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1 #!/usr/bin/env python
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2
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3 __author__= "Gianmarco Piccinno"
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4 __version__ = "1.0.0"
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5
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6 import Bio
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7 from Bio import SeqIO
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8 from Bio.Seq import Seq
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9 from Bio.Alphabet import IUPAC
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10 from Bio.Data import IUPACData
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11 from Bio.Data import CodonTable
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12 import re
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13 import sre_yield
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14
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15 import re
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16 import itertools
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17 from functools import reduce
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18
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19 import Bio
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20 from Bio import Data
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21 from Bio.Data import IUPACData
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22 from Bio.Data import CodonTable
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23
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24 from pprint import pprint
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25
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26 import pandas as pd
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27
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28 def _check_bases(seq_string):
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29 """
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30 Check characters in a string (PRIVATE).
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31 Remove digits and white space present in string. Allows any valid ambiguous
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32 IUPAC DNA single letters codes (ABCDGHKMNRSTVWY, upper case are converted).
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33
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34 Other characters (e.g. symbols) trigger a TypeError.
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35
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36 Returns the string WITH A LEADING SPACE (!). This is for backwards
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37 compatibility, and may in part be explained by the fact that
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38 Bio.Restriction doesn't use zero based counting.
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39 """
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40 # Remove white space and make upper case:
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41 seq_string = "".join(seq_string.split()).upper()
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42 # Remove digits
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43 for c in "0123456789":
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44 seq_string = seq_string.replace(c, "")
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45 # Check only allowed IUPAC letters
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46 if not set(seq_string).issubset(set("ABCDGHKMNRSTVWY")):
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47 raise TypeError("Invalid character found in %s" % repr(seq_string))
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48 return " " + seq_string
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49
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50 matching = {'A': 'ARWMHVDN', 'C': 'CYSMHBVN', 'G': 'GRSKBVDN',
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51 'T': 'TYWKHBDN', 'R': 'ABDGHKMNSRWV', 'Y': 'CBDHKMNSTWVY',
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52 'W': 'ABDHKMNRTWVY', 'S': 'CBDGHKMNSRVY', 'M': 'ACBDHMNSRWVY',
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53 'K': 'BDGHKNSRTWVY', 'H': 'ACBDHKMNSRTWVY',
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54 'B': 'CBDGHKMNSRTWVY', 'V': 'ACBDGHKMNSRWVY',
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55 'D': 'ABDGHKMNSRTWVY', 'N': 'ACBDGHKMNSRTWVY'}
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56
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57 class pattern(object):
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58
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59
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60 def __init__(self, pattern_input):
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61 s = str(pattern_input)
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62 self.upper = s.isupper()
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63 self.data = _check_bases(s)
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64 self.pattern = s
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65
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66 def plan_ambiguity(self):
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67 val = Bio.Data.IUPACData.ambiguous_dna_values
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68 re_pattern = ""
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69 for el in self.pattern:
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70 re_pattern = re_pattern + "[" + val[el] + "]"
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71 return re_pattern
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72
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73 class annotated_genome(object):
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74
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75 def __init__(self, seq):
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76 s = str(seq)
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77 self.upper = s.isupper()
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78 self.data = _check_bases(s)
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79 self.seq = s
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80
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81 def codon_usage(self, codonTable):
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82
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83 codon_usage = {}
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84 tmp = [x for x in re.split(r'(\w{3})', self.seq) if x != ""]
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85
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86 b_cod_table = CodonTable.unambiguous_dna_by_name["Bacterial"].forward_table
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87 aas = set(b_cod_table.values())
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88
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89 for aa in aas:
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90 codon_usage[aa] = {}
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91 for codon in b_cod_table.keys():
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92 if b_cod_table[codon] == aa:
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93 codon_usage[aa][codon] = tmp.count(codon)
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94
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95 tups = {(outerKey, innerKey): values for outerKey, innerDict in codon_usage.iteritems() for innerKey, values in innerDict.iteritems()}
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96
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97 codon_usage_ = pd.DataFrame(pd.Series(tups), columns = ["Count"])
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98 codon_usage_.index = codon_usage_.index.set_names(["AA", "Codon"])
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99 codon_usage_['Proportion'] = codon_usage_.groupby(level=0).transform(lambda x: (x / x.sum()).round(2))
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100
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101 codon_usage_.reset_index(inplace=True)
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102 codon_usage_.index = codon_usage_["Codon"]
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103
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104 return {"Dictionary": codon_usage, "Tuples": tups, "Table": codon_usage_}
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105
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106 class plasmid(object):
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107 """
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108 This class represents a circular plasmid
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109 """
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110
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111 def __init__(self, seq = "", circular=True, features = None):
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112
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113 if type(seq) in [Bio.SeqRecord.SeqRecord, plasmid, Seq]:
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114 s = str(seq.seq)
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115 self.features = seq.features
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116 else:
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117 s = str(seq)
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118 if features != None:
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119 self.features = features
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120 self.upper = s.isupper()
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121 #self.data = _check_bases(s)
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122 self.data = s
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123 self.circular = circular
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124 self.Class = s.__class__
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125 self.size = len(s)
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126 self.seq = self.data
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127
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128 def __len__(self):
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129 return len(self.data)
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130
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131 def __repr__(self):
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132 return "plasmid(%s, circular=%s)" % (repr(self[:]),repr(self.circular))
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133
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134 def __eq__(self, other):
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135 if isinstance(other, plasmid):
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136 if repr(self) == repr(other):
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137 return True
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138 else:
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139 return False
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140 return False
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141
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142 def __getitem__(self, i):
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143 if self.upper:
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144 return self.Class((self.data[i]).upper())
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145 return self.Class(self.data[i])
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146
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147 def sequence(self):
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148 return self.data
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149
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150
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151 def extract(self, feature):
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152 return self.data[feature.location.start:feature.location.end]#[::feature.strand]
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153
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154
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155 def findpattern(self, pattern, ambiguous_pattern):
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156 """
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157 Return a list of a given pattern which occurs in the sequence.
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158 The list is made of tuple (location, pattern.group).
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159 The latter is used with non palindromic sites.
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160 Pattern is the regular expression pattern corresponding to the
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161 enzyme restriction site.
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162 Size is the size of the restriction enzyme recognition-site size.
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163 """
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164 if not self.circular:
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165 data = self.data
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166 else:
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167 data = self.data + self.data[:len(ambiguous_pattern)]
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168 print(data)
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169 print(pattern)
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170 return [(data[i.start():i.end()], i.start(), i.end(), "innner") if (i.end()<=self.size) else (data[i.start():i.end()], i.start(), i.end()-self.size, "junction") for i in re.finditer(pattern, data)]
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171
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172 def findpatterns(self, patterns, ambiguous_patterns):
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173 """
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174 Return a list of a given pattern which occurs in the sequence.
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175 The list is made of tuple (location, pattern.group).
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176 The latter is used with non palindromic sites.
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177 Pattern is the regular expression pattern corresponding to the
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178 enzyme restriction site.
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179 Size is the size of the restriction enzyme recognition-site size.
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180 """
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181 patts = {}
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182 for i in range(len(patterns)):
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183 #print(patterns[i])
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184 #print(ambiguous_patterns[i])
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185 if not self.circular:
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186 data = self.data
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187 else:
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188 data = self.data + self.data[:len(ambiguous_patterns[i])]
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189 #print(data)
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190 patts[ambiguous_patterns[i]] = [(data[j.start():j.end()], j.start(), j.end(), "innner") if (j.end()<=self.size) else (data[j.start():j.end()], j.start(), j.end()-self.size, "junction") for j in re.finditer(patterns[i], data)]
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191
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192 return patts
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193
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194
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195 def codon_switch(self, patterns, annotation, codon_usage):
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196
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197 table = {}
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198
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199 for pattern in patterns:
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200 if pattern[1] >= annotation & pattern[2] <= annotation:
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201 # the pattern is in a coding sequence
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202 # do codon switch
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203 print("Do codon switch!")
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204 else:
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205 next
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206 return table
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207
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208 def transversion(self, patterns):
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209 return
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210
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211 def read_patterns(input):
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212
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213 patterns = []
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214
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215 with open(input, "rU") as handle:
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216 for pat in handle.readlines():
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217
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218 tmp = Seq(pat.rstrip(), IUPAC.ambiguous_dna)
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219
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220 patterns.append(tmp)
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221 #patterns.append(tmp.reverse_complement())
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222
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223
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224 return patterns
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225
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226
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227 def codon_usage(seqs, codonTable):
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228
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229 codon_usage = {}
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230 tmp = [x for x in re.split(r'(\w{3})', seqs) if x != ""]
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231
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232 b_cod_table = CodonTable.unambiguous_dna_by_name[codonTable].forward_table
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233
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234 for cod in CodonTable.unambiguous_dna_by_name[codonTable].stop_codons:
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235 b_cod_table[cod] = "_Stop"
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236
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237 for cod in CodonTable.unambiguous_dna_by_name[codonTable].start_codons:
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238 #print(cod)
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239 b_cod_table[cod] = b_cod_table[cod]
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240
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241 aas = set(b_cod_table.values())
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242
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243 for aa in aas:
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244 #print(aa)
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245 #codon_usage[aa] = {}
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246 for codon in b_cod_table.keys():
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247 if b_cod_table[codon] == aa:
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248 codon_usage[codon] = tmp.count(codon.split(" ")[0])
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249
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250 return codon_usage
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251
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252
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253 def read_annotated_genome(data="example.fna", type_="fasta"):
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254 """
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255 Accepted formats:
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256 - fasta (multifasta)
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257 - gbk
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258
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259 """
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260
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261 seqs = ""
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262
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263 if type_ == "fasta":
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264 with open(data, "rU") as handle:
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265 for record in SeqIO.parse(handle, type_):
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266 seqs = seqs + str(record.seq)
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267
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268 elif type_ == "genbank":
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269 with open(data, "rU") as input_handle:
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270 types = []
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271 for record in SeqIO.parse(input_handle, "genbank"):
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272 for feature in record.features:
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273 types.append(feature.type)
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274 if feature.type == "CDS":
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275 if feature.location.strand == +1:
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276 seq = record.seq[feature.location.start:feature.location.end]
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277 seqs = seqs + str(seq)
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278 elif feature.location.strand == -1:
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279 seq = record.seq[feature.location.start:
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280 feature.location.end].reverse_complement()
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281 seqs = seqs + str(seq)
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282 return seqs
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283
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284
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285 def synonims_(table_name="Bacterial"):
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286
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287 b_cod_table = CodonTable.unambiguous_dna_by_name[table_name].forward_table
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288
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289 print(b_cod_table)
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290
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291 for cod in CodonTable.unambiguous_dna_by_name[table_name].stop_codons:
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292 b_cod_table[cod] = "_Stop"
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293
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294 for cod in CodonTable.unambiguous_dna_by_name[table_name].start_codons:
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295 b_cod_table[cod] = "_Start"
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296
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297 #pprint(b_cod_table)
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298 codons = {}
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299
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300 aas = set(b_cod_table.values())
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301
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302 for aa in aas:
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303 codons[aa] = []
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304 for codon in b_cod_table.keys():
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305 if b_cod_table[codon] == aa:
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306 codons[aa].append(codon)
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307
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308 #break
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309
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310 synonims = {}
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311
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312 for el1 in codons.keys():
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313 print(el1)
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314 for el2 in codons[el1]:
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315 print(el2)
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316 synonims[el2] = codons[el1]
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317 #synonims[el2] = []
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318 #for el3 in codons[el1]#set.difference(set(codons[el1]), {el2}):
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319 # print(el3)
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320 # synonims[el2].append(el3)
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321 #break
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322 #break
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323 #break
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324
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325
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326 anti_codons = {}
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327
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328 for codon in synonims.keys():
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329 tmp_codon = Bio.Seq.Seq(codon, IUPAC.unambiguous_dna)
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330 tmp_anticodon = str(tmp_codon.reverse_complement())
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331
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332 anti_codons[tmp_anticodon] = []
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333
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334 for synonim in synonims[codon]:
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335 tmp_synonim = Bio.Seq.Seq(synonim, IUPAC.unambiguous_dna)
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336 tmp_antisynonim = str(tmp_synonim.reverse_complement())
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337 anti_codons[tmp_anticodon].append(tmp_antisynonim)
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338
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339 check = Bio.Seq.Seq("CTT")
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340 anti_check = check.reverse_complement()
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341 print("\nCheck:\n" + str(check))
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342 print("\nCodons:\n")
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343
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344 for key in codons.keys():
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345 if str(check) in codons[key]:
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346 print(codons[key])
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347
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348 #pprint(codons)
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349 print("\nSynonims:\n")
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350 pprint(synonims[str(check)])
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351 print("\nAnti_Codons:\n")
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352 pprint(anti_codons[str(anti_check)])
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353
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354 #i = synonims.keys()
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355 #right = True
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356 #while len(i) > 0:
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357 # tmp = i.pop()
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358 # check = Bio.Seq.Seq(tmp)
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359 # anti_check = check.reverse_complement()
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360
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361
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362 return {"synonims":synonims, "anti_synonims":anti_codons}
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