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.. jupyter-execute::
:hide-code:
import set_working_directory
Loading unaligned sequence data
-------------------------------
We can load unaligned sequence data using the ``load_unaligned`` app, this will return a ``SequenceCollection``.
Loading unaligned DNA sequences from a single fasta file
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, we load unaligned DNA sequences from a single fasta file using the ``load_unaligned`` app. We specify the molecular type ``(moltype="protein")`` and the file format ``(format="fasta")``.
.. jupyter-execute::
:raises:
from cogent3 import get_app
load_unaligned_app = get_app("load_unaligned", format="fasta", moltype="protein")
seqs = load_unaligned_app("data/inseqs_protein.fasta")
seqs
Loading unaligned DNA sequences from multiple fasta files
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
To load unaligned DNA sequences from multiple fasta files, we need two things, a data store that identifies the files we are interested in and a process composed of our apps of interest.
1. A data store that identifies the files we are interested in
""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Here we open a read-only (``mode="r"``) data store that identifies all fasta files in the data directory, limiting the data store to two members as a minimum example.
.. jupyter-execute::
:hide-code:
from tempfile import TemporaryDirectory
tmpdir = TemporaryDirectory(dir=".")
path_to_dir = tmpdir.name
.. jupyter-execute::
:raises:
from cogent3 import get_app, open_data_store
fasta_seq_dstore = open_data_store("data", suffix="fasta", mode="r", limit=2)
2. A composed process that defines our workflow
"""""""""""""""""""""""""""""""""""""""""""""""
In this example, our process loads the unaligned sequences using ``load_unaligned``, then applies ``jaccard_dist`` to estimate a kmer-based genetic distance, which we write out to a data store using ``write_tabular``.
.. note:: Apps that are "writers" require a data store to write to, learn more about writers :ref:`here! <writers>`.
.. jupyter-execute::
:raises:
out_dstore = open_data_store(path_to_dir, suffix="tsv", mode="w")
load_unaligned_app = get_app("load_unaligned", format="fasta", moltype="dna")
jdist = get_app("jaccard_dist")
writer = get_app("write_tabular", out_dstore, format="tsv")
process = load_unaligned_app + jdist + writer
.. tip:: When running this code on your machine, remember to replace ``path_to_dir`` with an actual directory path.
Now we're good to go! We can apply ``process`` to our data store of fasta sequences. ``result`` is a data store, which you can index to see individual data members. We can inspect a given data member using the ``.read()`` on data members.
.. jupyter-execute::
:raises:
result = process.apply_to(fasta_seq_dstore)
print(result[1].read())
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