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(
data=[["seq1", "-AAACCCCCA"], ["seq2", "TTTT--TTTT"]], array_align=False
)
# 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 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(
data=[["seq1", "-AAACCCCCA"], ["seq2", "TTTT--TTTT"]], array_align=False
)
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 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#
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")
3.15 s ± 12.9 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
GffAnnotationDb(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 load features and associate them with an existing sequence#
We can use the annotate_from_gff() method to associate the features from a GFF file with the existing Sequence.
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.
# loaded_seq = < loaded / created the seq>
loaded_seq.annotate_from_gff("data/C-elegans-chromosome-I.gff")
loaded_seq.annotation_db
GffAnnotationDb(source=':memory:', total_records=60166)
If the feature coordinates precede the sequence, for example, a sequence corresponds to a gene that starts 600 base pairs from the beginning of chromosome, but the annotation file is for the entire chromosome, we need to provide an offset to the annotate_from_gff() method.
# sub_seq = <genomic region starting at the 600th nt>
sub_seq.annotate_from_gff("data/C-elegans-chromosome-I.gff", offset=600)
sub_seq.annotation_db
GffAnnotationDb(source=':memory:', total_records=60166)
How to load features and associate them with sequences in an existing alignment#
To annotate one or more Sequence in an Alignment, call annotate_from_gff() on the Alignment instance, passing in the path to the GFF annotation file and a list of sequence names to annotate to the seq_ids argument.
For example, first we load an alignment of the brca1 gene in primates.
from cogent3 import load_aligned_seqs
brca1_aln = load_aligned_seqs(
"data/primate_brca1.fasta", array_align=False, 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.
brca1_aln.annotate_from_gff("data/brca1_hsa_shortened.gff", seq_ids=["Human"])
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)
Note
Alignment.annotate_from_gff() does not support setting an offset. If you need to set the offset for a sequence within an alignment, you can do so directly using the Sequence.annotation_offset attribute.
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=[348424:343068]/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=[2576006:2575840, 2575629:2575438, 2574584:2574520, 2574444:2574312, 2574267:2573998, 2573941:2573740, 2572736:2572569, 2572519:2572355, 2572300:2572092, 2572039:2571987]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434)),
Feature(seqid='I', biotype='CDS', name='WBGene00000010', map=[11396658:11396567, 11396432:11396253, 11395177:11395068, 11395007:11394926, 11394878:11394746, 11393875:11393721, 11393519:11393291, 11392185:11392047, 11391134:11390947, 11390188:11390088, 11390003:11389874, 11389238:11389140]/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=[348364:348304, 347044:346792, 346742:346560, 346516:346374, 345830:345020, 344033:343893, 343649:343540]/15072434, parent=DnaSequence(GCCTAAG... 15,072,434)),
Feature(seqid='I', biotype='CDS', name='WBGene00021661', map=[347113:347092, 347044:346792, 346742:346560, 346516:346374, 345830:345020, 344033:343893, 343649:343540]/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))
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
# first load alignment and annotate the human seq
aln = load_aligned_seqs(
"data/primate_brca1.fasta", array_align=False, moltype="dna"
)
aln.annotate_from_gff("data/brca1_hsa_shortened.gff", seq_ids=["Human"])
# 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=[1507:1]/2814, parent=7 x 2814 alignment: Galago[TGTGGCAAAAA...], HowlerMon[TGTGGCACAAA...], Rhesus[TGTGGCACAAA...], ...)]
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 alignment: Galago[TGTGGCAAAAA...], HowlerMon[TGTGGCACAAA...], Rhesus[TGTGGCACAAA...], ...),
Feature(seqid="from 'Human'", biotype='exon', name='unknown-0', map=[1507:1]/2814, parent=7 x 2814 alignment: Galago[TGTGGCAAAAA...], HowlerMon[TGTGGCACAAA...], Rhesus[TGTGGCACAAA...], ...)]
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=None, biotype='pseudogene', name='pseudogene1', map=[420:666]/2814, parent=7 x 2814 alignment: Galago[TGTGGCAAAAA...], HowlerMon[TGTGGCACAAA...], Rhesus[TGTGGCACAAA...], ...)]
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(
data=[["x", "C-CCCAAAAA"], ["y", "-T----TTTT"]],
array_align=False,
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 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 | |
| I | 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 | |
| I | 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()
[(23600, 22881)]
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, 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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, parent=DnaSequence(GCCTAAG... 15,072,434))
Taking the “shadow” of all genes will return the intergenic region as a valid Feature
intergenic = genes.shadow()
intergenic
Feature(seqid='I', biotype='not gene', name='not 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, 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13526247:13527365, 13527963:13528689, 13531462:13534736, 13542395:13543067, 13545188:13548073, 13559361:13560090, 13563465:13564566, 13572047:13572378, 13575805:13576147, 13581611:13581697, 13585295:13586036, 13588831:13589177, 13598505:13598608, 13603393:13603905, 13626225:13626517, 13628447:13629226, 13633492:13635041, 13635958:13637211, 13637277:13637844, 13639311:13640304, 13648405:13652523, 13652727:13652948, 13653704:13654008, 13658167:13658170, 13659128:13659430, 13662332:13663351, 13674997:13676254, 13677792:13680414, 13681366:13682136, 13684547:13685298, 13687586:13692684, 13702954:13709778, 13714128:13714297, 13716916:13720124, 13724453:13724576, 13732046:13734957, 13751124:13751233, 13756354:13756729, 13771773:13773066, 13778784:13778894, 13792249:13792636, 13797272:13797344, 13802223:13802392, 13806801:13806916, 13814322:13814755, 13827988:13828101, 13832147:13834875, 13834941:13835339, 13840047:13840703, 13843996:13844738, 13846957:13849500, 13850893:13852385, 13854047:13854637, 13859699:13861467, 13887640:13890341, 13891406:13893132, 13894249:13899084, 13914008:13915663, 13917809:13919374, 13922122:13922584, 13922659:13923060, 13923763:13926735, 13927556:13927945, 13928792:13938809, 13942016:13942543, 13951630:13951749, 13952052:13952160, 13953203:13957198, 13960612:13961010, 13969571:13969843, 13970877:13971282, 13971419:13972616, 13973412:13978429, 13980130:13980857, 13980954:13983225, 13989668:13992993, 13996221:13997362, 14002972:14004427, 14008484:14009870, 14011531:14012368, 14013629:14015753, 14018267:14018798, 14019759:14020360, 14021312:14027235, 14027588:14027779, 14029212:14029278, 14029364:14031146, 14031667:14034953, 14036057:14037150, 14037777:14041626, 14043478:14043530, 14045694:14046131, 14048022:14048093, 14051010:14052135, 14054154:14054592, 14056894:14058838, 14058910:14059311, 14062314:14062748, 14068803:14070555, 14070619:14075066, 14075163:14075836, 14082582:14083044, 14084207:14084511, 14085525:14085687, 14086685:14088086, 14089858:14090729, 14096348:14097491, 14101090:14101321, 14104470:14104514, 14113108:14113635, 14114085:14114193, 14118650:14118750, 14127202:14128590, 14142207:14150627, 14151067:14151840, 14152282:14153227, 14154195:14154768, 14155296:14155428, 14156336:14157610, 14158078:14158507, 14161664:14162845, 14162939:14163529, 14168307:14171053, 14179360:14182594, 14196295:14198610, 14199136:14200019, 14204687:14205716, 14208726:14209147, 14212501:14213448, 14213515:14213680, 14215850:14216700, 14220624:14220692, 14231360:14233034, 14235948:14236394, 14241181:14241897, 14244452:14245499, 14245555:14246227, 14247314:14251082, 14252416:14252956, 14254513:14255683, 14257016:14263296, 14271321:14271892, 14277653:14278780, 14278828:14282074, 14282140:14283469, 14285325:14285933, 14287089:14287825, 14295954:14297286, 14298325:14298754, 14299780:14305069, 14307220:14311756, 14312842:14316649, 14318522:14319113, 14319913:14320341, 14320592:14322236, 14328965:14331084, 14339839:14340790, 14343815:14347548, 14358326:14358561, 14367594:14367615, 14369773:14371137, 14376086:14376094, 14376115:14378385, 14379425:14381359, 14384737:14390332, 14400226:14400682, 14405649:14406027, 14417934:14418196, 14431913:14432790, 14433750:14434641, 14435706:14436809, 14451889:14461037, 14465145:14465379, 14468214:14468331, 14469103:14469499, 14486767:14486876, 14496560:14496815, 14498295:14499734, 14510552:14511430, 14518719:14529528, 14546870:14552029, 14565853:14566306, 14566783:14567168, 14616523:14620182, 14620637:14621168, 14631094:14634452, 14642568:14642713, 14646601:14647781, 14651288:14652115, 14657275:14657354, 14661495:14662523, 14663141:14664448, 14674841:14675599, 14682566:14688626, 14692144:14694153, 14700392:14701121, 14706522:14707058, 14707875:14708194, 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!
intergenic.get_slice()
| 0 | |
| I | 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(
data=[["x", "C-CCCAAAAAGGGAA"], ["y", "-T----TTTTG-GTT"]],
moltype="dna",
array_align=False,
)
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 | |
| x | ?-???AAAAAGGGAA |
| y | -T----TTTT.-.TT |
2 x 15 dna alignment
After a reverse complement operation
rc = aln.rc()
rc
2 x 15 dna alignment
these persist.
rc.with_masked_annotations("exon", mask_char="?")
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(
data=[["x", "-AAAAAAAAA"], ["y", "TTTT--TTTT"]],
array_align=False,
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 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 | |
| None | 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 | |
| None | AAAAAAAAAAAAA |
DnaSequence, length=13