Source of the materials: Biopython cookbook (adapted) Status: Draft
This chapter is about Multiple Sequence Alignments, by which we mean a
collection of multiple sequences which have been aligned together –
usually with the insertion of gap characters, and addition of leading or
trailing gaps – such that all the sequence strings are the same length.
Such an alignment can be regarded as a matrix of letters, where each row
is held as a SeqRecord object internally.
We will introduce the MultipleSeqAlignment object which holds this
kind of data, and the Bio.AlignIO module for reading and writing them
as various file formats (following the design of the Bio.SeqIO module
from the previous chapter). Note that both Bio.SeqIO and Bio.AlignIO
can read and write sequence alignment files. The appropriate choice will
depend largely on what you want to do with the data.
The final part of this chapter is about our command line wrappers for common multiple sequence alignment tools like ClustalW and MUSCLE.
We have two functions for reading in sequence alignments,
Bio.AlignIO.read() and Bio.AlignIO.parse() which following the
convention introduced in Bio.SeqIO are for files containing one or
multiple alignments respectively.
Using Bio.AlignIO.parse() will return an *iterator* which
gives MultipleSeqAlignment objects. Iterators are typically used in a
for loop. Examples of situations where you will have multiple different
alignments include resampled alignments from the PHYLIP tool seqboot,
or multiple pairwise alignments from the EMBOSS tools water or
needle, or Bill Pearson’s FASTA tools.
However, in many situations you will be dealing with files which contain
only a single alignment. In this case, you should use the
Bio.AlignIO.read() function which returns a single
MultipleSeqAlignment object.
Both functions expect two mandatory arguments:
The first argument is a *handle* to read the data from, typically an open file (see Section [sec:appendix-handles]), or a filename.
The second argument is a lower case string specifying the
alignment format. As in Bio.SeqIO we don’t try and guess the file
format for you! See http://biopython.org/wiki/AlignIO for a full
listing of supported formats.
There is also an optional seq_count argument which is discussed in
Section [sec:AlignIO-count-argument] below for dealing with ambiguous
file formats which may contain more than one alignment.
A further optional alphabet argument allowing you to specify the
expected alphabet. This can be useful as many alignment file formats do
not explicitly label the sequences as RNA, DNA or protein – which means
Bio.AlignIO will default to using a generic alphabet.
As an example, consider the following annotation rich protein alignment in the PFAM or Stockholm file format:
# STOCKHOLM 1.0
#=GS COATB_BPIKE/30-81 AC P03620.1
#=GS COATB_BPIKE/30-81 DR PDB; 1ifl ; 1-52;
#=GS Q9T0Q8_BPIKE/1-52 AC Q9T0Q8.1
#=GS COATB_BPI22/32-83 AC P15416.1
#=GS COATB_BPM13/24-72 AC P69541.1
#=GS COATB_BPM13/24-72 DR PDB; 2cpb ; 1-49;
#=GS COATB_BPM13/24-72 DR PDB; 2cps ; 1-49;
#=GS COATB_BPZJ2/1-49 AC P03618.1
#=GS Q9T0Q9_BPFD/1-49 AC Q9T0Q9.1
#=GS Q9T0Q9_BPFD/1-49 DR PDB; 1nh4 A; 1-49;
#=GS COATB_BPIF1/22-73 AC P03619.2
#=GS COATB_BPIF1/22-73 DR PDB; 1ifk ; 1-50;
COATB_BPIKE/30-81 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA
#=GR COATB_BPIKE/30-81 SS -HHHHHHHHHHHHHH--HHHHHHHH--HHHHHHHHHHHHHHHHHHHHH----
Q9T0Q8_BPIKE/1-52 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA
COATB_BPI22/32-83 DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA
COATB_BPM13/24-72 AEGDDP...AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
#=GR COATB_BPM13/24-72 SS ---S-T...CHCHHHHCCCCTCCCTTCHHHHHHHHHHHHHHHHHHHHCTT--
COATB_BPZJ2/1-49 AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA
Q9T0Q9_BPFD/1-49 AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
#=GR Q9T0Q9_BPFD/1-49 SS ------...-HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH--
COATB_BPIF1/22-73 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA
#=GR COATB_BPIF1/22-73 SS XX-HHHH--HHHHHH--HHHHHHH--HHHHHHHHHHHHHHHHHHHHHHH---
#=GC SS_cons XHHHHHHHHHHHHHHHCHHHHHHHHCHHHHHHHHHHHHHHHHHHHHHHHC--
#=GC seq_cons AEssss...AptAhDSLpspAT-hIu.sWshVsslVsAsluIKLFKKFsSKA
//This is the seed alignment for the Phage_Coat_Gp8 (PF05371) PFAM entry, downloaded from a now out of date release of PFAM from http://pfam.sanger.ac.uk/. We can load this file as follows (assuming it has been saved to disk as “PF05371_seed.sth” in the current working directory):
In [1]:
from Bio import AlignIO
alignment = AlignIO.read("data/PF05371_seed.sth", "stockholm")
This code will print out a summary of the alignment:
In [2]:
print(alignment)
You’ll notice in the above output the sequences have been truncated. We
could instead write our own code to format this as we please by
iterating over the rows as SeqRecord objects:
In [3]:
from Bio import AlignIO
alignment = AlignIO.read("data/PF05371_seed.sth", "stockholm")
print("Alignment length %i" % alignment.get_alignment_length())
In [4]:
for record in alignment:
print("%s - %s" % (record.seq, record.id))
You could also use the alignment object’s format method to show it in
a particular file format – see Section [sec:alignment-format-method]
for details.
Did you notice in the raw file above that several of the sequences include database cross-references to the PDB and the associated known secondary structure? Try this:
In [5]:
for record in alignment:
if record.dbxrefs:
print("%s %s" % (record.id, record.dbxrefs))
To have a look at all the sequence annotation, try this:
In [6]:
for record in alignment:
print(record)
Sanger provide a nice web interface at http://pfam.sanger.ac.uk/family?acc=PF05371 which will actually let you download this alignment in several other formats. This is what the file looks like in the FASTA file format:
>COATB_BPIKE/30-81
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA
>Q9T0Q8_BPIKE/1-52
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA
>COATB_BPI22/32-83
DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA
>COATB_BPM13/24-72
AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
>COATB_BPZJ2/1-49
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA
>Q9T0Q9_BPFD/1-49
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
>COATB_BPIF1/22-73
FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRANote the website should have an option about showing gaps as periods (dots) or dashes, we’ve shown dashes above. Assuming you download and save this as file “PF05371_seed.faa” then you can load it with almost exactly the same code:
from Bio import AlignIO
alignment = AlignIO.read("PF05371_seed.faa", "fasta")
print(alignment)All that has changed in this code is the filename and the format string.
You’ll get the same output as before, the sequences and record
identifiers are the same. However, as you should expect, if you check
each SeqRecord there is no annotation nor database cross-references
because these are not included in the FASTA file format.
Note that rather than using the Sanger website, you could have used
Bio.AlignIO to convert the original Stockholm format file into a FASTA
file yourself (see below).
With any supported file format, you can load an alignment in exactly the same way just by changing the format string. For example, use “phylip” for PHYLIP files, “nexus” for NEXUS files or “emboss” for the alignments output by the EMBOSS tools. There is a full listing on the wiki page (http://biopython.org/wiki/AlignIO) and in the built in documentation (also online):
In [7]:
from Bio import AlignIO
help(AlignIO)
5 6
Alpha AACAAC
Beta AACCCC
Gamma ACCAAC
Delta CCACCA
Epsilon CCAAACIf you wanted to bootstrap a phylogenetic tree using the PHYLIP tools,
one of the steps would be to create a set of many resampled alignments
using the tool bootseq. This would give output something like this,
which has been abbreviated for conciseness:
5 6
Alpha AAACCA
Beta AAACCC
Gamma ACCCCA
Delta CCCAAC
Epsilon CCCAAA
5 6
Alpha AAACAA
Beta AAACCC
Gamma ACCCAA
Delta CCCACC
Epsilon CCCAAA
5 6
Alpha AAAAAC
Beta AAACCC
Gamma AACAAC
Delta CCCCCA
Epsilon CCCAAC
...
5 6
Alpha AAAACC
Beta ACCCCC
Gamma AAAACC
Delta CCCCAA
Epsilon CAAACCIf you wanted to read this in using Bio.AlignIO you could use:
In [8]:
from Bio import AlignIO
alignments = AlignIO.parse("data/resampled.phy", "phylip")
for alignment in alignments:
print(alignment)
print("")
As with the function Bio.SeqIO.parse(), using Bio.AlignIO.parse()
returns an iterator. If you want to keep all the alignments in memory at
once, which will allow you to access them in any order, then turn the
iterator into a list:
In [10]:
from Bio import AlignIO
alignments = list(AlignIO.parse("data/resampled.phy", "phylip"))
last_align = alignments[-1]
first_align = alignments[0]
Many alignment file formats can explicitly store more than one alignment, and the division between each alignment is clear. However, when a general sequence file format has been used there is no such block structure. The most common such situation is when alignments have been saved in the FASTA file format. For example consider the following:
>Alpha
ACTACGACTAGCTCAG--G
>Beta
ACTACCGCTAGCTCAGAAG
>Gamma
ACTACGGCTAGCACAGAAG
>Alpha
ACTACGACTAGCTCAGG--
>Beta
ACTACCGCTAGCTCAGAAG
>Gamma
ACTACGGCTAGCACAGAAGThis could be a single alignment containing six sequences (with repeated identifiers). Or, judging from the identifiers, this is probably two different alignments each with three sequences, which happen to all have the same length.
What about this next example?
>Alpha
ACTACGACTAGCTCAG--G
>Beta
ACTACCGCTAGCTCAGAAG
>Alpha
ACTACGACTAGCTCAGG--
>Gamma
ACTACGGCTAGCACAGAAG
>Alpha
ACTACGACTAGCTCAGG--
>Delta
ACTACGGCTAGCACAGAAGAgain, this could be a single alignment with six sequences. However this time based on the identifiers we might guess this is three pairwise alignments which by chance have all got the same lengths.
This final example is similar:
>Alpha
ACTACGACTAGCTCAG--G
>XXX
ACTACCGCTAGCTCAGAAG
>Alpha
ACTACGACTAGCTCAGG
>YYY
ACTACGGCAAGCACAGG
>Alpha
--ACTACGAC--TAGCTCAGG
>ZZZ
GGACTACGACAATAGCTCAGGIn this third example, because of the differing lengths, this cannot be treated as a single alignment containing all six records. However, it could be three pairwise alignments.
Clearly trying to store more than one alignment in a FASTA file is not
ideal. However, if you are forced to deal with these as input files
Bio.AlignIO can cope with the most common situation where all the
alignments have the same number of records. One example of this is a
collection of pairwise alignments, which can be produced by the EMBOSS
tools needle and water – although in this situation, Bio.AlignIO
should be able to understand their native output using “emboss” as the
format string.
To interpret these FASTA examples as several separate alignments, we can
use Bio.AlignIO.parse() with the optional seq_count argument which
specifies how many sequences are expected in each alignment (in these
examples, 3, 2 and 2 respectively). For example, using the third example
as the input data:
In [11]:
for alignment in AlignIO.parse(handle, "fasta", seq_count=2):
print("Alignment length %i" % alignment.get_alignment_length())
for record in alignment:
print("%s - %s" % (record.seq, record.id))
print("")
Using Bio.AlignIO.read() or Bio.AlignIO.parse() without the
seq_count argument would give a single alignment containing all six
records for the first two examples. For the third example, an exception
would be raised because the lengths differ preventing them being turned
into a single alignment.
If the file format itself has a block structure allowing Bio.AlignIO
to determine the number of sequences in each alignment directly, then
the seq_count argument is not needed. If it is supplied, and doesn’t
agree with the file contents, an error is raised.
Note that this optional seq_count argument assumes each alignment in
the file has the same number of sequences. Hypothetically you may come
across stranger situations, for example a FASTA file containing several
alignments each with a different number of sequences – although I would
love to hear of a real world example of this. Assuming you cannot get
the data in a nicer file format, there is no straight forward way to
deal with this using Bio.AlignIO. In this case, you could consider
reading in the sequences themselves using Bio.SeqIO and batching them
together to create the alignments as appropriate.
We’ve talked about using Bio.AlignIO.read() and Bio.AlignIO.parse()
for alignment input (reading files), and now we’ll look at
Bio.AlignIO.write() which is for alignment output (writing files).
This is a function taking three arguments: some MultipleSeqAlignment
objects (or for backwards compatibility the obsolete Alignment
objects), a handle or filename to write to, and a sequence format.
Here is an example, where we start by creating a few
MultipleSeqAlignment objects the hard way (by hand, rather than by
loading them from a file). Note we create some SeqRecord objects to
construct the alignment from.
In [9]:
from Bio.Alphabet import generic_dna
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Align import MultipleSeqAlignment
align1 = MultipleSeqAlignment([
SeqRecord(Seq("ACTGCTAGCTAG", generic_dna), id="Alpha"),
SeqRecord(Seq("ACT-CTAGCTAG", generic_dna), id="Beta"),
SeqRecord(Seq("ACTGCTAGDTAG", generic_dna), id="Gamma"),
])
align2 = MultipleSeqAlignment([
SeqRecord(Seq("GTCAGC-AG", generic_dna), id="Delta"),
SeqRecord(Seq("GACAGCTAG", generic_dna), id="Epsilon"),
SeqRecord(Seq("GTCAGCTAG", generic_dna), id="Zeta"),
])
align3 = MultipleSeqAlignment([
SeqRecord(Seq("ACTAGTACAGCTG", generic_dna), id="Eta"),
SeqRecord(Seq("ACTAGTACAGCT-", generic_dna), id="Theta"),
SeqRecord(Seq("-CTACTACAGGTG", generic_dna), id="Iota"),
])
my_alignments = [align1, align2, align3]
Now we have a list of Alignment objects, we’ll write them to a PHYLIP
format file:
In [10]:
from Bio import AlignIO
AlignIO.write(my_alignments, "my_example.phy", "phylip")
Out[10]:
And if you open this file in your favourite text editor it should look like this:
3 12
Alpha ACTGCTAGCT AG
Beta ACT-CTAGCT AG
Gamma ACTGCTAGDT AG
3 9
Delta GTCAGC-AG
Epislon GACAGCTAG
Zeta GTCAGCTAG
3 13
Eta ACTAGTACAG CTG
Theta ACTAGTACAG CT-
Iota -CTACTACAG GTGIts more common to want to load an existing alignment, and save that, perhaps after some simple manipulation like removing certain rows or columns.
Suppose you wanted to know how many alignments the Bio.AlignIO.write()
function wrote to the handle? If your alignments were in a list like the
example above, you could just use len(my_alignments), however you
can’t do that when your records come from a generator/iterator.
Therefore the Bio.AlignIO.write() function returns the number of
alignments written to the file.
Note - If you tell the Bio.AlignIO.write() function to write to a
file that already exists, the old file will be overwritten without any
warning.
Converting between sequence alignment file formats with Bio.AlignIO
works in the same way as converting between sequence file formats with
Bio.SeqIO (Section [sec:SeqIO-conversion]). We load generally the
alignment(s) using Bio.AlignIO.parse() and then save them using the
Bio.AlignIO.write() – or just use the Bio.AlignIO.convert() helper
function.
For this example, we’ll load the PFAM/Stockholm format file used earlier and save it as a Clustal W format file:
In [11]:
from Bio import AlignIO
count = AlignIO.convert("data/PF05371_seed.sth", "stockholm", "PF05371_seed.aln", "clustal")
print("Converted %i alignments" % count)
Or, using Bio.AlignIO.parse() and Bio.AlignIO.write():
In [12]:
from Bio import AlignIO
alignments = AlignIO.parse("data/PF05371_seed.sth", "stockholm")
count = AlignIO.write(alignments, "PF05371_seed.aln", "clustal")
print("Converted %i alignments" % count)
The Bio.AlignIO.write() function expects to be given multiple
alignment objects. In the example above we gave it the alignment
iterator returned by Bio.AlignIO.parse().
In this case, we know there is only one alignment in the file so we
could have used Bio.AlignIO.read() instead, but notice we have to pass
this alignment to Bio.AlignIO.write() as a single element list:
In [13]:
from Bio import AlignIO
alignment = AlignIO.read("data/PF05371_seed.sth", "stockholm")
AlignIO.write([alignment], "PF05371_seed.aln", "clustal")
Out[13]:
Either way, you should end up with the same new Clustal W format file “PF05371_seed.aln” with the following content:
CLUSTAL X (1.81) multiple sequence alignment
COATB_BPIKE/30-81 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSS
Q9T0Q8_BPIKE/1-52 AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVS
COATB_BPI22/32-83 DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSS
COATB_BPM13/24-72 AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS
COATB_BPZJ2/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFAS
Q9T0Q9_BPFD/1-49 AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS
COATB_BPIF1/22-73 FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVS
COATB_BPIKE/30-81 KA
Q9T0Q8_BPIKE/1-52 RA
COATB_BPI22/32-83 KA
COATB_BPM13/24-72 KA
COATB_BPZJ2/1-49 KA
Q9T0Q9_BPFD/1-49 KA
COATB_BPIF1/22-73 RAAlternatively, you could make a PHYLIP format file which we’ll name “PF05371_seed.phy”:
In [14]:
from Bio import AlignIO
AlignIO.convert("data/PF05371_seed.sth", "stockholm", "PF05371_seed.phy", "phylip")
Out[14]:
This time the output looks like this:
7 52
COATB_BPIK AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
Q9T0Q8_BPI AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
COATB_BPI2 DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
COATB_BPM1 AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
COATB_BPZJ AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
Q9T0Q9_BPF AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
COATB_BPIF FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS
KA
RA
KA
KA
KA
KA
RAOne of the big handicaps of the original PHYLIP alignment file format is that the sequence identifiers are strictly truncated at ten characters. In this example, as you can see the resulting names are still unique - but they are not very readable. As a result, a more relaxed variant of the original PHYLIP format is now quite widely used:
In [15]:
from Bio import AlignIO
AlignIO.convert("data/PF05371_seed.sth", "stockholm", "PF05371_seed.phy", "phylip-relaxed")
Out[15]:
This time the output looks like this, using a longer indentation to allow all the identifers to be given in full::
7 52
COATB_BPIKE/30-81 AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
Q9T0Q8_BPIKE/1-52 AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
COATB_BPI22/32-83 DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
COATB_BPM13/24-72 AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
COATB_BPZJ2/1-49 AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
Q9T0Q9_BPFD/1-49 AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
COATB_BPIF1/22-73 FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS
KA
RA
KA
KA
KA
KA
RAIf you have to work with the original strict PHYLIP format, then you may need to compress the identifers somehow – or assign your own names or numbering system. This following bit of code manipulates the record identifiers before saving the output:
In [16]:
from Bio import AlignIO
alignment = AlignIO.read("data/PF05371_seed.sth", "stockholm")
name_mapping = {}
for i, record in enumerate(alignment):
name_mapping[i] = record.id
record.id = "seq%i" % i
print(name_mapping)
AlignIO.write([alignment], "PF05371_seed.phy", "phylip")
Out[16]:
Here is the new (strict) PHYLIP format output:
7 52
seq0 AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
seq1 AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
seq2 DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
seq3 AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
seq4 AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
seq5 AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
seq6 FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS
KA
RA
KA
KA
KA
KA
RAIn general, because of the identifier limitation, working with strict PHYLIP file formats shouldn’t be your first choice. Using the PFAM/Stockholm format on the other hand allows you to record a lot of additional annotation too.
The Bio.AlignIO interface is based on handles, which means if you want
to get your alignment(s) into a string in a particular file format you
need to do a little bit more work (see below). However, you will
probably prefer to take advantage of the alignment object’s format()
method. This takes a single mandatory argument, a lower case string
which is supported by Bio.AlignIO as an output format. For example:
from Bio import AlignIO
alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
print(alignment.format("clustal"))As described in Section [sec:SeqRecord-format], the SeqRecord object
has a similar method using output formats supported by Bio.SeqIO.
Internally the format() method is using the StringIO string based
handle and calling Bio.AlignIO.write(). You can do this in your own
code if for example you are using an older version of Biopython:
from Bio import AlignIO
from StringIO import StringIO
alignments = AlignIO.parse("PF05371_seed.sth", "stockholm")
out_handle = StringIO()
AlignIO.write(alignments, out_handle, "clustal")
clustal_data = out_handle.getvalue()
print(clustal_data)Now that we’ve covered loading and saving alignments, we’ll look at what else you can do with them.
First of all, in some senses the alignment objects act like a Python
list of SeqRecord objects (the rows). With this model in mind
hopefully the actions of len() (the number of rows) and iteration
(each row as a SeqRecord) make sense:
In [17]:
from Bio import AlignIO
alignment = AlignIO.read("data/PF05371_seed.sth", "stockholm")
print("Number of rows: %i" % len(alignment))
In [18]:
for record in alignment:
print("%s - %s" % (record.seq, record.id))
You can also use the list-like append and extend methods to add more
rows to the alignment (as SeqRecord objects). Keeping the list
metaphor in mind, simple slicing of the alignment should also make sense
In [19]:
print(alignment)
In [20]:
print(alignment[3:7])
What if you wanted to select by column? Those of you who have used the NumPy matrix or array objects won’t be surprised at this - you use a double index.
In [21]:
print(alignment[2, 6])
Using two integer indices pulls out a single letter, short hand for this:
In [22]:
print(alignment[2].seq[6])
You can pull out a single column as a string like this:
In [23]:
print(alignment[:, 6])
You can also select a range of columns. For example, to pick out those same three rows we extracted earlier, but take just their first six columns:
In [24]:
print(alignment[3:6, :6])
Leaving the first index as : means take all the rows:
In [25]:
print(alignment[:, :6])
This brings us to a neat way to remove a section. Notice columns 7, 8 and 9 which are gaps in three of the seven sequences:
In [26]:
print(alignment[:, 6:9])
Again, you can slice to get everything after the ninth column:
In [27]:
print(alignment[:, 9:])
Now, the interesting thing is that addition of alignment objects works by column. This lets you do this as a way to remove a block of columns:
In [28]:
edited = alignment[:, :6] + alignment[:, 9:]
print(edited)
Another common use of alignment addition would be to combine alignments
for several different genes into a meta-alignment. Watch out though -
the identifiers need to match up (see Section [sec:SeqRecord-addition]
for how adding SeqRecord objects works). You may find it helpful to
first sort the alignment rows alphabetically by id:
In [29]:
edited.sort()
print(edited)
In [30]:
import numpy as np
from Bio import AlignIO
alignment = AlignIO.read("data/PF05371_seed.sth", "stockholm")
align_array = np.array([list(rec) for rec in alignment], np.character)
print("Array shape %i by %i" % align_array.shape)
If you will be working heavily with the columns, you can tell NumPy to store the array by column (as in Fortran) rather then its default of by row (as in C):
In [31]:
align_array = np.array([list(rec) for rec in alignment], np.character, order="F")
Note that this leaves the original Biopython alignment object and the NumPy array in memory as separate objects - editing one will not update the other!
There are lots of algorithms out there for aligning sequences, both pairwise alignments and multiple sequence alignments. These calculations are relatively slow, and you generally wouldn’t want to write such an algorithm in Python. Instead, you can use Biopython to invoke a command line tool on your behalf. Normally you would:
Prepare an input file of your unaligned sequences, typically this
will be a FASTA file which you might create using Bio.SeqIO
(see Chapter [chapter:Bio.SeqIO]).
Call the command line tool to process this input file, typically via one of Biopython’s command line wrappers (which we’ll discuss here).
Read the output from the tool, i.e. your aligned sequences,
typically using Bio.AlignIO (see earlier in this chapter).
All the command line wrappers we’re going to talk about in this chapter
follow the same style. You create a command line object specifying the
options (e.g. the input filename and the output filename), then invoke
this command line via a Python operating system call (e.g. using the
subprocess module).
Most of these wrappers are defined in the Bio.Align.Applications
module:
In [32]:
import Bio.Align.Applications
dir(Bio.Align.Applications)
Out[32]:
(Ignore the entries starting with an underscore – these have special
meaning in Python.) The module Bio.Emboss.Applications has wrappers
for some of the EMBOSS suite,
including needle and water, which are described below in
Section [seq:emboss-needle-water], and wrappers for the EMBOSS
packaged versions of the PHYLIP tools (which EMBOSS refer to as one of
their EMBASSY packages - third party tools with an EMBOSS style
interface). We won’t explore all these alignment tools here in the
section, just a sample, but the same principles apply.
ClustalW is a popular command line tool for multiple sequence alignment
(there is also a graphical interface called ClustalX). Biopython’s
Bio.Align.Applications module has a wrapper for this alignment tool
(and several others).
Before trying to use ClustalW from within Python, you should first try running the ClustalW tool yourself by hand at the command line, to familiarise yourself the other options. You’ll find the Biopython wrapper is very faithful to the actual command line API:
In [33]:
from Bio.Align.Applications import ClustalwCommandline
help(ClustalwCommandline)
For the most basic usage, all you need is to have a FASTA input file, such as opuntia.fasta (available online or in the Doc/examples subdirectory of the Biopython source code). This is a small FASTA file containing seven prickly-pear DNA sequences (from the cactus family Opuntia).
By default ClustalW will generate an alignment and guide tree file with
names based on the input FASTA file, in this case opuntia.aln and
opuntia.dnd, but you can override this or make it explicit:
In [34]:
from Bio.Align.Applications import ClustalwCommandline
cline = ClustalwCommandline("clustalw2", infile="data/opuntia.fasta")
print(cline)
Notice here we have given the executable name as clustalw2, indicating
we have version two installed, which has a different filename to version
one (clustalw, the default). Fortunately both versions support the
same set of arguments at the command line (and indeed, should be
functionally identical).
You may find that even though you have ClustalW installed, the above command doesn’t work – you may get a message about “command not found” (especially on Windows). This indicated that the ClustalW executable is not on your PATH (an environment variable, a list of directories to be searched). You can either update your PATH setting to include the location of your copy of ClustalW tools (how you do this will depend on your OS), or simply type in the full path of the tool. For example:
In [35]:
import os
from Bio.Align.Applications import ClustalwCommandline
clustalw_exe = r"C:\Program Files\new clustal\clustalw2.exe"
clustalw_cline = ClustalwCommandline(clustalw_exe, infile="data/opuntia.fasta")
In [37]:
assert os.path.isfile(clustalw_exe), "Clustal W executable missing"
stdout, stderr = clustalw_cline()
Remember, in Python strings \n and \t are by default interpreted as
a new line and a tab – which is why we’re put a letter “r” at the start
for a raw string that isn’t translated in this way. This is generally
good practice when specifying a Windows style file name.
Internally this uses the subprocess module which is now the
recommended way to run another program in Python. This replaces older
options like the os.system() and the os.popen* functions.
Now, at this point it helps to know about how command line tools “work”. When you run a tool at the command line, it will often print text output directly to screen. This text can be captured or redirected, via two “pipes”, called standard output (the normal results) and standard error (for error messages and debug messages). There is also standard input, which is any text fed into the tool. These names get shortened to stdin, stdout and stderr. When the tool finishes, it has a return code (an integer), which by convention is zero for success.
When you run the command line tool like this via the Biopython wrapper, it will wait for it to finish, and check the return code. If this is non zero (indicating an error), an exception is raised. The wrapper then returns two strings, stdout and stderr.
In the case of ClustalW, when run at the command line all the important output is written directly to the output files. Everything normally printed to screen while you wait (via stdout or stderr) is boring and can be ignored (assuming it worked).
What we care about are the two output files, the alignment and the guide
tree. We didn’t tell ClustalW what filenames to use, but it defaults to
picking names based on the input file. In this case the output should be
in the file opuntia.aln. You should be able to work out how to read in
the alignment using Bio.AlignIO by now:
In [38]:
from Bio import AlignIO
align = AlignIO.read("data/opuntia.aln", "clustal")
print(align)
In case you are interested (and this is an aside from the main thrust of
this chapter), the opuntia.dnd file ClustalW creates is just a
standard Newick tree file, and Bio.Phylo can parse these:
In [39]:
from Bio import Phylo
tree = Phylo.read("data/opuntia.dnd", "newick")
Phylo.draw_ascii(tree)
Chapter [sec:Phylo] covers Biopython’s support for phylogenetic trees in more depth.
MUSCLE is a more recent multiple sequence alignment tool than ClustalW,
and Biopython also has a wrapper for it under the
Bio.Align.Applications module. As before, we recommend you try using
MUSCLE from the command line before trying it from within Python, as the
Biopython wrapper is very faithful to the actual command line API:
In [40]:
from Bio.Align.Applications import MuscleCommandline
help(MuscleCommandline)
For the most basic usage, all you need is to have a FASTA input file, such as opuntia.fasta (available online or in the Doc/examples subdirectory of the Biopython source code). You can then tell MUSCLE to read in this FASTA file, and write the alignment to an output file:
In [41]:
from Bio.Align.Applications import MuscleCommandline
cline = MuscleCommandline(input="data/opuntia.fasta", out="opuntia.txt")
print(cline)
Note that MUSCLE uses “-in” and “-out” but in Biopython we have to use “input” and “out” as the keyword arguments or property names. This is because “in” is a reserved word in Python.
By default MUSCLE will output the alignment as a FASTA file (using
gapped sequences). The Bio.AlignIO module should be able to read this
alignment using format=fasta. You can also ask for ClustalW-like
output:
In [42]:
from Bio.Align.Applications import MuscleCommandline
cline = MuscleCommandline(input="data/opuntia.fasta", out="opuntia.aln", clw=True)
print(cline)
Or, strict ClustalW output where the original ClustalW header line is used for maximum compatibility:
In [43]:
from Bio.Align.Applications import MuscleCommandline
cline = MuscleCommandline(input="data/opuntia.fasta", out="opuntia.aln", clwstrict=True)
print(cline)
The Bio.AlignIO module should be able to read these alignments using
format=clustal.
MUSCLE can also output in GCG MSF format (using the msf argument), but
Biopython can’t currently parse that, or using HTML which would give a
human readable web page (not suitable for parsing).
You can also set the other optional parameters, for example the maximum number of iterations. See the built in help for details.
You would then run MUSCLE command line string as described above for
ClustalW, and parse the output using Bio.AlignIO to get an alignment
object.
Using a MUSCLE command line as in the examples above will write the alignment to a file. This means there will be no important information written to the standard out (stdout) or standard error (stderr) handles. However, by default MUSCLE will write the alignment to standard output (stdout). We can take advantage of this to avoid having a temporary output file! For example:
In [44]:
from Bio.Align.Applications import MuscleCommandline
muscle_cline = MuscleCommandline(input="data/opuntia.fasta")
print(muscle_cline)
If we run this via the wrapper, we get back the output as a string. In
order to parse this we can use StringIO to turn it into a handle.
Remember that MUSCLE defaults to using FASTA as the output format:
In [45]:
from Bio.Align.Applications import MuscleCommandline
muscle_cline = MuscleCommandline(input="data/opuntia.fasta")
stdout, stderr = muscle_cline()
from io import StringIO
from Bio import AlignIO
align = AlignIO.read(StringIO(stdout), "fasta")
print(align)
The above approach is fairly simple, but if you are dealing with very
large output text the fact that all of stdout and stderr is loaded into
memory as a string can be a potential drawback. Using the subprocess
module we can work directly with handles instead:
In [49]:
import subprocess
import sys
from Bio.Align.Applications import MuscleCommandline
muscle_cline = MuscleCommandline(input="data/opuntia.fasta")
child = subprocess.Popen(str(muscle_cline),
stdout=subprocess.PIPE, stderr=subprocess.PIPE,
shell=(sys.platform != "win32"),
universal_newlines=True)
from Bio import AlignIO
align = AlignIO.read(child.stdout, "fasta")
print(align)
We don’t actually need to have our FASTA input sequences prepared in a file, because by default MUSCLE will read in the input sequence from standard input! Note this is a bit more advanced and fiddly, so don’t bother with this technique unless you need to.
First, we’ll need some unaligned sequences in memory as SeqRecord
objects. For this demonstration I’m going to use a filtered version of
the original FASTA file (using a generator expression), taking just six
of the seven sequences:
In [73]:
from Bio import SeqIO
records = (r for r in SeqIO.parse("data/opuntia.fasta", "fasta") if len(r) < 900)
Then we create the MUSCLE command line, leaving the input and output to their defaults (stdin and stdout). I’m also going to ask for strict ClustalW format as for the output.
In [90]:
from Bio.Align.Applications import MuscleCommandline
muscle_cline = MuscleCommandline(clwstrict=True)
print(muscle_cline)
Now for the fiddly bits using the subprocess module, stdin and stdout:
In [91]:
import subprocess
import sys
child = subprocess.Popen(str(cline),
stdin=subprocess.PIPE, stdout=subprocess.PIPE,
stderr=subprocess.PIPE, universal_newlines=True,
shell=(sys.platform != "win32"))
That should start MUSCLE, but it will be sitting waiting for its FASTA input sequences, which we must supply via its stdin handle:
In [92]:
SeqIO.write(records, child.stdin, "fasta")
In [93]:
child.stdin.close()
After writing the six sequences to the handle, MUSCLE will still be waiting to see if that is all the FASTA sequences or not – so we must signal that this is all the input data by closing the handle. At that point MUSCLE should start to run, and we can ask for the output:
In [94]:
from Bio import AlignIO
align = AlignIO.read(child.stdout, "clustal")
print(align)
Wow! There we are with a new alignment of just the six records, without having created a temporary FASTA input file, or a temporary alignment output file. However, a word of caution: Dealing with errors with this style of calling external programs is much more complicated. It also becomes far harder to diagnose problems, because you can’t try running MUSCLE manually outside of Biopython (because you don’t have the input file to supply). There can also be subtle cross platform issues (e.g. Windows versus Linux, Python 2 versus Python 3), and how you run your script can have an impact (e.g. at the command line, from IDLE or an IDE, or as a GUI script). These are all generic Python issues though, and not specific to Biopython.
If you find working directly with subprocess like this scary, there is
an alternative. If you execute the tool with muscle_cline() you can
supply any standard input as a big string, muscle_cline(stdin=...).
So, provided your data isn’t very big, you can prepare the FASTA input
in memory as a string using StringIO (see
Section [sec:appendix-handles]):
In [95]:
from Bio import SeqIO
records = (r for r in SeqIO.parse("data/opuntia.fasta", "fasta") if len(r) < 900)
handle = StringIO()
SeqIO.write(records, handle, "fasta")
Out[95]:
In [96]:
data = handle.getvalue()
You can then run the tool and parse the alignment as follows:
In [97]:
stdout, stderr = muscle_cline(stdin=data)
from Bio import AlignIO
align = AlignIO.read(StringIO(stdout), "clustal")
print(align)
You might find this easier, but it does require more memory (RAM) for the strings used for the input FASTA and output Clustal formatted data.
The EMBOSS suite includes the water
and needle tools for Smith-Waterman algorithm local alignment, and
Needleman-Wunsch global alignment. The tools share the same style
interface, so switching between the two is trivial – we’ll just use
needle here.
Suppose you want to do a global pairwise alignment between two sequences, prepared in FASTA format as follows:
>HBA_HUMAN
MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHG
KKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTP
AVHASLDKFLASVSTVLTSKYRin a file alpha.fasta, and secondly in a file beta.fasta:
>HBB_HUMAN
MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPK
VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFG
KEFTPPVQAAYQKVVAGVANALAHKYHLet’s start by creating a complete needle command line object in one
go:
In [98]:
from Bio.Emboss.Applications import NeedleCommandline
needle_cline = NeedleCommandline(asequence="data/alpha.faa", bsequence="data/beta.faa",
gapopen=10, gapextend=0.5, outfile="needle.txt")
print(needle_cline)
Why not try running this by hand at the command prompt? You should see
it does a pairwise comparison and records the output in the file
needle.txt (in the default EMBOSS alignment file format).
Even if you have EMBOSS installed, running this command may not work – you might get a message about “command not found” (especially on Windows). This probably means that the EMBOSS tools are not on your PATH environment variable. You can either update your PATH setting, or simply tell Biopython the full path to the tool, for example:
In [99]:
from Bio.Emboss.Applications import NeedleCommandline
needle_cline = NeedleCommandline(r"C:\EMBOSS\needle.exe",
asequence="data/alpha.faa", bsequence="data/beta.faa",
gapopen=10, gapextend=0.5, outfile="needle.txt")
Remember in Python that for a default string \n or \t means a new
line or a tab – which is why we’re put a letter “r” at the start for a
raw string.
At this point it might help to try running the EMBOSS tools yourself by hand at the command line, to familiarise yourself the other options and compare them to the Biopython help text:
In [100]:
from Bio.Emboss.Applications import NeedleCommandline
help(NeedleCommandline)
Note that you can also specify (or change or look at) the settings like this:
In [101]:
from Bio.Emboss.Applications import NeedleCommandline
needle_cline = NeedleCommandline()
needle_cline.asequence="data/alpha.faa"
needle_cline.bsequence="data/beta.faa"
needle_cline.gapopen=10
needle_cline.gapextend=0.5
needle_cline.outfile="needle.txt"
print(needle_cline)
In [102]:
print(needle_cline.outfile)
Next we want to use Python to run this command for us. As explained
above, for full control, we recommend you use the built in Python
subprocess module, but for simple usage the wrapper object usually
suffices:
In [103]:
stdout, stderr = needle_cline()
print(stdout + stderr)
Next we can load the output file with Bio.AlignIO as discussed earlier
in this chapter, as the emboss format:
In [104]:
from Bio import AlignIO
align = AlignIO.read("needle.txt", "emboss")
print(align)
In [ ]: