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annotate scripts/S01b_extract_variable_nuc.py @ 0:acc3674e515b draft default tip

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planemo upload for repository htpps://github.com/abims-sbr/adaptearch commit 3c7982d775b6f3b472f6514d791edcb43cd258a1-dirty
author abims-sbr
date Fri, 01 Feb 2019 10:28:50 -0500
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1 #!/usr/bin/env python
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2 #coding: utf-8
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4 import argparse, os
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5 from functions import dico, write_output, fill_with_NaN
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7 """ Functions for nucleic format """
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9 def all_nuc_counts(seq):
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10 """ Counts the number of nucleotides in a sequence
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12 Args :
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13 - seq (String) : a nucleic sequence
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15 Return:
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16 - nuc_counts (dict) : counts on nucleotides (key/value : nucleotide/count)
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17 """
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18 nuc_counts = {}
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19 seqU = seq.upper()
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20 LN =['A','C','T','G']
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22 for base in LN:
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23 nuc_counts[base] = seqU.count(base)
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25 return nuc_counts
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27 def ratios(nuc_counts):
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28 """ Compute GC and purine ratios from the counts of nucleotides in a sequence
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30 Args :
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31 - nuc_counts (dict) : dictionary of nucleotides counts (output of all_nuc_counts())
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33 Return :
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34 - ratios (dict) : dictionary with ratios
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35 """
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37 # Search for compositional bias in genome as marker of thermal adaptation
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38 ratios = {}
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39 ratios['GC_percent'] = float(nuc_counts['C'] + nuc_counts['G'])/(sum(nuc_counts.values())*100)
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40 ratios['purine_percent'] = float(nuc_counts['A'] + nuc_counts['G'])/(sum(nuc_counts.values())*100)
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42 ratios['DIFF_GC'] = nuc_counts['G'] - nuc_counts['C']
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43 ratios['DIFF_AT'] = nuc_counts['A'] - nuc_counts['T']
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45 # Per bp
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46 ratios['PLI_GC'] = float(ratios['DIFF_GC'])/sum(nuc_counts.values())
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47 ratios['PLI_AT'] = float(ratios['DIFF_AT'])/sum(nuc_counts.values())
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49 # Per 1000 bp
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50 ratios['PLI_GC_1000'] = ratios['PLI_GC']*1000
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51 ratios['PLI_AT_1000'] = ratios['PLI_AT']*1000
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53 return ratios
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55 def main():
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56 parser = argparse.ArgumentParser()
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57 parser.add_argument("species_list", help="List of species separated by commas")
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58 args = parser.parse_args()
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60 LN =['A','C','T','G']
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61 Lratios = ['GC_percent', 'purine_percent', 'DIFF_GC', 'DIFF_AT', 'PLI_GC', 'PLI_AT', 'PLI_GC_1000', 'PLI_AT_1000']
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63 list_inputs = []
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65 path_inputs = '01_input_files'
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66 list_inputs = os.listdir(path_inputs)
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68 lsp = args.species_list.split(',')
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69 lsp = sorted(lsp)
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70 flsp = ''
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71 for el in lsp:
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72 flsp += el+','
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74 path_outputs_1 = '02_tables_per_nucleotide'
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75 path_outputs_2 = '02_tables_per_nuc_variable'
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76 os.mkdir(path_outputs_1)
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77 os.mkdir(path_outputs_2)
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79 dict_for_files_nuc = {}
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80 dict_for_file_var = {}
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82 # All counts
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83 for file in list_inputs:
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84 # iterate over input files
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85 sequences = dico(file, path_inputs)
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86
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87 # TEMPORARY CORRECTION FOR SEQUENCES CONTAINING ONLY INDELS
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88 # It appears than CDS_Search can bug sometimes and return an alignement where a species' sequence is made of indels only
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89 # This causes a crash here (in the ratios function). The correction skip the whole file.
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90 # When CDS_Search is corrected, lines with 'skip' can be removed
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91 skip = False
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92 for key in sequences.keys():
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93 if all(x == '-' for x in sequences[key]):
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94 skip = True
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95
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96 if not skip:
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98 nuc_counts_per_seq = {}
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99 nuc_var_per_seq = {}
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101 for key in sequences.keys():
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102 # iterate over sequences in the file
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103 nuc_counts_per_seq[key] = all_nuc_counts(sequences[key])
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104 nuc_var_per_seq[key] = ratios(nuc_counts_per_seq[key])
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106 # Add NaN for missing species
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107 for key in set(lsp).difference(set(sequences.keys())):
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108 nuc_counts_per_seq[key] = fill_with_NaN(LN)
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109 nuc_var_per_seq[key] = fill_with_NaN(Lratios)
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110
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111 # Add computations to final dicts
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112 dict_for_files_nuc[file] = nuc_counts_per_seq
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113 dict_for_file_var[file] = nuc_var_per_seq
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114
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115
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116 # Try with pandas ?
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117 write_output(LN, flsp, path_outputs_1, dict_for_files_nuc) # one file per nuc
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118 write_output(Lratios, flsp, path_outputs_2, dict_for_file_var) # one file per nuc_variable
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119
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120 if __name__ == '__main__':
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121 main()