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Loading a sequence from a file#

It’s also possible to load a sequence from a url.

from cogent3 import load_seq

seq = load_seq("data/mycoplasma-genitalium.fa", moltype="dna")
seq
0
NC_000908.2 Mycoplasmoides genitalium G37, complete sequenceTAAGTTATTATTTAGTTAATACTTTTAACAATATTATTAAGGTATTTAAAAAATACTATT

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
FlyingFoxTGTGGCACAAATGCTCATGCCAGCTCTTTACAGCATGAGAAC---AGTTTATTATACACT

DnaSequence, length=3,009 (truncated to 60)

Note

The filename suffix is used to infer the data format.

Loading an alignment from a file or url#

Loading aligned sequences#

from cogent3 import load_aligned_seqs

aln = load_aligned_seqs("data/long_testseqs.fasta", moltype="dna")
type(aln)

cogent3.core.alignment.ArrayAlignment

The load functions record the origin of the data in the info attribute under a “source” key.

aln.info.source

'data/long_testseqs.fasta'

Note

The function load_aligned_seqs() returns an ArrayAlignment by default. If you set the argument array_align=False, you will get an Alignment. (That class can be annotated.)

Loading unaligned sequences#

The load_unaligned_seqs() function returns a sequence collection.

from cogent3 import load_unaligned_seqs

seqs = load_unaligned_seqs("data/long_testseqs.fasta", moltype="dna")
type(seqs)

cogent3.core.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
DogFacedTGTGGCACAAATACTCATGCCAACTCATTACAGCATGAGAACAGCAGTTTATTATACACT
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#

Simple case of loading a list of aligned amino acid sequences in FASTA format, with and without moltype specification. When moltype is not specified it defaults to BYTES for the ArrayAlignment class, ASCII for the Alignment class.

from cogent3 import make_aligned_seqs

protein_seqs = {"seq1": "DEKQL-RG", "seq2": "DDK--SRG"}
proteins_loaded = make_aligned_seqs(protein_seqs)
proteins_loaded.moltype
proteins_loaded
0
seq1DEKQL-RG
seq2.D.--S..

2 x 8 bytes alignment

proteins_loaded = make_aligned_seqs(protein_seqs, moltype="protein")
proteins_loaded
0
seq1DEKQL-RG
seq2.D.--S..

2 x 8 protein alignment

Note

This applies to both the load_* or make_* functions.

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)

From a series of strings#

from cogent3 import make_aligned_seqs

seqs = {"seq1": "AATCG-A", "seq2": "AATCGGA"}
seqs_loaded = make_aligned_seqs(seqs)
seqs_loaded
0
seq2AATCGGA
seq1.....-.

2 x 7 bytes alignment

Stripping label characters 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, stripping is accomplished by passing a function that removes everything after the first whitespace to the label_to_name parameter.

from cogent3 import make_aligned_seqs

DNA_seqs = {
    "sample1 Mus musculus": "AACCTGC--C",
    "sample2 Gallus gallus": "AAC-TGCAAC",
}
loaded_seqs = make_aligned_seqs(
    DNA_seqs, moltype="dna", label_to_name=lambda x: x.split()[0]
)
loaded_seqs
0
sample2AAC-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

<cogent3.core.alignment.SequenceCollection at 0x7f4e349ab320>

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 the MinimalFastaParser.

Note

This returns the sequences as strings.

from cogent3.parse.fasta import MinimalFastaParser

f = open("data/long_testseqs.fasta")
seqs = [(name, seq) for name, seq in MinimalFastaParser(f)]
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, MinimalFastaParser

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 MinimalFastaParser(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]
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