Loading nucleotide, protein sequences

LoadSeqs from a file

As an alignment

The function LoadSeqs() creates either a sequence collection or an alignment depending on the keyword argument aligned (the default is True).

>>> from cogent import LoadSeqs, DNA
>>> aln = LoadSeqs('data/long_testseqs.fasta', moltype=DNA)
>>> type(aln)
<class 'cogent.core.alignment.Alignment'>

This example and some of the following use the long_testseqs.fasta file.

As a sequence collection (unaligned)

Setting the LoadSeqs() function keyword argument aligned=False returns a sequence collection.

>>> from cogent import LoadSeqs, DNA
>>> seqs = LoadSeqs('data/long_testseqs.fasta', moltype=DNA, aligned=False)
>>> print type(seqs)
<class 'cogent.core.alignment.SequenceCollection'>


An alignment can be sliced, but a SequenceCollection can not.

Specifying the file format

LoadSeqs() uses the filename suffix to infer the file format. This can be overridden using the format argument.

>>> from cogent import LoadSeqs, DNA
>>> aln = LoadSeqs('data/long_testseqs.fasta', moltype=DNA,
...                  format='fasta')
>>> aln
5 x 2532 dna alignment: Human[TGTGGCACAAA...

LoadSeqs from a series of strings

>>> from cogent import LoadSeqs
>>> seqs = ['>seq1','AATCG-A','>seq2','AATCGGA']
>>> seqs_loaded = LoadSeqs(data=seqs)
>>> print seqs_loaded

LoadSeqs from a dict of strings

>>> from cogent import LoadSeqs
>>> seqs = {'seq1': 'AATCG-A','seq2': 'AATCGGA'}
>>> seqs_loaded = LoadSeqs(data=seqs)

Specifying the sequence molecular type

Simple case of loading a list of aligned amino acid sequences in FASTA format, with and without molecule type specification. When the MolType is not specified it defaults to ASCII.

>>> from cogent import LoadSeqs
>>> from cogent import DNA, PROTEIN
>>> protein_seqs = ['>seq1','DEKQL-RG','>seq2','DDK--SRG']
>>> proteins_loaded = LoadSeqs(data=protein_seqs)
>>> proteins_loaded.MolType
MolType(('a', 'b', 'c', 'd', 'e', ...
>>> print proteins_loaded

>>> proteins_loaded = LoadSeqs(data=protein_seqs, moltype=PROTEIN)
>>> print proteins_loaded

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 cogent import LoadSeqs, DNA
>>> DNA_seqs = ['>sample1 Mus musculus','AACCTGC--C','>sample2 Gallus gallus','AAC-TGCAAC']
>>> loaded_seqs = LoadSeqs(data=DNA_seqs, moltype=DNA, label_to_name=lambda x: x.split()[0])
>>> print loaded_seqs

Using alternative constructors for the Alignment object

An example of using an alternative constructor is given below. A constructor is passed to the aligned parameter in lieu of True or False.

>>> from cogent import LoadSeqs
>>> from cogent.core.alignment import DenseAlignment
>>> seqs = ['>seq1','AATCG-A','>seq2','AATCGGA']
>>> seqs_loaded = LoadSeqs(data=seqs,aligned=DenseAlignment)
>>> print seqs_loaded

Loading sequences using format parsers

LoadSeqs is just a convenience interface 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.


This returns the sequences as strings.

>>> from cogent.parse.fasta import MinimalFastaParser
>>> f=open('data/long_testseqs.fasta')
>>> seqs = [(name, seq) for name, seq in MinimalFastaParser(f)]
>>> print seqs

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 decided which part we use as the sequence name. We show how convert a field into something specific.

>>> from cogent.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 'cogent.parse.fasta.RichLabel'>
chimp <class 'cogent.parse.fasta.RichLabel'>

The RichLabel objects have an Info object as an attribute, allowing specific reference to all the specified label fields.

>>> from cogent.parse.fasta import MinimalFastaParser, LabelParser
>>> fasta_data = ['>gi|10047090|ref|NP_055147.1| small muscle protein, X-linked [Homo sapiens]',
... '>gi|10047092|ref|NP_037391.1| neuronal protein [Homo sapiens]',
>>> 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
 small muscle protein, X-linked [Homo sapiens]
 neuronal protein [Homo sapiens]