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_name="fasta").

from cogent3 import get_app

load_unaligned_app = get_app("load_unaligned", format_name="fasta", moltype="protein")
seqs = load_unaligned_app("data/inseqs_protein.fasta")
seqs

	0
1091044_fragment	IPLDFDKEFRDKTVVIVAIPGAFTPT
13541053_fragment	KKKNTEVISVSEDTVYVHKAWVQYD
15605725_fragment	FEILAINMDPENLTGFLKNNP

3 x {min=21, median=25.0, max=26} protein sequence collection

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.

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 here!.

out_dstore = open_data_store(path_to_dir, suffix="tsv", mode="w")

load_unaligned_app = get_app("load_unaligned", format_name="fasta", moltype="dna")
jdist = get_app("jaccard_dist")
writer = get_app("write_tabular", out_dstore, format_name="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.

result = process.apply_to(fasta_seq_dstore)
print(result[1].read())

dim-1	dim-2	value
Human	RoundEare	0.9375736160188457
Human	Wombat	0.9464285714285714
RoundEare	Human	0.9375736160188457
RoundEare	Wombat	0.9840909090909091
Wombat	Human	0.9464285714285714
Wombat	RoundEare	0.9840909090909091