple Alignment Format (MAF)). The associated AutoSql file bigMaf.as is provided by UCSC. To create a bigMaf file, you can either use the mafToBigMaf and bedToBigBed programs from UCSC. or you can use Biopython by calling the Bio.Align.write function with fmt="bigmaf". While the two methods should result in identical bigMaf files, the UCSC tools are much faster and may be more reliable, as it is the gold standard. As bigMaf files are bigBed files, they come with a built-in index, allowing you to quickly search a specific region of the reference genome.

The file ucsc_test.bb in the Tests/MAF subdirectory of the Biopython distribution is an example of a bigMaf file. This file is equivalent to the MAF file ucsc_test.maf (see section Multiple Alignment Format (MAF)). To parse this file, use

>>> from Bio import Align
>>> alignments = Align.parse("ucsc_test.bb", "bigmaf")
>>> len(alignments)
3
>>> print(alignments.declaration)
table bedMaf
"Bed3 with MAF block"
(
string  chrom;         "Reference sequence chromosome or scaffold"
uint    chromStart;    "Start position in chromosome"
uint    chromEnd;      "End position in chromosome"
lstring mafBlock;      "MAF block"
)


The declaration contains the specification of the columns as defined by the bigMaf.as AutoSql file from UCSC.

The bigMaf file does not store the header information found in the MAF file, but it does define a reference genome. The corresponding SeqRecord is stored in the targets attribute of the alignments object:

>>> alignments.reference
'hg16'
>>> alignments.targets
[SeqRecord(seq=Seq(None, length=158545518), id='hg16.chr7', ...)]


By iterating over the alignments we obtain one Alignment object for each alignment block in the bigMaf file:

>>> alignment = next(alignments)
>>> alignment.score
23262.0
>>> {seq.id: len(seq) for seq in alignment.sequences}
{'hg16.chr7': 158545518,
'panTro1.chr6': 161576975,
'baboon': 4622798,
'mm4.chr6': 151104725,
'rn3.chr4': 187371129}
>>> print(alignment.coordinates)
[[27578828 27578829 27578831 27578831 27578850 27578850 27578866]
[28741140 28741141 28741143 28741143 28741162 28741162 28741178]
[  116834   116835   116837   116837   116856   116856   116872]
[53215344 53215344 53215346 53215347 53215366 53215366 53215382]
[81344243 81344243 81344245 81344245 81344264 81344267 81344283]]
>>> print(alignment)
hg16.chr7  27578828 AAA-GGGAATGTTAACCAAATGA---ATTGTCTCTTACGGTG 27578866
panTro1.c  28741140 AAA-GGGAATGTTAACCAAATGA---ATTGTCTCTTACGGTG 28741178
baboon       116834 AAA-GGGAATGTTAACCAAATGA---GTTGTCTCTTATGGTG   116872
mm4.chr6   53215344 -AATGGGAATGTTAAGCAAACGA---ATTGTCTCTCAGTGTG 53215382
rn3.chr4   81344243 -AA-GGGGATGCTAAGCCAATGAGTTGTTGTCTCTCAATGTG 81344283

>>> print(format(alignment, "phylip"))
5 42
hg16.chr7 AAA-GGGAATGTTAACCAAATGA---ATTGTCTCTTACGGTG
panTro1.chAAA-GGGAATGTTAACCAAATGA---ATTGTCTCTTACGGTG
baboon    AAA-GGGAATGTTAACCAAATGA---GTTGTCTCTTATGGTG
mm4.chr6  -AATGGGAATGTTAAGCAAACGA---ATTGTCTCTCAGTGTG
rn3.chr4  -AA-GGGGATGCTAAGCCAATGAGTTGTTGTCTCTCAATGTG


Information in the “i”, “e”, and “q” lines is stored in the same way as in the corresponding MAF file (see section Multiple Alignment Format (MAF)):

>>> from Bio import Align
>>> alignments = Align.parse("ucsc_mm9_chr10.bb", "bigmaf")
>>> for i in range(10):
...     alignment = next(alignments)
...
>>> alignment.score
19159.0
>>> print(alignment)
mm9.chr10   3014644 CCTGTACC---CTTTGGTGAGAATTTTTGTTTCAGTGTTAAAAGTTTG   3014689
hg18.chr6 155029206 CCTATACCTTTCTTTTATGAGAA-TTTTGTTTTAATCCTAAAC-TTTT 155029160
panTro2.c 157519257 CCTATACCTTTCTTTTATGAGAA-TTTTGTTTTAATCCTAAAC-TTTT 157519211
calJac1.C      6182 CCTATACCTTTCTTTCATGAGAA-TTTTGTTTGAATCCTAAAC-TTTT      6228
loxAfr1.s      9407 ------------TTTGGTTAGAA-TTATGCTTTAATTCAAAAC-TTCC      9373

>>> print(format(alignment, "MAF"))
a score=19159.000000
s mm9.chr10                         3014644   45 + 129993255 CCTGTACC---CTTTGGTGAGAATTTTTGTTTCAGTGTTAAAAGTTTG
s hg18.chr6                        15870786   46 - 170899992 CCTATACCTTTCTTTTATGAGAA-TTTTGTTTTAATCCTAAAC-TTTT
i hg18.chr6                        I 9085 C 0
s panTro2.chr6                     16389355   46 - 173908612 CCTATACCTTTCTTTTATGAGAA-TTTTGTTTTAATCCTAAAC-TTTT
q panTro2.chr6                                               99999999999999999999999-9999999999999999999-9999
i panTro2.chr6                     I 9106 C 0
s calJac1.Contig6394                   6182   46 +    133105 CCTATACCTTTCTTTCATGAGAA-TTTTGTTTGAATCCTAAAC-TTTT
i calJac1.Contig6394               N 0 C 0
s loxAfr1.scaffold_75566               1167   34 -     10574 ------------TTTGGTTAGAA-TTATGCTTTAATTCAAAAC-TTCC
q loxAfr1.scaffold_75566                                     ------------99999699899-9999999999999869998-9997
i loxAfr1.scaffold_75566           N 0 C 0
e tupBel1.scaffold_114895.1-498454   167376 4145 -    498454 I
e echTel1.scaffold_288249             87661 7564 +    100002 I
e otoGar1.scaffold_334.1-359464      181217 2931 -    359464 I
e ponAbe2.chr6                     16161448 8044 - 174210431 I

>>> alignment.sequences[1].annotations
{'leftStatus': 'I', 'leftCount': 9085, 'rightStatus': 'C', 'rightCount': 0}
>>> alignment.sequences[2].annotations["quality"]
'9999999999999999999999999999999999999999999999'
>>> alignment.sequences[4].annotations["quality"]
'9999969989999999999999998699989997'
>>> alignment.annotations["empty"]
[(SeqRecord(seq=Seq(None, length=498454), id='tupBel1.scaffold_114895.1-498454', name='', description='', dbxrefs=[]), (331078, 326933), 'I'),
(SeqRecord(seq=Seq(None, length=100002), id='echTel1.scaffold_288249', name='', description='', dbxrefs=[]), (87661, 95225), 'I'),
(SeqRecord(seq=Seq(None, length=359464), id='otoGar1.scaffold_334.1-359464', name='', description='', dbxrefs=[]), (178247, 175316), 'I'),
(SeqRecord(seq=Seq(None, length=174210431), id='ponAbe2.chr6', name='', description='', dbxrefs=[]), (158048983, 158040939), 'I')]


To write a complete bigMaf file, use Bio.Align.write(alignments, "myfilename.bb", fmt="bigMaf"), where myfilename.bb is the name of the output bigMaf file. Alternatively, you can use a (binary) stream for output. If you are creating the alignments from scratch, you can use the Alignments (plural) class to create a list-like alignments object (see Section The Alignments class) and give it a targets attribute. The latter must be a list of SeqRecord objects for the chromosomes for the reference species in the order in which they appear in the alignments. Alternatively, you can use the targets keyword argument when calling Bio.Align.write. The id of each SeqRecord must be of the form reference.chromosome, where reference refers to the reference species. Bio.Align.write has the additional keyword argument compress (True by default) specifying whether the data should be compressed using zlib. Further optional arguments are blockSize (default value is 256), and itemsPerSlot (default value is 512). See the documentation of UCSC’s bedToBigBed program for a description of these arguments.

As a bigMaf file is a special case of a bigBed file, you can use the search method on the alignments object to find alignments to specific regions of the reference species. For example, we can look for regions on chr10 between positions 3018000 and 3019000 on chromosome 10:

>>> selected_alignments = alignments.search("mm9.chr10", 3018000, 3019000)
>>> for alignment in selected_alignments:
...     start, end = alignment.coordinates[0, 0], alignment.coordinates[0, -1]
...     print(start, end)
...
3017743 3018161
3018161 3018230
3018230 3018359
3018359 3018482
3018482 3018644
3018644 3018822
3018822 3018932
3018932 3019271


The chromosome name may be None to include all chromosomes, and the start and end positions may be None to start searching from position 0 or to continue searching until the end of the chromosome, respectively. Note that we can search on genomic position for the reference species only.

Searching a bigMaf file can be faster by using compress=False and itemsPerSlot=1 when creating the bigMaf file.

### UCSC chain file format

Chain files describe a pairwise alignment between two nucleotide sequences, allowing gaps in both sequences. Only the length of each aligned subsequences and the gap lengths are stored in a chain file; the sequences themselves are not stored. Chain files are typically used to store alignments between two genome assembly versions, allowing alignments to one genome assembly version to be lifted over to the other genome assembly. This is an example of a chain file (available as psl_34_001.chain in the Tests/Blat subdirectory of the Biopython distribution):

chain 16 chr4 191154276 + 61646095 61646111 hg18_dna 33 + 11 27 1
16
chain 33 chr1 249250621 + 10271783 10271816 hg18_dna 33 + 0 33 2
33
chain 17 chr2 243199373 + 53575980 53575997 hg18_dna 33 - 8 25 3
17
chain 35 chr9 141213431 + 85737865 85737906 hg19_dna 50 + 9 50 4
41
chain 41 chr8 146364022 + 95160479 95160520 hg19_dna 50 + 8 49 5
41
chain 30 chr22 51304566 + 42144400 42144436 hg19_dna 50 + 11 47 6
36
chain 41 chr2 243199373 + 183925984 183926028 hg19_dna 50 + 1 49 7
6       0       4
38
chain 31 chr19 59128983 + 35483340 35483510 hg19_dna 50 + 10 46 8
25      134     0
11
chain 39 chr18 78077248 + 23891310 23891349 hg19_dna 50 + 10 49 9
39
...


This file was generated by running UCSC’s pslToChain program on the PSL file psl_34_001.psl. According to the chain file format specification, there should be a blank line after each chain block, but some tools (including pslToChain) apparently do not follow this rule.

To parse this file, use

>>> from Bio import Align
>>> alignments = Align.parse("psl_34_001.chain", "chain")


Iterate over alignments to get one Alignment object for each chain:

>>> for alignment in alignments:
...     print(alignment.target.id, alignment.query.id)
...
chr4 hg18_dna
chr1 hg18_dna
chr2 hg18_dna
chr9 hg19_dna
chr8 hg19_dna
chr22 hg19_dna
chr2 hg19_dna
...
chr1 hg19_dna


Iterate from the start until we reach the seventh alignment:

>>> alignments = iter(alignments)
>>> for i in range(7):
...     alignment = next(alignments)
...


Check the alignment score and chain ID (the first and last number, respectively, in the header line of each chain block) to confirm that we got the seventh alignment:

>>> alignment.score
41.0
>>> alignment.annotations["id"]
'7'


We can print the alignment in the chain file format. The alignment coordinates are consistent with the information in the chain block, with an aligned section of 6 nucleotides, a gap of 4 nucleotides, and an aligned section of 38 nucleotides:

>>> print(format(alignment, "chain"))
chain 41 chr2 243199373 + 183925984 183926028 hg19_dna 50 + 1 49 7
6   0   4
38

>>> alignment.coordinates
array([[183925984, 183925990, 183925990, 183926028],
[        1,         7,        11,        49]])
>>> print(alignment)
chr2      183925984 ??????----?????????????????????????????????????? 183926028
0 ||||||----||||||||||||||||||||||||||||||||||||||        48
hg19_dna          1 ????????????????????????????????????????????????        49


We can also print the alignment in a few other alignment fite formats:

>>> print(format(alignment, "BED"))
chr2    183925984   183926028   hg19_dna    41  +   183925984   183926028   0   2   6,38,   0,6,

>>> print(format(alignment, "PSL"))
44  0   0   0   1   4   0   0   +   hg19_dna    50  1   49  chr2    243199373   183925984   183926028   2   6,38,   1,11,   183925984,183925990,

>>> print(format(alignment, "exonerate"))
vulgar: hg19_dna 1 49 + chr2 183925984 183926028 + 41 M 6 6 G 4 0 M 38 38

>>> print(alignment.format("exonerate", "cigar"))
cigar: hg19_dna 1 49 + chr2 183925984 183926028 + 41 M 6 I 4 M 38

>>> print(format(alignment, "sam"))
hg19_dna    0   chr2    183925985   255 1S6M4I38M1S *   0   0   *   *   AS:i:41 id:A:7