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