Loading a single sequence from a file#
Note
These docs now use the new_type
core objects via the following setting.
import os
# using new types without requiring an explicit argument
os.environ["COGENT3_NEW_TYPE"] = "1"
In this case, the filename suffix is used to infer the data format.
from cogent3 import load_seq
seq = load_seq("data/mycoplasma-genitalium.fa", moltype="dna")
seq
0 | |
NC_000908.2 Mycoplasmoides genitalium G37, complete sequence | TAAGTTATTATTTAGTTAATACTTTTAACAATATTATTAAGGTATTTAAAAAATACTATT |
DnaSequence, length=580,076 (truncated to 60)
Warning
If a file has more than one sequence, only the first one is loaded.
seq = load_seq("data/brca1-bats.fasta", moltype="dna")
seq
0 | |
FlyingFox | TGTGGCACAAATGCTCATGCCAGCTCTTTACAGCATGAGAAC---AGTTTATTATACACT |
DnaSequence, length=3,009 (truncated to 60)
Note
It’s also possible to load a sequence from a url.
Loading an alignment from a file or url#
Loading aligned sequences#
Any file in which the sequences have exactly the same length can be loaded as an alignment.
from cogent3 import load_aligned_seqs
aln = load_aligned_seqs("data/long_testseqs.fasta", moltype="dna")
type(aln)
cogent3.core.new_alignment.Alignment
Note
The load functions record the origin of the data in the info
attribute under a “source” key.
aln.info.source
'data/long_testseqs.fasta'
Loading unaligned sequences#
Files containing sequences that may differ in length can be loaded using load_unaligned_seqs()
, which returns a sequence collection.
from cogent3 import load_unaligned_seqs
seqs = load_unaligned_seqs("data/long_testseqs.fasta", moltype="dna")
type(seqs)
cogent3.core.new_alignment.SequenceCollection
Loading from a url#
The cogent3
load functions support loading from a url. We load the above fasta file directly from GitHub.
from cogent3 import load_aligned_seqs
aln = load_aligned_seqs(
"https://raw.githubusercontent.com/cogent3/cogent3/develop/doc/data/long_testseqs.fasta",
moltype="dna",
)
Specifying the file format#
The loading functions use the filename suffix to infer the file format. This can be overridden using the format
argument.
from cogent3 import load_aligned_seqs
aln = load_aligned_seqs("data/long_testseqs.fasta", moltype="dna", format="fasta")
aln
0 | |
DogFaced | TGTGGCACAAATACTCATGCCAACTCATTACAGCATGAGAACAGCAGTTTATTATACACT |
Human | ......................G...............................CT.... |
HowlerMon | ......................G...........................G...CT.... |
Mouse | .........G..G.........G...........C.....C............GCT..T. |
NineBande | .........................T............................CT.... |
5 x 2532 (truncated to 5 x 60) dna alignment
Specifying the sequence molecular type#
from cogent3 import make_aligned_seqs
protein_seqs = {"seq1": "DEKQL-RG", "seq2": "DDK--SRG"}
proteins_loaded = make_aligned_seqs(protein_seqs, moltype="protein")
proteins_loaded.moltype
proteins_loaded
0 | |
seq1 | DEKQL-RG |
seq2 | .D.--S.. |
2 x 8 protein alignment
Making an alignment from standard python objects#
From a dict of strings#
from cogent3 import make_aligned_seqs
seqs = {"seq1": "AATCG-A", "seq2": "AATCGGA"}
seqs_loaded = make_aligned_seqs(seqs, moltype="dna")
From a dict of numpy arrays#
from cogent3 import make_aligned_seqs
from numpy import array, uint8
seqs = {
"seq1": array([2, 2, 0, 1, 3, 9, 2], dtype=uint8),
"seq2": array([2, 2, 0, 1, 3, 3, 2], dtype=uint8),
}
seqs_loaded = make_aligned_seqs(seqs, moltype="dna")
From a series of strings#
The sequence names will be automatically created.
from cogent3 import make_aligned_seqs
data = ["AATCG-A", "AATCGGA"]
coll = make_aligned_seqs(data, moltype="dna", new_type=True)
coll
0 | |
seq_1 | AATCGGA |
seq_0 | .....-. |
2 x 7 dna alignment
Changing sequence labels on loading#
Load a list of aligned nucleotide sequences, while specifying the DNA molecule type and stripping the comments from the label. In this example, we rename sequences by passing a function that removes everything after the first whitespace to the label_to_name
parameter.
from cogent3 import make_aligned_seqs
data = {
"sample1 Mus musculus": "AACCTGC--C",
"sample2 Gallus gallus": "AAC-TGCAAC",
}
loaded_seqs = make_aligned_seqs(
data, moltype="dna", label_to_name=lambda x: x.split()[0]
)
loaded_seqs
0 | |
sample2 | AAC-TGCAAC |
sample1 | ...C...--. |
2 x 10 dna alignment
Making a sequence collection from standard python objects#
This is done using make_unaligned_seqs()
, which returns a SequenceCollection
instance. The function arguments match those of make_aligned_seqs()
. We demonstrate only for the case where the input data is a dict
.
from cogent3 import make_unaligned_seqs
seqs = {"seq1": "AATCA", "seq2": "AATCGGA"}
seqs = make_unaligned_seqs(data=seqs, moltype="dna")
seqs
0 | |
seq1 | AATCA |
seq2 | AATCGGA |
2 x {min=5, median=6.0, max=7} dna sequence collection
Loading sequences using format parsers#
load_aligned_seqs()
and load_unaligned_seqs()
are just convenience interfaces to format parsers. It can sometimes be more effective to use the parsers directly, say when you don’t want to load everything into memory.
Loading FASTA sequences from an open file or list of lines#
To load FASTA formatted sequences directly, you can use iter_fasta_records
. This parser returns data as python strings.
Note
This returns the sequences as strings.
from cogent3.parse.fasta import iter_fasta_records
seqs = list(iter_fasta_records("data/long_testseqs.fasta"))
seqs
[('Human',
'TGTGGCACAAATACTCATGCCAGCTCATTACAGCATGAGAACAGCAGTTTATTACTCACTAAAGACAGAATGAATGTAGAAAAGGCTGAATTCTGTAATAAAAGCAAACAGCCTGGCTTAGCAAGGAGCCAACATAACAGATGGGCTGGAAGTAAGGAAACATGTAATGATAGGCGGACTCCCAGCGAAAAAAAGGTAGATCTGAATGCTGATCCCCTGTGTGAGAGAAAAGAATGGAATAAGCAGAAACTGCCATGCTCAGAGAATCCTAGAGATACTGAAGATGTTCCTTGGATAACACTAAATAGCAGCATTCAGAAAGTTAATGAGTGGTTTTCCAGAAGTGATGAACTGTTAGGTTCTGATGACTCACATGATGGGGAGTCTGAATCAAATGCCTTGGACGTTCTAAATGAGGTAGATGAATATTCTGGTTCTTCAGAGAAAATAGACTTACTGGCCAGTGATCCTCATGAGGCTTTAATATGTGAAAGAGTTCACTCCAAATCAGTAGAGAGTAATATTGAAGACAAAATATTTGGGAAAACCTATCGGAAGAAGGCAAGCCTCCCCAACTTAAGCCATGTAACTGAAATTATAGGAGCATTTGTTACTGAGCCACAGATAATACAAGAGCGTCCCCTCACAAATAAATTAAAGCGTAAAAGGACATCAGGCCTTCATCCTGAGGATTTTATCAAGAAAGCAGATTTGGCAGTTCAAAAGACTCCTGAAATGATAAATCAGGGAACTAACCAAACGGAGCAGAATGGTCAAGTGATGAATATTACTAATAGTGGTCATGAGAATAAAACAAAAGGTGATTCTATTCAGAATGAGAAAAATCCTAACCCAATAGAATCACTCGAAAAAGAATCTTTCAAAACGAAAGCTGAACCTATAAGCAGCAGTATAAGCAATATGGAACTCGAATTAAATATCCACAATTCAAAAGCACCTAAAAAGAATCTGAGGAGGAAGTCTACCAGGCATATTCATGCGCTTGAACTAGTCAGTAGAAATCTAAGCCCACCTAATTGTACTGAATTGCAAATTGATAGTTGTTCTAGCAGTGAAGAGATAAAGAAAAAAAAGTACAACCAAATGCCAGTCAGGCACAGCAGAAACCTACAACTCATGGAAGGTAAAGAACCTGCAACTGGAGCCAAGAAGAACAAGCCAAATGAACAGACAAGTAAAAGACATGACAGCGATACTTTCCCAGAGCTGAAGAATGCACCTGGTTCTTTTACTAAGTGTTCAAATACCAGTGAACTTAAAGAATTTAATCCTAGCCTTCCAAGAGAAGAAAAAGAGAAACTAGAAACAGTTAAAGTGTCTAATAATGCTGAAGACCCCAAAGATCTCATGTTAAGTGGAGAAAGGGTTTTGCAAACTGAAAGATCTGTAGAGAGTAGCAGTATTTCATTGGTACCTGGTACTGATTATGGCACTCAGGAAAGTATCTCGTTACTGGAAGTTAGCACTCTAGGGAAGGCAAAAACAGAACCAAATAAATGTGTGAGTCAGTGTGCAGCATTTGAAAACCCCAAGGGACTAATTCATGGTTCCAAAGATAATAGAAATGACACAGAAGGCTTTAAGTATCCATTGGGACATGAAGTTAACCACTCAAATCCAGAAGAGGAATGTGCACACTCTGGGTCCTTAAAGAAACAAAGTCCAAAAGTCACTTTTGAATGTGAACAAAAGGAAAATCAAGGAAAGAATGAGTCTAATAAGCCTGTACAGACAGTTAATATCACTGCAGGCTTTCCTGTGGTTGGTCAGAAAGATAAGCCAGTTGATAATGCCAAATGTAAAGGAGGCTCTAGGTTTTGTCTATCATCTCAGTTCAGAGGCAACGAAACTGGACTCATTACTCCAAATAAACATGGACTTTTACAAAACCCATATCGTATACCACCACTTTTTCCCATCAAGTCATTTGTTAAAACTAAATGTAAGAAAAATCTGCTAGAGGAAAACTTTGAGGAACATTCAATGTCACCTGAAAGAGAAATGGGAAATGAGAACATTCCAAGTACAGTGAGCACAATTAGCCGTAATAACAGAGAAAATGTTTTTAAAGAAGCCAGCTCAAGCAATATTAATGAAGTAGGTTCCAGTGATGAAAACATTCAAGCAGAACTAGGTAGAAACAGAGGGCCAAAATTGAATGCTATGCTTAGATTAGGGGTTTTGCAACCTGAGGTCTATAAACAAAGTCTTCCTGGAAGTAATAAGCATCCTGAAATAAAAAAGCAAGAAGTTCAGACTGTTAATACAGATTTCTCTCCACTGATTTCAGATAACTTAGAACAGCCTATGAGTAGTCATGCATCTCAGGTTTGTTCTGAGACACCTGATGACCTGTTAGATGATGGTGAAATAAAGGAAGATACTAGTTTTGCTGAAAATGACATTAAGGAAAGTTCTGCTGTTTTTAGCAAAAGCGTCCAGAAAGGAGAGCTTAGCAGGAGTCCTAGCCCTTTCACCCATACACATTTGGCTCAGGGTTACCGAAGAGGG'),
('HowlerMon',
'TGTGGCACAAATACTCATGCCAGCTCATTACAGCATGAGAACAGCAGTTTGTTACTCACTAAAGACACACTGAATGTAGAAAAGGCTGAATTCTGTAATAAAAGCAAACAGCCTGGCTTAGCAAGGAGCCAACATAACAGATGGGCTGAAAGTGAGGAAACATGTAATGATAGGCAGACTCCCAGCGAGAAAAAGGTAGATGTGGATGCTGATCCCCTGCATGGGAGAAAAGAATGGAATAAGCAGAAACCTCCGTGCTCTGAGAATCCTAGAGATACTGAAGATGTTGCTTGGATAATGCTAAATAGCAGCATTCAGAAAGTTAATGAGTGGTTTTCCAGAAGTGATGAACTGTTAACTTCTGATGACTCACATGATGGGGGGTCTGAATCAAATGCCTTGGAAGTTCTAAATGAGGTAGATGGATATTCTAGTTCTTCAGAGAAAATAGACTTACTGGCCAGTGATCCTCATGATCATTTGATATGTGAAAGAGTTCACTGCAAATCAGTAGAGAGTAGTATTGAAGATAAAATATTTGGGAAAACCTATCGGAGGAAGGCAAGCCTCCCTAACTTGAGCCACGTAACTGAAATTATAGGAGCATTTGTTACTGAGCCACAGATAATACAAGAGCATCCTCTCACAAATAAATTAAAGCGTAAAAGGACATCAGGACTTCATCCTGAGGATTTTATCAAGAAAGCAGATTTGGCAGTTCAAAAGACTCCTGAAAAGATAAATCAGGGAACTAACCAAACAGAGCGGAATGATCAAGTGATGAATATTACTAACAGTGGTCATGAGAATAAAACAAAAGGTGATTCTATTCAGAATGAGAACAATCCTAACCCAGTAGAATCACTGGAAAAAGAATCATTCAAAAGTAAAGCTGAACCTATAAGCAGTAGTATAAGCAATATGGAATTAGAATTGAATGTCCACAATTCCAAAGCATCTAAAAAGAATCTGAGAAGGAAGTCTACCAGGCATATTCATGAGCTTGAACTAGTCAGTAGAAATCTAAGCCCACCTAATTATACTGAAGTACAAATTGATAGTTGTTCTAGCAGTGAAGAGATAAAGAAAAAAAATTACAACCAAATGCCAGTCAGGCACAGCAGAAAGCTACAACTCATGGAAGATAAAGAACGTGCAGCTAGAGCCAAAAAGAGCAAGCCAAATGAACAAACAAGTAAAAGACATGCCAGTGATACTTTCCCAGAACTGAGGAACATACCTGGTTCTTTTACTAACTGTTCAAATACTAATGAATTTAAAGAATTTAATCCTAGCCTTCCAAGAGAACAAACAGAGAAACTAGAAACAGTTAAACTGTCTAATAATGCCAAAGACCCCAAAGATCTCATGTTAAGTGGAGAAAGTGTTTTGCAAATTGAAAGATCTGTAGAGAGTAGCAGTATTTTGTTGATACCTGGTACTGATTATGGCACTCAGGAAAGTATCTCATTACTGGAAGTTAGCACTCTGGGGAAGGCAAAAACAGAACCAAATAAATGTGTGAGTCAGTGTGCAGCATTTGAAAACCCCAAGGAACTAATTCATGGTTCTAAAGATACTAGAAATGGCACAGAAGGCTTGAAGTATCCATTGGGACCTGAAGTTAACTACTCAAATCCAGAAAAGGAATGTGCATGCTCTAGGTCCTTAAAGAAACAAAGTCCAAAGGTCACTCCTGAATGTGAACAAAAGGAAAATCAAGGAGAGAAAGAGTCTAATGAGCTTGTAGAGACAGTTAATACCACTGCAGGCTTTCCTATGGTTTGTCAGAAAGATAAGCCAGTTGATTATGCCAGATGTGAAGGAGGCTCTAGGCTTTGTCTATCATCTCAGTTCAGAGGCAACGAAACTGGACTCATTATTCCAAATAAACATGGACTTTTACAGAACCCATATCATATGTCACCGCTTATTCCCACCAGGTCATTTGTTAAAACTAAATGTAAGAAAAACCTGCTAGAAGAAAACTCTGAGGAACATTCAATGTCACCTGAAAGAGCAATGGGAAACAAGAACATTCCAAGTACAGTGAGCACAATTAGCCATAATAACAGAGAAAATGCTTTTAAAGAAACCAGCTCAAGCAGTATTTATGAAGTAGGTTCCAGTGATGAAAACATTCAAGCAGAGCTAGGTAGAAACAGAAGGCCAAAATTGAATGCTATGCTTAGATTAGGGCTTCTGCAACCTGAGATTTGTAAGCAAAGTCTTCCTATAAGTGATAAACATCCTGAAATTAAAAAGCAAGAAGTTCAGACTGTTAATACAGACGTCTCTCTACTGATTTCATATAACCTAGAACAGCATATGAGCAGTCATACATCTCAGGTTTGTTCTGAGACACCTGACAACCTGTTAGATGATGGTGAAATAAAGGAAGATACTAGTTTTGCTGAATATGGCATTAAGGAGACTTCTACTGTTTTTAGCAAAAGTGTCCAGAGAGGAGAGCTCAGCAGGAGCCCTAGCCCTTTCACCCATACACATTTGGCTCAGGTTTACCAAAGAGGG'),
('Mouse',
'TGTGGCACAGATGCTCATGCCAGCTCATTACAGCCTGAGACCAGCAGTTTATTGCTCATTGAAGACAGAATGAATGCAGAAAAGGCTGAATTCTGTAATAAAAGCAAACAGCCTGGCATAGCAGTGAGCCAGCAGAGCAGATGGGCTGCAAGTAAAGGAACATGTAACGACAGGCAGGTTCCCAGCGGGGAAAAGGTAGGTCCAAACGCTGACTCCCTTAGTGATAGAGAGAAGTGGACTCACCCGCAAAGTCTGTGCCCTGAGAATTCTGGAGCTACCACCGATGTTCCTTGGATAACACTAAATAGCAGCGTTCAGAAAGTTAATGAGTGGTTTTCCAGAACTGGTGAAATGTTAACTTCTGACAGCGCATCTGCCAGGAGGCACGAGTCAAATGCTTTGGAAGTTTCAAACGAAGTGGATGGGGGTTTTAGTTCTTCAAGGAAAACAGACTTAGTAACCCCCGACCCCCATCATACTTTAATGTGTGGAAGAGACTTCTCCAAACCAGTAGAGGATAATATCAGTGATAAAATATTTGGGAAATCCTATCAGAGAAAGGGAAGCCGCCCTCACCTGAACCATGTGACTGAAATTATAGGCACATTTATTACAGAACCACAGATAACACAAGAGCAGCCCTTCACAAATAAATTAAAACGTAAGAGAAGTACATCCCTTCAACCTGAGGACTTCATCAAGAAAGCAGATTCAGCAGGTCAAAGGACTCCTGACAACATAAATCAGGGAACTGACCTAATGGAGCCAAATGAGCAAGCAGTGAGTACTACCAGTAACTGTCAGGAGAACAAAATAGCAGGTAGTAATCTCCAGAAAGAGAAAAGCGCTCATCCAACTGAATCATTGAGAAAGGAACCTTCCACAGCAGGAGCCAAATCTATAAGCAACAGTGTAAGTGATTTGGAGGTAGAATTAAACGTCCACAGTTCAAAAGCACCTAAGAAAAATCTGAGGAGGAAGTCTATCAGGTGTGCTCTTCCACTTGAACCAATCAGTAGAAATCCAAGCCCACCTACTTGTGCTGAGCTTCAAATCGATAGTTGTGGTAGCAGTGAAGAAACAAAGAAAAACCATTCCAACCAACAGCCAGCCGGGCACCTTAGAGAGCCTCAACTCATCGAAGACACTGAACCTGCAGCGGATGCCAAGAAGAACGAGCCAAATGAACACATAAGGAAGAGACGTGCCAGCGATGCTTTCCCAGAAGAGAAAAACAAAGCTGGTTTATTAACTAGCTGTTCAAGTCCTAGAAAATCTCAAGGGCCTAATCCCAGCCCTCAGAGAACAGGAACAGAGCAACTTGAAACACGCCAAATGTCTGACAGTGCCAAAGAACTCGGGGATCGGGTCCTAGGAGGAGAGCCCAGTGGCAAAACTGACCGATCTGAGGAGAGCACCAGCGTATCCTTGGTACCTGACACTGACTACGACACTCAGAACAGTGTCTCAGTCCTGGACGCTCACACTGTCAGATATGCAAGAACAGGATCCGCTCAGTGTATGACTCAGTTTGTAGCAAGCGAAAACCCCAAGGAACTCGTCCATGGCTCTAACAATGCTGGGAGTGGCACAGAGGGTCTCAAGCCCCCCTTGAGACACGCGCTTAACCTCTCAAAACCTCAAAAGGACTGTGCTCACTCTGTGCCCTCAAAGGAACTGAGTCCAAAGGTGACAGCTAAAGGTAAACAAAAAGAACGTCAGGGACAGGAAGAATTTGAAAGTCACGTACAAGCAGTTGCGGCCACAGTGGGCTTACCTGTGCCCTGTCAAGAAGGTAAGCTAGCTGCTGATACAATGTGTGATAGAGGTTGTAGGCTTTGTCCATCATCTCATTACAGAAGCGGGGAGAATGGACTCAGCGCCACAGGTAAATCAGGAATTTCACAAAACTCACATTTTAAACAATCAGTTTCTCCCATCAGGTCATCTATAAAAACTGACAATAGGAAACCTCTGACAGAGGGACGATTTGAGAGACATACATCATCAACTGAGATGGCGGTGGGAAATGAGAACCTTCAGAGTACAGTGCACACAGTTAGCCTGAATAACAGAGGAAATGCTTGTCAAGAAGCCGGCTCGGGCAGTATTCATGAAGTATGTTCCACTGGTGACTCCTTCCCAGGACAACTAGGTAGAAACAGAGGGCCTAAGGTGAACACTGTGCCTCCATTAGATAGTATGCAGCCTGGTGTCTGTCAGCAAAGTGTTCCTGTAAGTGATAAGTATCTTGAAATAAAAAAGCAGGAGGGTGAGGCTGTCTGTGCAGACTTCTCTCCACTATTCTCAGACCATCTTGAGCAATCTATGAGTGGTAAGGTTTTTCAGGTTTGCTCTGAGACACCTGATGACCTGCTGGATGATGTTGAAATACAGGGACATACTAGCTTTGGTGAAGGTGACATAATGGAGAGATCTGCTGTCTTTAACGGAAGCATCCTGAGAAGGGAGTCCAGTAGGAGCCCTAGTCCTGTAACCCATGCATCGAAGTCTCAGAGTCTCCACAGAGCG'),
('NineBande',
'TGTGGCACAAATACTCATGCCAACTTATTACAGCATGAGAACAGCAGTTTATTACTCACTAAAGACAGAATGAATGTAGAAAAGGCTGAATTCTGTAATAAAAGCAAACAGCCTGGCTTAGCAAGGCGCCAACAGAGCAGATGGGCTGAAAGTAAGGAAACATGTAATGATAGGCAGACTCCCAGCGAGAAAAAGGTAGATGTGGATGCTGATCCCCTGTATGGGCGAAAAGAACTGAATAAGCAGAAACCTCCATGCTCTGAGAGTCATAGAGATACCCAAGATATTCCTTGGATAATGCTGAATAGTAGCATTCAGAAAGTTAACGAGTGGTTTTCCAGAGGTGATGACATATTAACTTCTGATGACTCACACGATAGGGGGTCTGAATTAAATGCATTGAAAGTTTCAAAAGAAGTAGATGAATATTCTAGTTTTTCAGAGAAGATAGACTTAATGGCCATTAATCCTCATGATACTTTACAATTTGAAAGAGTCCAATTGAAACCAGCAGAGAGTAACATCAAAGATAAAATATTTGGGAAAACCTATCATAGGAAGGCAAGCCTCCCTAACTTGAGCCACATAACCCGATTTATAGGAGCTATTGCTGCAGAGCCCAAGATAACACAAGAGCATTCCCTCCAAAATAAAATAAAGCGTAAAAGGGCATCAGGCCTTCGTCCTGAGGATTTATCCAAGAAAGTAGATTTGACAGTTCAAAAAACCCCTGAAAAGATAAATCAGGGAACTGACCAAATGGAGCAGAATGATCCAGTGATGAATATTGCTAATAGTGGTCATGAGAATGAAACAAAAGGTGATTGTGTTCAGAAAGAGAAAAATGCTAATCCGACAGAATCATTGGGAAAAGAATCTTTCAGAACTAAAGGCGAACCTATAAGCAGCAGTATAAGCAATATGGAACTAGAATTAAATATTTTAAATTCAAAAGCATCTAAGAAGAATCCGAAGAGGATGTCCACCAGGCATATTCATGCACTTGAACTAGGCAGTAGAAATCCAAGCCCACCTAATCATACTGAACTACAAATTGATAGTTGTTCTAGCATTGAAGAGATAGAGAAAATAAATTCTAACCAAAAGCCAATCAGACACAACAGAATGCTTCAACTCACGAAAGAAAAAGAAACCACAACTGGAGCCAAAAAGAATAAGCCAAATGAACAAATAAGTGAAAGACATGCCAGTGATGCTTTCCTAGAACTTAAAAATGTAACTGATTTTCTTCCTAAATGTTCAAGTTCTGATAAACTTCAAAAATTTAATTCTAGCCTGCAAGGAGAAGTAGCAGAGAACCTAGAAACAATTCAAGTGTCTGATAGTACCAGGGACCCTGAAGATCTGGTGGTAAGTGGAGAAAAGTGTTTGCAAACTGAAAGATCTGCAGAGAGTACCGGTATTTCAGTGGTACCTGATACTGATTATGGCACTCAAGACAGTATCTCATTACTGGAAGCTGACACCCTGGGGAAGGCAAAAACAGCACTAAATCAACATGTGAGTCAGTATGTAGCAATTAGAAATGCCACTGAACTTTCCCATGGTTCTAAAGACACTAGAAATGACACTGAAGATTTTAAGGATTCATTGAGACATGAAGTTAACCACTCGAATCCAGAAAATGAATGTGCACACTCCAGGTTCTTAGGGAAACAAAGTCCAAAAGTCACCTTTGAATGTAGACATAAAGAAAATCAGGGGAAGAAAGAGTCTAAAAAACATGTGCAGGTAATTCACACAACTGCAGGCTTTCCTATAGTTTGTCAGAAAGATAAGCCAGGTGATTATGCCAAAGGTCAAGGAGTCTCTAGGCTTTGTCAGTCCTCTCAGGCCAGAGGCAATGAATCTGAACTCATTAATTCAAATGAACATGAAATTTCACAAAACCCAGATCAAATGCCATCACTTTCTCACATGAAGTCATCTGTTAAAACTAAATGTAAGGAAAACCTGTCAGAGGAAAAGTTTGAGGAACTTACAGTGTCACTTGAAAGAACAATGGTAAATGAGAACATTCAAAGTACAGTAAGCACAATTAGCCACAGTAACAGAGAAAACACTTTTAAAGAAGCCAGCTCAAGCAGTATTAATGAAGTAGGGTCCAGTGATGAGAACATTCAAGCAGAAGTAGGTAGAAACAGAGCACCTAAATTAAATGCTATGCTCAGATTAGGTCTTATGCAACCTGAAGTCTATAAGCAAAGTCTTCCTATAACCAATAAATATCCTGAAATAAAAAGTCAAGGAATTCGGGCTGTTGATATAGACTTCTCTCCACTAATTTCAGATAACCTACAACTACCTATGAATAGTTGTGCTTCCCAGATTTGTTCTGAGACACCTGATGACTTGTTAGATGATGATGAAATAAAGGAAAATAACTGCTTTGCTGAAAGTGACATTAAGGAAAGATCTGCTATTTTTAGCAAAACTGTCCAGAAAAGAGAGTTCAGAAGGAGCCCTAGCCCTTTAGTCCATACAAGTTTTGCTCAGGGTCACCAAAGAAAG'),
('DogFaced',
'TGTGGCACAAATACTCATGCCAACTCATTACAGCATGAGAACAGCAGTTTATTATACACTAAAGACAGAATGAATGTAGAAAAGACTGACTTCTGTAATAAAAGCAAACAGCCTGGCTTAGCAAGGAGCCAGCAGAACAGATGGGTTGAAACTAAGGAAACATGTAATGATAGGCAGACTTCCAGCGAGAAAAAGGTAGTTCTGAATGCTGATCCCCTGAATGGAAGAATAAAACTGAATAAGCAGAAACCTCCATGCTCTGACAGTCCTAGAGATTCCAAAGATATTCCTTGGATAACACGGAATAGTAGCATACAGAAAGTTAATGAGTGGTTTTCCAGACGTGATGAAACATTAACTTCTGATGTCTTACTTGATGAGAGGTCTGAATCAAATGTGGTAGAAGTTCCAAATGAAGTAGATGGATACTCTGGTGCTTCAGAGGAAATAGCCTTAAAGGCCAGTGATCCTCATGGTGCTTTAATATGTGAAAGAGTTCACTCCAAATTGATAGAAAGTAATATTGAAGATAAAATATTTGGGAAAACATATCGGAGGAAAGCAAGCCTCCCTAACTTAAGCCACATAACTGAAATTACAAGAGCATCTGCTACAGAACCTCAGATAACACAAGAGTGCCCCCTCACAAATAAACTAAAACGTAAAAGAACATCAGGCCTTCATCCTGAGGATTTTATCAAGAAAATAGATTTGACAACTCAAAAAACTTCTGAAAATATAATTGAGGGAACTGACCAAATAGAGCAGAATGGTCATGTGATGAATAGTTCTAATGATGGTCATGAGAATGAAACAAAAGGTGATTATGTTCAGAAGAAGAAAAATACAAACCCAACAGAATCATTGGAAAAAGAATCTTTCAGAACTAAAGTTGAGTCTGTACCCAACAACATAAGCAATGTGGAACTAGAATTAAATATTCACGGTTCAAAAGCACTCAAGAAGAATCTGAGGAGGAAGTCCACCAGGCATATTCATGCACTTGAACTAGTCAATAGAAATTCAAGCCCACCTAATCATACTGAACTACAAATTGATAGTTGTTCCAGCAGTGAAGAACTGAAGGAAAAAAATTCTGACCGAATGCCAGACAGACACAGCAAAAAACTTCAGTTCGTAGAAGATAAAGAATCTGCAACTGGAGCCAAGAAGAACATGCCAAATGAGGCAATAAATAAAAGACTTTCCAGTGAAGCTTTTCCCGAATTAAATAACGTACCTGGTTTTTTTACTAATGGTTCAAGTTCTAATAAACGTCAAGAGTTTAATCCTAGCCTTCAAGGAGAAGAAATAGAGAATCTACGAACAATTCAAGTGTCTAATAGCACCAAAGACCCCAAAATTCTAATCTTTGGTGAAGGAAGAGGTTCACAAACTGATCGATCTACAGAGAGTACCAGTATTTTATTGGGACCTGAAACGGATTATGGCACTCAAGATAGTATCTCATTACTGGAATCTGACATCCCAGGGAGGGCAAAGACAGCACCAAACCAACATGCAGATCTGTGTGCAGCAATTGAAAACCCCAGAGAACTTATTCATGATTTTAAAGAAACTAGAAATGACACAGAGAGCTTTAAAGATCCATTGAGACATGAAGTTAACTCCTCAGACCCAGAAAAGGAATGTGCACACTCCAGGTCCTTGATAAAACAAAGTCCAAAAGTCACTCTTGAATGTGACCGAAAAGGAAATCAGGGAAAGAAAGAGTCTAACGAGCATGTGCAGGCAGTTTATACAACTATAGGCTTTCCTGGGGTTTCTGAGAAAGACAAACCAGGAGATTATGCCAGATATAAAGAAGTCTCTAGGCTTTGTCAGTCATTTCAGTCTAGAAGAAATGAAACTGAGCTCACTATTGCAAATAAACTTGGACTTTCACAAAACCCATATCATATGCCATCCATTTCTCCCATCAAGTCATCTGTTAAAACTATATGTAAGAAAAATCTGTCAGAGGAAAAGTTTGAAGAACATTCAATATTCCCTGAAAGAGCAATAGGAAATGAGACCATTCAAAGTACAGTGGGCACAATTAGCCAAAATAACAGAGAAAGCACTTTTAAAGAAGGCAGCTCAAGCGGTATTTATGAAGCAGGTTCCAGTGGTGAAAACATTCAAGCAGAACTAAGTAGAAACAGAGGACCAAAATTAAATGCTGTGCTTCAGTTGGGTCTCATGCAGCCTGAAGTCTATGAGCAAAGCCTTCCTCTAAGTAATAAACATTCTGAAATAAAAAGGCAAGGAGTTCAGGCTGTTAATGCAGATGTCTCTCCACAAATTTCAGATAACTTAGAGCAACCTATGAACAGTAATATTTCTCAGGTTTGTTCTGAGACACCGGATGACCTGTTAAATGATGACAAAATAAAGGACAATATCAGCTTTGATGAAAGTGGCATTCAGGAAAGATCTGCTGTTTTTAGCAAAAATGTCCAGAAAGGAGAATTCAGAAGGAGCCCTAGTCCCTTAGCCCATGCAAGTTTGTCTCAAGGTCGCCCAAGAAGG')]
Handling overloaded FASTA sequence labels#
The FASTA label field is frequently overloaded, with different information fields present in the field and separated by some delimiter. This can be flexibly addressed using the LabelParser
. By creating a custom label parser, we can decide which part we use as the sequence name. We show how to convert a field into something specific.
from cogent3.parse.fasta import LabelParser
def latin_to_common(latin):
return {"Homo sapiens": "human", "Pan troglodtyes": "chimp"}[latin]
label_parser = LabelParser(
"%(species)s", [[1, "species", latin_to_common]], split_with=":"
)
for label in ">abcd:Homo sapiens:misc", ">abcd:Pan troglodtyes:misc":
label = label_parser(label)
print(label, type(label))
human <class 'cogent3.parse.fasta.RichLabel'>
chimp <class 'cogent3.parse.fasta.RichLabel'>
RichLabel
objects have an Info
object as an attribute, allowing specific reference to all the specified label fields.
from cogent3.parse.fasta import LabelParser, iter_fasta_records
fasta_data = [
">gi|10047090|ref|NP_055147.1| small muscle protein, X-linked [Homo sapiens]",
"MNMSKQPVSNVRAIQANINIPMGAFRPGAGQPPRRKECTPEVEEGVPPTSDEEKKPIPGAKKLPGPAVNL",
"SEIQNIKSELKYVPKAEQ",
">gi|10047092|ref|NP_037391.1| neuronal protein [Homo sapiens]",
"MANRGPSYGLSREVQEKIEQKYDADLENKLVDWIILQCAEDIEHPPPGRAHFQKWLMDGTVLCKLINSLY",
"PPGQEPIPKISESKMAFKQMEQISQFLKAAETYGVRTTDIFQTVDLWEGKDMAAVQRTLMALGSVAVTKD",
]
label_to_name = LabelParser(
"%(ref)s",
[[1, "gi", str], [3, "ref", str], [4, "description", str]],
split_with="|",
)
for name, seq in iter_fasta_records(fasta_data, label_to_name=label_to_name):
print(name)
print(name.info.gi)
print(name.info.description)
NP_055147.1
10047090
small muscle protein, X-linked [Homo sapiens]
NP_037391.1
10047092
neuronal protein [Homo sapiens]
Using a third-party plugin for sequence storage#
Sequence collections and alignments have a .storage
attribute which holds the underlying sequence data and provides basic functions for obtaining it. Users can install a third-party plugin which is customized for different types of sequence data. The following examples require you install the cogent3-h5seqs
plugin. This project provides alternative storage for both unaligned sequences and for alignments.
$ pip install cogent3-h5seqs
Selecting an alternate storage backend#
Specify the storage using the storage_backend
argument.
from cogent3 import load_aligned_seqs
aln = load_aligned_seqs(
"data/long_testseqs.fasta", moltype="dna", storage_backend="h5seqs_aligned"
)
aln
0 | |
DogFaced | TGTGGCACAAATACTCATGCCAACTCATTACAGCATGAGAACAGCAGTTTATTATACACT |
HowlerMon | ......................G...........................G...CT.... |
Human | ......................G...............................CT.... |
Mouse | .........G..G.........G...........C.....C............GCT..T. |
NineBande | .........................T............................CT.... |
5 x 2532 (truncated to 5 x 60) dna alignment
That’s it!
type(aln.storage)
cogent3_h5seqs.AlignedSeqsData
For the cogent3-h5seqs
package you specify a different storage backend for unaligned sequences.
from cogent3 import load_unaligned_seqs
seqs = load_unaligned_seqs(
"data/long_testseqs.fasta", moltype="dna", storage_backend="h5seqs_unaligned"
)
type(seqs.storage)
cogent3_h5seqs.UnalignedSeqsData
Set the default storage#
You can set the default storage process-wide, so you don’t need to use the storage_backend
argument.
import cogent3
cogent3.set_storage_defaults(
unaligned_seqs="h5seqs_unaligned", aligned_seqs="h5seqs_aligned"
)
aln = cogent3.get_dataset("brca1")
type(aln.storage)
cogent3_h5seqs.AlignedSeqsData
When you apply operations, the new backend storage setting is applied.
coll = aln.degap()
type(coll.storage)
cogent3_h5seqs.UnalignedSeqsData
Note
To revert to the cogent3
defaults, use the reset
argument.
cogent3.set_storage_defaults(reset=True)