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1 #!/usr/bin/env python
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2
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3 import Bio as Bio
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4 from Bio import SeqIO
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5 from Bio.Data import CodonTable
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6 import re
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7 from pprint import pprint
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8 import argparse as ap
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9 import sys
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10 import os
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11 import pandas as pd
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12
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13
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14 def read_input(data = "example.fna", type_ = "fasta"):
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15
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16 """
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17 Accepted formats:
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18 - fasta (multifasta)
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19 - gff
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20 - gbk
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21
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22 """
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23
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24
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25 seqs = ""
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26
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27 if type_ == "fasta":
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28 with open(data, "rU") as handle:
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29 for record in SeqIO.parse(handle, type_):
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30 seqs = seqs + str(record.seq)
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31
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32
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33 #elif type_ == "gff":
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34 # with open(data, "rU") as handle:
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35 # for record in GFF.parse(handle):
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36 # seqs = seqs + str(record.seq)
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37
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38 elif type_ == "gbk":
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39 with open(data, "rU") as input_handle:
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40 for record in SeqIO.parse(input_handle, "genbank"):
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41 seqs = seqs + str(record.seq)
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42
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43
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44 return seqs
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45
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46 def codon_usage(seqs, codonTable):
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47
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48 codon_usage = {}
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49 tmp = [x for x in re.split(r'(\w{3})', seqs) if x != ""]
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50
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51 b_cod_table = CodonTable.unambiguous_dna_by_name[codonTable].forward_table
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52
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53
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54 for cod in CodonTable.unambiguous_dna_by_name[codonTable].stop_codons:
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55 b_cod_table[cod] = "_Stop"
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56
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57 for cod in CodonTable.unambiguous_dna_by_name[codonTable].start_codons:
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58 b_cod_table[cod + " Start"] = b_cod_table[cod]
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59 b_cod_table.pop(cod)
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60
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61 aas = set(b_cod_table.values())
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62
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63
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64 for aa in aas:
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65 codon_usage[aa] = {}
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66 for codon in b_cod_table.keys():
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67 if b_cod_table[codon] == aa:
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68 codon_usage[aa][codon] = tmp.count(codon.split(" ")[0])
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69
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70
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71 tups = {(outerKey, innerKey): values for outerKey, innerDict in codon_usage.iteritems() for innerKey, values in innerDict.iteritems()}
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72
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73 codon_usage_ = pd.DataFrame(pd.Series(tups), columns = ["Count"])
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74 codon_usage_.index = codon_usage_.index.set_names(["AA", "Codon"])
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75 codon_usage_['Proportion'] = codon_usage_.groupby(level=0).transform(lambda x: (x / x.sum()).round(2))
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76
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77 return {"Dictionary": codon_usage, "Tuples": tups, "Table": codon_usage_}
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78
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79 if __name__ == '__main__':
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80
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81 parser = ap.ArgumentParser(description=
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82 'This script takes as input gff, gbk and single or multifasta files and \n'
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83 'compute the codon usage for a specified codon table.\n'
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84 'Usage:\n'
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85 'python codon_usage.py -i example.gbk -t genebank -o gbk_example -c Bacterial\n'
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86 'python codon_usage.py -i example.ffn -t fasta -o fasta_example -c Bacterial\n'
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87 'python codon_usage.py -i example.gff -t gff -o gff_example -c Bacterial\n',
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88 formatter_class=ap.RawTextHelpFormatter)
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89
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90 parser.add_argument('-i','--input', help='The path to the input file',required=True)
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91 parser.add_argument('-t','--type', help=
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92 'The format of the file [genebank, fasta, gff ...]', required=True)
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93 parser.add_argument('-c','--codonTable', help=
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94 'The codon table to be used [Standard, Bacterial, Archaeal ...]\n'
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95 'Alternative Flatworm Mitochondrial,\\n'
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96 'Alternative Yeast Nuclear,\n'
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97 'Archaeal,\n'
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98 'Ascidian Mitochondrial,\n'
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99 'Bacterial,\n'
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100 'Blastocrithidia Nuclear,\n'
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101 'Blepharisma Macronuclear,\n'
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102 'Candidate Division SR1,\n'
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103 'Chlorophycean Mitochondrial,\n'
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104 'Ciliate Nuclear,\n'
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105 'Coelenterate Mitochondrial,\n'
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106 'Condylostoma Nuclear,\n'
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107 'Dasycladacean Nuclear,\n'
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108 'Echinoderm Mitochondrial,\n'
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109 'Euplotid Nuclear,\n'
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110 'Flatworm Mitochondrial,\n'
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111 'Gracilibacteria,\n'
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112 'Hexamita Nuclear,\n'
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113 'Invertebrate Mitochondrial,\n'
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114 'Karyorelict Nuclear,\n'
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115 'Mesodinium Nuclear,\n'
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116 'Mold Mitochondrial,\n'
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117 'Mycoplasma,\n'
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118 'Pachysolen tannophilus Nuclear,\n'
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119 'Peritrich Nuclear,\n'
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120 'Plant Plastid,\n'
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121 'Protozoan Mitochondrial,\n'
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122 'Pterobranchia Mitochondrial,\n'
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123 'SGC0,\n'
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124 'SGC1,\n'
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125 'SGC2,\n'
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126 'SGC3,\n'
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127 'SGC4,\n'
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128 'SGC5,\n'
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129 'SGC8,\n'
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130 'SGC9,\n'
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131 'Scenedesmus obliquus Mitochondrial,\n'
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132 'Spiroplasma,\n'
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133 'Standard,\n'
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134 'Thraustochytrium Mitochondrial,\n'
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135 'Trematode Mitochondrial,\n'
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136 'Vertebrate Mitochondrial,\n'
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137 'Yeast Mitochondrial\n', required=True)
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138
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139 parser.add_argument('-o','--output', help='Description for bar argument', required=True)
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140 args = vars(parser.parse_args())
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141
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142 seqs = read_input(data=args['input'], type_=args['type'])
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143 out = codon_usage(seqs, args['codonTable'])
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144
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145 with open(args['output'], "w") as outf:
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146 out["Table"].to_csv(outf, sep="\t", index_label=["AA", "Codon"])
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147
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148 |
