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], andSeqC[0:2]are aligned to each other;SeqA[3:4]andSeqC[2:3]are aligned to each other, with a gap of one nucleotide inseqB;SeqA[4:7],SeqB[2:5], andSeqC[3:6]are aligned to each other;SeqA[7:9]is not aligned toseqBorseqC.
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
alphabetattribute of thePairwiseAlignerobject that created the alignment (see section Generalized pairwise alignments).
For pairwise alignments (meaning an alignment of two sequences), the properties
targetandqueryare aliases forsequences[0]andsequences[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 thePairwiseAligner(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.
Adding alignments
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 = Align.read("Blat/panTro5ToPanTro6.over.chain", "chain")
>>> chain.sequences[0].id
'chr1'
>>> len(chain.sequences[0].seq)
228573443
>>> chain.sequences[1].id
'chr1'
>>> len(chain.sequences[1].seq)
224244399
>>> 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"
>>> genome_alignment = Align.read(path, "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
Alignmentsobject is its own iterator, consistent with iterators returned byBio.Align.parse(see section Reading alignments) or iterators returned by the pairwise aligner (see Section Pairwise sequence alignment). Callingiteron the iterator will always return theAlignmentsobject itself. In contrast, callingiteron a list object creates a new iterator each time, allowing you to have multiple independent iterators for a given list.In this example,
alignment_iterator1andalignment_iterator2are 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_iterator1andalignment_iterator2obtained by callingiteron anAlignmentsobject 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
iteron anAlignmentsobject resets the iterator to its first item, so you can loop over it again. You can also iterate over the alignments multiple times using afor-loop, which implicitly callsiteron 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
Alignmentsobject, whereas a plainlistdoes 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
Reading and writing alignments
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.
Reading alignments
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'
Instead, you can ask for a full slice of the alignments:
>>> 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
>>> alignments.metadata
{'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"}
>>> alignments.metadata = metadata
>>> 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
|
Description |
text / binary |
Supported
by
|
Subsection |
|
A2M |
text |
yes |
|
|
Browser Extensible Data (BED) |
text |
yes |
|
|
bigBed |
binary |
yes |
|
|
bigMaf |
binary |
yes |
|
|
bigPsl |
binary |
yes |
|
|
UCSC chain file |
text |
yes |
|
|
ClustalW |
text |
yes |
|
|
EMBOSS |
text |
no |
|
`` exonerate`` |
Exonerate |
text |
yes |
|
|
Aligned FASTA |
text |
yes |
|
|
HH-suite output files |
text |
no |
|
|
Multiple Alignment Format (MAF) |
text |
yes |
|
|
Mauve eXtended Multi-FastA (xmfa) format |
text |
yes |
|
|
GCG Multiple Sequence Format (MSF) |
text |
no |
|
|
NEXUS |
text |
yes |
|
|
PHYLIP output files |
text |
yes |
|
|
Pattern Space Layout (PSL) |
text |
yes |
|
|
Sequence Al ignment/Map (SAM) |
text |
yes |
|
`` stockholm`` |
Stockholm |
text |
yes |
|
|
Tabular output from BLAST or FASTA |
text |
no |
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
--VKLGADSGALEFVPKTLTIKSGETVNFVNNAGFPHNIVFDEDAIPSG-VN-ADAISRDDYLNAPGETYSVKLTA---AGEYGYYCEPHQGAGMVGKIIV
>plas_anava
--VKLGSDKGLLVFEPAKLTIKPGDTVEFLNNKVPPHNVVFDAALNPAKSADLAKSLSHKQLLMSPGQSTSTTFPADAPAGEYTFYCEPHRGAGMVGKITV
>plas_proho
VQIKMGTDKYAPLYEPKALSISAGDTVEFVMNKVGPHNVIFDK--VPAG-ES-APALSNTKLRIAPGSFYSVTLGT---PGTYSFYCTPHRGAGMVGTITV
>azup_achcy
VHMLNKGKDGAMVFEPASLKVAPGDTVTFIPTDK-GHNVETIKGMIPDG-AE-A-------FKSKINENYKVTFTA---PGVYGVKCTPHYGMGMVGVVEV
To read this file, use
>>> from Bio import Align
>>> alignment = Align.read("probcons.fa", "fasta")
>>> 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_chlr 0 --VKLGADSGALEFVPKTLTIKSGETVNFVNNAGFPHNIVFDEDAIPSG-VN-ADAISRD
plas_anav 0 --VKLGSDKGLLVFEPAKLTIKPGDTVEFLNNKVPPHNVVFDAALNPAKSADLAKSLSHK
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_anav 58 QLLMSPGQSTSTTFPADAPAGEYTFYCEPHRGAGMVGKITV 99
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
--VKLGADSGALEFVPKTLTIKSGETVNFVNNAGFPHNIVFDEDAIPSG-VN-ADAISRDDYLNAPGETYSVKLTA---AGEYGYYCEPHQGAGMVGKIIV
>plas_anava
--VKLGSDKGLLVFEPAKLTIKPGDTVEFLNNKVPPHNVVFDAALNPAKSADLAKSLSHKQLLMSPGQSTSTTFPADAPAGEYTFYCEPHRGAGMVGKITV
>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_chlre --VKLGADSGALEFVPKTLTIKSGETVNFVNNAGFPHNIVFDEDAIPSG-
plas_anava --VKLGSDKGLLVFEPAKLTIKPGDTVEFLNNKVPPHNVVFDAALNPAKS
plas_proho VQIKMGTDKYAPLYEPKALSISAGDTVEFVMNKVGPHNVIFDK--VPAG-
azup_achcy VHMLNKGKDGAMVFEPASLKVAPGDTVTFIPTDK-GHNVETIKGMIPDG-
plas_horvu VD-VSKISQEEYLTAPGETFSVTLTV---PGTYGFYCEPHAGAGMVGKVT
plas_chlre VN-ADAISRDDYLNAPGETYSVKLTA---AGEYGYYCEPHQGAGMVGKII
plas_anava ADLAKSLSHKQLLMSPGQSTSTTFPADAPAGEYTFYCEPHRGAGMVGKIT
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
--VKLGADSGALEFVPKTLTIKSGETVNFVNNAGFPHNIVFDEDAIPSG-VN-ADAISRDDYLNAPGETYSVKLTA---AGEYGYYCEPHQGAGMVGKIIV
>plas_anava
--VKLGSDKGLLVFEPAKLTIKPGDTVEFLNNKVPPHNVVFDAALNPAKSADLAKSLSHKQLLMSPGQSTSTTFPADAPAGEYTFYCEPHRGAGMVGKITV
>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
>>> alignment = Align.read("opuntia.aln", "clustal")
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 = Align.read("pfam2.seed.txt", "stockholm")
>>> 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
FLEFQDKFNK KY-SHEEYLE RFEIFKSNLG KIEELNLIAI NHKADTKFGV NKFADLSSDE
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
FLEFQDKFNK KY-SHEEYLE RFEIFKSNLG KIEELNLIAI NHKADTKFGV NKFADLSSDE
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 = Align.read("sequential.phy", "phylip")
>>> alignment
<Alignment object (3 rows x 384 columns) at ...>
>>> alignment2 = Align.read("interlaced.phy", "phylip")
>>> 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
ALEU_HORV 0 MAHARVLLLALAVLATAAVAVASSSSFADSNPIRPVTDRAASTLESAVLGALGRTRHALR
CATH_HUMA 0 ------MWATLPLLCAGAWLLGV--------PVCGAAELSVNSLEK------------FH
CYS1_DICD 28 FLEFQDKFNKKY-SHEEYLERFEIFKSNLGKIEELNLIAINHKADTKFGVNKFADLSSDE
ALEU_HORV 60 FARFAVRYGKSYESAAEVRRRFRIFSESLEEVRSTN----RKGLPYRLGINRFSDMSWEE
CATH_HUMA 34 FKSWMSKHRKTY-STEEYHHRLQTFASNWRKINAHN----NGNHTFKMALNQFSDMSFAE
CYS1_DICD 87 FKNYYLNNKEAIFTDDLPVADYLDDEFINSIPTAFDWRTRG-AVTPVKNQGQCGSCWSFS
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
CYS1_DICD 206 GIQTESSYPYTAETGTQCNFNSANIGAKISNFTMIP-KNETVMAGYIVSTGPLAIAADAV
ALEU_HORV 224 GIDTEESYPYKGVNGV-CHYKAENAAVQVLDSVNITLNAEDELKNAVGLVRPVSVAFQVI
CATH_HUMA 197 GIMGEDTYPYQGKDGY-CKFQPGKAIGFVKDVANITIYDEEAMVEAVALYNPVSFAFEVT
CYS1_DICD 265 E-WQFYIGGVF-DIPCN--PNSLDHGILIVGYSAKNTIFRKNMPYWIVKNSWGADWGEQG
ALEU_HORV 283 DGFRQYKSGVYTSDHCGTTPDDVNHAVLAVGYGVENGV-----PYWLIKNSWGADWGDNG
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
CYS1_DICDI-----MKVILLFVLAVFTVFVSS---------------RGIPPEEQ------------SQFLEFQDKFNKKY-SHEEYLERFEIFKSNLGKIEELNLIAINHKADTKFGVNKFADLSSDEFKNYYLNNKEAIFTDDLPVADYLDDEFINSIPTAFDWRTRG-AVTPVKNQGQCGSCWSFSTTGNVEGQHFISQNKLVSLSEQNLVDCDHECMEYEGEEACDEGCNGGLQPNAYNYIIKNGGIQTESSYPYTAETGTQCNFNSANIGAKISNFTMIP-KNETVMAGYIVSTGPLAIAADAVE-WQFYIGGVF-DIPCN--PNSLDHGILIVGYSAKNTIFRKNMPYWIVKNSWGADWGEQGYIYLRRGKNTCGVSNFVSTSII--
ALEU_HORVUMAHARVLLLALAVLATAAVAVASSSSFADSNPIRPVTDRAASTLESAVLGALGRTRHALRFARFAVRYGKSYESAAEVRRRFRIFSESLEEVRSTN----RKGLPYRLGINRFSDMSWEEFQATRL-GAAQTCSATLAGNHLMRDA--AALPETKDWREDG-IVSPVKNQAHCGSCWTFSTTGALEAAYTQATGKNISLSEQQLVDCAGGFNNF--------GCNGGLPSQAFEYIKYNGGIDTEESYPYKGVNGV-CHYKAENAAVQVLDSVNITLNAEDELKNAVGLVRPVSVAFQVIDGFRQYKSGVYTSDHCGTTPDDVNHAVLAVGYGVENGV-----PYWLIKNSWGADWGDNGYFKMEMGKNMCAIATCASYPVVAA
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
>>> alignment3 = Align.read(stream, "phylip")
>>> 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
>>> alignment = Align.read("W_prot.msf", "msf")
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 0 GLTPFNGYTAATWTRTAVSSVGMNIPYHGASYLVRNQELRSWTAADKAAQMPWRRNRQSC
W*01:01:0 0 GLTPFNGYTAATWTRTAVSSVGMNIPYHGASYLVRNQELRSWTAADKAAQMPWRRNRQSC
W*01:01:0 0 GLTPFNGYTAATWTRTAVSSVGMNIPYHGASYLVRNQELRSWTAADKAAQMPWRRNRQSC
W*01:01:0 0 GLTPFNGYTAATWTRTAVSSVGMNIPYHGASYLVRNQELRSWTAADKAAQMPWRRNRQSC
W*01:01:0 0 GLTPFNGYTAATWTRTAVSSVGMNIPYHGASYLVRNQELRSWTAADKAAQMPWRRNRQSC
W*01:01:0 0 GLTPFNGYTAATWTRTAVSSVGMNIPYHGASYLVRNQELRSWTAADKAAQMPWRRNRQSC
W*02:01 0 GLTPSNGYTAATWTRTAASSVGMNIPYDGASYLVRNQELRSWTAADKAAQMPWRRNMQSC
W*03:01:0 0 GLTPSSGYTAATWTRTAVSSVGMNIPYHGASYLVRNQELRSWTAADKAAQMPWRRNRQSC
W*03:01:0 0 GLTPSSGYTAATWTRTAVSSVGMNIPYHGASYLVRNQELRSWTAADKAAQMPWRRNRQSC
W*04:01 0 GLTPSNGYTAATWTRTAASSVGMNIPYDGASYLVRNQELRSWTAADKAAQMPWRRNMQSC
W*05:01 0 GLTPSSGYTAATWTRTAVSSVGMNIPYHGASYLVRNQELRSWTAADKAAQMPWRRNRQSC
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
Seq('GLTPFNGYTAATWTRTAVSSVGMNIPYHGASYLVRNQELRSWTAADKAAQMPWR...PHL')
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',
'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'}
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
>>> alignment = Align.read("codonposset.nex", "nexus")
>>> 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 header with general information about the alignments;
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
2uvo_A 60 CSQYGYCGFGAEYCGAGCQGGPCRADIKCGSQAGGKLCPNNLCCSQWGFCGLGSEFCGGG
60 ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
2UVO:A|PD 60 CSQYGYCGFGAEYCGAGCQGGPCRADIKCGSQAGGKLCPNNLCCSQWGFCGLGSEFCGGG
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
-.VKLGADSGALEFVPKTLTIKSGETVNFVNNAGFPHNIVFDEDAIPSG.VN.ADAISRDDYLNAPGETYSVKLTA...AGEYGYYCEPHQGAGMVGKII
V
>plas_anava
-.VKLGSDKGLLVFEPAKLTIKPGDTVEFLNNKVPPHNVVFDAALNPAKsADlAKSLSHKQLLMSPGQSTSTTFPAdapAGEYTFYCEPHRGAGMVGKIT
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 = Align.read("probcons.a2m", "a2m")
>>> alignment
<Alignment object (5 rows x 101 columns) at ...>
>>> print(alignment)
plas_horv 0 D-VLLGANGGVLVFEPNDFSVKAGETITFKNNAGYPHNVVFDEDAVPSG-VD-VSKISQE
plas_chlr 0 --VKLGADSGALEFVPKTLTIKSGETVNFVNNAGFPHNIVFDEDAIPSG-VN-ADAISRD
plas_anav 0 --VKLGSDKGLLVFEPAKLTIKPGDTVEFLNNKVPPHNVVFDAALNPAKSADLAKSLSHK
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_anav 58 QLLMSPGQSTSTTFPADAPAGEYTFYCEPHRGAGMVGKITV 99
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
-.VKLGADSGALEFVPKTLTIKSGETVNFVNNAGFPHNIVFDEDAIPSG.VN.ADAISRDDYLNAPGETYSVKLTA...AGEYGYYCEPHQGAGMVGKIIV
>plas_anava
-.VKLGSDKGLLVFEPAKLTIKPGDTVEFLNNKVPPHNVVFDAALNPAKsADlAKSLSHKQLLMSPGQSTSTTFPAdapAGEYTFYCEPHRGAGMVGKITV
>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")
>>> metadata = alignments.metadata
>>> identifiers = alignments.identifiers
>>> alignments = list(alignments) # this drops the attributes
>>> alignments.metadata
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'
>>> Align.write(alignments, stream, "mauve", metadata=metadata, identifiers=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.metadata = metadata
>>> alignments.identifiers = identifiers
>>> stream = StringIO()
>>> Align.write(alignments, stream, "mauve", metadata=metadata, identifiers=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
=
When printing a single alignment in Mauve format, use keyword
arguments to provide the metadata and identifiers:
>>> alignment = alignments[0]
>>> print(alignment.format("mauve", metadata=metadata, identifiers=identifiers))
> 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
>>> alignments = Align.parse("ex1_header.sam", "sam")
>>> 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)
>>> queryA = SeqRecord(seqA, id="readA")
>>> sequences = [target1, queryA]
>>> coordinates = np.array([[4300, 4320], [0, 20]])
>>> alignment = Align.Alignment(sequences, coordinates)
>>> alignments.append(alignment)
>>> seqB = Seq(None, length=25)
>>> queryB = SeqRecord(seqB, id="readB")
>>> sequences = [target1, queryB]
>>> coordinates = np.array([[5900, 5925], [25, 0]])
>>> alignment = Align.Alignment(sequences, coordinates)
>>> alignments.append(alignment)
>>> seqC = Seq(None, length=40)
>>> queryC = SeqRecord(seqC, id="readC")
>>> 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:
... autosql_data = stream.read()
...
>>> 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")
>>> alignments.metadata
{'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,
>>> line = alignment.format("psl", mask="lower")
>>> 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: IfTrue(default), compress data using zlib; ifFalse, do not compress data.extraIndex: List of strings with the names of extra columns to be indexed.cds: IfTrue, look for a query feature of type CDS and write it in NCBI style in the PSL file (default:False).fa: IfTrue, 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 therepMatchesfield instead of in thematchesfield. Acceptable values areNone: 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 thenCountfield instead of in thematches,misMatches, orrepMatchesfields. 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