r""" Sequences (:mod:`skbio.sequence`) ================================= .. currentmodule:: skbio.sequence This module provides classes for storing and working with sequences, including generic/nonbiological sequences which have no alphabet restrictions (``Sequence``) and sequences based on IUPAC-defined alphabets (``DNA``, ``RNA``, ``Protein``). 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. Additionally this module defines the ``GeneticCode`` class, which represents an immutable object that translates DNA or RNA sequences into protein sequences. 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. Classes ------- .. autosummary:: :toctree: generated/ Sequence GrammaredSequence DNA RNA Protein GeneticCode Subpackages ----------- .. autosummary:: :toctree: generated/ distance Examples -------- 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': 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'] """ # ---------------------------------------------------------------------------- # Copyright (c) 2013--, scikit-bio development team. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file LICENSE.txt, distributed with this software. # ---------------------------------------------------------------------------- from ._sequence import Sequence from ._protein import Protein from ._dna import DNA from ._rna import RNA from ._genetic_code import GeneticCode from ._grammared_sequence import GrammaredSequence __all__ = ['Sequence', 'Protein', 'DNA', 'RNA', 'GeneticCode', 'GrammaredSequence']