Collections and Alignments

For loading collections of unaligned or aligned sequences see Loading nucleotide, protein sequences.

Basic Collection objects

Constructing a SequenceCollection or Alignment object from strings

>>> from cogent3 import make_aligned_seqs, make_unaligned_seqs
>>> dna  = {'seq1': 'ATGACC',
...         'seq2': 'ATCGCC'}
>>> seqs = make_aligned_seqs(data=dna, moltype="dna")
>>> print(type(seqs))
<class 'cogent3.core.alignment.ArrayAlignment'>
>>> seqs = make_unaligned_seqs(dna, moltype="dna")
>>> print(type(seqs))
<class 'cogent3.core.alignment.SequenceCollection'>

Constructing a ArrayAlignment using make_aligned_seqs

>>> from cogent3 import make_aligned_seqs
>>> dna  = {'seq1': 'ATGACC',
...         'seq2': 'ATCGCC'}
>>> seqs = make_aligned_seqs(data=dna, moltype="dna", array_align=True)
>>> print(type(seqs))
<class 'cogent3.core.alignment.ArrayAlignment'>
>>> print(seqs)
>seq1
ATGACC
>seq2
ATCGCC

Converting a SequenceCollection to FASTA format

>>> from cogent3 import load_unaligned_seqs
>>> seqs = load_unaligned_seqs('data/test.paml')
>>> print(seqs)  
>DogFaced
GCAAGGAGCCAGCAGAACAGATGGGTTGAAACTAAGGAAACATGTAATGATAGGCAGACT
>HowlerMon
GCAAGGAGCCAACATAACAGATGGGCTGAAAGTGAGGAAACATGTAATGATAGGCAGACT
>Human
GCAAGGAGCCAACATAACAGATGGGCTGGAAGTAAGGAAACATGTAATGATAGGCGGACT
>Mouse
GCAGTGAGCCAGCAGAGCAGATGGGCTGCAAGTAAAGGAACATGTAACGACAGGCAGGTT
>NineBande
GCAAGGCGCCAACAGAGCAGATGGGCTGAAAGTAAGGAAACATGTAATGATAGGCAGACT

Adding new sequences to an existing collection or alignment

New sequences can be either appended or inserted using the add_seqs method. More than one sequence can be added at the same time. Note that add_seqs does not modify the existing collection/alignment, it creates a new one.

Appending the sequences

add_seqs without additional parameters will append the sequences to the end of the collection/alignment.

>>> from cogent3 import make_aligned_seqs
>>> aln = make_aligned_seqs([('seq1', 'ATGAA------'),
...                       ('seq2', 'ATG-AGTGATG'),
...                       ('seq3', 'AT--AG-GATG')], moltype="dna")
>>> print(aln)
>seq1
ATGAA------
>seq2
ATG-AGTGATG
>seq3
AT--AG-GATG

>>> new_seqs = make_aligned_seqs([('seq0', 'ATG-AGT-AGG'),
...                           ('seq4', 'ATGCC------')], moltype="dna")
>>> new_aln = aln.add_seqs(new_seqs)
>>> print(new_aln)
>seq1
ATGAA------
>seq2
ATG-AGTGATG
>seq3
AT--AG-GATG
>seq0
ATG-AGT-AGG
>seq4
ATGCC------

Note

The order is not preserved if you use to_fasta method, which sorts sequences by name.

Inserting the sequences

Sequences can be inserted into an alignment at the specified position using either the before_name or after_name arguments.

>>> new_aln = aln.add_seqs(new_seqs, before_name='seq2')
>>> print(new_aln)
>seq1
ATGAA------
>seq0
ATG-AGT-AGG
>seq4
ATGCC------
>seq2
ATG-AGTGATG
>seq3
AT--AG-GATG

>>> new_aln = aln.add_seqs(new_seqs, after_name='seq2')
>>> print(new_aln)
>seq1
ATGAA------
>seq2
ATG-AGTGATG
>seq0
ATG-AGT-AGG
>seq4
ATGCC------
>seq3
AT--AG-GATG

Inserting sequence(s) based on their alignment to a reference sequence

Already aligned sequences can be added to an existing Alignment object and aligned at the same time using the add_from_ref_aln method. The alignment is performed based on their alignment to a reference sequence (which must be present in both alignments). The method assumes the first sequence in ref_aln.names[0] is the reference.

>>> from cogent3 import make_aligned_seqs
>>> aln = make_aligned_seqs([('seq1', 'ATGAA------'),
...                      ('seq2', 'ATG-AGTGATG'),
...                      ('seq3', 'AT--AG-GATG')], moltype="dna")
>>> ref_aln = make_aligned_seqs([('seq3', 'ATAGGATG'),
...                          ('seq0', 'ATG-AGCG'),
...                          ('seq4', 'ATGCTGGG')], moltype="dna")
>>> new_aln = aln.add_from_ref_aln(ref_aln)
>>> print(new_aln)
>seq1
ATGAA------
>seq2
ATG-AGTGATG
>seq3
AT--AG-GATG
>seq0
AT--G--AGCG
>seq4
AT--GC-TGGG

add_from_ref_aln has the same arguments as add_seqs so before_name and after_name can be used to insert the new sequences at the desired position.

Note

This method does not work with the ArrayAlignment class.

Removing all columns with gaps in a named sequence

>>> from cogent3 import make_aligned_seqs
>>> aln = make_aligned_seqs([('seq1', 'ATGAA---TG-'),
...                      ('seq2', 'ATG-AGTGATG'),
...                      ('seq3', 'AT--AG-GATG')], moltype="dna")
>>> new_aln = aln.get_degapped_relative_to('seq1')
>>> print(new_aln)
>seq1
ATGAATG
>seq2
ATG-AAT
>seq3
AT--AAT

The elements of a collection or alignment

Accessing individual sequences from a collection or alignment by name

Using the get_seq method allows for extracting an unaligned sequence from a collection or alignment by name.

>>> from cogent3 import make_aligned_seqs
>>> aln = make_aligned_seqs([('seq1', 'ATGAA------'),
...                      ('seq2', 'ATG-AGTGATG'),
...                      ('seq3', 'AT--AG-GATG')],
...                 moltype="dna", array_align=False)
>>> seq = aln.get_seq('seq1')
>>> seq.name
'seq1'
>>> type(seq)
<class 'cogent3.core.sequence.DnaSequence'>
>>> seq.is_gapped()
False

Alternatively, if you want to extract the aligned (i.e., gapped) sequence from an alignment, you can use get_gapped_seq.

>>> seq = aln.get_gapped_seq('seq1')
>>> seq.is_gapped()
True
>>> print(seq)
ATGAA------

To see the names of the sequences in a sequence collection, you can use either the Names attribute or get_seq_names method.

>>> aln.names
['seq1', 'seq2', 'seq3']
>>> aln.names
['seq1', 'seq2', 'seq3']

Slice the sequences from an alignment like a list

The usual approach is to access a SequenceCollection or Alignment object as a dictionary, obtaining the individual sequences using the titles as “keys” (above). However, one can also iterate through the collection like a list.

>>> from cogent3 import load_unaligned_seqs, load_aligned_seqs
>>> fn = 'data/long_testseqs.fasta'
>>> seqs = load_unaligned_seqs(fn, moltype="dna")
>>> my_seq = seqs.seqs[0]
>>> my_seq[:24]
DnaSequence(TGTGGCA... 24)
>>> str(my_seq[:24])
'TGTGGCACAAATACTCATGCCAGC'
>>> type(my_seq)
<class 'cogent3.core.sequence.DnaSequence'>
>>> aln = load_aligned_seqs(fn, moltype="dna")
>>> aln.seqs[0][:24]
DnaSequence(TGTGGCA... 24)
>>> print(aln.seqs[0][:24])
TGTGGCACAAATACTCATGCCAGC

Getting a subset of sequences from the alignment

>>> from cogent3 import load_aligned_seqs
>>> aln = load_aligned_seqs('data/test.paml', moltype="dna")
>>> aln.names
['NineBande', 'Mouse', 'Human', 'HowlerMon', 'DogFaced']
>>> new = aln.take_seqs(['Human', 'HowlerMon'])
>>> new.names
['Human', 'HowlerMon']

Note, if you set array_align=False, then the subset contain references to the original sequences, not copies.

>>> from cogent3 import load_aligned_seqs
>>> aln = load_aligned_seqs('data/test.paml', array_align=False, moltype="dna")
>>> seq = aln.get_seq('Human')
>>> new = aln.take_seqs(['Human', 'HowlerMon'])
>>> id(new.get_seq('Human')) == id(aln.get_seq('Human'))
True

Alignments

Creating an Alignment object from a SequenceCollection

>>> from cogent3 import load_unaligned_seqs
>>> from cogent3.core.alignment import Alignment
>>> seq = load_unaligned_seqs('data/test.paml')
>>> aln = Alignment(seq)
>>> fasta_1 = seq
>>> fasta_2 = aln
>>> assert fasta_1 == fasta_2

Convert alignment to DNA, RNA or PROTEIN moltypes

This is useful if you’ve loaded a sequence alignment without specifying the moltype and later need to convert it.

>>> from cogent3 import make_aligned_seqs
>>> data = [('a', 'ACG---'), ('b', 'CCTGGG')]
>>> aln = make_aligned_seqs(data=data)
>>> dna = aln.to_dna()
>>> dna
2 x 6 dna alignment: a[ACG---], b[CCTGGG]

To RNA

>>> from cogent3 import make_aligned_seqs
>>> data = [('a', 'ACG---'), ('b', 'CCUGGG')]
>>> aln = make_aligned_seqs(data=data)
>>> rna = aln.to_rna()
>>> rna
2 x 6 rna alignment: a[ACG---], b[CCUGGG]

To PROTEIN

>>> from cogent3 import make_aligned_seqs
>>> data = [('x', 'TYV'), ('y', 'TE-')]
>>> aln = make_aligned_seqs(data=data)
>>> prot = aln.to_protein()
>>> prot
2 x 3 protein alignment: x[TYV], y[TE-]

Handling gaps

Remove all gaps from an alignment in FASTA format

This necessarily returns a SequenceCollection.

>>> from cogent3 import load_aligned_seqs
>>> aln = load_aligned_seqs("data/primate_cdx2_promoter.fasta")
>>> degapped = aln.degap()
>>> print(type(degapped))
<class 'cogent3.core.alignment.SequenceCollection'>

Writing sequences to file

Both collection and alignment objects have a write method. The output format is inferred from the filename suffix,

>>> from cogent3 import make_aligned_seqs
>>> dna  = {'seq1': 'ATGACC',
...         'seq2': 'ATCGCC'}
>>> aln = make_aligned_seqs(data=dna, moltype="dna")
>>> aln.write('sample.fasta')

or by the format argument.

>>> aln.write('sample', format='fasta')

Converting an alignment to FASTA format

>>> from cogent3 import load_aligned_seqs
>>> from cogent3.core.alignment import Alignment
>>> seq = load_aligned_seqs('data/long_testseqs.fasta')
>>> aln = Alignment(seq)
>>> fasta_align = aln

Converting an alignment into Phylip format

>>> from cogent3 import load_aligned_seqs
>>> from cogent3.core.alignment import Alignment
>>> seq = load_aligned_seqs('data/test.paml')
>>> aln = Alignment(seq)
>>> got = aln.to_phylip()
>>> print(got)
5  60
NineBande GCAAGGCGCCAACAGAGCAGATGGGCTGAAAGTAAGGAAACATGTAATGATAGGCAGACT
Mouse     GCAGTGAGCCAGCAGAGCAGATGGGCTGCAAGTAAAGGAACATGTAACGACAGGCAGGTT
Human     GCAAGGAGCCAACATAACAGATGGGCTGGAAGTAAGGAAACATGTAATGATAGGCGGACT
HowlerMon GCAAGGAGCCAACATAACAGATGGGCTGAAAGTGAGGAAACATGTAATGATAGGCAGACT
DogFaced  GCAAGGAGCCAGCAGAACAGATGGGTTGAAACTAAGGAAACATGTAATGATAGGCAGACT

Converting an alignment to a list of strings

>>> from cogent3 import load_aligned_seqs
>>> from cogent3.core.alignment import Alignment
>>> seq = load_aligned_seqs('data/test.paml')
>>> aln = Alignment(seq)
>>> string_list = aln.to_dict().values()

Slicing an alignment

By rows (sequences)

An Alignment can be sliced

>>> from cogent3 import load_aligned_seqs
>>> fn = 'data/long_testseqs.fasta'
>>> aln = load_aligned_seqs(fn, moltype="dna")
>>> print(aln[:24])
>Human
TGTGGCACAAATACTCATGCCAGC
>HowlerMon
TGTGGCACAAATACTCATGCCAGC
>Mouse
TGTGGCACAGATGCTCATGCCAGC
>NineBande
TGTGGCACAAATACTCATGCCAAC
>DogFaced
TGTGGCACAAATACTCATGCCAAC

but a SequenceCollection cannot be sliced

>>> from cogent3 import load_unaligned_seqs
>>> fn = 'data/long_testseqs.fasta'
>>> seqs = load_unaligned_seqs(fn)
>>> print(seqs[:24])
Traceback (most recent call last):
TypeError: 'SequenceCollection' object...

Getting a single column from an alignment

>>> from cogent3 import load_aligned_seqs
>>> seq = load_aligned_seqs('data/test.paml')
>>> column_four = aln[3]

Getting a region of contiguous columns

>>> from cogent3 import load_aligned_seqs
>>> aln = load_aligned_seqs('data/long_testseqs.fasta')
>>> region = aln[50:70]

Iterating over alignment positions

>>> from cogent3 import load_aligned_seqs
>>> aln = load_aligned_seqs('data/primate_cdx2_promoter.fasta')
>>> col = aln[113:115].iter_positions()
>>> type(col)
<class 'generator'>
>>> list(col)
[[ByteSequence(A), ByteSequence(A), ByteSequence(A)], [ByteSequence(T)...

Getting codon 3rd positions from Alignment

We’ll do this by specifying the position indices of interest, creating a sequence Feature and using that to extract the positions.

>>> from cogent3 import make_aligned_seqs
>>> aln = make_aligned_seqs(data={'seq1': 'ATGATGATG---',
...                      'seq2': 'ATGATGATGATG'}, array_align=False)
>>> list(range(len(aln))[2::3])
[2, 5, 8, 11]
>>> indices = [(i, i+1) for i in range(len(aln))[2::3]]
>>> indices
[(2, 3), (5, 6), (8, 9), (11, 12)]
>>> pos3 = aln.add_feature('pos3', 'pos3', indices)
>>> pos3 = pos3.get_slice()
>>> print(pos3)  
>seq2
GGGG
>seq1
GGG-

Getting codon 3rd positions from ArrayAlignment

We can use more conventional slice notation in this instance. Note, because Python counts from 0, the 3rd position starts at index 2.

>>> from cogent3 import make_aligned_seqs
>>> aln = make_aligned_seqs(data={'seq1': 'ATGATGATG---',
...                      'seq2': 'ATGATGATGATG'}, array_align=True)
>>> pos3 = aln[2::3]
>>> print(pos3)  
>seq1
GGG-
>seq2
GGGG

Filtering positions

Trim terminal stop codons

For evolutionary analyses that use codon models we need to exclude terminating stop codons. For the case where the sequences are all of length divisible by 3.

>>> from cogent3 import make_aligned_seqs
>>> aln = make_aligned_seqs(data={'seq1': 'ACGTAA---',
...                      'seq2': 'ACGACA---',
...                      'seq3': 'ACGCAATGA'}, moltype="dna")
...
>>> new = aln.trim_stop_codons()
>>> print(new)  
>seq3
ACGCAA
>seq2
ACGACA
>seq1
ACG---

If the alignment contains sequences not divisible by 3, use the allow_partial argument.

>>> aln = make_aligned_seqs(data={'seq1': 'ACGTAA---',
...                      'seq2': 'ACGAC----', # terminal codon incomplete
...                      'seq3': 'ACGCAATGA'}, moltype="dna")
...
>>> new = aln.trim_stop_codons(allow_partial=True)
>>> print(new)  
>seq3
ACGCAA
>seq2
ACGAC-
>seq1
ACG---

Eliminating columns with non-nucleotide characters

We sometimes want to eliminate ambiguous or gap data from our alignments. We show how to exclude alignment columns by the characters they contain. In the first instance we do this just for single nucleotide columns, then for trinucleotides (equivalent for handling codons). Both are done using the no_degenerates method.

>>> from cogent3 import make_aligned_seqs
>>> aln = make_aligned_seqs(data= [('seq1', 'ATGAAGGTG---'),
...                       ('seq2', 'ATGAAGGTGATG'),
...                       ('seq3', 'ATGAAGGNGATG')], moltype="dna")

We apply to nucleotides,

>>> nucs = aln.no_degenerates()
>>> print(nucs)
>seq1
ATGAAGGG
>seq2
ATGAAGGG
>seq3
ATGAAGGG

Applying the same filter to trinucleotides (specified by setting motif_length=3).

>>> trinucs = aln.no_degenerates(motif_length=3)
>>> print(trinucs)
>seq1
ATGAAG
>seq2
ATGAAG
>seq3
ATGAAG

Getting all variable positions from an alignment

>>> from cogent3 import load_aligned_seqs
>>> aln = load_aligned_seqs('data/long_testseqs.fasta')
>>> pos = aln.variable_positions()
>>> just_variable_aln = aln.take_positions(pos)
>>> print(just_variable_aln[:10])
>Human
AAGCAAAACT
>HowlerMon
AAGCAAGACT
>Mouse
GGGCCCAGCT
>NineBande
AAATAAAACT
>DogFaced
AAACAAAATA

Getting all constant positions from an alignment

>>> from cogent3 import load_aligned_seqs
>>> aln = load_aligned_seqs('data/long_testseqs.fasta')
>>> pos = aln.variable_positions()
>>> just_constant_aln = aln.take_positions(pos, negate=True)
>>> print(just_constant_aln[:10])
>Human
TGTGGCACAA
>HowlerMon
TGTGGCACAA
>Mouse
TGTGGCACAA
>NineBande
TGTGGCACAA
>DogFaced
TGTGGCACAA

Getting all variable codons from an alignment

This is done using the filtered method using the motif_length argument. We demonstrate this first for the ArrayAlignment.

>>> from cogent3 import load_aligned_seqs
>>> aln = load_aligned_seqs('data/long_testseqs.fasta')
>>> variable_codons = aln.filtered(lambda x: len(set(map(tuple, x))) > 1,
...                                motif_length=3)
>>> print(just_variable_aln[:9])
>Human
AAGCAAAAC
>HowlerMon
AAGCAAGAC
>Mouse
GGGCCCAGC
>NineBande
AAATAAAAC
>DogFaced
AAACAAAAT

Then for the standard Alignment by first converting the ArrayAlignment.

>>> aln = aln.to_type(array_align=False)
>>> variable_codons = aln.filtered(lambda x: len(set(''.join(x))) > 1,
...                                motif_length=3)
>>> print(just_variable_aln[:9])
>Human
AAGCAAAAC...

Filtering sequences

Extracting sequences by sequence identifier into a new alignment object

You can use take_seqs to extract some sequences by sequence identifier from an alignment to a new alignment object:

>>> from cogent3 import load_aligned_seqs
>>> aln = load_aligned_seqs('data/long_testseqs.fasta')
>>> aln.take_seqs(['Human','Mouse'])
2 x 2532 bytes alignment: Human[TGTGGCACAAA...], Mouse[TGTGGCACAGA...]

Alternatively, you can extract only the sequences which are not specified by passing negate=True:

>>> aln.take_seqs(['Human','Mouse'], negate=True)  
3 x 2532 bytes alignment: NineBande[TGTGGCACAAA...], HowlerMon[TGTGGCACAAA...], DogFaced[TGTGGCACAAA...]

Extracting sequences using an arbitrary function into a new alignment object

You can use take_seqs_if to extract sequences into a new alignment object based on whether an arbitrary function applied to the sequence evaluates to True. For example, to extract sequences which don’t contain any N bases you could do the following:

>>> from cogent3 import make_aligned_seqs
>>> aln = make_aligned_seqs(data= [('seq1', 'ATGAAGGTG---'),
...                       ('seq2', 'ATGAAGGTGATG'),
...                       ('seq3', 'ATGAAGGNGATG')], moltype="dna")
>>> def no_N_chars(s):
...     return 'N' not in s
>>> aln.take_seqs_if(no_N_chars)
2 x 12 dna alignment: seq1[ATGAAGGTG--...], seq2[ATGAAGGTGAT...]

You can additionally get the sequences where the provided function evaluates to False:

>>> aln.take_seqs_if(no_N_chars,negate=True)
1 x 12 dna alignment: seq3[ATGAAGGNGAT...]

Computing alignment statistics

Getting motif counts

We state the motif length we want and whether to allow gap or ambiguous characters. The latter only has meaning for IPUAC character sets (the DNA, RNA or PROTEIN moltypes). We illustrate this for the DNA moltype with motif lengths of 1 and 3.

>>> from cogent3 import make_aligned_seqs
>>> aln = make_aligned_seqs(data= [('seq1', 'ATGAAGGTG---'),
...                       ('seq2', 'ATGAAGGTGATG'),
...                       ('seq3', 'ATGAAGGNGATG')], moltype="dna")
>>> counts = aln.counts()
>>> print(counts) 
Counter({'G': 14, 'A': 11, 'T': 7})
>>> counts = aln.counts(motif_length=3)
>>> print(counts) 
Counter({'ATG': 5, 'AAG': 3, 'GTG': 2})
>>> counts = aln.counts(include_ambiguity=True)
>>> print(counts) 
Counter({'G': 14, 'A': 11, 'T': 7, 'N': 1})

Note

Only the observed motifs are returned, rather than all defined by the alphabet.

Computing motif probabilities from an alignment

The method get_motif_probs of Alignment objects returns the probabilities for all motifs of a given length. For individual nucleotides:

>>> from cogent3 import load_aligned_seqs
>>> aln = load_aligned_seqs('data/primate_cdx2_promoter.fasta', moltype="dna")
>>> motif_probs = aln.get_motif_probs()
>>> print(motif_probs) 
{'A': 0.24...

For dinucleotides or longer, we need to pass in an Alphabet with the appropriate word length. Here is an example with trinucleotides:

>>> from cogent3 import load_aligned_seqs, DNA
>>> trinuc_alphabet = DNA.alphabet.get_word_alphabet(3)
>>> aln = load_aligned_seqs('data/primate_cdx2_promoter.fasta', moltype="dna")
>>> motif_probs = aln.get_motif_probs(alphabet=trinuc_alphabet)
>>> for m in sorted(motif_probs, key=lambda x: motif_probs[x],
...                 reverse=True):
...     print("%s  %.3f" % (m, motif_probs[m]))
...
CAG  0.037
CCT  0.034
CGC  0.030...

The same holds for other arbitrary alphabets, as long as they match the alignment MolType.

Some calculations in cogent3 require all non-zero values in the motif probabilities, in which case we use a pseudo-count. We illustrate that here with a simple example where T is missing. Without the pseudo-count, the frequency of T is 0.0, with the pseudo-count defined as 1e-6 then the frequency of T will be slightly less than 1e-6.

>>> aln = make_aligned_seqs(data=[('a', 'AACAAC'),('b', 'AAGAAG')], moltype="dna")
>>> motif_probs = aln.get_motif_probs()
>>> assert motif_probs['T'] == 0.0
>>> motif_probs = aln.get_motif_probs(pseudocount=1e-6)
>>> assert 0 < motif_probs['T'] <= 1e-6

It is important to notice that motif probabilities are computed by treating sequences as non-overlapping tuples. Below is a very simple pair of identical sequences where there are clearly 2 ‘AA’ dinucleotides per sequence but only the first one is ‘in-frame’ (frame width = 2).

We then create a dinucleotide Alphabet object and use this to get dinucleotide probabilities. These frequencies are determined by breaking each aligned sequence up into non-overlapping dinucleotides and then doing a count. The expected value for the ‘AA’ dinucleotide in this case will be 2/8 = 0.25.

>>> seqs = [('a', 'AACGTAAG'), ('b', 'AACGTAAG')]
>>> aln = make_aligned_seqs(data=seqs, moltype="dna")
>>> dinuc_alphabet = DNA.alphabet.get_word_alphabet(2)
>>> motif_probs = aln.get_motif_probs(alphabet=dinuc_alphabet)
>>> assert motif_probs['AA'] == 0.25

What about counting the total incidence of dinucleotides including those not in-frame? A naive application of the Python string object’s count method will not work as desired either because it “returns the number of non-overlapping occurrences”.

>>> seqs = [('my_seq', 'AAAGTAAG')]
>>> aln = make_aligned_seqs(data=seqs, moltype="dna")
>>> my_seq = aln.get_seq('my_seq')
>>> my_seq.count('AA')
2
>>> 'AAA'.count('AA')
1
>>> 'AAAA'.count('AA')
2

To count all occurrences of a given dinucleotide in a DNA sequence, one could use a standard Python approach such as list comprehension:

>>> from cogent3 import make_seq
>>> seq = make_seq(moltype="dna", seq='AAAGTAAG')
>>> seq
DnaSequence(AAAGTAAG)
>>> di_nucs = [seq[i:i+2] for i in range(len(seq)-1)]
>>> sum([nn == 'AA' for nn in di_nucs])
3

Working with alignment gaps

Filtering extracted columns for the gap character

>>> from cogent3 import load_aligned_seqs
>>> aln = load_aligned_seqs('data/primate_cdx2_promoter.fasta')
>>> col = aln[113:115].iter_positions()
>>> c1, c2 = list(col)
>>> c1, c2
([ByteSequence(A), ByteSequence(A), ByteSequence(A)], [ByteSequence(T),...
>>> list(filter(lambda x: x == '-', c1))
[]
>>> list(filter(lambda x: x == '-', c2))
[ByteSequence(-), ByteSequence(-)]

Calculating the gap fraction

>>> from cogent3 import load_aligned_seqs
>>> aln = load_aligned_seqs('data/primate_cdx2_promoter.fasta')
>>> for column in aln[113:150].iter_positions():
...     ungapped = list(filter(lambda x: x == '-', column))
...     gap_fraction = len(ungapped) * 1.0 / len(column)
...     print(gap_fraction)
0.0
0.66666...

Extracting maps of aligned to unaligned positions (i.e., gap maps)

It’s often important to know how an alignment position relates to a position in one or more of the sequences in the alignment. The gap_maps method of the individual sequences is useful for this. To get a map of sequence to alignment positions for a specific sequence in your alignment, do the following:

>>> from cogent3 import make_aligned_seqs
>>> aln = make_aligned_seqs(data=[('seq1', 'ATGAAGG-TG--'),
...                      ('seq2', 'ATG-AGGTGATG'),
...                      ('seq3', 'ATGAAG--GATG')], moltype="dna")
>>> seq_to_aln_map = aln.get_gapped_seq('seq1').gap_maps()[0]

It’s now possible to look up positions in the seq1, and find out what they map to in the alignment:

>>> seq_to_aln_map[3]
3
>>> seq_to_aln_map[8]
9

This tells us that in position 3 in seq1 corresponds to position 3 in aln, and that position 8 in seq1 corresponds to position 9 in aln.

Notice that we grabbed the first result from the call to gap_maps. This is the sequence position to alignment position map. The second value returned is the alignment position to sequence position map, so if you want to find out what sequence positions the alignment positions correspond to (opposed to what alignment positions the sequence positions correspond to) for a given sequence, you would take the following steps:

>>> aln_to_seq_map = aln.get_gapped_seq('seq1').gap_maps()[1]
>>> aln_to_seq_map[3]
3
>>> aln_to_seq_map[8]
7

If an alignment position is a gap, and therefore has no corresponding sequence position, you’ll get a KeyError.

>>> seq_pos = aln_to_seq_map[7]
Traceback (most recent call last):
KeyError: 7

Note

The first position in alignments and sequences is always numbered position 0.

Filtering alignments based on gaps

Note

An alternate, computationally faster, approach to removing gaps is to use the filtered method as discussed in Filtering positions.

The omit_gap_runs method can be applied to remove long stretches of gaps in an alignment. In the following example, we remove sequences that have more than two adjacent gaps anywhere in the aligned sequence.

>>> aln = make_aligned_seqs(data=[('seq1', 'ATGAA---TG-'),
...                      ('seq2', 'ATG-AGTGATG'),
...                      ('seq3', 'AT--AG-GATG')], moltype="dna")
>>> print(aln.omit_gap_runs(2))  
>seq2
ATG-AGTGATG
>seq3
AT--AG-GATG

If instead, we just wanted to remove positions from the alignment which are gaps in more than a certain percentage of the sequences, we could use the omit_gap_pos function. For example:

>>> aln = make_aligned_seqs(data=[('seq1', 'ATGAA---TG-'),
...                      ('seq2', 'ATG-AGTGATG'),
...                      ('seq3', 'AT--AG-GATG')], moltype="dna")
>>> print(aln.omit_gap_pos(0.40))  
>seq1
ATGA--TG-
>seq2
ATGAGGATG
>seq3
AT-AGGATG

You’ll notice that the 4th and 7th columns of the alignment have been removed because they contained 66% gaps – more than the allowed 40%.

If you wanted to remove sequences which contain more than a certain percent gap characters, you could use the omit_gap_seqs method. This is commonly applied to filter partial sequences from an alignment.

>>> aln = make_aligned_seqs(data=[('seq1', 'ATGAA------'),
...                      ('seq2', 'ATG-AGTGATG'),
...                      ('seq3', 'AT--AG-GATG')], moltype="dna")
>>> filtered_aln = aln.omit_gap_seqs(0.50)
>>> print(filtered_aln)  
>seq2
ATG-AGTGATG
>seq3
AT--AG-GATG

Note that following this call to omit_gap_seqs, the 4th column of filtered_aln is 100% gaps. This is generally not desirable, so a call to omit_gap_seqs is frequently followed with a call to omit_gap_pos with no parameters – this defaults to removing positions which are all gaps:

>>> print(filtered_aln.omit_gap_pos())  
>seq2
ATGAGTGATG
>seq3
AT-AG-GATG