Biological Sequences (skbio.sequence)#

This module provides functionality for storing and working with sequences, including molecular sequences based on IUPAC-defined alphabets (DNA, RNA, Protein), sequences based on custom alphabets (GrammaredSequence), and generic/non-biological sequences with no alphabet restrictions (Sequence).

Additionally, this module defines the GeneticCode class, which represents an immutable object that translates DNA or RNA sequences into protein sequences, and the SubstitutionMatrix class, which stores scores of substitutions between sequence characters.

Sequence types#

Sequence(sequence[, metadata, ...])

Store generic sequence data and optional associated metadata.

GrammaredSequence(sequence[, metadata, ...])

Store sequence data conforming to a character set.

DNA(sequence[, metadata, ...])

Store DNA sequence data and optional associated metadata.

RNA(sequence[, metadata, ...])

Store RNA sequence data and optional associated metadata.

Protein(sequence[, metadata, ...])

Store protein sequence data and optional associated metadata.

Sequence utilities#

GeneticCode(amino_acids, starts[, name])

Genetic code for translating codons to amino acids.

SubstitutionMatrix(alphabet, scores, **kwargs)

Scoring matrix between characters in biological sequences.

Distance calculation#

distance

Sequence distance metrics (skbio.sequence.distance)

Tutorial#

The primary information stored for each different type of sequence object is the underlying sequence data itself. This is stored as an immutable NumPy array. Additionally, each type of sequence may include optional metadata and positional metadata. Note that metadata and positional metadata are mutable.

Common operations are defined as methods, for example computing the reverse complement of a DNA sequence, or searching for N-glycosylation motifs in protein sequences. Class attributes provide valid character sets, complement maps for different sequence types, and degenerate character definitions.

New sequences are created with optional metadata and positional metadata. Metadata is stored as a Python dict, while positional metadata is stored as a pandas DataFrame.

>>> from skbio import DNA, RNA
>>> d = DNA('ACCGGGTA', metadata={'id':"my-sequence", 'description':"GFP"},
...          positional_metadata={'quality':[22, 25, 22, 18, 23, 25, 25, 25]})
>>> d
DNA
-----------------------------
Metadata:
    'description': 'GFP'
    'id': 'my-sequence'
Positional metadata:
    'quality': <dtype: int64>
Stats:
    length: 8
    has gaps: False
    has degenerates: False
    has definites: True
    GC-content: 62.50%
-----------------------------
0 ACCGGGTA

New sequences can also be created from existing sequences, for example as their reverse complement or degapped (i.e., unaligned) version.

>>> d1 = DNA('.ACC--GGG-TA...', metadata={'id':'my-sequence'})
>>> d2 = d1.degap()
>>> d2
DNA
--------------------------
Metadata:
    'id': 'my-sequence'
Stats:
    length: 8
    has gaps: False
    has degenerates: False
    has definites: True
    GC-content: 62.50%
--------------------------
0 ACCGGGTA
>>> d3 = d2.reverse_complement()
>>> d3
DNA
--------------------------
Metadata:
    'id': 'my-sequence'
Stats:
    length: 8
    has gaps: False
    has degenerates: False
    has definites: True
    GC-content: 62.50%
--------------------------
0 TACCCGGT

It’s also straightforward to compute distances between sequences (optionally using user-defined distance metrics, the default is Hamming distance which requires that the sequences being compared are the same length) for use in sequence clustering, phylogenetic reconstruction, etc.

>>> r1 = RNA('GACCCGCUUU')
>>> r2 = RNA('GCCCCCCUUU')
>>> r1.distance(r2)
0.2

Similarly, you can calculate the percent (dis)similarity between a pair of aligned sequences.

>>> r3 = RNA('ACCGUUAGUC')
>>> r4 = RNA('ACGGGU--UC')
>>> r3.match_frequency(r4, relative=True)
0.6
>>> r3.mismatch_frequency(r4, relative=True)
0.4

Sequences can be searched for known motif types. This returns the slices that describe the matches.

>>> r5 = RNA('AGG-GGACUGAA')
>>> for motif in r5.find_motifs('purine-run', min_length=2):
...     motif
slice(0, 3, None)
slice(4, 7, None)
slice(9, 12, None)

Those slices can be used to extract the relevant subsequences.

>>> for motif in r5.find_motifs('purine-run', min_length=2):
...     r5[motif]
...     print('')
RNA
--------------------------
Stats:
    length: 3
    has gaps: False
    has degenerates: False
    has definites: True
    GC-content: 66.67%
--------------------------
0 AGG

RNA
--------------------------
Stats:
    length: 3
    has gaps: False
    has degenerates: False
    has definites: True
    GC-content: 66.67%
--------------------------
0 GGA

RNA
--------------------------
Stats:
    length: 3
    has gaps: False
    has degenerates: False
    has definites: True
    GC-content: 33.33%
--------------------------
0 GAA

And gaps or other features can be ignored while searching, as these may disrupt otherwise meaningful motifs.

>>> for motif in r5.find_motifs('purine-run', min_length=2, ignore=r5.gaps()):
...     r5[motif]
...     print('')
RNA
--------------------------
Stats:
    length: 7
    has gaps: True
    has degenerates: False
    has definites: True
    GC-content: 66.67%
--------------------------
0 AGG-GGA

RNA
--------------------------
Stats:
    length: 3
    has gaps: False
    has degenerates: False
    has definites: True
    GC-content: 33.33%
--------------------------
0 GAA

In the above example, removing gaps from the resulting motif matches is easily achieved, as the sliced matches themselves are sequences of the same type as the input.

>>> for motif in r5.find_motifs('purine-run', min_length=2, ignore=r5.gaps()):
...     r5[motif].degap()
...     print('')
RNA
--------------------------
Stats:
    length: 6
    has gaps: False
    has degenerates: False
    has definites: True
    GC-content: 66.67%
--------------------------
0 AGGGGA

RNA
--------------------------
Stats:
    length: 3
    has gaps: False
    has degenerates: False
    has definites: True
    GC-content: 33.33%
--------------------------
0 GAA

Sequences can similarly be searched for arbitrary patterns using regular expressions.

>>> for match in r5.find_with_regex('(G+AC[UT])'):
...     match
slice(4, 9, None)

DNA can be transcribed to RNA:

>>> dna = DNA('ATGTGTATTTGA')
>>> rna = dna.transcribe()
>>> rna
RNA
--------------------------
Stats:
    length: 12
    has gaps: False
    has degenerates: False
    has definites: True
    GC-content: 25.00%
--------------------------
0 AUGUGUAUUU GA

Both DNA and RNA can be translated into a protein sequence. For example, let’s translate our DNA and RNA sequences using NCBI’s standard genetic code (table ID 1, the default genetic code in scikit-bio):

>>> protein_from_dna = dna.translate()
>>> protein_from_dna
Protein
--------------------------
Stats:
    length: 4
    has gaps: False
    has degenerates: False
    has definites: True
    has stops: True
--------------------------
0 MCI*
>>> protein_from_rna = rna.translate()
>>> protein_from_rna
Protein
--------------------------
Stats:
    length: 4
    has gaps: False
    has degenerates: False
    has definites: True
    has stops: True
--------------------------
0 MCI*

The two translations are equivalent:

>>> protein_from_dna == protein_from_rna
True

Class-level methods contain information about the molecule types.

>>> sorted(DNA.degenerate_map['B'])
['C', 'G', 'T']

>>> sorted(RNA.degenerate_map['B'])
['C', 'G', 'U']
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