Sequence Collections and Alignments

Note

These docs now use the new_type core objects via the following setting.

import os

# using new types without requiring an explicit argument
os.environ["COGENT3_NEW_TYPE"] = "1"

For loading collections of unaligned or aligned sequences see Loading an alignment from a file or url.

Basic Collection objects

Constructing a SequenceCollection or Alignment object from strings

from cogent3 import make_aligned_seqs

data = {"seq1": "ATGACC", "seq2": "ATCGCC"}
# for an alignment, sequences must be the same length
seqs = make_aligned_seqs(data=data, moltype="dna")
type(seqs)

cogent3.core.new_alignment.Alignment

from cogent3 import make_unaligned_seqs

data = {"seq1": "ATGCC", "seq2": "ATCG"}
seqs = make_unaligned_seqs(data, moltype="dna")
type(seqs)

cogent3.core.new_alignment.SequenceCollection

Converting a SequenceCollection to FASTA format

from cogent3 import load_unaligned_seqs

seqs = load_unaligned_seqs("data/test.paml", moltype="dna")
print(seqs.to_fasta())

>NineBande
GCAAGGCGCCAACAGAGCAGATGGGCTGAAAGTAAGGAAACATGTAATGATAGGCAGACT
>Mouse
GCAGTGAGCCAGCAGAGCAGATGGGCTGCAAGTAAAGGAACATGTAACGACAGGCAGGTT
>Human
GCAAGGAGCCAACATAACAGATGGGCTGGAAGTAAGGAAACATGTAATGATAGGCGGACT
>HowlerMon
GCAAGGAGCCAACATAACAGATGGGCTGAAAGTGAGGAAACATGTAATGATAGGCAGACT
>DogFaced
GCAAGGAGCCAGCAGAACAGATGGGTTGAAACTAAGGAAACATGTAATGATAGGCAGACT

Adding new sequences to an existing collection or alignment

More than one sequence can be added to a collection simultaneously. Note that add_seqs() does not modify the existing collection/alignment, it creates a new one.

The elements of a collection or alignment

Accessing individual sequences from a collection or alignment by name

We can get a sequence by name by indexing the .seqs attribute.

from cogent3 import make_unaligned_seqs

seqcoll = make_unaligned_seqs(
    [("seq1", "ATGAA"), ("seq2", "ATGAGTGATG"), ("seq3", "ATAGGATG")],
    moltype="dna",
)
seq = seqcoll.seqs["seq1"]
seq

	0
seq1	ATGAA

DnaSequence, length=5

For an alignment, the result is an Aligned instance.

from cogent3 import make_aligned_seqs

aln = make_aligned_seqs(
    [("seq1", "ATGAA------"), ("seq2", "ATG-AGTGATG"), ("seq3", "AT--AG-GATG")],
    moltype="dna",
)
aligned = aln.seqs["seq1"]
aligned

Aligned(name='seq1', seq='ATGAA--... 11', moltype='dna')

For the alignment case, you can get the ungapped sequence by accessing the .seq attribute of the aligned instance.

aligned.seq

	0
seq1	ATGAA

DnaSequence, length=5

seq = aln.get_seq("seq1")
seq.name
type(seq)
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()
seq

	0
seq1	ATGAA------

DnaSequence, length=11

To see the names of the sequences in a sequence collection, use the names attribute.

aln.names

('seq1', 'seq2', 'seq3')

Slice the sequences from a collection like a list

Use the .seqs attribute. We can index a single sequence

from cogent3 import load_unaligned_seqs

fn = "data/long_testseqs.fasta"
seqs = load_unaligned_seqs(fn, moltype="dna")
my_seq = seqs.seqs[0]
my_seq

	0
Human	TGTGGCACAAATACTCATGCCAGCTCATTACAGCATGAGAACAGCAGTTTATTACTCACT

DnaSequence, length=2,532 (truncated to 60)

but you cannot index a slice (Use .take_seqs() for that).

seqs.seqs[:2]

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[13], line 1
----> 1 seqs.seqs[:2]

File /opt/hostedtoolcache/Python/3.12.10/x64/lib/python3.12/functools.py:949, in singledispatchmethod.__get__.<locals>._method(*args, **kwargs)
    947 def _method(*args, **kwargs):
    948     method = self.dispatcher.dispatch(args[0].__class__)
--> 949     return method.__get__(obj, cls)(*args, **kwargs)

File ~/work/cogent3.github.io/cogent3.github.io/.venv/lib/python3.12/site-packages/cogent3/core/new_alignment.py:4509, in _IndexableSeqs.__getitem__(self, key)
   4503 @singledispatchmethod
   4504 def __getitem__(
   4505     self,
   4506     key: str | int | slice,
   4507 ) -> new_sequence.Sequence | Aligned:
   4508     msg = f"indexing not supported for {type(key)}, try .take_seqs()"
-> 4509     raise TypeError(msg)

TypeError: indexing not supported for <class 'slice'>, try .take_seqs()

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')

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

new = aln.take_seqs(["Human", "HowlerMon"], negate=True)
new.names

('NineBande', 'Mouse', 'DogFaced')

Note

The subset contains references to the original sequences, not copies.

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")

from cogent3.util.io import remove_files

remove_files(["sample", "sample.fasta"], error_on_missing=False)

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", moltype="dna")
seq

	0
NineBande	GCAAGGCGCCAACAGAGCAGATGGGCTGAAAGTAAGGAAACATGTAATGATAGGCAGACT
Mouse	GCAGTGAGCCAGCAGAGCAGATGGGCTGCAAGTAAAGGAACATGTAACGACAGGCAGGTT
Human	GCAAGGAGCCAACATAACAGATGGGCTGGAAGTAAGGAAACATGTAATGATAGGCGGACT
HowlerMon	GCAAGGAGCCAACATAACAGATGGGCTGAAAGTGAGGAAACATGTAATGATAGGCAGACT
DogFaced	GCAAGGAGCCAGCAGAACAGATGGGTTGAAACTAAGGAAACATGTAATGATAGGCAGACT

5 x {min=60, median=60.0, max=60} dna sequence collection

aln = Alignment(seq.seqs)
aln

	0
DogFaced	GCAAGGAGCCAGCAGAACAGATGGGTTGAAACTAAGGAAACATGTAATGATAGGCAGACT
NineBande	......C....A....G........C.....G............................
Mouse	...GT...........G........C..C..G...A.G.........C..C......GT.
Human	...........A..T..........C..G..G.......................G....
HowlerMon	...........A..T..........C.....G.G..........................

5 x 60 dna alignment

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 using the dedicated method

from cogent3 import make_aligned_seqs

data = [("a", "ACG---"), ("b", "CCTGGG")]
aln = make_aligned_seqs(data=data, moltype="text")
dna = aln.to_dna()
dna

	0
b	CCTGGG
a	A.G---

2 x 6 dna alignment

Or using the generic to_moltype() method

aln.to_moltype("dna")

	0
b	CCTGGG
a	A.G---

2 x 6 dna alignment

To RNA

from cogent3 import make_aligned_seqs

data = [("a", "ACG---"), ("b", "CCUGGG")]
aln = make_aligned_seqs(data=data, moltype="text")
rna = aln.to_rna()
rna

	0
b	CCUGGG
a	A.G---

2 x 6 rna alignment

To PROTEIN

from cogent3 import make_aligned_seqs

data = [("x", "TYV"), ("y", "TE-")]
aln = make_aligned_seqs(data=data, moltype="text")
prot = aln.to_moltype("protein")
prot

	0
x	TYV
y	.E-

2 x 3 protein alignment

Handling gaps

Remove all gaps from an alignment

This necessarily returns a SequenceCollection.

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/primate_cdx2_promoter.fasta", moltype="dna")
degapped = aln.degap()
degapped

	0
human	AGCGCCCGCGGGTTCTGAGAGCGCTCAAAGCCGCCGAGTCAGGCTGCCCAGCCCGCCGGG
macaque	AGCGCCCGCGGGTTCTGAGAGCGCTCAAAGCCACCGAGTCAGGCTGCCCAGCCCGCCGGG
chimp	AGCGCCCGCGGGTTCTGAGAGCGCTCAAAGCCGCCGAGTCAGGCTGCCCAGCCCGCCGGG

3 x {min=1500, median=1503.0, max=1511} dna sequence collection

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")
new_aln

	0
seq1	ATGAATG
seq2	...-.AT
seq3	..--.AT

3 x 7 dna alignment

Converting an alignment to FASTA format

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/long_testseqs.fasta", moltype="dna")
fasta_align = aln.to_fasta()

Converting an alignment to Phylip format

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/test.paml", moltype="dna")
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

aln = load_aligned_seqs("data/test.paml", moltype="dna")
data = [str(s) for s in aln.seqs]

Converting an alignment to a list of arrays

import numpy
from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/test.paml", moltype="dna")
data = [numpy.array(s) for s in aln.seqs]
data

[array([3, 1, 2, 2, 3, 3, 1, 3, 1, 1, 2, 2, 1, 2, 3, 2, 3, 1, 2, 3, 2, 0,
        3, 3, 3, 1, 0, 3, 2, 2, 2, 3, 0, 2, 2, 3, 3, 2, 2, 2, 1, 2, 0, 3,
        0, 2, 2, 0, 3, 2, 0, 2, 3, 3, 1, 2, 3, 2, 1, 0], dtype=uint8),
 array([3, 1, 2, 3, 0, 3, 2, 3, 1, 1, 2, 3, 1, 2, 3, 2, 3, 1, 2, 3, 2, 0,
        3, 3, 3, 1, 0, 3, 1, 2, 2, 3, 0, 2, 2, 2, 3, 3, 2, 2, 1, 2, 0, 3,
        0, 2, 2, 1, 3, 2, 1, 2, 3, 3, 1, 2, 3, 3, 0, 0], dtype=uint8),
 array([3, 1, 2, 2, 3, 3, 2, 3, 1, 1, 2, 2, 1, 2, 0, 2, 2, 1, 2, 3, 2, 0,
        3, 3, 3, 1, 0, 3, 3, 2, 2, 3, 0, 2, 2, 3, 3, 2, 2, 2, 1, 2, 0, 3,
        0, 2, 2, 0, 3, 2, 0, 2, 3, 3, 1, 3, 3, 2, 1, 0], dtype=uint8),
 array([3, 1, 2, 2, 3, 3, 2, 3, 1, 1, 2, 2, 1, 2, 0, 2, 2, 1, 2, 3, 2, 0,
        3, 3, 3, 1, 0, 3, 2, 2, 2, 3, 0, 3, 2, 3, 3, 2, 2, 2, 1, 2, 0, 3,
        0, 2, 2, 0, 3, 2, 0, 2, 3, 3, 1, 2, 3, 2, 1, 0], dtype=uint8),
 array([3, 1, 2, 2, 3, 3, 2, 3, 1, 1, 2, 3, 1, 2, 3, 2, 2, 1, 2, 3, 2, 0,
        3, 3, 3, 0, 0, 3, 2, 2, 2, 1, 0, 2, 2, 3, 3, 2, 2, 2, 1, 2, 0, 3,
        0, 2, 2, 0, 3, 2, 0, 2, 3, 3, 1, 2, 3, 2, 1, 0], dtype=uint8)]

Getting an alignment as an array

The rows are sequences and their order corresponds to that of aln.names.

import numpy
from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/test.paml", moltype="dna")
aln.array_seqs

array([[3, 1, 2, 2, 3, 3, 1, 3, 1, 1, 2, 2, 1, 2, 3, 2, 3, 1, 2, 3, 2, 0,
        3, 3, 3, 1, 0, 3, 2, 2, 2, 3, 0, 2, 2, 3, 3, 2, 2, 2, 1, 2, 0, 3,
        0, 2, 2, 0, 3, 2, 0, 2, 3, 3, 1, 2, 3, 2, 1, 0],
       [3, 1, 2, 3, 0, 3, 2, 3, 1, 1, 2, 3, 1, 2, 3, 2, 3, 1, 2, 3, 2, 0,
        3, 3, 3, 1, 0, 3, 1, 2, 2, 3, 0, 2, 2, 2, 3, 3, 2, 2, 1, 2, 0, 3,
        0, 2, 2, 1, 3, 2, 1, 2, 3, 3, 1, 2, 3, 3, 0, 0],
       [3, 1, 2, 2, 3, 3, 2, 3, 1, 1, 2, 2, 1, 2, 0, 2, 2, 1, 2, 3, 2, 0,
        3, 3, 3, 1, 0, 3, 3, 2, 2, 3, 0, 2, 2, 3, 3, 2, 2, 2, 1, 2, 0, 3,
        0, 2, 2, 0, 3, 2, 0, 2, 3, 3, 1, 3, 3, 2, 1, 0],
       [3, 1, 2, 2, 3, 3, 2, 3, 1, 1, 2, 2, 1, 2, 0, 2, 2, 1, 2, 3, 2, 0,
        3, 3, 3, 1, 0, 3, 2, 2, 2, 3, 0, 3, 2, 3, 3, 2, 2, 2, 1, 2, 0, 3,
        0, 2, 2, 0, 3, 2, 0, 2, 3, 3, 1, 2, 3, 2, 1, 0],
       [3, 1, 2, 2, 3, 3, 2, 3, 1, 1, 2, 3, 1, 2, 3, 2, 2, 1, 2, 3, 2, 0,
        3, 3, 3, 0, 0, 3, 2, 2, 2, 1, 0, 2, 2, 3, 3, 2, 2, 2, 1, 2, 0, 3,
        0, 2, 2, 0, 3, 2, 0, 2, 3, 3, 1, 2, 3, 2, 1, 0]], dtype=uint8)

Slicing an alignment

Alignments can be thought of as a matrix, with sequences along the rows and alignment positions as the columns. However, all slicing is only along positions.

from cogent3 import load_aligned_seqs

fn = "data/long_testseqs.fasta"
aln = load_aligned_seqs(fn, moltype="dna")
aln[:24]

	0
DogFaced	TGTGGCACAAATACTCATGCCAAC
Human	......................G.
HowlerMon	......................G.
Mouse	.........G..G.........G.
NineBande	........................

5 x 24 dna alignment

Warning

A SequenceCollection cannot be sliced!

Getting a single column from an alignment

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/test.paml", moltype="dna")
column_four = aln[3]
column_four

	0
DogFaced	A
NineBande	.
Mouse	G
Human	.
HowlerMon	.

5 x 1 dna alignment

Getting a region of contiguous columns

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/long_testseqs.fasta", moltype="dna")
region = aln[50:70]

Iterating over alignment positions

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/primate_cdx2_promoter.fasta", moltype="dna")
col = list(aln[113:115].iter_positions())
col

[['A', 'A', 'A'], ['T', '-', '-']]

Getting codon 3rd positions

We can use conventional slice notation. 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"},
    moltype="dna",
)
aln[2::3]

	0
seq2	GGGG
seq1	...-

2 x 4 dna alignment

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()
new

	0
seq2	ACGACA---
seq1	...---...
seq3	...CA....

3 x 9 dna alignment

To detect if the alignment contains sequences not divisible by 3, use the strict argument. This argument covers both allowing partial terminating codons / not divisible by 3.

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

Eliminating columns with non-nucleotide characters

We sometimes want to eliminate ambiguous or gap data from our alignments. We demonstrate how to exclude alignment columns based on 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()
nucs

	0
seq1	ATGAAGGG
seq2	........
seq3	........

3 x 8 dna alignment

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

trinucs = aln.no_degenerates(motif_length=3)
trinucs

	0
seq1	ATGAAG
seq2	......
seq3	......

3 x 6 dna alignment

Getting all variable positions from an alignment

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/long_testseqs.fasta", moltype="dna")
pos = aln.variable_positions()
just_variable_aln = aln.take_positions(pos)
just_variable_aln[:10]

	0
DogFaced	AAACAAAATA
Human	..G.....CT
HowlerMon	..G...G.CT
Mouse	GGG.CC.GCT
NineBande	...T....CT

5 x 10 dna alignment

Getting all constant positions from an alignment

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/long_testseqs.fasta", moltype="dna")
pos = aln.variable_positions()
just_constant_aln = aln.take_positions(pos, negate=True)
just_constant_aln[:10]

	0
DogFaced	TGTGGCACAA
Human	..........
HowlerMon	..........
Mouse	..........
NineBande	..........

5 x 10 dna alignment

Getting all variable codons from an alignment

This is done using the filtered method using the motif_length argument.

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/long_testseqs.fasta", moltype="dna")
pos = aln.variable_positions(motif_length=3)
variable_codons = aln.take_positions(pos)
just_variable_aln[:9]

	0
DogFaced	AAACAAAAT
Human	..G.....C
HowlerMon	..G...G.C
Mouse	GGG.CC.GC
NineBande	...T....C

5 x 9 dna alignment

Filtering sequences

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 str(s)

aln.take_seqs_if(no_N_chars)

	0
seq2	ATGAAGGTGATG
seq1	.........---

2 x 12 dna alignment

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

aln.take_seqs_if(no_N_chars, negate=True)

	0
seq3	ATGAAGGNGATG

1 x 12 dna alignment

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()
counts

A	C	G	T
11	0	14	7

counts = aln.counts(motif_length=3)
counts

AAA	AAC	AAG	AAT	ACA	ACC	ACG	ACT	AGA	AGC	AGG	AGT	ATA	ATC	ATG	ATT	CAA	CAC	CAG	CAT	CCA	CCC	CCG	CCT	CGA	CGC	CGG	CGT	CTA	CTC	CTG	CTT	GAA	GAC	GAG	GAT	GCA	GCC	GCG	GCT	GGA	GGC	GGG	GGT	GTA	GTC	GTG	GTT	TAA	TAC	TAG	TAT	TCA	TCC	TCG	TCT	TGA	TGC	TGG	TGT	TTA	TTC	TTG	TTT
0	0	3	0	0	0	0	0	0	0	0	0	0	0	5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

counts = aln.counts(include_ambiguity=True)
counts

A	C	G	N	T
11	0	14	1	7

Note

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

Getting motif counts per sequence

from cogent3 import make_aligned_seqs

aln = make_aligned_seqs(
    data=[
        ("seq1", "ATGAAGGTG---"),
        ("seq2", "ATGAAGGTGATG"),
        ("seq3", "ATGAAGGNGATG"),
    ],
    moltype="dna",
)
counts = aln.counts_per_seq()
counts

	A	C	G	T
seq1	3	0	4	2
seq2	4	0	5	3
seq3	4	0	5	2

Note

There are also .probs_per_seq() and .entropy_per_seq() methods.

Getting motif counts per position

from cogent3 import make_aligned_seqs

aln = make_aligned_seqs(
    data=[
        ("seq1", "ATGAAGGTG---"),
        ("seq2", "ATGAAGGTGATG"),
        ("seq3", "ATGAAGGNGATG"),
    ],
    moltype="dna",
)
counts = aln.counts_per_pos()
counts

	T	C	A	G
0	0	0	3	0
1	3	0	0	0
2	0	0	0	3
3	0	0	3	0
4	0	0	3	0
5	0	0	0	3
6	0	0	0	3
7	2	0	0	0
8	0	0	0	3
9	0	0	2	0
10	2	0	0	0
11	0	0	0	2

Note

There are also .probs_per_pos() and .entropy_per_pos() methods.

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()
motif_probs

{'T': 0.25520602569782896,
 'C': 0.25808595480726626,
 'A': 0.24390784226849802,
 'G': 0.24280017722640673}

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

from cogent3 import load_aligned_seqs, get_moltype, make_table

trinuc_alphabet = get_moltype("dna").alphabet.get_kmer_alphabet(3)
aln = load_aligned_seqs("data/primate_cdx2_promoter.fasta", moltype="dna")
motif_probs = aln.get_motif_probs(alphabet=trinuc_alphabet)
table = make_table(header=["motif", "freq"], data=list(motif_probs.items()))
table

motif	freq
TTT	0.0201
TTC	0.0241
TTA	0.0114
TTG	0.0167
TCT	0.0288
...	...
GAG	0.0241
GGT	0.0194
GGC	0.0140
GGA	0.0207
GGG	0.0261

Top 5 and bottom 5 rows from 64 rows x 2 columns

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)
motif_probs

{'T': 8.333330555556482e-08,
 'C': 0.16666669444443521,
 'A': 0.6666665277778241,
 'G': 0.16666669444443521}

Note

For alignments, motif probabilities are computed by treating sequences as non-overlapping tuples. To get all possible k-mers, use the iter_kmers() method on the sequence classes.

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", moltype="dna")
col = aln[113:115].iter_positions()
c1, c2 = list(col)
c1, c2
list(filter(lambda x: x == "-", c1))
list(filter(lambda x: x == "-", c2))

['-', '-']

Calculating the gaps per position

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/primate_cdx2_promoter.fasta", moltype="dna")
gap_counts = aln.count_gaps_per_pos()
gap_counts # this is a DictArray

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To turn that into grap fraction

gap_frac = gap_counts.array / aln.num_seqs

Filtering alignments based on gaps

If we want to remove positions from the alignment which are gaps in more than a certain percentage of the sequences, use the omit_gap_pos().

aln = make_aligned_seqs(
    data=[
        ("seq1", "ATGAA---TG-"),
        ("seq2", "ATG-AGTGATG"),
        ("seq3", "AT--AG-GATG"),
    ],
    moltype="dna",
)
filtered_aln = aln.omit_gap_pos(allowed_gap_frac=0.40)
filtered_aln

	0
seq2	ATGAGGATG
seq1	....--TG-
seq3	..-......

3 x 9 dna alignment

Note

The default for filtered_aln.omit_gap_pos() is to remove columns with gaps in all the sequences. This can occur after sequences have been removed from the alignment.