Sequence collections and alignments (skbio.core.alignment)

This module provides functionality for working with biological sequence collections and alignments. These can be composed of generic sequences, nucelotide sequences, DNA sequences, and RNA sequences. By default, input is not validated, except that sequence ids must be unique, but all contructor methods take a validate option which checks different features of the input based on SequenceCollection type.

Data Structures

SequenceCollection(seqs[, validate]) Class for storing collections of biological sequences.
Alignment(seqs[, validate]) Class for storing alignments of biological sequences.

Alignment Algorithms

StripedSmithWaterman Performs a striped (banded) Smith Waterman Alignment.
AlignmentStructure Wraps the result of an alignment c struct so it is accessible to Python
align_striped_smith_waterman Align query and target sequences with Striped Smith-Waterman.

Data Structure Examples

>>> from StringIO import StringIO
>>> from skbio.core.alignment import SequenceCollection, Alignment
>>> from skbio.core.sequence import DNA
>>> seqs = [DNA("ACC--G-GGTA..", id="seq1"),
...     DNA("TCC--G-GGCA..", id="seqs2")]
>>> a1 = Alignment(seqs)
>>> a1
<Alignment: n=2; mean +/- std length=13.00 +/- 0.00>

>>> seqs = [DNA("ACCGGG", id="seq1"),
...     DNA("TCCGGGCA", id="seq2")]
>>> s1 = SequenceCollection(seqs)
>>> s1
<SequenceCollection: n=2; mean +/- std length=7.00 +/- 1.00>

>>> from skbio.parse.sequences import parse_fasta
>>> fasta_f = StringIO('>seq1\n'
...                    'CGATGTCGATCGATCGATCGATCAG\n'
...                    '>seq2\n'
...                    'CATCGATCGATCGATGCATGCATGCATG\n')
>>> s1 = SequenceCollection.from_fasta_records(parse_fasta(fasta_f), DNA)
>>> s1
<SequenceCollection: n=2; mean +/- std length=26.50 +/- 1.50>

Alignment Algorithm Examples

Using the convenient align_striped_smith_waterman function:

>>> from skbio.core.alignment import align_striped_smith_waterman
>>> alignment = align_striped_smith_waterman(
...                 "ACTAAGGCTCTCTACCCCTCTCAGAGA",
...                 "ACTAAGGCTCCTAACCCCCTTTTCTCAGA"
...             )
>>> print alignment
{
    'optimal_alignment_score': 27,
    'suboptimal_alignment_score': 21,
    'query_begin': 0,
    'query_end': 24,
    'target_begin': 0,
    'target_end_optimal': 28,
    'target_end_suboptimal': 12,
    'cigar': '10M1I2M1D5M4D7M',
    'query_sequence': 'ACTAAGGCTCTCTACCCCTCTCAGAGA',
    'target_sequence': 'ACTAAGGCTCCTAACCCCCTTTTCTCAGA'
}

Using the StripedSmithWaterman object:

>>> from skbio.core.alignment import StripedSmithWaterman
>>> query = StripedSmithWaterman("ACTAAGGCTCTCTACCCCTCTCAGAGA")
>>> alignment = query("AAAAAACTCTCTAAACTCACTAAGGCTCTCTACCCCTCTTCAGAGAAGTCGA")
>>> print alignment
{
    'optimal_alignment_score': 49,
    'suboptimal_alignment_score': 24,
    'query_begin': 0,
    'query_end': 26,
    'target_begin': 18,
    'target_end_optimal': 45,
    'target_end_suboptimal': 29,
    'cigar': '20M1D7M',
    'query_sequence': 'ACTAAGGCTCTCTACCCCTCTCAGAGA',
    'target_sequence': 'AAAAAACTCTCTAAACTCACTAAGGCTCTCTACCCCTCTTCAGAGAAGTCGA'
}

Using the StripedSmithWaterman object for multiple targets in an efficient way and finding the aligned sequence representations:

>>> from skbio.core.alignment import StripedSmithWaterman
>>> alignments = []
>>> target_sequences = [
...     "GCTAACTAGGCTCCCTTCTACCCCTCTCAGAGA",
...     "GCCCAGTAGCTTCCCAATATGAGAGCATCAATTGTAGATCGGGCC",
...     "TCTATAAGATTCCGCATGCGTTACTTATAAGATGTCTCAACGG",
...     "TAGAGATTAATTGCCACTGCCAAAATTCTG"
... ]
>>> query_sequence = "ACTAAGGCTCTCTACCCCTCTCAGAGA"
>>> query = StripedSmithWaterman(query_sequence)
>>> for target_sequence in target_sequences:
...     alignment = query(target_sequence)
...     alignments.append(alignment)
...
>>> print alignments[0]
{
    'optimal_alignment_score': 38,
    'suboptimal_alignment_score': 14,
    'query_begin': 0,
    'query_end': 26,
    'target_begin': 4,
    'target_end_optimal': 32,
    'target_end_suboptimal': 15,
    'cigar': '3M1I6M3D17M',
    'query_sequence': 'ACTAAGGCTCTCTACCCCTCTCAGAGA',
    'target_sequence': 'GCTAACTAGGCTCCCTTCTACCCCTCTCAGAGA'
}
>>> print alignments[0].get_aligned_query_sequence()
ACTAAGGCT---CTCTACCCCTCTCAGAGA
>>> print alignments[0].get_aligned_target_sequence()
ACT-AGGCTCCCTTCTACCCCTCTCAGAGA