Features

This guide provides instructions on creating, querying, and utilising features to manipulate biological sequence data.

How to create a custom Feature

Via add_feature

We can dynamically create a Feature via the add_feature() method, providing the key information about the feature.

The new feature will be added to the annotation_db attribute of the Sequence and/or Alignment. A Feature instance will be returned.

On a Sequence

We use add_feature to add a Feature to a Sequence, providing the biotype, name/id, and a list of start and stop indices.

from cogent3 import make_seq

# create an example sequence
seq = make_seq(
    "GCCTAATACTAAGCCTAAGCCTAAGACTAAGCCTAATACTAAGCCTAAGCCTAAGACTAA",
    name="seq1",
    moltype="dna",
)

# Add a feature
seq.add_feature(biotype="gene", name="a_gene", spans=[(12, 48)], seqid="seq1")

Feature(seqid='seq1', biotype='gene', name='a_gene', map=[12:48]/60, parent=DnaSequence(GCCTAAT... 60))

A feature can also be added as a series of non-overlapping genomic segments:

# Add a feature as a series
seq.add_feature(
    biotype="cpgsite",
    name="a_cpgsite",
    spans=[(12, 18), (21, 29), (35, 48)],
    seqid="seq1",
)

Feature(seqid='seq1', biotype='cpgsite', name='a_cpgsite', map=[12:18, 21:29, 35:48]/60, parent=DnaSequence(GCCTAAT... 60))

On a Sequence within an Alignment

We use add_feature and provide a value for seqid to add a feature to a sequence within an Alignment or SequenceCollection.

Note the difference between the value provided to spans, and the value of map in the returned Feature… the resulting feature is in alignment coordinates!

from cogent3 import make_aligned_seqs

# make demo alignment
aln1 = make_aligned_seqs([["seq1", "-AAACCCCCA"], ["seq2", "TTTT--TTTT"]],
    moltype="dna",
)
# add feature to seq1
aln1.add_feature(
    seqid="seq1", biotype="exon", name="A", spans=[(3, 8)], on_alignment=False
)

Feature(seqid="from 'seq1'", biotype='exon', name='A', map=[4:9]/10, parent=2 x 10 dna alignment: seq1[-AAACCCCCA], seq2[TTTT--TTTT])

The Feature specifies that seqid="from 'seq1'", indicating that it “belongs” to seq1.

On an Alignment

We use add_feature to add a Feature to an Alignment.

from cogent3 import make_aligned_seqs

# make demo alignment
aln1 = make_aligned_seqs([["seq1", "-AAACCCCCA"], ["seq2", "TTTT--TTTT"]],
    moltype="dna",
)

aln1.add_feature(
    biotype="exon",
    name="aligned_exon",
    spans=[(0, 8)],
    on_alignment=True,
)

Feature(seqid=None, biotype='exon', name='aligned_exon', map=[0:8]/10, parent=2 x 10 dna alignment: seq1[-AAACCCCCA], seq2[TTTT--TTTT])

The Feature specifies that seqid=None, indicating that it belongs to the alignment

Via an AnnotationDb

We can use add_feature to add a feature directly into an AnnotationDb, and assign it to the annotation_db attribute of a Sequence or Alignment. For extensive documentation on handling features directly via an AnnotationDb see Annotation Databases.

from cogent3 import make_seq
from cogent3.core.annotation_db import BasicAnnotationDb

# init empty db and add feature
db = BasicAnnotationDb()
db.add_feature(seqid="seq1", biotype="exon", name="C", spans=[(45, 48)])

# make demo seq
s1 = make_seq(
    "AAGAAGAAGACCCCCAAAAAAAAAATTTTTTTTTTAAAAAGGGAACCCT", name="seq1", moltype="dna"
)

# assign db to sequence
s1.annotation_db = db
s1.annotation_db

BasicAnnotationDb(source=':memory:', total_records=1)

How to load bulk Features from a File

Typically, we want to load bulk features from a genomic annotation file, such as a GFF or Genbank file. For the following examples, we will use Caenorhabditis elegans chromosome I.

Note

See the list of Data Files Used in the Documentation to download the data used in the following examples.

To load features from a genomic annotation file along with the corresponding sequence, we can use the load_seq function. The features are stored in a AnnotationDb and assigned to the annotation_db attribute of the sequence.

From a Genbank file

Directly using the genbank parser to get features as Python primitives

This approach returns features as Python primitives, except for the coordinates for features, which is a Location object.

from cogent3.parse.genbank import iter_genbank_records

label, seq, anns = list(iter_genbank_records("data/mycoplasma-genitalium.gb"))[0]
anns.keys()

dict_keys(['locus', 'length', 'mol_type', 'topology', 'db', 'date', 'definition', 'accession', 'version', 'dblink', 'keywords', 'source', 'species', 'taxonomy', 'references', 'comment', 'features', 'contig'])

As the output indicates, variable anns is a dictionary. The features in the GenBank feature table are available as a list under the "features" key. Listing just the first two.

features = anns["features"]
features[:2]

[{'type': 'source',
  'raw_location': ['1..580076'],
  'location': LocationList(1..580076),
  'organism': ['Mycoplasmoides genitalium G37'],
  'mol_type': ['genomic DNA'],
  'strain': ['G-37'],
  'type_material': ['type strain of Mycoplasma genitalium'],
  'db_xref': ['ATCC:33530', 'taxon:243273'],
  'note': ['strain coidentity: G37 = ATCC 33530']},
 {'type': 'gene',
  'raw_location': ['686..1828'],
  'location': LocationList(686..1828),
  'gene': ['dnaN'],
  'locus_tag': ['MG_RS00005'],
  'old_locus_tag': ['MG_001']}]

Illustrating how to get specific coordinate data (which are converted from the 1-based indices of GenBank to 0-based Python indices).

gene = features[1]
gene_loc = gene["location"]
gene_loc.get_coordinates(), gene_loc.strand

([(685, 1828)], 1)

How to load features and sequence data

To load the sequence and all 40,578 features from C. elegans Chromosome 1, we use the load_seq function ⚡️

from cogent3 import load_seq

%timeit load_seq("data/C-elegans-chromosome-I.gb", moltype="dna")

2.54 s ± 8.56 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

The features are stored in the annotation_db attribute.

seq.annotation_db

GenbankAnnotationDb(seqid='I', source=':memory:', namer=None, total_records=40578)

Now that the Sequence is annotated, we can query it for specific features. For more details on querying, skip to Querying for Features.

From a GFF file

How to load features and sequence data

Given a FASTA file with sequence data and a GFF file with annotations, we can use load_seq to load both the sequence and its corresponding features.

from cogent3 import load_seq

seq = load_seq(
    filename="data/C-elegans-chromosome-I.fa",
    annotation_path="data/C-elegans-chromosome-I.gff",
    moltype="dna",
)
seq.annotation_db

BasicAnnotationDb(source=':memory:', total_records=0)

Warning

total_records=0? 🤔 This is because load_seq assumes the sequence names match exactly between files! If the names are different, you need to provide function to the label_to_name argument.

Because the names above are different, for FASTA its "I dna:chromosome chromosome:WBcel235:I:1:15072434:1 REF" and for GFF its "I", we need a label_to_name argument. We provide a lambda function.

from cogent3 import load_seq

seq = load_seq(
    "data/C-elegans-chromosome-I.fa",
    annotation_path="data/C-elegans-chromosome-I.gff",
    label_to_name=lambda x: x.split()[0],
    moltype="dna",
)
seq.annotation_db

GffAnnotationDb(source=':memory:', total_records=60166)

How to merge features from multiple files into a single annotation db

We can create one annotation db from all files in a directory that match a wildcard (or glob) pattern (e.g. "path/to/dir/*.<filename suffix>") with the load_annotations() function.

from cogent3 import load_annotations

anndb = load_annotations(path="data/*.gb")
anndb.count_distinct(biotype=True).head()

biotype	count
source	7
gene	4,776
mRNA	4,693
CDS	5,223
misc_RNA	1,489

Top 5 rows from 12 rows x 2 columns

How to load features and associate them with an existing sequence

We load with the load_annotations() function and directly assign to the .annotation_db attribute.

If we know that the features and the sequence share the same coordinate space, then we only need to provide the path to the annotation file.

from cogent3 import load_seq, load_annotations

loaded_seq = load_seq(
    "data/C-elegans-chromosome-I.fa",
    label_to_name=lambda x: x.split()[0],
    moltype="dna",
)
db = load_annotations(path="data/C-elegans-chromosome-I.gff")
loaded_seq.annotation_db = db
loaded_seq.annotation_db

GffAnnotationDb(source=':memory:', total_records=60166)

How to load features and associate them with sequences in an existing alignment

For example, first we load an alignment of the brca1 gene in primates.

from cogent3 import load_aligned_seqs, load_annotations

brca1_aln = load_aligned_seqs(
    "data/primate_brca1.fasta", moltype="dna"
)
brca1_aln

	0
Chimpanzee	TGTGGCACAAATACTCATGCCAGCTCATTACAGCATGAGAACAGTTTATTACTCACTAAA
Galago	.......A................................G...................
HowlerMon	...............................................G............
Rhesus	...............................................G............
Orangutan	............................................................
Gorilla	............................................................
Human	............................................................

7 x 2814 (truncated to 7 x 60) dna alignment

Next, we annotate with a GFF file that contains features specific to the human gene.

ann_db = load_annotations(path="data/brca1_hsa_shortened.gff", seqids=["Human"])
brca1_aln.annotation_db = ann_db
brca1_aln.annotation_db

GffAnnotationDb(source=':memory:', total_records=11)

Note that the AnnotationDb is accessible via the Alignment (above) and Sequence (below) attribute.

brca1_aln.get_seq("Human").annotation_db

GffAnnotationDb(source=':memory:', total_records=11)

How to query a Sequence or Alignment for Features

The method get_features yields all features that match the given arguments. You can provide conditions for the name, biotype, and start/stop location of a feature.

Querying a Sequence for Features

Querying via Feature Name

We can search for a gene given its name (AKA its unique ID). For example we can search for a gene with name="WBGene00021661".

from cogent3 import load_seq

seq = load_seq("data/C-elegans-chromosome-I.gb", moltype="dna")

# note we wrap `get_features` in `list` as generator is returned
gene = list(seq.get_features(name="WBGene00021661", biotype="gene"))
gene

[Feature(seqid='I', biotype='gene', name='WBGene00021661', map=[343068:348424]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434))]

Querying via Feature Biotype

We can search for features with a certain biotype, for example, all coding sequences (CDS):

from cogent3 import load_seq

seq = load_seq("data/C-elegans-chromosome-I.gb", moltype="dna")
cds = list(seq.get_features(biotype="CDS"))
cds[:3]

[Feature(seqid='I', biotype='CDS', name='WBGene00000001', map=[5107843:5108016, 5108063:5108323, 5108504:5108770, 5108997:5109429, 5109509:5109947]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434)),
 Feature(seqid='I', biotype='CDS', name='WBGene00000006', map=[2571987:2572039, 2572092:2572300, 2572355:2572519, 2572569:2572736, 2573740:2573941, 2573998:2574267, 2574312:2574444, 2574520:2574584, 2575438:2575629, 2575840:2576006]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434)),
 Feature(seqid='I', biotype='CDS', name='WBGene00000010', map=[11389140:11389238, 11389874:11390003, 11390088:11390188, 11390947:11391134, 11392047:11392185, 11393291:11393519, 11393721:11393875, 11394746:11394878, 11394926:11395007, 11395068:11395177, 11396253:11396432, 11396567:11396658]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434))]

We can also provide combinations of argument to search, for example, all CDS with a given name:

cds = list(seq.get_features(biotype="CDS", name="WBGene00021661"))
cds

[Feature(seqid='I', biotype='CDS', name='WBGene00021661', map=[343540:343649, 343893:344033, 345020:345830, 346374:346516, 346560:346742, 346792:347044, 348304:348364]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434)),
 Feature(seqid='I', biotype='CDS', name='WBGene00021661', map=[343540:343649, 343893:344033, 345020:345830, 346374:346516, 346560:346742, 346792:347044, 347092:347113]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434))]

Querying via region of interest

We can provide start and end arguments to get_features() and all features within the coordinates will be returned.

from cogent3 import load_seq

seq = load_seq("data/C-elegans-chromosome-I.gb", moltype="dna")
region_features = list(seq.get_features(start=10148, stop=26732))
region_features[:3]

[Feature(seqid='I', biotype='gene', name='WBGene00022276', map=[11494:16837]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434)),
 Feature(seqid='I', biotype='mRNA', name='WBGene00022276', map=[11494:11561, 11617:11689, 14950:15160, 16472:16837]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434)),
 Feature(seqid='I', biotype='CDS', name='WBGene00022276', map=[11640:11689, 14950:15160, 16472:16585]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434))]

We can again provide a combination of conditions, for example, querying for all features with biotype="mRNA" within a certain range, and returning the first match.

mRNA = list(seq.get_features(start=10148, stop=29322, biotype="mRNA"))[0]
mRNA

Feature(seqid='I', biotype='mRNA', name='WBGene00022276', map=[11494:11561, 11617:11689, 14950:15160, 16472:16837]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434))

Limiting the number of returned Features

Both Sequence.get_features() and the equivalent methods on SequenceCollection and Alignment accept an optional limit argument that caps the total number of features yielded. This is handy when a query would otherwise return a very large set.

first_three = list(seq.get_features(biotype="CDS", limit=3))
first_three

[Feature(seqid='I', biotype='CDS', name='WBGene00000001', map=[5107843:5108016, 5108063:5108323, 5108504:5108770, 5108997:5109429, 5109509:5109947]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434)),
 Feature(seqid='I', biotype='CDS', name='WBGene00000006', map=[2571987:2572039, 2572092:2572300, 2572355:2572519, 2572569:2572736, 2573740:2573941, 2573998:2574267, 2574312:2574444, 2574520:2574584, 2575438:2575629, 2575840:2576006]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434)),
 Feature(seqid='I', biotype='CDS', name='WBGene00000010', map=[11389140:11389238, 11389874:11390003, 11390088:11390188, 11390947:11391134, 11392047:11392185, 11393291:11393519, 11393721:11393875, 11394746:11394878, 11394926:11395007, 11395068:11395177, 11396253:11396432, 11396567:11396658]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434))]

limit must be a positive integer; limit=None (the default) returns all matching features. For an Alignment the limit applies to the combined total of sequence-level and alignment-level features.

Querying a Sequence (via an Alignment) for Features

To query for a particular Sequence within an Alignment or SequenceCollection, we can use get_features as shown above for a Sequence, but providing the seqid for the sequence of interest.

For example, given an alignment of primates, we can search for features that are just on the human sequence as follows:

from cogent3 import load_aligned_seqs, load_annotations

# first load alignment and annotate the human seq
aln = load_aligned_seqs(
    "data/primate_brca1.fasta",
    moltype="dna",
)
# load the annotation data
ann_db = load_annotations(path="data/brca1_hsa_shortened.gff", seqids=["Human"])
aln.annotation_db = ann_db
# query alignment providing seqid of interest
human_exons = list(aln.get_features(biotype="exon", seqid="Human"))
human_exons

[Feature(seqid="from 'Human'", biotype='exon', name='unknown-0', map=[1:1507]/2814, parent=7 x 2814 dna alignment: Galago[TGTGGCAAAA...], HowlerMon[TGTGGCACAA...], Rhesus[TGTGGCACAA...], ...)]

Note that seqid="from'Human'" indicated this feature belongs to this particular sequence.

Querying an Alignment for Features

Querying for features on any Sequence in an Alignment

todo: on_alignment=False and dont provide seqid

from cogent3 import make_aligned_seqs

# add a feature to the alignment we created above on difference sequence
aln.add_feature(biotype="gene", name="gene:101", spans=[(40, 387)], seqid="Rhesus")

any_feature = list(aln.get_features(on_alignment=False))
any_feature

[Feature(seqid="from 'Rhesus'", biotype='gene', name='gene:101', map=[40:387]/2814, parent=7 x 2814 dna alignment: Galago[TGTGGCAAAA...], HowlerMon[TGTGGCACAA...], Rhesus[TGTGGCACAA...], ...),
 Feature(seqid="from 'Human'", biotype='exon', name='unknown-0', map=[1:1507]/2814, parent=7 x 2814 dna alignment: Galago[TGTGGCAAAA...], HowlerMon[TGTGGCACAA...], Rhesus[TGTGGCACAA...], ...)]

Note there are features from both Rhesus, which we just added, and Human, which we annotated above

Querying for features on an Alignment

todo: on_alignment=True and dont provide seqid

Using add_feature we add a feature to the brca1 alignment we have been using above, by specifying on_alignment=True this feature will be on the Alignment.

To query for features on the alignment, we use get_features, again specifying on_alignment=True.

from cogent3 import make_aligned_seqs

# first we add the feature to the alignment
aln.add_feature(
    biotype="pseudogene", name="pseudogene1", spans=[(420, 666)], on_alignment=True
)

# query for features on the alignment
aln_features = list(aln.get_features(on_alignment=True))
aln_features

[Feature(seqid='', biotype='pseudogene', name='pseudogene1', map=[420:666]/2814, parent=7 x 2814 dna alignment: Galago[TGTGGCAAAA...], HowlerMon[TGTGGCACAA...], Rhesus[TGTGGCACAA...], ...)]

Note how even though we annotated the Human and Rhesus sequences in the above examples, only the pseudogene we added to Alignment is returned by this query.

Querying features that span gaps in alignments

If you query for a Feature from a Sequence (i.e. the feature is in sequence coordinates), its alignment coordinates may be discontinuous. This will lead to an omission of data from other sequences!

from cogent3 import make_aligned_seqs

aln3 = make_aligned_seqs([["x", "C-CCCAAAAA"], ["y", "-T----TTTT"]],
    moltype="dna",
)
exon = aln3.add_feature(
    seqid="x", biotype="exon", name="ex1", spans=[(0, 4)], on_alignment=False
)
exon.get_slice()

	0
x	CCCC
y	----

2 x 4 dna alignment

aln_exons = list(aln3.get_features(seqid="x", biotype="exon"))[0]
aln_exons

Feature(seqid="from 'x'", biotype='exon', name='ex1', map=[0:1, 2:5]/10, parent=2 x 10 dna alignment: x[C-CCCAAAAA], y[-T----TTTT])

Note

In the above, the T in sequence Y opposite the gap is missing since this approach only returns positions directly corresponding to the feature.

To include the gaps, use the allow_gaps argument

exon.get_slice(allow_gaps=True)

	0
x	C-CCC
y	-T---

2 x 5 dna alignment

Examples using the methods available on Features

A Feature has many methods to manipulate the sequence or alignment that they are bound to.

How to slice a Sequence or Alignment by its features

Given a Feature, we can directly slice its parent sequence to return its sequence information

from cogent3 import load_seq

seq = load_seq(
    "data/C-elegans-chromosome-I.fa",
    annotation_path="data/C-elegans-chromosome-I.gff",
    label_to_name=lambda x: x.split()[0],
    moltype="dna",
)
pseudogene = list(seq.get_features(start=10148, stop=26732, biotype="pseudogene"))[0]
seq[pseudogene]

	0
gene:WBGene00235381	CAGCAAGAAGAACTTCCACAAGACATTTATGAAGGACGTGACTGCGGAATTGGCCAGAAT

DnaSequence, length=719 (truncated to 60)

Note

This only works for the Sequence that the Feature “belongs” to.

We can also achieve this via get_slice()

pseudogene.get_slice()

	0
gene:WBGene00235381	CAGCAAGAAGAACTTCCACAAGACATTTATGAAGGACGTGACTGCGGAATTGGCCAGAAT

DnaSequence, length=719 (truncated to 60)

How to display the features of a Sequence

We can display all the features on a sequence using .get_drawable(), or a subset of biotypes. We do this for only the first 50,000 base pairs. The plotly figure returned, as displayed below, is interactive! 🤩 Zoom in on the dark vertical lines in the big gene and you will see small genes on the opposite strand. Hover your cursor over each block and the gene name is displayed.

from cogent3 import load_seq

seq = load_seq("data/C-elegans-chromosome-I.gb", moltype="dna")
subseq = seq[25000:35000]
fig = subseq.get_drawable(biotype=("gene", "mRNA", "CDS", "misc_RNA"))
fig.show()

Note

If a feature extends beyond the sequence region selected, its name includes the text “(incomplete)”.

How to find the coordinates of a feature

pseudogene.get_coordinates()

[(22881, 23600)]

These are useful for doing custom things, e.g. if the introns are not annotated for a gene, we can generate the introns from the coordinates of the exons as follows:

from cogent3 import load_seq

seq = load_seq("data/C-elegans-chromosome-I.gb", moltype="dna")
cds = list(seq.get_features(biotype="CDS"))[0]
exon_coords = cds.get_coordinates()

exon_coords

[(5107843, 5108016),
 (5108063, 5108323),
 (5108504, 5108770),
 (5108997, 5109429),
 (5109509, 5109947)]

We generate the intron coordinates from the second element of the first tuple, and the first element of the second tuple and so on:

intron_coords = []

for i in range(len(exon_coords) - 1):
    intron_coords.append((exon_coords[i][1], exon_coords[i + 1][0]))

intron_coords

[(5108016, 5108063),
 (5108323, 5108504),
 (5108770, 5108997),
 (5109429, 5109509)]

We can then add the introns as a Feature to the sequence!

intron = seq.add_feature(
    biotype="intron", name="intron:Y74C9A.3.1", seqid="I", spans=intron_coords
)
intron

Feature(seqid='I', biotype='intron', name='intron:Y74C9A.3.1', map=[5108016:5108063, 5108323:5108504, 5108770:5108997, 5109429:5109509]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434))

How to take the union of features

We can create a feature that is the union of all coding sequence.

from cogent3 import load_seq

seq = load_seq("data/C-elegans-chromosome-I.gb", moltype="dna")
cds = list(seq.get_features(biotype="CDS"))
union_cds = cds[0].union(cds[1:])

How to get the shadow of a Feature

The “shadow” of a feature is a new feature containing all of the sequence except the feature!

How to use the shadow of a Feature to return the intergenic sequence

We first need to query our sequence for all genes. Using the union() method we combine all genes into a single feature.

from cogent3 import load_seq

seq = load_seq("data/C-elegans-chromosome-I.gb", moltype="dna")
genes = list(seq.get_features(biotype="gene"))
genes = genes[0].union(genes[1:])
genes

Feature(seqid='I', biotype='gene', name='WBGene00000001, WBGene00000006, WBGene00000010, WBGene00000012, WBGene00000013, WBGene00000020, WBGene00000023, WBGene00000036, WBGene00000045, WBGene00000050, WBGene00000058, WBGene00000079, WBGene00000084, WBGene00000088, WBGene00000089, WBGene00000095, WBGene00000096, WBGene00000099, WBGene00000138, WBGene00000140, WBGene00000147, WBGene00000148, WBGene00000150, WBGene00000155, WBGene00000156, WBGene00000158, WBGene00000163, WBGene00000165, WBGene00000177, WBGene00000197, WBGene00000199, WBGene00000205, WBGene00000209, WBGene00000227, WBGene00000230, WBGene00000232, WBGene00000236, WBGene00000237, WBGene00000241, WBGene00000253, WBGene00000254, WBGene00000269, WBGene00000272, WBGene00000275, WBGene00000284, WBGene00000368, WBGene00000371, WBGene00000382, WBGene00000386, WBGene00000408, WBGene00000409, WBGene00000410, WBGene00000413, WBGene00000415, WBGene00000426, WBGene00000429, WBGene00000430, WBGene00000431, WBGene00000433, WBGene00000434, WBGene00000436, WBGene00000440, WBGene00000466, WBGene00000467, WBGene00000468, WBGene00000469, WBGene00000473, WBGene00000474, WBGene00000476, WBGene00000483, WBGene00000485, WBGene00000492, WBGene00000493, WBGene00000496, WBGene00000500, WBGene00000502, WBGene00000527, WBGene00000540, WBGene00000544, WBGene00000583, WBGene00000596, WBGene00000612, WBGene00000622, WBGene00000623, WBGene00000624, WBGene00000625, WBGene00000626, WBGene00000627, WBGene00000628, WBGene00000629, WBGene00000630, WBGene00000631, WBGene00000632, WBGene00000633, WBGene00000634, WBGene00000636, WBGene00000637, WBGene00000638, WBGene00000639, WBGene00000640, WBGene00000641, WBGene00000642, WBGene00000761, WBGene00000764, WBGene00000772, WBGene00000777, WBGene00000779, WBGene00000780, WBGene00000787, WBGene00000788, WBGene00000794, WBGene00000800, WBGene00000801, WBGene00000812, WBGene00000814, WBGene00000821, WBGene00000832, WBGene00000843, WBGene00000867, WBGene00000869, WBGene00000871, WBGene00000881, WBGene00000890, WBGene00000891, WBGene00000896, WBGene00000904, WBGene00000912, WBGene00000917, WBGene00000931, WBGene00000937, WBGene00000938, WBGene00000962, WBGene00000963, WBGene00000965, WBGene00000966, WBGene00000967, WBGene00000968, WBGene00001008, WBGene00001022, WBGene00001030, WBGene00001039, WBGene00001045, WBGene00001046, WBGene00001047, WBGene00001048, WBGene00001049, WBGene00001050, WBGene00001056, WBGene00001067, WBGene00001075, WBGene00001112, WBGene00001115, WBGene00001117, WBGene00001121, WBGene00001131, WBGene00001136, WBGene00001145, WBGene00001147, WBGene00001156, WBGene00001167, WBGene00001196, WBGene00001214, WBGene00001215, WBGene00001226, WBGene00001228, WBGene00001231, WBGene00001232, WBGene00001234, WBGene00001309, WBGene00001333, WBGene00001334, WBGene00001337, WBGene00001365, WBGene00001372, WBGene00001377, WBGene00001404, WBGene00001405, WBGene00001414, WBGene00001427, WBGene00001430, WBGene00001432, WBGene00001433, WBGene00001442, WBGene00001481, WBGene00001483, WBGene00001488, WBGene00001491, WBGene00001497, WBGene00001501, WBGene00001542, WBGene00001555, WBGene00001568, WBGene00001574, WBGene00001582, WBGene00001590, WBGene00001592, WBGene00001595, WBGene00001596, WBGene00001598, WBGene00001599, WBGene00001600, WBGene00001601, WBGene00001614, WBGene00001627, WBGene00001640, WBGene00001641, WBGene00001642, WBGene00001643, WBGene00001644, WBGene00001647, WBGene00001648, WBGene00001651, WBGene00001676, WBGene00001677, WBGene00001678, WBGene00001680, WBGene00001682, WBGene00001704, WBGene00001707, WBGene00001725, WBGene00001745, WBGene00001746, WBGene00001773, WBGene00001791, WBGene00001814, WBGene00001816, WBGene00001819, WBGene00001832, WBGene00001833, WBGene00001836, WBGene00001843, WBGene00001856, WBGene00001859, WBGene00001860, WBGene00001941, WBGene00001942, WBGene00001949, WBGene00001960, WBGene00001973, WBGene00001979, WBGene00001980, WBGene00001985, WBGene00001990, WBGene00002000, WBGene00002004, WBGene00002026, WBGene00002027, WBGene00002034, WBGene00002035, WBGene00002040, WBGene00002042, WBGene00002047, WBGene00002069, WBGene00002070, WBGene00002073, WBGene00002075, WBGene00002077, WBGene00002079, WBGene00002101, WBGene00002107, WBGene00002108, WBGene00002109, WBGene00002110, WBGene00002111, WBGene00002112, WBGene00002113, WBGene00002116, WBGene00002119, WBGene00002134, WBGene00002135, WBGene00002136, WBGene00002137, WBGene00002138, WBGene00002139, WBGene00002141, WBGene00002142, WBGene00002143, WBGene00002144, WBGene00002153, WBGene00002174, WBGene00002179, WBGene00002181, WBGene00002189, WBGene00002191, WBGene00002196, WBGene00002198, WBGene00002205, WBGene00002213, WBGene00002225, WBGene00002226, WBGene00002232, WBGene00002240, WBGene00002248, WBGene00002257, WBGene00002258, WBGene00002276, WBGene00002324, WBGene00002348, WBGene00002393, WBGene00002583, WBGene00002601, WBGene00002694, WBGene00002696, WBGene00002717, WBGene00002783, WBGene00002804, WBGene00002977, WBGene00002978, WBGene00002979, WBGene00002989, WBGene00002999, WBGene00003000, WBGene00003006, WBGene00003014, WBGene00003020, WBGene00003026, WBGene00003029, WBGene00003036, WBGene00003040, WBGene00003041, WBGene00003052, WBGene00003059, WBGene00003060, WBGene00003061, WBGene00003062, WBGene00003068, WBGene00003071, WBGene00003072, WBGene00003074, WBGene00003080, WBGene00003083, WBGene00003098, WBGene00003111, WBGene00003123, WBGene00003132, WBGene00003136, WBGene00003156, WBGene00003170, WBGene00003172, WBGene00003182, WBGene00003183, WBGene00003184, WBGene00003186, WBGene00003209, WBGene00003221, WBGene00003229, WBGene00003233, WBGene00003236, WBGene00003238, WBGene00003258, WBGene00003260, WBGene00003261, WBGene00003278, WBGene00003299, WBGene00003307, WBGene00003316, WBGene00003328, WBGene00003338, WBGene00003339, WBGene00003387, WBGene00003396, WBGene00003397, WBGene00003400, WBGene00003418, WBGene00003422, WBGene00003471, WBGene00003472, WBGene00003475, WBGene00003499, WBGene00003508, WBGene00003516, WBGene00003524, WBGene00003548, WBGene00003552, WBGene00003564, WBGene00003569, WBGene00003578, WBGene00003581, WBGene00003584, WBGene00003585, WBGene00003587, WBGene00003596, WBGene00003601, WBGene00003622, WBGene00003639, WBGene00003652, WBGene00003654, WBGene00003659, WBGene00003663, WBGene00003664, WBGene00003667, WBGene00003671, WBGene00003672, WBGene00003675, WBGene00003679, WBGene00003703, WBGene00003735, WBGene00003742, WBGene00003746, WBGene00003750, WBGene00003753, WBGene00003777, WBGene00003788, WBGene00003790, WBGene00003792, WBGene00003793, WBGene00003797, WBGene00003798, WBGene00003799, WBGene00003800, WBGene00003803, WBGene00003815, WBGene00003818, WBGene00003836, WBGene00003842, WBGene00003847, WBGene00003878, WBGene00003902, WBGene00003904, WBGene00003905, WBGene00003906, WBGene00003921, WBGene00003924, WBGene00003925, WBGene00003926, WBGene00003934, WBGene00003948, WBGene00003950, WBGene00003951, WBGene00003953, WBGene00003964, WBGene00003977, WBGene00003980, WBGene00003989, WBGene00003996, WBGene00004014, WBGene00004028, WBGene00004043, WBGene00004049, WBGene00004063, WBGene00004068, WBGene00004077, WBGene00004087, WBGene00004093, WBGene00004094, WBGene00004109, WBGene00004110, WBGene00004111, WBGene00004113, WBGene00004115, WBGene00004117, WBGene00004131, WBGene00004143, WBGene00004147, WBGene00004154, WBGene00004168, WBGene00004178, WBGene00004181, WBGene00004186, WBGene00004190, WBGene00004196, WBGene00004202, WBGene00004217, WBGene00004224, WBGene00004225, WBGene00004226, WBGene00004231, WBGene00004235, WBGene00004237, WBGene00004246, WBGene00004268, WBGene00004272, WBGene00004273, WBGene00004274, WBGene00004275, WBGene00004298, WBGene00004305, WBGene00004312, WBGene00004318, WBGene00004324, WBGene00004332, WBGene00004368, WBGene00004372, WBGene00004378, WBGene00004380, WBGene00004389, WBGene00004393, WBGene00004404, WBGene00004408, WBGene00004409, WBGene00004412, WBGene00004415, WBGene00004418, WBGene00004425, WBGene00004426, WBGene00004429, WBGene00004431, WBGene00004436, WBGene00004437, WBGene00004439, WBGene00004441, WBGene00004444, WBGene00004445, WBGene00004464, WBGene00004466, WBGene00004475, WBGene00004476, WBGene00004479, WBGene00004484, WBGene00004486, WBGene00004488, WBGene00004489, WBGene00004495, WBGene00004505, WBGene00004508, WBGene00004509, WBGene00004512, WBGene00004513, WBGene00004567, WBGene00004622, WBGene00004677, WBGene00004684, WBGene00004706, WBGene00004721, WBGene00004743, WBGene00004753, WBGene00004771, WBGene00004773, WBGene00004775, WBGene00004778, WBGene00004803, WBGene00004807, WBGene00004808, WBGene00004836, WBGene00004838, WBGene00004839, WBGene00004841, WBGene00004875, WBGene00004879, WBGene00004880, WBGene00004883, WBGene00004887, WBGene00004888, WBGene00004889, WBGene00004890, WBGene00004895, WBGene00004900, WBGene00004901, WBGene00004915, WBGene00004920, WBGene00004924, WBGene00004931, WBGene00004944, WBGene00004946, WBGene00004952, WBGene00004953, WBGene00004955, WBGene00004958, WBGene00004959, WBGene00004962, WBGene00004963, WBGene00004965, WBGene00004966, WBGene00004969, WBGene00004978, WBGene00005000, WBGene00005002, WBGene00005009, WBGene00005010, WBGene00005023, WBGene00005043, WBGene00005044, WBGene00005045, WBGene00005046, WBGene00005047, WBGene00005048, WBGene00005049, WBGene00005050, WBGene00005051, WBGene00005077, WBGene00005078, WBGene00005131, WBGene00005171, WBGene00005272, WBGene00005273, WBGene00005274, WBGene00005517, WBGene00005523, WBGene00005524, WBGene00005525, WBGene00005526, WBGene00005527, WBGene00005528, WBGene00005529, WBGene00005710, WBGene00005835, WBGene00005839, WBGene00005840, WBGene00005858, WBGene00006044, WBGene00006047, WBGene00006048, WBGene00006058, WBGene00006061, WBGene00006062, WBGene00006272, WBGene00006306, WBGene00006324, WBGene00006349, WBGene00006352, WBGene00006377, WBGene00006379, WBGene00006382, WBGene00006385, WBGene00006386, WBGene00006394, WBGene00006399, WBGene00006407, WBGene00006423, WBGene00006440, WBGene00006445, WBGene00006447, WBGene00006461, WBGene00006484, WBGene00006492, WBGene00006503, WBGene00006505, WBGene00006508, WBGene00006518, WBGene00006525, WBGene00006528, WBGene00006529, WBGene00006531, WBGene00006532, WBGene00006565, WBGene00006574, WBGene00006577, WBGene00006578, WBGene00006593, WBGene00006594, WBGene00006595, WBGene00006607, WBGene00006616, WBGene00006618, WBGene00006624, WBGene00006625, WBGene00006633, WBGene00006641, WBGene00006645, WBGene00006656, WBGene00006681, WBGene00006682, WBGene00006689, WBGene00006700, WBGene00006702, WBGene00006707, WBGene00006709, WBGene00006711, WBGene00006720, WBGene00006726, WBGene00006730, WBGene00006732, WBGene00006740, WBGene00006751, WBGene00006752, WBGene00006753, WBGene00006754, WBGene00006765, WBGene00006771, WBGene00006773, WBGene00006774, WBGene00006776, WBGene00006789, WBGene00006790, WBGene00006791, WBGene00006793, WBGene00006797, WBGene00006805, WBGene00006807, WBGene00006819, WBGene00006820, WBGene00006823, WBGene00006824, WBGene00006829, WBGene00006833, WBGene00006844, WBGene00006845, WBGene00006876, WBGene00006888, WBGene00006889, WBGene00006902, WBGene00006919, WBGene00006932, WBGene00006936, WBGene00006958, WBGene00006961, WBGene00006963, WBGene00006976, WBGene00006978, WBGene00006984, WBGene00007001, WBGene00007009, WBGene00007018, WBGene00007024, WBGene00007026, WBGene00007042, WBGene00007043, WBGene00007047, WBGene00007053, WBGene00007054, WBGene00007057, WBGene00007094, WBGene00007155, WBGene00007156, WBGene00007158, WBGene00007159, WBGene00007213, WBGene00007214, WBGene00007215, WBGene00007216, WBGene00007217, WBGene00007218, WBGene00007219, WBGene00007252, WBGene00007253, WBGene00007254, WBGene00007255, WBGene00007256, WBGene00007257, WBGene00007258, WBGene00007274, WBGene00007275, WBGene00007277, WBGene00007278, WBGene00007297, WBGene00007299, WBGene00007300, WBGene00007301, WBGene00007302, WBGene00007303, WBGene00007533, WBGene00007597, WBGene00007598, WBGene00007599, WBGene00007600, WBGene00007601, WBGene00007602, WBGene00007603, WBGene00007620, WBGene00007621, WBGene00007637, WBGene00007638, WBGene00007639, WBGene00007640, WBGene00007641, WBGene00007642, WBGene00007643, WBGene00007644, WBGene00007645, WBGene00007646, WBGene00007647, WBGene00007654, WBGene00007655, WBGene00007656, WBGene00007657, WBGene00007658, WBGene00007659, WBGene00007660, WBGene00007661, WBGene00007662, WBGene00007705, WBGene00007707, WBGene00007708, WBGene00007709, WBGene00007710, WBGene00007711, WBGene00007712, WBGene00007713, WBGene00007739, WBGene00007740, WBGene00007741, WBGene00007742, WBGene00007743, WBGene00007766, WBGene00007767, WBGene00007768, WBGene00007769, WBGene00007770, WBGene00007854, WBGene00007855, WBGene00007856, WBGene00007857, WBGene00007858, WBGene00007911, WBGene00007912, WBGene00007913, WBGene00007914, WBGene00007971, WBGene00007972, WBGene00007973, WBGene00007974, WBGene00007975, WBGene00007976, WBGene00007977, WBGene00007978, WBGene00007979, WBGene00007984, WBGene00007985, WBGene00007986, WBGene00007988, WBGene00007989, WBGene00007990, WBGene00007991, WBGene00007992, WBGene00008061, WBGene00008062, WBGene00008063, WBGene00008064, WBGene00008107, WBGene00008130, WBGene00008131, WBGene00008132, WBGene00008133, WBGene00008134, WBGene00008157, WBGene00008158, WBGene00008159, WBGene00008160, WBGene00008161, WBGene00008162, WBGene00008163, WBGene00008184, WBGene00008185, WBGene00008186, WBGene00008187, WBGene00008188, WBGene00008190, WBGene00008191, WBGene00008216, WBGene00008289, WBGene00008290, WBGene00008291, WBGene00008292, WBGene00008293, WBGene00008294, WBGene00008295, WBGene00008311, WBGene00008312, WBGene00008313, WBGene00008314, WBGene00008315, WBGene00008316, WBGene00008317, WBGene00008318, WBGene00008381, WBGene00008382, WBGene00008383, WBGene00008384, WBGene00008385, WBGene00008386, WBGene00008387, WBGene00008397, WBGene00008398, WBGene00008399, WBGene00008400, WBGene00008401, WBGene00008411, WBGene00008412, WBGene00008413, WBGene00008414, WBGene00008415, WBGene00008417, WBGene00008418, WBGene00008419, WBGene00008420, WBGene00008421, WBGene00008439, WBGene00008440, WBGene00008470, WBGene00008471, WBGene00008472, WBGene00008473, WBGene00008474, WBGene00008475, WBGene00008476, WBGene00008477, WBGene00008478, WBGene00008479, WBGene00008528, WBGene00008529, WBGene00008530, WBGene00008531, WBGene00008532, WBGene00008533, WBGene00008541, WBGene00008542, WBGene00008543, WBGene00008564, WBGene00008565, WBGene00008566, WBGene00008567, WBGene00008568, WBGene00008569, WBGene00008570, WBGene00008610, WBGene00008611, WBGene00008612, WBGene00008613, WBGene00008648, WBGene00008649, WBGene00008650, WBGene00008651, WBGene00008652, WBGene00008661, WBGene00008662, WBGene00008663, WBGene00008664, WBGene00008665, WBGene00008666, WBGene00008667, WBGene00008681, WBGene00008695, WBGene00008762, WBGene00008763, WBGene00008764, WBGene00008765, WBGene00008766, WBGene00008767, WBGene00008768, WBGene00008769, WBGene00008770, WBGene00008779, WBGene00008780, WBGene00008781, WBGene00008782, WBGene00008783, WBGene00008784, WBGene00008785, WBGene00008786, WBGene00008787, WBGene00008852, WBGene00008853, WBGene00008854, WBGene00008855, WBGene00008856, WBGene00008857, WBGene00008858, WBGene00008870, WBGene00008876, WBGene00008877, WBGene00008878, WBGene00008885, WBGene00008886, WBGene00008887, WBGene00008889, WBGene00008890, WBGene00008905, WBGene00008906, WBGene00008907, WBGene00008908, WBGene00008909, WBGene00008934, WBGene00008935, WBGene00008990, WBGene00008991, WBGene00009001, WBGene00009002, WBGene00009004, WBGene00009005, WBGene00009050, WBGene00009051, WBGene00009052, WBGene00009054, WBGene00009055, WBGene00009056, WBGene00009061, WBGene00009063, WBGene00009064, WBGene00009065, WBGene00009112, WBGene00009113, WBGene00009114, WBGene00009116, WBGene00009117, WBGene00009118, WBGene00009119, WBGene00009120, WBGene00009121, WBGene00009122, WBGene00009123, WBGene00009124, WBGene00009125, WBGene00009126, WBGene00009127, WBGene00009128, WBGene00009129, WBGene00009130, WBGene00009140, WBGene00009141, WBGene00009142, WBGene00009143, WBGene00009144, WBGene00009145, WBGene00009157, WBGene00009158, WBGene00009159, WBGene00009160, WBGene00009161, WBGene00009162, WBGene00009163, WBGene00009164, WBGene00009165, WBGene00009173, WBGene00009174, WBGene00009176, WBGene00009177, WBGene00009178, WBGene00009187, WBGene00009188, WBGene00009189, WBGene00009190, WBGene00009191, WBGene00009210, WBGene00009220, WBGene00009243, WBGene00009250, WBGene00009251, WBGene00009252, WBGene00009254, WBGene00009260, WBGene00009261, WBGene00009262, WBGene00009263, WBGene00009264, WBGene00009266, WBGene00009267, WBGene00009268, WBGene00009269, WBGene00009270, WBGene00009271, WBGene00009272, WBGene00009274, WBGene00009276, WBGene00009277, WBGene00009284, WBGene00009285, WBGene00009286, WBGene00009287, WBGene00009288, WBGene00009304, WBGene00009305, WBGene00009306, 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parent=DnaSequence(GCCTAAG... 15,072,434))

Taking the “shadow” of all genes will return the intergenic region as a valid Feature

intergenic = genes.shadow("not gene")
intergenic

Feature(seqid='I', biotype='not gene', name='not gene', map=[0:3746, 3909:4115, 10230:11494, 16837:17483, 26781:27588, 33150:43732, 44677:47460, 49860:49920, 54426:55292, 64066:71424, 81156:81192, 91296:91403, 92720:93021, 95312:96458, 108223:108681, 110057:110550, 110771:111037, 113751:113806, 114708:115655, 115676:115686, 117498:118092, 120901:127284, 134270:134336, 137282:137844, 144565:144810, 154855:160171, 169763:171222, 176040:178231, 178291:178523, 182772:183363, 208384:209280, 211738:212255, 215491:216027, 217002:217927, 219137:219734, 221575:231577, 237853:238615, 240250:240490, 245414:248623, 251580:252106, 259157:263501, 263618:272602, 272627:280113, 289185:291214, 305477:305810, 310790:310980, 314989:316634, 318066:318548, 323352:323854, 330987:332603, 337845:343068, 348424:348459, 357236:357239, 363901:364509, 373465:373570, 381902:382825, 394415:398890, 418677:420921, 423713:444002, 461605:461994, 467137:471134, 473890:477581, 483880:484038, 488392:488606, 489905:489968, 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14719027:14719166, 14720477:14720587, 14726131:14726280, 14730738:14732930, 14733669:14735222, 14737195:14737701, 14738796:14738881, 14742671:14742764, 14747789:14748617, 14753777:14755153, 14756412:14756903, 14759473:14759929, 14760626:14760637, 14768989:14770688, 14775007:14775183, 14776663:14777136, 14787804:14788124, 14789517:14790243, 14790934:14791039, 14797167:14797659, 14798705:14806328, 14806473:14807461, 14820235:14820404, 14821651:14822049, 14823996:14824492, 14825952:14826557, 14827779:14828199, 14829594:14829731, 14834841:14838916, 14839138:14839855, 14843128:14846475, 14855827:14855900, 14863482:14867923, 14871365:14872111, 14875436:14875822, 14900322:14906269, 14906399:14906791, 14913083:14913765, 14952226:14953489, 14955492:14955635, 14957097:14961496, 14965361:14967300, 14968540:14975418, 14980596:14984177, 14990016:14993823, 14993959:14994580, 15001691:15001852, 15013904:15014836, 15016595:15017282, 15022990:15023167, 15030708:15031140, 15033039:15034514, 15035467:15036008, 15041274:15041432, 15050700:15050796, 15052420:15053009, 15058994:15060298, 15061159:15062082, 15063836:15064300, 15064453:15064837, 15068355:15069279, 15071033:15072265, 15072426:15072434]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434))

We can slice the sequence by this new Feature to return the complete intergenic sequence! However, because the automated naming produces a crazy long name, we reset that.

intergenic.get_slice()

	0
not gene	GCCTAAGCCTAAGCCTAAGCCTAAGCCTAAGCCTAAGCCTAAGCCTAAGCCTAAGCCTAA

DnaSequence, length=4,010,835 (truncated to 60)

How to mask annotated regions

Masking annotated regions on a sequence

We can mask a certain annotation using with_masked_annotations()

from cogent3 import load_seq

seq = load_seq("data/C-elegans-chromosome-I.gb", moltype="dna")
no_cds = seq.with_masked_annotations("CDS")
no_cds[2575800:2575900]

	0
I	TTTTTTAAACTCCTGTAGCAATTTTATTTAAATTACTTAC????????????????????

DnaSequence, length=100 (truncated to 60)

The above sequence could then have positions filtered so no position with the ambiguous character ‘?’ was present.

Masking annotated regions on an Alignment

We can mask exons on an alignment.

from cogent3 import make_aligned_seqs

aln = make_aligned_seqs([["x", "C-CCCAAAAAGGGAA"], ["y", "-T----TTTTG-GTT"]],
    moltype="dna",
)
exon = aln.add_feature(
    seqid="x",
    biotype="exon",
    name="exon-be-gone",
    spans=[(0, 4)],
    on_alignment=False,
)
aln.with_masked_annotations("exon", mask_char="?")

	0
y	-T----TTTTG-GTT
x	?-???AAAAA.G.AA

2 x 15 dna alignment

After a reverse complement operation

rc = aln.rc()
rc

	0
x	TTCCCTTTTTGGG-G
y	AA.-.AAAA----A-

2 x 15 dna alignment

these persist.

rc.with_masked_annotations("exon", mask_char="?")

	0
y	AAC-CAAAA----A-
x	TT.C.TTTTT???-?

2 x 15 dna alignment

How to find the “children” of a Feature

To find the “children” of a feature, we can use the get_children() method. A “child” refers to a feature that is nested within or contained by another “parent” feature. For example, a child feature could be an exon contained within a gene or a CDS contained within a transcript.

This method returns a generator that yields all the child features of the specified feature.

For example, let’s find the children of the gene “WBGene00021661”:

from cogent3 import load_seq

seq = load_seq(
    "data/C-elegans-chromosome-I.fa",
    annotation_path="data/C-elegans-chromosome-I.gff",
    label_to_name=lambda x: x.split()[0],
    moltype="dna",
)
gene = list(seq.get_features(name="gene:WBGene00022276", biotype="gene"))[0]
children = list(gene.get_children())
children

[Feature(seqid='I', biotype='mRNA', name='transcript:Y74C9A.2a.3', map=[11494:16793]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434)),
 Feature(seqid='I', biotype='mRNA', name='transcript:Y74C9A.2a.1', map=[11494:16837]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434)),
 Feature(seqid='I', biotype='mRNA', name='transcript:Y74C9A.2a.2', map=[11617:16833]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434)),
 Feature(seqid='I', biotype='mRNA', name='transcript:Y74C9A.2b.1', map=[15102:16585]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434))]

How to find the “parent” of a Feature

To find the “parent” of a feature, we can use the get_parent() method, which achieves the inverse of the above method.

For example, we can use the first “child” we returned above, "transcript:Y74C9A.2a.3", to find the original parent gene!

child = list(seq.get_features(name="transcript:Y74C9A.2a.3", biotype="mRNA"))[0]
parent = list(child.get_parent())
parent

[Feature(seqid='I', biotype='gene', name='gene:WBGene00022276', map=[11494:16837]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434))]

How to copy features

We can copy features onto sequences with the same name. Note that the AnnotationDb instance bound to the alignment and its member sequences is the same.

aln2 = make_aligned_seqs([["x", "-AAAAAAAAA"], ["y", "TTTT--TTTT"]],
    moltype="dna",
)
x, y = aln2.get_seq("x"), aln2.get_seq("y")
x.annotation_db is y.annotation_db is aln2.annotation_db

True

Warning

Despite this, it is possible for the attributes to get out-of-sync. So, any copy annotations should be done using alignment.copy_annotations(), not alignment.get_seq("x").copy_annotations().

seq = make_seq("CCCCCCCCCCCCCCCCCCCC", name="x", moltype="dna")
match_exon = seq.add_feature(biotype="exon", name="A", spans=[(3, 8)])
aln2.copy_annotations(seq.annotation_db)
aln2.annotation_db

BasicAnnotationDb(source=':memory:', total_records=1)

However, if the feature lies outside the sequence being copied to, you get a lost span

copied = list(aln2.get_features(seqid="x", biotype="exon"))
copied

[Feature(seqid="from 'x'", biotype='exon', name='A', map=[4:9]/10, parent=2 x 10 dna alignment: x[-AAAAAAAAA], y[TTTT--TTTT])]

How to get the positions of a feature as one span

as_one_span unifies features with discontinuous alignment coordinates and returns positions spanned by a feature, including gaps.

unified = aln_exons.as_one_span()
aln3[unified]

	0
x	C-CCC
y	-T---

2 x 5 dna alignment

Behaviour of annotations on nucleic acid sequences

Reverse complementing a sequence does not reverse features. Features are considered to have strand specific meaning (e.g. CDS, exons) and so they retain the reference to the frame for which they were defined.

plus = make_seq("CCCCCAAAAAAAAAATTTTTTTTTTAAAGG", moltype="dna")
plus_rpt = plus.add_feature(biotype="blah", name="a", spans=[(5, 15), (25, 28)])
plus[plus_rpt]

	0
a	AAAAAAAAAAAAA

DnaSequence, length=13

minus = plus.rc()
minus

	0
None	CCTTTAAAAAAAAAATTTTTTTTTTGGGGG

DnaSequence, length=30

minus_rpt = list(minus.get_features(biotype="blah"))[0]
minus[minus_rpt]

	0
a	AAAAAAAAAAAAA

DnaSequence, length=13