.. jupyter-execute::
    :hide-code:

    import set_working_directory

.. _howto-features:

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.

.. jupyter-execute::

    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")

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

.. jupyter-execute::

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

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**!

.. jupyter-execute::

    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
    )

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``.

.. jupyter-execute::

    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,
    )

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 :ref:`anno_db`.

.. jupyter-execute::

    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

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 :ref:`data-links` 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.

.. _genbank-features:

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.

.. jupyter-execute::

    from cogent3.parse.genbank import iter_genbank_records

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

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.

.. jupyter-execute::

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

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

.. jupyter-execute::

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

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 ⚡️

.. jupyter-execute::

    from cogent3 import load_seq
    
    %timeit load_seq("data/C-elegans-chromosome-I.gb", moltype="dna")

.. jupyter-execute::
    :hide-code:

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

The features are stored in the ``annotation_db`` attribute.

.. jupyter-execute::

    seq.annotation_db

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

.. jupyter-execute::

    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

.. 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.

.. jupyter-execute::

    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

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.

.. jupyter-execute::

    from cogent3 import load_annotations

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

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.

.. jupyter-execute::

    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

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.

.. jupyter-execute::

    from cogent3 import load_aligned_seqs, load_annotations

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

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

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

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

.. jupyter-execute::

    brca1_aln.get_seq("Human").annotation_db

.. _query_for_features:

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"``.

.. jupyter-execute::

    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

Querying via Feature Biotype
++++++++++++++++++++++++++++

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

.. jupyter-execute::

    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]

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

.. jupyter-execute::

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

Querying via region of interest
+++++++++++++++++++++++++++++++

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

.. jupyter-execute::

    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]

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.

.. jupyter-execute::

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

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.

.. jupyter-execute::

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

``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:

.. jupyter-execute::

    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

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

.. jupyter-execute::

    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

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``. 

.. jupyter-execute::

    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

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!

.. jupyter-execute::

    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()

.. jupyter-execute::

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

.. 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

.. jupyter-execute::

    exon.get_slice(allow_gaps=True)

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

.. jupyter-execute::

    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]

.. note:: This only works for the ``Sequence`` that the ``Feature`` "belongs" to.

We can also achieve this via ``get_slice()``

.. jupyter-execute::

    pseudogene.get_slice()

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.

.. jupyter-execute::

    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
""""""""""""""""""""""""""""""""""""""""

.. jupyter-execute::

    pseudogene.get_coordinates()

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:

.. jupyter-execute::

    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

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

.. jupyter-execute::

    intron_coords = []

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

    intron_coords

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

.. jupyter-execute::

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

How to take the union of features
"""""""""""""""""""""""""""""""""

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

.. jupyter-execute::

    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.

.. jupyter-execute::

    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

Taking the "shadow" of all genes will return the intergenic region as a valid ``Feature``

.. jupyter-execute::

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

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.

.. jupyter-execute::

    intergenic.get_slice()

How to mask annotated regions
"""""""""""""""""""""""""""""

Masking annotated regions on a sequence
+++++++++++++++++++++++++++++++++++++++

We can mask a certain annotation using ``with_masked_annotations()``

.. jupyter-execute::

    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]

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.

.. jupyter-execute::

    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="?")

After a reverse complement operation

.. jupyter-execute::

    rc = aln.rc()
    rc

these persist.

.. jupyter-execute::

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

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":

.. jupyter-execute::

    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

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!

.. jupyter-execute::

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

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**.

.. jupyter-execute::

    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

.. 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()``.

.. jupyter-execute::

    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

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

.. jupyter-execute::

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

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.

.. jupyter-execute::

    unified = aln_exons.as_one_span()
    aln3[unified]

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.

.. jupyter-execute::

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

.. jupyter-execute::

    minus = plus.rc()
    minus

.. jupyter-execute::

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