/usr/lib/python2.7/dist-packages/biotools/align.py is in python-biotools 1.2.12-2.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | #!/usr/bin/env python
'''
This module is used to align sequences. Currently, there is only a single
alignment algorithm implementented; it is a hybrid between Needleman-Wunsch
and Smith-Waterman and is used to find the subsequence within a larger sequence
that best aligns to a reference.
'''
from biotools.translate import translate
import biotools.analysis.options as options
DIAG_MARK, VGAP_MARK, HGAP_MARK = 3, 2, 1
bl = {
'*': {'*': 0, 'A': -9, 'C': -9, 'E': -9, 'D': -9, 'G': -9, 'F': -9, 'I': -9,
'H': -9, 'K': -9, 'M': -9, 'L': -9, 'N': -9, 'Q': -9, 'P': -9, 'S': -9,
'R': -9, 'T': -9, 'W': -9, 'V': -9, 'Y': -9, 'X': 0},
'A': {'*': -9, 'A': 4, 'C': 0, 'E': -1, 'D': -2, 'G': 0, 'F': -2, 'I': -1,
'H': -2, 'K': -1, 'M': -1, 'L': -1, 'N': -1, 'Q': -1, 'P': -1, 'S': 1,
'R': -1, 'T': -1, 'W': -3, 'V': -2, 'Y': -2, 'X': 0},
'C': {'*': -9, 'A': 0, 'C': 9, 'E': -4, 'D': -3, 'G': -3, 'F': -2, 'I': -1,
'H': -3, 'K': -3, 'M': -1, 'L': -1, 'N': -3, 'Q': -3, 'P': -3, 'S': -1,
'R': -3, 'T': -1, 'W': -2, 'V': -1, 'Y': -2, 'X': 0},
'E': {'*': -9, 'A': -1, 'C': -4, 'E': 5, 'D': 2, 'G': -2, 'F': -3, 'I': -3,
'H': 0, 'K': 1, 'M': -2, 'L': -3, 'N': 0, 'Q': 2, 'P': -1, 'S': 0,
'R': 0, 'T': 0, 'W': -3, 'V': -3, 'Y': -2, 'X': 0},
'D': {'*': -9, 'A': -2, 'C': -3, 'E': 2, 'D': 6, 'G': -1, 'F': -3, 'I': -3,
'H': -1, 'K': -1, 'M': -3, 'L': -4, 'N': 1, 'Q': 0, 'P': -1, 'S': 0,
'R': -2, 'T': 1, 'W': -4, 'V': -3, 'Y': -3, 'X': 0},
'G': {'*': -9, 'A': 0, 'C': -3, 'E': -2, 'D': -1, 'G': 6, 'F': -3, 'I': -4,
'H': -2, 'K': -2, 'M': -3, 'L': -4, 'N': -2, 'Q': -2, 'P': -2, 'S': 0,
'R': -2, 'T': 1, 'W': -2, 'V': 0, 'Y': -3, 'X': 0},
'F': {'*': -9, 'A': -2, 'C': -2, 'E': -3, 'D': -3, 'G': -3, 'F': 6, 'I': 0,
'H': -1, 'K': -3, 'M': 0, 'L': 0, 'N': -3, 'Q': -3, 'P': -4, 'S': -2,
'R': -3, 'T': -2, 'W': 1, 'V': -1, 'Y': 3, 'X': 0},
'I': {'*': -9, 'A': -1, 'C': -1, 'E': -3, 'D': -3, 'G': -4, 'F': 0, 'I': 4,
'H': -3, 'K': -3, 'M': 1, 'L': 2, 'N': -3, 'Q': -3, 'P': -3, 'S': -2,
'R': -3, 'T': -2, 'W': -3, 'V': 1, 'Y': -1, 'X': 0},
'H': {'*': -9, 'A': -2, 'C': -3, 'E': 0, 'D': 1, 'G': -2, 'F': -1, 'I': -3,
'H': 8, 'K': -1, 'M': -2, 'L': -3, 'N': 1, 'Q': 0, 'P': -2, 'S': -1,
'R': 0, 'T': 0, 'W': -2, 'V': -2, 'Y': 2, 'X': 0},
'K': {'*': -9, 'A': -1, 'C': -3, 'E': 1, 'D': -1, 'G': -2, 'F': -3, 'I': -3,
'H': -1, 'K': 5, 'M': -1, 'L': -2, 'N': 0, 'Q': 1, 'P': -1, 'S': 0,
'R': 2, 'T': 0, 'W': -3, 'V': -3, 'Y': -2, 'X': 0},
'M': {'*': -9, 'A': -1, 'C': -1, 'E': -2, 'D': -3, 'G': -3, 'F': 0, 'I': 1,
'H': -2, 'K': -1, 'M': 5, 'L': 2, 'N': -2, 'Q': 0, 'P': -2, 'S': -1,
'R': -1, 'T': -1, 'W': -1, 'V': -2, 'Y': -1, 'X': 0},
'L': {'*': -9, 'A': -1, 'C': -1, 'E': -3, 'D': -4, 'G': -4, 'F': 0, 'I': 2,
'H': -3, 'K': -2, 'M': 2, 'L': 4, 'N': -3, 'Q': -2, 'P': -3, 'S': -2,
'R': -2, 'T': -2, 'W': -2, 'V': 3, 'Y': -1, 'X': 0},
'N': {'*': -9, 'A': -2, 'C': -3, 'E': 0, 'D': 1, 'G': 0, 'F': -3, 'I': -3,
'H': -1, 'K': 0, 'M': -2, 'L': -3, 'N': 6, 'Q': 0, 'P': -2, 'S': 1,
'R': 0, 'T': 0, 'W': -4, 'V': -3, 'Y': -2, 'X': 0},
'Q': {'*': -9, 'A': -1, 'C': -3, 'E': 2, 'D': 0, 'G': -2, 'F': -3, 'I': -3,
'H': 0, 'K': 1, 'M': 0, 'L': -2, 'N': 0, 'Q': 5, 'P': -1, 'S': 0,
'R': 1, 'T': 0, 'W': -2, 'V': -2, 'Y': -1, 'X': 0},
'P': {'*': -9, 'A': -1, 'C': -3, 'E': -1, 'D': -1, 'G': -2, 'F': -4, 'I': -3,
'H': -2, 'K': -1, 'M': -2, 'L': -3, 'N': -1, 'Q': -1, 'P': 7, 'S': -1,
'R': -2, 'T': 1, 'W': -4, 'V': -2, 'Y': -3, 'X': 0},
'S': {'*': -9, 'A': 1, 'C': -1, 'E': 0, 'D': 0, 'G': 0, 'F': -2, 'I': -2,
'H': -1, 'K': 0, 'M': -1, 'L': -2, 'N': 1, 'Q': 0, 'P': -1, 'S': 4,
'R': -1, 'T': 1, 'W': -3, 'V': -2, 'Y': -2, 'X': 0},
'R': {'*': -9, 'A': -1, 'C': -3, 'E': 0, 'D': -2, 'G': -2, 'F': -3, 'I': -3,
'H': 0, 'K': 2, 'M': -1, 'L': -2, 'N': 0, 'Q': 1, 'P': -2, 'S': -1,
'R': 5, 'T': -1, 'W': -3, 'V': -3, 'Y': -2, 'X': 0},
'T': {'*': -9, 'A': -1, 'C': -1, 'E': 0, 'D': 1, 'G': 1, 'F': -2, 'I': -2,
'H': 0, 'K': 0, 'M': -1, 'L': -2, 'N': 0, 'Q': 0, 'P': 1, 'S': 1,
'R': -1, 'T': 4, 'W': -3, 'V': -2, 'Y': -2, 'X': 0},
'W': {'*': -9, 'A': -3, 'C': -2, 'E': -3, 'D': -4, 'G': -2, 'F': 1, 'I': -3,
'H': -2, 'K': -3, 'M': -1, 'L': -2, 'N': -4, 'Q': -2, 'P': -4, 'S': -3,
'R': -3, 'T': -3, 'W': 11, 'V': -3, 'Y': 2, 'X': 0},
'V': {'*': -9, 'A': 0, 'C': -1, 'E': -2, 'D': -3, 'G': -3, 'F': -1, 'I': 3,
'H': -3, 'K': -2, 'M': 1, 'L': 1, 'N': -3, 'Q': -2, 'P': -2, 'S': -2,
'R': -3, 'T': -2, 'W': -3, 'V': 4, 'Y': -1, 'X': 0},
'Y': {'*': -9, 'A': -2, 'C': -2, 'E': -2, 'D': -3, 'G': -3, 'F': 3, 'I': -1,
'H': 2, 'K': -2, 'M': -1, 'L': -1, 'N': -2, 'Q': -1, 'P': -3, 'S': -2,
'R': -2, 'T': -2, 'W': 2, 'V': -1, 'Y': 7, 'X': 0},
'X': {'*': 0, 'A': 0, 'C': 0, 'E': 0, 'D': 0, 'G': 0, 'F': 0, 'I': 0,
'H': 0, 'K': 0, 'M': 0, 'L': 0, 'N': 0, 'Q': 0, 'P': 0, 'S': 0,
'R': 0, 'T': 0, 'W': 0, 'V': 0, 'Y': 0, 'X': 0}
}
def OptimalCTether(reference, translation, extend=1, create=10):
'''
This function will take two sequences: a `reference` sequence and another
protein sequence (`translation`; usually, this is an open reading frame
that has been translated). Needleman-Wunsch alignment will be performed
and the substring of translation with the highest identity that begins
with a start codon [default: `['ATG']`] is reported.
This function returns a dictionary of relevent information from the
alignment; specifically, the alignments itself [keys: `query`, `subject`],
the score [key: `score`], the length of the alignment [key: `length`], the
length of the substring of translation used [key: `sublength`], the number
of identities [key: `identities`], and the number of gaps [key: `gaps`].
'''
starts = set(translate(s) for s in options.START_CODONS)
v, w = reference, translation
try:
v = v.seq
except AttributeError:
pass
try:
w = w.seq
except AttributeError:
pass
if not starts & set(w):
raise ValueError("Open reading frame does not contain a start codon.")
v, w = v[::-1], w[::-1]
lv, lw = len(v), len(w)
rv, rw = range(lv + 1), range(lw + 1)
gpc = [[create * int(not (i | j)) for i in rw] for j in rv]
mat = [[-(i + j) * extend - create * (not (i | j) and w[0] != v[0])
for i in rw] for j in rv]
pnt = [[VGAP_MARK if i > j else HGAP_MARK if j > i else DIAG_MARK
for i in rw] for j in rv]
ids = [[0 for i in rw] for j in rv]
optimal = [None, 0, 0]
for i in range(lv):
for j in range(lw):
vals = [[mat[i][j] + bl[v[i]][w[j]], DIAG_MARK],
[mat[i + 1][j] - extend - gpc[i + 1][j], VGAP_MARK],
[mat[i][j + 1] - extend - gpc[i][j + 1], HGAP_MARK]]
mat[i + 1][j + 1], pnt[i + 1][j + 1] = max(vals)
gpc[i + 1][j + 1] = create * int(pnt[i + 1][j + 1] == DIAG_MARK)
if (optimal[0] is None or mat[i + 1][j + 1] > optimal[0]) and \
abs(lv - i) / float(lv) <= options.LENGTH_ERR and \
w[j] in starts:
optimal = [mat[i + 1][j + 1], i + 1, j + 1]
i, j = optimal[1], optimal[2]
seq, ids = ['', ''], 0
gapcount, length, sublen = 0, 0, 0
methods = {
VGAP_MARK:
lambda s, i, j, l, g, n:
(['-' + s[0], w[j - 1] + s[1]], i, j - 1, l + 1, g + 1, n),
DIAG_MARK:
lambda s, i, j, l, g, n:
([v[i - 1] + s[0], w[j - 1] + s[1]], i - 1, j - 1,
l + 1, g, n + (w[j - 1] == v[i - 1])),
HGAP_MARK:
lambda s, i, j, l, g, n:
([v[i - 1] + s[0], '-' + s[1]], i - 1, j, l, g + 1, n)
}
while [i, j] != [0, 0]:
length += 1
state = (seq, i, j, sublen, gapcount, ids)
seq, i, j, sublen, gapcount, ids = methods[pnt[i][j]](*state)
return {
'subject': seq[0][::-1],
'query': seq[1][::-1],
'score': optimal[0],
'gaps': gapcount,
'length': length,
'sublength': sublen,
'identities': ids
}
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