# Structures to handle biological sequences.
#
# This file is part of gepyto.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative
# Commons, PO Box 1866, Mountain View, CA 94042, USA.
from __future__ import division
__author__ = "Marc-Andre Legault"
__copyright__ = ("Copyright 2014 Marc-Andre Legault and Louis-Philippe "
"Lemieux Perreault. All rights reserved.")
__license__ = "Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)"
try:
from string import maketrans, translate
except ImportError:
maketrans = str.maketrans
translate = str.translate
import textwrap
import collections
import multiprocessing
import itertools
import logging
logger = logging.getLogger(__name__)
import numpy as np
from six.moves import range as xrange
from .. import reference
from .. import settings
DNA_GENETIC_CODE = dict(
GCT="A", GCC="A", GCA="A", GCG="A",
CGT="R", CGC="R", CGA="R", CGG="R", AGA="R", AGG="R",
AAT="N", AAC="N", GAT="D", GAC="D", TGT="C", TGC="C", CAA="Q", CAG="Q",
GAA="E", GAG="E",
GGT="G", GGC="G", GGA="G", GGG="G",
CAT="H", CAC="H", ATT="I", ATC="I", ATA="I",
TTA="L", TTG="L", CTT="L", CTC="L", CTA="L", CTG="L",
AAA="K", AAG="K", ATG="M", TTT="F", TTC="F",
CCT="P", CCC="P", CCA="P", CCG="P",
TCT="S", TCC="S", TCA="S", TCG="S", AGT="S", AGC="S",
ACT="T", ACC="T", ACA="T", ACG="T",
TGG="W", TAT="Y", TAC="Y",
GTT="V", GTC="V", GTA="V", GTG="V",
)
REVERSE_COMPLEMENT_DNA = dict(
A="T", C="G", G="C", T="A", M="K", R="Y", W="W", S="S", Y="R", K="M",
V="B", H="D", D="H", B="V",
)
# For both DNA and RNA.
STOP_CODONS = {"TAA", "TAG", "TGA", "UAA", "UAG", "UGA"}
def _build_coding_sequences(orf, s=None):
if type(orf) is tuple:
orf, s = orf # map passes arguments as a tuple.
if s is None:
raise TypeError("_build_coding_sequences() takes either a tuple "
"of (orf name, sequence) or the two corresponding "
"distinct arguments.")
# Sequences are actually: (orf, start, end, sequence)
sequences = []
start_codons = []
i = 0
while i + 3 <= len(s):
codon = s[i:(i+3)]
if codon == "ATG":
start_codons.append(i)
elif codon in STOP_CODONS:
# Close all open sequences.
for j in start_codons:
if orf > 0:
start = j + orf - 1
end = (i+3) + orf - 1
else:
start = len(s) - (i+3)
end = len(s) - j
sequences.append(
(orf, start, end, s[j:i])
)
start_codons = []
elif codon not in DNA_GENETIC_CODE:
# This should not happen in a real protein, so we'll close the
# sequences.
start_codons = []
i += 3
return sequences
class Sequence(object):
[docs] """Object to represent biological sequences.
:param uid: The identifier for this sequence.
:type uid: str
:param s: The actual sequence.
:type s: str
:param seq_type: The sequence type (DNA, RNA or AA).
:type seq_type: str
:param info: A python dict of extra parameters (optional).
:type info: dict
Common examples for the info attributes:
* ``species``: Homo sapiens
* ``species_ncbi_tax_id``: 9606
* ``description``: dystroglycan 1
* ``db_name``: RefSeq
* ``db_acc``: NM_004393
"""
types = set(["DNA", "RNA", "AA"])
def __init__(self, uid, s, seq_type, info=None):
if seq_type not in Sequence.types:
raise ValueError("Invalid sequence type {}. Allowed types are: "
"{}".format(seq_type, list(Sequence.types)))
self.uid = uid
self.seq = str(s.upper())
self.seq_type = seq_type
self.info = info
self._annotations = []
def __repr__(self):
return "<Sequence: {}>".format(self.uid)
def __eq__(self, seq1):
return bool(
self.uid == seq1.uid and
self.seq == seq1.seq and
self.seq_type == seq1.seq_type
)
def __len__(self):
return len(self.seq)
@classmethod
def from_reference(cls, chrom, start, end=None, length=None):
[docs] """Create a Sequence object from a given locus."""
with reference.Reference() as ref:
seq = ref.get_sequence(chrom, start, end, length)
if length:
end = start + length - 1
uid = "chr{}:{}-{}".format(chrom, start, end)
seq_type = "DNA"
info = {
"species": "Homo sapiens",
"species_ncbi_tax_id": 9606,
"build": settings.BUILD
}
return cls(uid, seq, seq_type, info)
def to_fasta(self, line_len=80, full_header=False):
[docs] """Converts the sequence to a valid fasta string.
:param line_len: The maximum line length for the sequence.
:type line_len: int
:param full_header: Add the contents of the info field to the header.
(default: False).
:type full_header: bool
:returns: A fasta string.
:rtype: str
"""
s = "> {}".format(self.uid)
if full_header:
s += " - "
s += ", ".join(
["'{}'='{}'".format(k, v) for k, v in self.info.items()]
)
s = [s, ] + textwrap.wrap(self.seq, line_len)
return "\n".join(s) + "\n"
def get_annotations(self):
[docs] """Return a list of bound SequenceAnnotation objects.
:returns: A list of annotations for the sequence representing
different kind of information about sub-sequences like
protein domains.
:rtype: list
"""
return self._annotations
def translate(self, no_check=False):
[docs] """Use the genetic code to translate a DNA or RNA sequence into an
amino acid sequence.
"""
if self.seq_type not in ("DNA", "RNA"):
raise Exception("Can only translate DNA or RNA sequences.")
if self.seq_type == "RNA":
# We need to convert the genetic code to RNA.
code = {}
for k, v in DNA_GENETIC_CODE.items():
k = k.replace("T", "U")
code[k] = v
else:
code = DNA_GENETIC_CODE
s = self.seq
if len(s) % 3 != 0:
raise Exception("Invalid sequence length for translation.")
if not no_check:
if s[:3] not in ("ATG", "AUG"):
raise Exception("Sequence does not start with START codon "
"(ATG).")
if s[-3:] not in STOP_CODONS:
if not no_check:
raise Exception("Sequence does not end with STOP codon.")
else:
# Sequence ends with stop codon, we'll remove it for translation.
s = s[:-3]
return Sequence(
uid="translated_{}".format(self.uid),
seq_type="AA",
s=Sequence._translate(s, code=code),
info=self.info
)
@staticmethod
def _translate(s, code=DNA_GENETIC_CODE):
"""Translate a string. Used internally for translation."""
try:
s = "".join(
[code[s[i:i+3]] for i in xrange(0, len(s) - 2, 3)]
)
except KeyError as e:
raise ValueError("Can't find amino acid for codon '{}'".format(
e.message
))
return s
def find_coding_sequences(self, cpu=6):
[docs] """Tries all the ORFs and translates every possible protein.
:returns: A tuple containing the information of the coding sequence.
(ORF, start, end, sequence)
:rtype: tuple
.. warning::
This is currently **untested**.
"""
orfs = collections.OrderedDict([
(1, self.seq),
(2, self.seq[1:]),
(3, self.seq[2:]),
(-1, Sequence._str_reverse_complement(self.seq))
])
orfs.update([
(-2, orfs[-1][1:]),
(-3, orfs[-1][2:]),
])
if cpu == 1:
# Single processor.
coding_sequences = []
for orf, seq in orfs.items():
coding_sequences.extend(_build_coding_sequences(orf, seq))
else:
p = multiprocessing.Pool(cpu)
coding_sequences = p.map(_build_coding_sequences, orfs.items())
coding_sequences = list(itertools.chain(*coding_sequences))
return coding_sequences
def find_translations(self, cpu=6):
[docs] """Finds and translates peptides from any ORF in the sequence."""
coding_sequences = self.find_coding_sequences(cpu)
peptides = []
for i, tu in enumerate(coding_sequences):
tu = list(tu)
tu[-1] = Sequence._translate(tu[-1])
peptides.append(tu)
return peptides
def reverse_complement(self):
[docs] """Reverse complement the sequence (compatible with IUPAC codes)."""
if self.seq_type != "DNA":
raise NotImplementedError("reverse_complement is only available "
"for DNA sequences.")
seq = Sequence._str_reverse_complement(self.seq)
return Sequence(
uid="reversed_compl_{}".format(self.uid),
seq_type="DNA",
s=seq,
info=self.info
)
@staticmethod
def _str_reverse_complement(seq):
seq = seq[::-1]
before, after = zip(*REVERSE_COMPLEMENT_DNA.items())
trans = maketrans("".join(before), "".join(after))
seq = translate(seq, trans)
return seq
def gc_content(self):
[docs] """Computes the GC content for the sequence."""
counter = collections.Counter(self.seq)
return (counter["G"] + counter["C"]) / sum(counter.values())
def bbc(self, k=10, alphabet=None):
[docs] """Shortcut to base_base_correlation.
"""
return self.base_base_correlation(k, alphabet)
def base_base_correlation(self, k=10, alphabet=None):
[docs] """Compute the base base correlation (BBC) for the sequence.
:param k: k is a parameter of the BBC. Intuitively, it represents
the maximum distance to observe correlation between bases.
:type k: int
:param alphabet: List of possible characters. This can be used to avoid
autodetection of the alphabet in the case where
sequences with missing letters are to be compared.
:type alphabet: iterable
:returns: A 16 dimensional vector representing the BBC.
:rtype: :py:class:`np.ndarray`
A description of the method can be found here:
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4272582
Liu, Zhi-Hua, et al. "Base-Base Correlation a Novel Sequence Feature
and its Applications." Bioinformatics and Biomedical Engineering, 2007.
ICBBE 2007. The 1st International Conference on. IEEE, 2007.
This implementation is generalized for any sequence type.
"""
s = self.seq
if k > len(s) - 2:
raise Exception("Sequence too short to compute BBC with "
"k={}".format(k))
if alphabet is None:
alphabet = set(s)
alphabet = sorted(list(alphabet))
alphabet = dict(zip(alphabet, xrange(len(alphabet))))
L = len(alphabet)
# Compute the base probabilities for every character.
p = np.zeros(L)
for c in s:
p[alphabet[c]] += 1
p /= np.sum(p)
p.shape = (1, L)
# Now we need to compute
bbc = np.zeros((L, L))
for l in xrange(1, k + 2):
# We need to compute $p_{ij}(l)$ representing the probability of
# observing the bases i and j separated by l "gaps".
# We will compute it for all 16 combinations of alleles.
l_dist_correlations = np.zeros((L, L))
for i in xrange(len(s) - l):
nuc1 = alphabet[s[i]]
nuc2 = alphabet[s[i + l]]
l_dist_correlations[nuc1][nuc2] += 1
l_dist_correlations /= np.sum(l_dist_correlations)
# We can now compute the D_{ij}(l) which is the deviation from
# statistical independance.
# $D_{ij}(l) = p_{ij}(l) - p_i p_j$
D = l_dist_correlations - np.dot(p.T, p)
bbc += D + (D ** 2 / 2 * np.dot(p.T ** 2, p ** 2)) + D ** 3
# We can now flatten the bbc into a 16 feature vector.
bbc.shape = (1, L * L)
return bbc
def smith_waterman(seq1, seq2, penalties=None, output="sequences"):
[docs] """Compute a pairwise local sequence alignment using the Smith Waterman
algorithm.
The output parameter determines how results will be represented:
If "sequences" is chosen, the two aligned sequences will be returned with
gaps represented by dashes ("-").
If "alignment" is chosen, a single encoded string will be reterned where
"M" represents matches, "I" represents insertions, "D" represents deletions
and "X" represents mismatches. This is done with respect to the first
sequence.
In any mode, the first returned element is always the raw similarity score.
Note that because this is local alignment, gaps at both ends won't be
penalized. Also, only one of the potentially many best alignments will be
output.
.. warning::
This implementation is not very optimized. It is not written in a low
level language. It can be used for small sequences or for low number of
comparisons, but should not be used in large scale products.
.. note::
Some functionality like affine gap penalties, or substitution matrices
are not implemented.
The default penalty scheme is the following:
.. code-block:: python
{
"match": 2,
"mismatch": -1,
"gap": -1
}
You can follow this pattern to set your own penalty scores.
"""
if isinstance(seq1, Sequence):
seq1 = seq1.seq
if isinstance(seq2, Sequence):
seq2 = seq2.seq
# The return mode.
SEQUENCES = "sequences"
ALIGNMENT = "alignment"
if output == SEQUENCES:
mode = SEQUENCES
elif output == ALIGNMENT:
mode = ALIGNMENT
else:
msg = "Invalid output mode '{}'. Accepted values are: {}."
raise TypeError(
msg.format(output, ", ".join([SEQUENCES, ALIGNMENT]))
)
# Default penalties.
if penalties is None:
default_penalties = {
"match": 2,
"mismatch": -1,
"gap": -1
}
penalties = {}
elif type(penalties) is not dict:
raise ValueError("The penalties dict should have keys for a subset of "
"match, mismatch and gap scores.")
default_penalties.update(penalties)
p = default_penalties
msg = ("Computing local alignment using the Smith Waterman algorithm "
"with match={}, mismatch={}, gap={}. Return mode: {}.")
logger.info(msg.format(p["match"], p["mismatch"], p["gap"], mode))
m = len(seq1) + 1
n = len(seq2) + 1
mat = np.empty((m, n), dtype=int)
pointers = np.zeros((m, n), dtype=int) - 1
mat[0, :] = 0
mat[:, 0] = 0
# This implementation will use the naive algorithm to generate the matrix.
# Better high performance implementations exist.
# Also, affine panelties are not implemented. It's a constant cost per
# gap.
for i in xrange(1, mat.shape[0]):
for j in xrange(1, mat.shape[1]):
if seq1[i - 1] == seq2[j - 1]:
diag_score = p["match"]
else:
diag_score = p["mismatch"]
choices = [
0,
mat[i - 1, j - 1] + diag_score,
mat[i - 1, j] + p["gap"],
mat[i, j - 1] + p["gap"]
]
max_idx = np.argmax(choices)
mat[i, j] = choices[max_idx]
pointers[i, j] = max_idx
# Take the max and trackback.
i, j = np.unravel_index(mat.argmax(), mat.shape)
# If we did not finish at the bottom right, we need to start off the
# alignment with the gaps.
align1 = ""
align2 = ""
# Case 1, seq 1 ends with gaps.
tail = collections.defaultdict(str)
if j < (mat.shape[1] - 1):
tail[2] += seq2[j:][::-1]
# Case 2, seq 2 ends with gaps.
if i < (mat.shape[0] - 1):
tail[1] += seq1[i:][::-1]
score = mat[i, j]
while mat[i][j] != 0:
op = pointers[i, j]
if op == 1:
align1 += seq1[i - 1]
align2 += seq2[j - 1]
i -= 1
j -= 1
elif op == 2:
align1 += seq1[i - 1]
align2 += "-"
i -= 1
elif op == 3:
align1 += "-"
align2 += seq2[j - 1]
j -= 1
# Case 3, seq 1 starts with gaps.
head = collections.defaultdict(str)
if j > 0:
head[2] += seq2[:j][::-1]
# Case 4, seq 2 starts with gaps.
if i > 0:
head[1] += seq1[:i][::-1]
if tail:
if tail.get(1):
align1 = tail[1] + align1
align2 = "-" * len(tail[1]) + align2
if tail.get(2):
align2 = tail[2] + align2
align1 = "-" * len(tail[2]) + align1
if head:
if head.get(1):
align1 += head[1]
align2 += "-" * len(head[1])
if head.get(2):
align2 += head[2]
align1 += "-" * len(head[2])
align1 = align1[::-1]
align2 = align2[::-1]
if mode == SEQUENCES:
return score, align1, align2
if mode == ALIGNMENT:
return score, _represent_alignment(align1, align2)
def _represent_alignment(s1, s2):
"""Represent two aligned sequences."""
n = len(s1)
if len(s2) != n:
raise ValueError("Can't represent an alignment between strings of "
"different lengths.")
alignment = ""
for i in xrange(n):
assert not (s1[i] == "-" == s2[i]), ("Gap in both sequences at "
"position {}.".format(i))
if s1[i] == s2[i]:
# Match.
alignment += "M"
elif s1[i] == "-":
# Deletion.
alignment += "D"
elif s2[i] == "-":
# Insertion.
alignment += "I"
elif s1[i] != "-" and s2[i] != "-" and s1[i] != s2[i]:
# Mismatch.
alignment += "X"
return alignment