# Copyright 2013 by Zheng Ruan (zruan1991@gmail.com). All rights reserved.
#
# This file is part of the Biopython distribution and governed by your
# choice of the "Biopython License Agreement" or the "BSD 3-Clause License".
# Please see the LICENSE file that should have been included as part of this
# package.
"""Code for dealing with Codon Alignment.
CodonAlignment class is inherited from MultipleSeqAlignment class. This is
the core class to deal with codon alignment in biopython.
"""
import warnings
from math import erfc
from math import sqrt
from Bio import BiopythonWarning
from Bio.Align import MultipleSeqAlignment
from Bio.codonalign.codonseq import _get_codon_list
from Bio.codonalign.codonseq import cal_dn_ds
from Bio.codonalign.codonseq import CodonSeq
from Bio.Data import CodonTable
from Bio.SeqRecord import SeqRecord
class CodonAlignment(MultipleSeqAlignment):
"""Codon Alignment class that inherits from MultipleSeqAlignment.
>>> from Bio.SeqRecord import SeqRecord
>>> a = SeqRecord(CodonSeq("AAAACGTCG"), id="Alpha")
>>> b = SeqRecord(CodonSeq("AAA---TCG"), id="Beta")
>>> c = SeqRecord(CodonSeq("AAAAGGTGG"), id="Gamma")
>>> print(CodonAlignment([a, b, c]))
CodonAlignment with 3 rows and 9 columns (3 codons)
AAAACGTCG Alpha
AAA---TCG Beta
AAAAGGTGG Gamma
"""
def __init__(self, records="", name=None):
"""Initialize the class."""
MultipleSeqAlignment.__init__(self, records)
# check the type of the alignment to be nucleotide
for rec in self:
if not isinstance(rec.seq, CodonSeq):
raise TypeError(
"CodonSeq objects are expected in each SeqRecord in CodonAlignment"
)
if self.get_alignment_length() % 3 != 0:
raise ValueError(
"Alignment length is not a multiple of "
"three (i.e. a whole number of codons)"
)
def __str__(self):
"""Return a multi-line string summary of the alignment.
This output is indicated to be readable, but large alignment
is shown truncated. A maximum of 20 rows (sequences) and
60 columns (20 codons) are shown, with the record identifiers.
This should fit nicely on a single screen. e.g.
"""
rows = len(self._records)
lines = [
"CodonAlignment with %i rows and %i columns (%i codons)"
% (rows, self.get_alignment_length(), self.get_aln_length())
]
if rows <= 60:
lines.extend([self._str_line(rec, length=60) for rec in self._records])
else:
lines.extend([self._str_line(rec, length=60) for rec in self._records[:18]])
lines.append("...")
lines.append(self._str_line(self._records[-1], length=60))
return "\n".join(lines)
def __getitem__(self, index):
"""Return a CodonAlignment object for single indexing."""
if isinstance(index, int):
return self._records[index]
elif isinstance(index, slice):
return CodonAlignment(self._records[index])
elif len(index) != 2:
raise TypeError("Invalid index type.")
# Handle double indexing
row_index, col_index = index
if isinstance(row_index, int):
return self._records[row_index][col_index]
elif isinstance(col_index, int):
return "".join(str(rec[col_index]) for rec in self._records[row_index])
else:
return MultipleSeqAlignment(
rec[col_index] for rec in self._records[row_index]
)
def __add__(self, other):
"""Combine two codonalignments with the same number of rows by adding them.
The method also allows to combine a CodonAlignment object with a
MultipleSeqAlignment object. The following rules apply:
* CodonAlignment + CodonAlignment -> CodonAlignment
* CodonAlignment + MultipleSeqAlignment -> MultipleSeqAlignment
"""
if isinstance(other, CodonAlignment):
if len(self) != len(other):
raise ValueError(
"When adding two alignments they must have the same length"
" (i.e. same number or rows)"
)
warnings.warn(
"Please make sure the two CodonAlignment objects are sharing the same codon table. This is not checked by Biopython.",
BiopythonWarning,
)
merged = (
SeqRecord(seq=CodonSeq(left.seq + right.seq))
for left, right in zip(self, other)
)
return CodonAlignment(merged)
elif isinstance(other, MultipleSeqAlignment):
if len(self) != len(other):
raise ValueError(
"When adding two alignments they must have the same length"
" (i.e. same number or rows)"
)
return self.toMultipleSeqAlignment() + other
else:
raise TypeError(
"Only CodonAlignment or MultipleSeqAlignment object can be"
f" added with a CodonAlignment object. {object(other)} detected."
)
def get_aln_length(self):
"""Get alignment length."""
return self.get_alignment_length() // 3
def toMultipleSeqAlignment(self):
"""Convert the CodonAlignment to a MultipleSeqAlignment.
Return a MultipleSeqAlignment containing all the
SeqRecord in the CodonAlignment using Seq to store
sequences
"""
alignments = [SeqRecord(rec.seq.toSeq(), id=rec.id) for rec in self._records]
return MultipleSeqAlignment(alignments)
def get_dn_ds_matrix(self, method="NG86", codon_table=None):
"""Available methods include NG86, LWL85, YN00 and ML.
Argument:
- method - Available methods include NG86, LWL85, YN00 and ML.
- codon_table - Codon table to use for forward translation.
"""
from Bio.Phylo.TreeConstruction import DistanceMatrix as DM
if codon_table is None:
codon_table = CodonTable.generic_by_id[1]
names = [i.id for i in self._records]
size = len(self._records)
dn_matrix = []
ds_matrix = []
for i in range(size):
dn_matrix.append([])
ds_matrix.append([])
for j in range(i + 1):
if i != j:
dn, ds = cal_dn_ds(
self._records[i],
self._records[j],
method=method,
codon_table=codon_table,
)
dn_matrix[i].append(dn)
ds_matrix[i].append(ds)
else:
dn_matrix[i].append(0.0)
ds_matrix[i].append(0.0)
dn_dm = DM(names, matrix=dn_matrix)
ds_dm = DM(names, matrix=ds_matrix)
return dn_dm, ds_dm
def get_dn_ds_tree(
self, dn_ds_method="NG86", tree_method="UPGMA", codon_table=None
):
"""Construct dn tree and ds tree.
Argument:
- dn_ds_method - Available methods include NG86, LWL85, YN00 and ML.
- tree_method - Available methods include UPGMA and NJ.
"""
from Bio.Phylo.TreeConstruction import DistanceTreeConstructor
if codon_table is None:
codon_table = CodonTable.generic_by_id[1]
dn_dm, ds_dm = self.get_dn_ds_matrix(
method=dn_ds_method, codon_table=codon_table
)
dn_constructor = DistanceTreeConstructor()
ds_constructor = DistanceTreeConstructor()
if tree_method == "UPGMA":
dn_tree = dn_constructor.upgma(dn_dm)
ds_tree = ds_constructor.upgma(ds_dm)
elif tree_method == "NJ":
dn_tree = dn_constructor.nj(dn_dm)
ds_tree = ds_constructor.nj(ds_dm)
else:
raise RuntimeError(
f"Unknown tree method ({tree_method})."
" Only NJ and UPGMA are accepted."
)
return dn_tree, ds_tree
@classmethod
def from_msa(cls, align):
"""Convert a MultipleSeqAlignment to CodonAlignment.
Function to convert a MultipleSeqAlignment to CodonAlignment.
It is the user's responsibility to ensure all the requirement
needed by CodonAlignment is met.
"""
rec = [SeqRecord(CodonSeq(str(i.seq)), id=i.id) for i in align._records]
return cls(rec)
def mktest(codon_alns, codon_table=None, alpha=0.05):
"""McDonald-Kreitman test for neutrality.
Implement the McDonald-Kreitman test for neutrality (PMID: 1904993)
This method counts changes rather than sites
(http://mkt.uab.es/mkt/help_mkt.asp).
Arguments:
- codon_alns - list of CodonAlignment to compare (each
CodonAlignment object corresponds to gene sampled from a species)
Return the p-value of test result.
"""
import copy
if codon_table is None:
codon_table = CodonTable.generic_by_id[1]
if not all(isinstance(i, CodonAlignment) for i in codon_alns):
raise TypeError("mktest accepts CodonAlignment list.")
codon_aln_len = [i.get_alignment_length() for i in codon_alns]
if len(set(codon_aln_len)) != 1:
raise RuntimeError(
"CodonAlignment object for mktest should be of equal length."
)
codon_num = codon_aln_len[0] // 3
# prepare codon_dict (taking stop codon as an extra amino acid)
codon_dict = copy.deepcopy(codon_table.forward_table)
for stop in codon_table.stop_codons:
codon_dict[stop] = "stop"
# prepare codon_lst
codon_lst = []
for codon_aln in codon_alns:
codon_lst.append([])
for i in codon_aln:
codon_lst[-1].append(_get_codon_list(i.seq))
codon_set = []
for i in range(codon_num):
uniq_codons = []
for j in codon_lst:
uniq_codon = {k[i] for k in j}
uniq_codons.append(uniq_codon)
codon_set.append(uniq_codons)
syn_fix, nonsyn_fix, syn_poly, nonsyn_poly = 0, 0, 0, 0
G, nonsyn_G = _get_codon2codon_matrix(codon_table=codon_table)
for i in codon_set:
all_codon = i[0].union(*i[1:])
if "-" in all_codon or len(all_codon) == 1:
continue
fix_or_not = all(len(k) == 1 for k in i)
if fix_or_not:
# fixed
nonsyn_subgraph = _get_subgraph(all_codon, nonsyn_G)
subgraph = _get_subgraph(all_codon, G)
this_non = _count_replacement(all_codon, nonsyn_subgraph)
this_syn = _count_replacement(all_codon, subgraph) - this_non
nonsyn_fix += this_non
syn_fix += this_syn
else:
# not fixed
nonsyn_subgraph = _get_subgraph(all_codon, nonsyn_G)
subgraph = _get_subgraph(all_codon, G)
this_non = _count_replacement(all_codon, nonsyn_subgraph)
this_syn = _count_replacement(all_codon, subgraph) - this_non
nonsyn_poly += this_non
syn_poly += this_syn
return _G_test([syn_fix, nonsyn_fix, syn_poly, nonsyn_poly])
def _get_codon2codon_matrix(codon_table):
"""Get codon codon substitution matrix (PRIVATE).
Elements in the matrix are number of synonymous and nonsynonymous
substitutions required for the substitution.
"""
import copy
base_tuple = ("A", "T", "C", "G")
codons = [
i
for i in list(codon_table.forward_table.keys()) + codon_table.stop_codons
if "U" not in i
]
# set up codon_dict considering stop codons
codon_dict = copy.deepcopy(codon_table.forward_table)
for stop in codon_table.stop_codons:
codon_dict[stop] = "stop"
# count site
num = len(codons)
G = {} # graph for substitution
nonsyn_G = {} # graph for nonsynonymous substitution
graph = {}
graph_nonsyn = {}
for i, codon in enumerate(codons):
graph[codon] = {}
graph_nonsyn[codon] = {}
for p, b in enumerate(codon):
for j in base_tuple:
tmp_codon = codon[0:p] + j + codon[p + 1 :]
if codon_dict[codon] != codon_dict[tmp_codon]:
graph_nonsyn[codon][tmp_codon] = 1
graph[codon][tmp_codon] = 1
else:
if codon != tmp_codon:
graph_nonsyn[codon][tmp_codon] = 0.1
graph[codon][tmp_codon] = 1
for codon1 in codons:
nonsyn_G[codon1] = {}
G[codon1] = {}
for codon2 in codons:
if codon1 == codon2:
nonsyn_G[codon1][codon2] = 0
G[codon1][codon2] = 0
else:
nonsyn_G[codon1][codon2] = _dijkstra(graph_nonsyn, codon1, codon2)
G[codon1][codon2] = _dijkstra(graph, codon1, codon2)
return G, nonsyn_G
def _dijkstra(graph, start, end):
"""Dijkstra's algorithm Python implementation (PRIVATE).
Algorithm adapted from
http://thomas.pelletier.im/2010/02/dijkstras-algorithm-python-implementation/.
However, an obvious bug in::
if D[child_node] >(<) D[node] + child_value:
is fixed.
This function will return the distance between start and end.
Arguments:
- graph: Dictionary of dictionary (keys are vertices).
- start: Start vertex.
- end: End vertex.
Output:
List of vertices from the beginning to the end.
"""
D = {} # Final distances dict
P = {} # Predecessor dict
# Fill the dicts with default values
for node in graph.keys():
D[node] = 100 # Vertices are unreachable
P[node] = "" # Vertices have no predecessors
D[start] = 0 # The start vertex needs no move
unseen_nodes = list(graph.keys()) # All nodes are unseen
while len(unseen_nodes) > 0:
# Select the node with the lowest value in D (final distance)
shortest = None
node = ""
for temp_node in unseen_nodes:
if shortest is None:
shortest = D[temp_node]
node = temp_node
elif D[temp_node] < shortest:
shortest = D[temp_node]
node = temp_node
# Remove the selected node from unseen_nodes
unseen_nodes.remove(node)
# For each child (ie: connected vertex) of the current node
for child_node, child_value in graph[node].items():
if D[child_node] > D[node] + child_value:
D[child_node] = D[node] + child_value
# To go to child_node, you have to go through node
P[child_node] = node
if node == end:
break
# Set a clean path
path = []
# We begin from the end
node = end
distance = 0
# While we are not arrived at the beginning
while node != start:
if path.count(node) == 0:
path.insert(0, node) # Insert the predecessor of the current node
node = P[node] # The current node becomes its predecessor
else:
break
path.insert(0, start) # Finally, insert the start vertex
for i in range(len(path) - 1):
distance += graph[path[i]][path[i + 1]]
return distance
def _count_replacement(codon_set, G):
"""Count replacement needed for a given codon_set (PRIVATE)."""
from math import floor
if len(codon_set) == 1:
return 0, 0
elif len(codon_set) == 2:
codons = list(codon_set)
return floor(G[codons[0]][codons[1]])
else:
codons = list(codon_set)
return _prim(G)
def _prim(G):
"""Prim's algorithm to find minimum spanning tree (PRIVATE).
Code is adapted from
http://programmingpraxis.com/2010/04/09/minimum-spanning-tree-prims-algorithm/
"""
from collections import defaultdict
from heapq import heapify
from heapq import heappop
from heapq import heappush
from math import floor
nodes = []
edges = []
for i in G.keys():
nodes.append(i)
for j in G[i]:
if (i, j, G[i][j]) not in edges and (j, i, G[i][j]) not in edges:
edges.append((i, j, G[i][j]))
conn = defaultdict(list)
for n1, n2, c in edges:
conn[n1].append((c, n1, n2))
conn[n2].append((c, n2, n1))
mst = [] # minimum spanning tree
used = set(nodes[0])
usable_edges = conn[nodes[0]][:]
heapify(usable_edges)
while usable_edges:
cost, n1, n2 = heappop(usable_edges)
if n2 not in used:
used.add(n2)
mst.append((n1, n2, cost))
for e in conn[n2]:
if e[2] not in used:
heappush(usable_edges, e)
length = 0
for p in mst:
length += floor(p[2])
return length
def _get_subgraph(codons, G):
"""Get the subgraph that contains all codons in list (PRIVATE)."""
subgraph = {}
for i in codons:
subgraph[i] = {}
for j in codons:
if i != j:
subgraph[i][j] = G[i][j]
return subgraph
def _G_test(site_counts):
"""G test for 2x2 contingency table (PRIVATE).
Arguments:
- site_counts - [syn_fix, nonsyn_fix, syn_poly, nonsyn_poly]
>>> print("%0.6f" % _G_test([17, 7, 42, 2]))
0.004924
"""
# TODO:
# Apply continuity correction for Chi-square test.
from math import log
G = 0
tot = sum(site_counts)
tot_syn = site_counts[0] + site_counts[2]
tot_non = site_counts[1] + site_counts[3]
tot_fix = sum(site_counts[:2])
tot_poly = sum(site_counts[2:])
exp = [
tot_fix * tot_syn / tot,
tot_fix * tot_non / tot,
tot_poly * tot_syn / tot,
tot_poly * tot_non / tot,
]
for obs, ex in zip(site_counts, exp):
G += obs * log(obs / ex)
# with only 1 degree of freedom for a 2x2 table,
# the cumulative chi-square distribution reduces to a simple form:
return erfc(sqrt(G))
if __name__ == "__main__":
from Bio._utils import run_doctest
run_doctest()