Mercurial > repos > galaxyp > unipept
annotate unipept.py @ 7:75b3b3d0adbf draft
"planemo upload for repository http://unipept.ugent.be/apidocs commit b6707ea113b2a89b0bb8072dfcc9ceeef4a1b708"
author | galaxyp |
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date | Tue, 06 Apr 2021 16:13:57 +0000 |
parents | 9aaa46d45472 |
children | 7863f1abcdda |
rev | line source |
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0 | 1 #!/usr/bin/env python |
2 """ | |
3 # | |
4 # Author: | |
5 # | |
6 # James E Johnson | |
7 # | |
8 #------------------------------------------------------------------------------ | |
9 """ | |
10 import json | |
11 import optparse | |
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12 import re |
0 | 13 import sys |
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14 import urllib.error |
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15 import urllib.parse |
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16 import urllib.request |
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17 |
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18 |
0 | 19 try: |
20 import xml.etree.cElementTree as ET | |
21 except ImportError: | |
22 import xml.etree.ElementTree as ET | |
23 | |
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24 |
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25 def warn_err(msg, exit_code=1): |
0 | 26 sys.stderr.write(msg) |
27 if exit_code: | |
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28 sys.exit(exit_code) |
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29 |
0 | 30 |
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31 go_types = ['biological process', 'molecular function', 'cellular component'] |
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32 ipr_types = ['Domain', 'Family', 'Homologous_superfamily', 'Repeat', 'Conserved_site', 'Active_site', 'Binding_site', 'PTM'] |
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33 ec_name_dict = { |
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34 '1': 'Oxidoreductase', |
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35 '1.1': 'act on the CH-OH group of donors', |
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36 '1.2': 'act on the aldehyde or oxo group of donors', |
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37 '1.3': 'act on the CH-CH group of donors', |
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38 '1.4': 'act on the CH-NH2 group of donors', |
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39 '1.5': 'act on CH-NH group of donors', |
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40 '1.6': 'act on NADH or NADPH', |
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41 '1.7': 'act on other nitrogenous compounds as donors', |
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42 '1.8': 'act on a sulfur group of donors', |
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43 '1.9': 'act on a heme group of donors', |
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44 '1.10': 'act on diphenols and related substances as donors', |
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45 '1.11': 'act on peroxide as an acceptor -- peroxidases', |
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46 '1.12': 'act on hydrogen as a donor', |
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47 '1.13': 'act on single donors with incorporation of molecular oxygen', |
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48 '1.14': 'act on paired donors with incorporation of molecular oxygen', |
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49 '1.15': 'act on superoxide radicals as acceptors', |
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50 '1.16': 'oxidize metal ions', |
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51 '1.17': 'act on CH or CH2 groups', |
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52 '1.18': 'act on iron-sulfur proteins as donors', |
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53 '1.19': 'act on reduced flavodoxin as donor', |
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54 '1.20': 'act on phosphorus or arsenic as donors', |
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55 '1.21': 'act on X-H and Y-H to form an X-Y bond', |
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56 '1.97': 'other oxidoreductases', |
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57 '2': 'Transferase', |
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58 '2.1': 'transfer one-carbon groups, Methylase', |
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59 '2.2': 'transfer aldehyde or ketone groups', |
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60 '2.3': 'acyltransferases', |
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61 '2.4': 'glycosyltransferases', |
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62 '2.5': 'transfer alkyl or aryl groups, other than methyl groups', |
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63 '2.6': 'transfer nitrogenous groups', |
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64 '2.7': 'transfer phosphorus-containing groups', |
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65 '2.8': 'transfer sulfur-containing groups', |
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66 '2.9': 'transfer selenium-containing groups', |
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67 '3': 'Hydrolase', |
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68 '3.1': 'act on ester bonds', |
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69 '3.2': 'act on sugars - glycosylases', |
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70 '3.3': 'act on ether bonds', |
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71 '3.4': 'act on peptide bonds - Peptidase', |
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72 '3.5': 'act on carbon-nitrogen bonds, other than peptide bonds', |
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73 '3.6': 'act on acid anhydrides', |
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74 '3.7': 'act on carbon-carbon bonds', |
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75 '3.8': 'act on halide bonds', |
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76 '3.9': 'act on phosphorus-nitrogen bonds', |
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77 '3.10': 'act on sulfur-nitrogen bonds', |
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78 '3.11': 'act on carbon-phosphorus bonds', |
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79 '3.12': 'act on sulfur-sulfur bonds', |
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80 '3.13': 'act on carbon-sulfur bonds', |
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81 '4': 'Lyase', |
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82 '4.1': 'carbon-carbon lyases', |
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83 '4.2': 'carbon-oxygen lyases', |
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84 '4.3': 'carbon-nitrogen lyases', |
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85 '4.4': 'carbon-sulfur lyases', |
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86 '4.5': 'carbon-halide lyases', |
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87 '4.6': 'phosphorus-oxygen lyases', |
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88 '5': 'Isomerase', |
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89 '5.1': 'racemases and epimerases', |
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90 '5.2': 'cis-trans-isomerases', |
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91 '5.3': 'intramolecular oxidoreductases', |
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92 '5.4': 'intramolecular transferases -- mutases', |
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93 '5.5': 'intramolecular lyases', |
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94 '5.99': 'other isomerases', |
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95 '6': 'Ligase', |
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96 '6.1': 'form carbon-oxygen bonds', |
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97 '6.2': 'form carbon-sulfur bonds', |
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98 '6.3': 'form carbon-nitrogen bonds', |
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99 '6.4': 'form carbon-carbon bonds', |
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100 '6.5': 'form phosphoric ester bonds', |
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101 '6.6': 'form nitrogen-metal bonds', |
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102 } |
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103 pept2lca_column_order = ['peptide', 'taxon_rank', 'taxon_id', 'taxon_name'] |
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104 pept2lca_extra_column_order = ['peptide', 'superkingdom', 'kingdom', 'subkingdom', 'superphylum', 'phylum', 'subphylum', 'superclass', 'class', 'subclass', 'infraclass', 'superorder', 'order', 'suborder', 'infraorder', 'parvorder', 'superfamily', 'family', 'subfamily', 'tribe', 'subtribe', 'genus', 'subgenus', 'species_group', 'species_subgroup', 'species', 'subspecies', 'varietas', 'forma'] |
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105 pept2lca_all_column_order = pept2lca_column_order + pept2lca_extra_column_order[2:] |
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106 pept2prot_column_order = ['peptide', 'uniprot_id', 'taxon_id'] |
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107 pept2prot_extra_column_order = pept2prot_column_order + ['taxon_name', 'ec_references', 'go_references', 'refseq_ids', 'refseq_protein_ids', 'insdc_ids', 'insdc_protein_ids'] |
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108 pept2ec_column_order = [['peptide', 'total_protein_count'], ['ec_number', 'protein_count']] |
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109 pept2ec_extra_column_order = [['peptide', 'total_protein_count'], ['ec_number', 'protein_count', 'name']] |
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110 pept2go_column_order = [['peptide', 'total_protein_count'], ['go_term', 'protein_count']] |
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111 pept2go_extra_column_order = [['peptide', 'total_protein_count'], ['go_term', 'protein_count', 'name']] |
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112 pept2interpro_column_order = [['peptide', 'total_protein_count'], ['code', 'protein_count']] |
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113 pept2interpro_extra_column_order = [['peptide', 'total_protein_count'], ['code', 'protein_count', 'type', 'name']] |
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114 pept2funct_column_order = ['peptide', 'total_protein_count', 'ec', 'go', 'ipr'] |
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115 |
1 | 116 |
117 def __main__(): | |
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118 version = '4.3' |
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119 pep_pat = '^([ABCDEFGHIKLMNPQRSTVWXYZ]+)$' |
1 | 120 |
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121 def read_tabular(filepath, col): |
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122 peptides = [] |
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123 with open(filepath) as fp: |
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124 for i, line in enumerate(fp): |
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125 if line.strip() == '' or line.startswith('#'): |
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126 continue |
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127 fields = line.rstrip('\n').split('\t') |
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128 peptide = fields[col] |
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129 if not re.match(pep_pat, peptide): |
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130 warn_err('"%s" is not a peptide (line %d column %d of tabular file: %s)\n' % (peptide, i, col, filepath), exit_code=invalid_ec) |
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131 peptides.append(peptide) |
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132 return peptides |
1 | 133 |
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134 def get_fasta_entries(fp): |
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135 name, seq = None, [] |
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136 for line in fp: |
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137 line = line.rstrip() |
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138 if line.startswith(">"): |
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139 if name: |
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140 yield (name, ''.join(seq)) |
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141 name, seq = line, [] |
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142 else: |
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143 seq.append(line) |
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144 if name: |
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145 yield (name, ''.join(seq)) |
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147 def read_fasta(filepath): |
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148 peptides = [] |
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149 with open(filepath) as fp: |
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150 for id, peptide in get_fasta_entries(fp): |
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151 if not re.match(pep_pat, peptide): |
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152 warn_err('"%s" is not a peptide (id %s of fasta file: %s)\n' % (peptide, id, filepath), exit_code=invalid_ec) |
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153 peptides.append(peptide) |
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154 return peptides |
1 | 155 |
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156 def read_mzid(fp): |
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157 peptides = [] |
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158 for event, elem in ET.iterparse(fp): |
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159 if event == 'end': |
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160 if re.search('PeptideSequence', elem.tag): |
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161 peptides.append(elem.text) |
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162 return peptides |
0 | 163 |
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164 def read_pepxml(fp): |
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165 peptides = [] |
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166 for event, elem in ET.iterparse(fp): |
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167 if event == 'end': |
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168 if re.search('search_hit', elem.tag): |
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169 peptides.append(elem.get('peptide')) |
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170 return peptides |
0 | 171 |
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172 def best_match(peptide, matches): |
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173 if not matches: |
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174 return None |
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175 elif len(matches) == 1: |
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176 return matches[0].copy() |
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177 elif 'taxon_rank' in matches[0]: |
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178 # find the most specific match (peptide is always the first column order field) |
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179 for col in reversed(pept2lca_extra_column_order[1:]): |
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180 col_id = col + "_id" if options.extra else col |
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181 for match in matches: |
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182 if 'taxon_rank' in match and match['taxon_rank'] == col: |
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183 return match.copy() |
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184 if col_id in match and match[col_id]: |
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185 return match.copy() |
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186 else: |
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187 return sorted(matches, key=lambda x: len(x['peptide']))[-1].copy() |
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188 return None |
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189 |
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190 def get_taxon_json(resp): |
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191 found_keys = set() |
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192 for i, pdict in enumerate(resp): |
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193 found_keys |= set(pdict.keys()) |
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194 taxa_cols = [] |
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195 for col in pept2lca_extra_column_order[-1:0:-1]: |
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196 if col + '_id' in found_keys: |
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197 taxa_cols.append(col) |
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198 id_to_node = dict() |
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200 def get_node(id, name, rank, child, seq): |
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201 if id not in id_to_node: |
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202 data = {'count': 0, 'self_count': 0, 'valid_taxon': 1, 'rank': rank, 'sequences': []} |
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203 node = {'id': id, 'name': name, 'children': [], 'kids': [], 'data': data} |
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204 id_to_node[id] = node |
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205 else: |
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206 node = id_to_node[id] |
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207 node['data']['count'] += 1 |
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208 if seq is not None and seq not in node['data']['sequences']: |
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209 node['data']['sequences'].append(seq) |
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210 if child is None: |
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211 node['data']['self_count'] += 1 |
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212 elif child['id'] not in node['kids']: |
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213 node['kids'].append(child['id']) |
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214 node['children'].append(child) |
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215 return node |
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216 root = get_node(1, 'root', 'no rank', None, None) |
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217 for i, pdict in enumerate(resp): |
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218 sequence = pdict.get('peptide', pdict.get('tryptic_peptide', None)) |
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219 seq = sequence |
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220 child = None |
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221 for col in taxa_cols: |
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222 col_id = col + '_id' |
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223 if col_id in pdict and pdict.get(col_id): |
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224 col_name = col if col in found_keys else col + '_name' |
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225 child = get_node(pdict.get(col_id, None), pdict.get(col_name, ''), col, child, seq) |
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226 seq = None |
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227 if child is not None: |
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228 get_node(1, 'root', 'no rank', child, None) |
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229 return root |
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230 |
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231 def get_ec_json(resp): |
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232 ecMap = dict() |
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233 for pdict in resp: |
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234 if 'ec' in pdict: |
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235 for ec in pdict['ec']: |
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236 ec_number = ec['ec_number'] |
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237 if ec_number not in ecMap: |
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238 ecMap[ec_number] = [] |
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239 ecMap[ec_number].append(pdict) |
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240 |
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241 def get_ids(ec): |
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242 ids = [] |
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243 i = len(ec) |
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244 while i >= 0: |
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245 ids.append(ec[:i]) |
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246 i = ec.rfind('.', 0, i - 1) |
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247 return ids |
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248 id_to_node = dict() |
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249 |
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250 def get_node(id, name, child, seq): |
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251 if id not in id_to_node: |
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252 data = {'count': 0, 'self_count': 0, 'sequences': []} |
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253 node = {'id': id, 'name': name, 'children': [], 'kids': [], 'data': data} |
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254 id_to_node[id] = node |
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255 else: |
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256 node = id_to_node[id] |
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257 node['data']['count'] += 1 |
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258 if seq is not None and seq not in node['data']['sequences']: |
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259 node['data']['sequences'].append(seq) |
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260 if child is None: |
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261 node['data']['self_count'] += 1 |
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262 elif child['id'] not in node['kids']: |
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263 node['kids'].append(child['id']) |
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264 node['children'].append(child) |
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265 return node |
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266 root = get_node(0, '-.-.-.-', None, None) |
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267 for i in range(1, 7): |
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268 child = get_node(str(i), '%s\n%s' % (str(i), ec_name_dict[str(i)]), None, None) |
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269 get_node(0, '-.-.-.-', child, None) |
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270 for i, pdict in enumerate(resp): |
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271 sequence = pdict.get('peptide', pdict.get('tryptic_peptide', None)) |
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272 seq = sequence |
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273 if 'ec' in pdict: |
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274 for ec in pdict['ec']: |
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275 child = None |
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276 ec_number = ec['ec_number'] |
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277 for ec_id in get_ids(ec_number): |
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278 ec_name = str(ec_id) |
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279 child = get_node(ec_id, ec_name, child, seq) |
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280 seq = None |
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281 if child: |
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282 get_node(0, '-.-.-.-', child, None) |
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283 return root |
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284 |
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285 def get_taxon_dict(resp, column_order, extra=False, names=False): |
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286 found_keys = set() |
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287 results = [] |
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288 for i, pdict in enumerate(resp): |
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289 results.append(pdict) |
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290 found_keys |= set(pdict.keys()) |
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291 # print >> sys.stderr, "%s\n%s" % (pdict.keys(), found_keys) |
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292 column_names = [] |
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293 column_keys = [] |
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294 for col in column_order: |
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295 if col in found_keys: |
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296 column_names.append(col) |
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297 column_keys.append(col) |
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298 elif names: |
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299 col_id = col + '_id' |
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300 col_name = col + '_name' |
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301 if extra: |
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302 if col_id in found_keys: |
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303 column_names.append(col_id) |
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304 column_keys.append(col_id) |
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305 if names: |
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306 if col_name in found_keys: |
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307 column_names.append(col) |
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308 column_keys.append(col_name) |
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309 else: |
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310 if col + '_name' in found_keys: |
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311 column_names.append(col) |
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312 column_keys.append(col + '_name') |
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313 elif col + '_id' in found_keys: |
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314 column_names.append(col) |
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315 column_keys.append(col + '_id') |
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316 # print >> sys.stderr, "%s\n%s" % (column_names, column_keys) |
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317 taxa = dict() # peptide: [taxonomy] |
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318 for i, pdict in enumerate(results): |
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319 peptide = pdict['peptide'] if 'peptide' in pdict else None |
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320 if peptide and peptide not in taxa: |
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321 vals = [str(pdict[x]) if x in pdict and pdict[x] else '' for x in column_keys] |
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322 taxa[peptide] = vals |
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323 return (taxa, column_names) |
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324 |
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325 def get_ec_dict(resp, extra=False): |
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326 ec_cols = ['ec_numbers', 'ec_protein_counts'] |
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327 if extra: |
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328 ec_cols.append('ec_names') |
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329 ec_dict = dict() |
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330 for i, pdict in enumerate(resp): |
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331 peptide = pdict['peptide'] |
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332 ec_numbers = [] |
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333 protein_counts = [] |
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334 ec_names = [] |
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335 if 'ec' in pdict: |
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336 for ec in pdict['ec']: |
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337 ec_numbers.append(ec['ec_number']) |
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338 protein_counts.append(str(ec['protein_count'])) |
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339 if extra: |
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340 ec_names.append(ec['name'] if 'name' in ec and ec['name'] else '') |
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341 vals = [','.join(ec_numbers), ','.join(protein_counts)] |
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342 if extra: |
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343 vals.append(','.join(ec_names)) |
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344 ec_dict[peptide] = vals |
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345 return (ec_dict, ec_cols) |
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346 |
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347 def get_go_dict(resp, extra=False): |
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348 go_cols = ['go_terms', 'go_protein_counts'] |
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349 if extra: |
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350 go_cols.append('go_names') |
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351 go_dict = dict() |
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352 for i, pdict in enumerate(resp): |
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353 peptide = pdict['peptide'] |
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354 go_terms = [] |
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355 protein_counts = [] |
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356 go_names = [] |
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357 if 'go' in pdict: |
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358 for go in pdict['go']: |
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359 if 'go_term' in go: |
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360 go_terms.append(go['go_term']) |
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361 protein_counts.append(str(go['protein_count'])) |
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362 if extra: |
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363 go_names.append(go['name'] if 'name' in go and go['name'] else '') |
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364 else: |
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365 for go_type in go_types: |
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366 if go_type in go: |
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367 for _go in go[go_type]: |
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368 go_terms.append(_go['go_term']) |
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369 protein_counts.append(str(_go['protein_count'])) |
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370 if extra: |
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371 go_names.append(_go['name'] if 'name' in _go and _go['name'] else '') |
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372 vals = [','.join(go_terms), ','.join(protein_counts)] |
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373 if extra: |
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374 vals.append(','.join(go_names)) |
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375 go_dict[peptide] = vals |
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376 return (go_dict, go_cols) |
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377 |
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378 def get_ipr_dict(resp, extra=False): |
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379 ipr_cols = ['ipr_codes', 'ipr_protein_counts'] |
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380 if extra: |
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381 ipr_cols.append('ipr_types') |
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382 ipr_cols.append('ipr_names') |
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383 ipr_dict = dict() |
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384 for i, pdict in enumerate(resp): |
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385 peptide = pdict['peptide'] |
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386 ipr_codes = [] |
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387 protein_counts = [] |
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388 ipr_names = [] |
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389 ipr_types = [] |
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390 if 'ipr' in pdict: |
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391 for ipr in pdict['ipr']: |
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392 if 'code' in ipr: |
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393 ipr_codes.append(ipr['code']) |
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394 protein_counts.append(str(ipr['protein_count'])) |
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395 if extra: |
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396 ipr_types.append(ipr['type'] if 'type' in ipr else '') |
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397 ipr_names.append(ipr['name'] if 'name' in ipr else '') |
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398 else: |
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399 for ipr_type in ipr_types: |
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400 if ipr_type in ipr: |
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401 for _ipr in ipr[ipr_type]: |
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402 ipr_codes.append(_ipr['code']) |
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403 protein_counts.append(str(_ipr['protein_count'])) |
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404 if extra: |
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405 ipr_types.append(ipr_type) |
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406 ipr_names.append(_ipr['name'] if 'name' in _ipr else '') |
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407 vals = [','.join(ipr_codes), ','.join(protein_counts)] |
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408 if extra: |
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409 vals.append(','.join(ipr_types)) |
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410 vals.append(','.join(ipr_names)) |
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411 ipr_dict[peptide] = vals |
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412 return (ipr_dict, ipr_cols) |
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413 |
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414 def write_ec_table(outfile, resp, column_order): |
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415 with open(outfile, 'w') as fh: |
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416 for i, pdict in enumerate(resp): |
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417 if 'ec' in pdict: |
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418 tvals = [str(pdict[x]) if x in pdict and pdict[x] else '' for x in column_order[0]] |
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419 for ec in pdict['ec']: |
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420 vals = [str(ec[x]) if x in ec and ec[x] else '' for x in column_order[-1]] |
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421 fh.write('%s\n' % '\t'.join(tvals + vals)) |
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422 |
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423 def write_go_table(outfile, resp, column_order): |
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424 with open(outfile, 'w') as fh: |
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425 for i, pdict in enumerate(resp): |
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426 if 'go' in pdict: |
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427 tvals = [str(pdict[x]) if x in pdict and pdict[x] else '' for x in column_order[0]] |
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428 for go in pdict['go']: |
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429 if 'go_term' in go: |
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430 vals = [str(go[x]) if x in go and go[x] else '' for x in column_order[-1]] |
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431 fh.write('%s\n' % '\t'.join(tvals + vals)) |
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432 else: |
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433 for go_type in go_types: |
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434 if go_type in go: |
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435 for _go in go[go_type]: |
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436 vals = [str(_go[x]) if x in _go and _go[x] else '' for x in column_order[-1]] |
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437 vals.append(go_type) |
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438 fh.write('%s\n' % '\t'.join(tvals + vals)) |
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439 |
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440 def write_ipr_table(outfile, resp, column_order): |
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441 with open(outfile, 'w') as fh: |
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442 for i, pdict in enumerate(resp): |
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443 if 'ipr' in pdict: |
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444 tvals = [str(pdict[x]) if x in pdict and pdict[x] else '' for x in column_order[0]] |
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445 for ipr in pdict['ipr']: |
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446 if 'code' in ipr: |
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447 vals = [str(ipr[x]) if x in ipr and ipr[x] else '' for x in column_order[-1]] |
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448 fh.write('%s\n' % '\t'.join(tvals + vals)) |
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449 else: |
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450 for ipr_type in ipr_types: |
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451 if ipr_type in ipr: |
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452 for _ipr in ipr[ipr_type]: |
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453 vals = [str(_ipr[x]) if x in _ipr and _ipr[x] else '' for x in column_order[-1]] |
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454 vals.append(ipr_type) |
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455 fh.write('%s\n' % '\t'.join(tvals + vals)) |
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456 |
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457 # Parse Command Line |
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458 parser = optparse.OptionParser() |
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459 # unipept API choice |
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460 parser.add_option('-a', '--api', dest='unipept', default='pept2lca', choices=['pept2lca', 'pept2taxa', 'pept2prot', 'pept2ec', 'pept2go', 'pept2interpro', 'pept2funct', 'peptinfo'], |
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461 help='The unipept application: pept2lca, pept2taxa, pept2prot, pept2ec, pept2go, pept2funct, or peptinfo') |
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462 # input files |
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463 parser.add_option('-t', '--tabular', dest='tabular', default=None, help='A tabular file that contains a peptide column') |
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464 parser.add_option('-c', '--column', dest='column', type='int', default=0, help='The column (zero-based) in the tabular file that contains peptide sequences') |
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465 parser.add_option('-f', '--fasta', dest='fasta', default=None, help='A fasta file containing peptide sequences') |
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466 parser.add_option('-m', '--mzid', dest='mzid', default=None, help='A mxIdentML file containing peptide sequences') |
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467 parser.add_option('-p', '--pepxml', dest='pepxml', default=None, help='A pepxml file containing peptide sequences') |
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468 # Unipept Flags |
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469 parser.add_option('-e', '--equate_il', dest='equate_il', action='store_true', default=False, help='isoleucine (I) and leucine (L) are equated when matching tryptic peptides to UniProt records') |
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470 parser.add_option('-x', '--extra', dest='extra', action='store_true', default=False, help='return the complete lineage of the taxonomic lowest common ancestor') |
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471 parser.add_option('-n', '--names', dest='names', action='store_true', default=False, help='return the names of all ranks in the lineage of the taxonomic lowest common ancestor') |
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472 parser.add_option('-D', '--domains', dest='domains', action='store_true', default=False, help='group response by GO namaspace: biological process, molecular function, cellular component') |
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473 parser.add_option('-M', '--max_request', dest='max_request', type='int', default=200, help='The maximum number of entries per unipept request') |
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474 |
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475 # output fields |
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476 parser.add_option('-A', '--allfields', dest='allfields', action='store_true', default=False, help='inlcude fields: taxon_rank,taxon_id,taxon_name csv and tsv outputs') |
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477 # Warn vs Error Flag |
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478 parser.add_option('-S', '--strict', dest='strict', action='store_true', default=False, help='Print exit on invalid peptide') |
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479 # output files |
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480 parser.add_option('-J', '--json', dest='json', default=None, help='Output file path for json formatted results') |
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481 parser.add_option('-j', '--ec_json', dest='ec_json', default=None, help='Output file path for json formatted results') |
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482 parser.add_option('-E', '--ec_tsv', dest='ec_tsv', default=None, help='Output file path for EC TAB-separated-values (.tsv) formatted results') |
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483 parser.add_option('-G', '--go_tsv', dest='go_tsv', default=None, help='Output file path for GO TAB-separated-values (.tsv) formatted results') |
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484 parser.add_option('-I', '--ipr_tsv', dest='ipr_tsv', default=None, help='Output file path for InterPro TAB-separated-values (.tsv) formatted results') |
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485 parser.add_option('-L', '--lineage_tsv', dest='lineage_tsv', default=None, help='Output file path for Lineage TAB-separated-values (.tsv) formatted results') |
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486 parser.add_option('-T', '--tsv', dest='tsv', default=None, help='Output file path for TAB-separated-values (.tsv) formatted results') |
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487 parser.add_option('-C', '--csv', dest='csv', default=None, help='Output file path for Comma-separated-values (.csv) formatted results') |
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488 parser.add_option('-U', '--unmatched', dest='unmatched', default=None, help='Output file path for peptide with no matches') |
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489 parser.add_option('-u', '--url', dest='url', default='http://api.unipept.ugent.be/api/v1/', help='unipept url http://api.unipept.ugent.be/api/v1/') |
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490 parser.add_option('-P', '--peptide_match', dest='peptide_match', choices=['best', 'full', 'report'], default='best', help='Match whole peptide') |
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491 parser.add_option('--unmatched_aa', dest='unmatched_aa', default=None, help='Show unmatched AA in peptide as') |
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492 # debug |
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493 parser.add_option('-g', '--get', dest='get', action='store_true', default=False, help='Use GET instead of POST') |
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494 parser.add_option('-d', '--debug', dest='debug', action='store_true', default=False, help='Turning on debugging') |
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495 parser.add_option('-v', '--version', dest='version', action='store_true', default=False, help='print version and exit') |
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496 (options, args) = parser.parse_args() |
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497 if options.version: |
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498 print('%s' % version) |
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499 sys.exit(0) |
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500 |
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501 def tryptic_match_string(peptide, tryptic_matches): |
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502 if options.unmatched_aa: |
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503 p = peptide.lower() |
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504 for m in tryptic_matches: |
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505 p = p.replace(m.lower(), m) |
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506 return re.sub('[a-z]', options.unmatched_aa, p) |
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507 else: |
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508 return ','.join(tryptic_matches) |
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509 |
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510 invalid_ec = 2 if options.strict else None |
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511 peptides = [] |
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512 # Get peptide sequences |
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513 if options.mzid: |
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514 peptides += read_mzid(options.mzid) |
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515 if options.pepxml: |
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516 peptides += read_pepxml(options.pepxml) |
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517 if options.tabular: |
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518 peptides += read_tabular(options.tabular, options.column) |
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519 if options.fasta: |
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520 peptides += read_fasta(options.fasta) |
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521 if args and len(args) > 0: |
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522 for i, peptide in enumerate(args): |
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523 if not re.match(pep_pat, peptide): |
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524 warn_err('"%s" is not a peptide (arg %d)\n' % (peptide, i), exit_code=invalid_ec) |
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525 peptides.append(peptide) |
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526 if len(peptides) < 1: |
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527 warn_err("No peptides input!", exit_code=1) |
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528 column_order = pept2lca_column_order |
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529 if options.unipept == 'pept2prot': |
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530 column_order = pept2prot_extra_column_order if options.extra else pept2prot_column_order |
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531 else: |
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532 if options.extra or options.names: |
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533 column_order = pept2lca_all_column_order if options.allfields else pept2lca_extra_column_order |
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534 else: |
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535 column_order = pept2lca_column_order |
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536 # map to tryptic peptides |
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537 if options.peptide_match == 'full': |
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538 pepToParts = {p: [p] for p in peptides} |
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539 else: |
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540 pepToParts = {p: re.split('\n', re.sub(r'(?<=[RK])(?=[^P])', '\n', p)) for p in peptides} |
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541 if options.debug: |
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542 print("column_order: %s\n" % (column_order), file=sys.stderr) |
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543 partToPeps = {} |
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544 for peptide, parts in pepToParts.items(): |
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545 if options.debug: |
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546 print("peptide: %s\ttryptic: %s\n" % (peptide, parts), file=sys.stderr) |
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547 for part in parts: |
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548 if len(part) > 50: |
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549 warn_err("peptide: %s tryptic fragment len %d > 50 for %s\n" % (peptide, len(part), part), exit_code=None) |
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550 if 5 <= len(part) <= 50: |
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551 partToPeps.setdefault(part, []).append(peptide) |
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552 trypticPeptides = list(partToPeps.keys()) |
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553 # unipept |
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554 unipept_resp = [] |
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555 idx = list(range(0, len(trypticPeptides), options.max_request)) |
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556 idx.append(len(trypticPeptides)) |
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557 for i in range(len(idx) - 1): |
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558 post_data = [] |
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559 if options.equate_il: |
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560 post_data.append(('equate_il', 'true')) |
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561 if options.names or options.json: |
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562 post_data.append(('extra', 'true')) |
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563 post_data.append(('names', 'true')) |
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564 elif options.extra or options.json: |
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565 post_data.append(('extra', 'true')) |
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566 if options.domains: |
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567 post_data.append(('domains', 'true')) |
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568 post_data += [('input[]', x) for x in trypticPeptides[idx[i]:idx[i + 1]]] |
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569 if options.debug: |
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570 print('post_data: %s\n' % (str(post_data)), file=sys.stderr) |
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571 headers = {'Content-Type': 'application/x-www-form-urlencoded', 'Accept': 'application/json'} |
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572 url = '%s/%s' % (options.url.rstrip('/'), options.unipept) |
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573 if options.get: |
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574 params = '&'.join(["%s=%s" % (i[0], i[1]) for i in post_data]) |
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575 url = '%s.json?%s' % (url, params) |
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576 req = urllib.request.Request(url) |
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577 else: |
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578 url = '%s.json' % (url) |
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579 data = urllib.parse.urlencode(post_data).encode() |
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580 params = '&'.join(["%s=%s" % (i[0], i[1]) for i in post_data]) |
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581 if options.debug: |
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582 print('data:\n%s\n' % (data), file=sys.stderr) |
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583 req = urllib.request.Request(url, headers=headers, data=urllib.parse.urlencode(post_data).encode(), method='POST') |
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584 if options.debug: |
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585 print("url: %s\n" % (str(url)), file=sys.stderr) |
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586 try: |
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587 resp = urllib.request.urlopen(req) |
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588 rdata = resp.read() |
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589 if options.debug: |
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590 print("%s %s\n" % (url, str(resp.getcode())), file=sys.stderr) |
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591 if resp.getcode() == 200: |
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592 if options.debug: |
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593 print("rdata: \n%s\n\n" % (rdata), file=sys.stderr) |
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594 unipept_resp += json.loads(rdata) |
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595 # unipept_resp += json.loads(urllib.request.urlopen(req).read()) |
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596 except Exception as e: |
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597 warn_err('HTTP Error %s\n' % (str(e)), exit_code=None) |
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598 unmatched_peptides = [] |
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599 peptideMatches = [] |
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600 if options.debug: |
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601 print("unipept response: %s\n" % str(unipept_resp), file=sys.stderr) |
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602 if options.unipept in ['pept2prot', 'pept2taxa']: |
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603 dupkey = 'uniprot_id' if options.unipept == 'pept2prot' else 'taxon_id' # should only keep one of these per input peptide |
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604 # multiple entries per trypticPeptide for pep2prot or pep2taxa |
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605 mapping = {} |
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606 for match in unipept_resp: |
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607 mapping.setdefault(match['peptide'], []).append(match) |
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608 for peptide in peptides: |
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609 # Get the intersection of matches to the tryptic parts |
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610 keyToMatch = None |
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611 tryptic_match = [] |
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612 for part in pepToParts[peptide]: |
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613 if part in mapping: |
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614 tryptic_match.append(part) |
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615 temp = {match[dupkey]: match for match in mapping[part]} |
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616 if keyToMatch: |
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617 dkeys = set(keyToMatch.keys()) - set(temp.keys()) |
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618 for k in dkeys: |
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619 del keyToMatch[k] |
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620 else: |
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621 keyToMatch = temp |
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622 # keyToMatch = keyToMatch.fromkeys([x for x in keyToMatch if x in temp]) if keyToMatch else temp |
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623 if not keyToMatch: |
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624 unmatched_peptides.append(peptide) |
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625 else: |
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626 for key, match in keyToMatch.items(): |
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627 match['tryptic_match'] = tryptic_match_string(peptide, tryptic_match) |
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628 match['tryptic_peptide'] = match['peptide'] |
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629 match['peptide'] = peptide |
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630 peptideMatches.append(match) |
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631 elif options.unipept in ['pept2lca', 'peptinfo']: |
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632 # should be one response per trypticPeptide for pep2lca |
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633 respMap = {v['peptide']: v for v in unipept_resp} |
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634 # map resp back to peptides |
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635 for peptide in peptides: |
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636 matches = list() |
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637 tryptic_match = [] |
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638 for part in pepToParts[peptide]: |
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639 if part in respMap: |
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640 tryptic_match.append(part) |
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641 matches.append(respMap[part]) |
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642 match = best_match(peptide, matches) |
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643 if not match: |
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644 unmatched_peptides.append(peptide) |
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645 longest_tryptic_peptide = sorted(pepToParts[peptide], key=lambda x: len(x))[-1] |
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646 match = {'peptide': longest_tryptic_peptide} |
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647 match['tryptic_match'] = tryptic_match_string(peptide, tryptic_match) |
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648 match['tryptic_peptide'] = match['peptide'] |
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649 match['peptide'] = peptide |
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650 peptideMatches.append(match) |
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651 else: |
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652 respMap = {v['peptide']: v for v in unipept_resp} |
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653 # map resp back to peptides |
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654 for peptide in peptides: |
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655 matches = list() |
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656 tryptic_match = [] |
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657 for part in pepToParts[peptide]: |
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658 if part in respMap and 'total_protein_count' in respMap[part]: |
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659 tryptic_match.append(part) |
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660 matches.append(respMap[part]) |
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661 match = best_match(peptide, matches) |
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662 if not match: |
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663 unmatched_peptides.append(peptide) |
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664 longest_tryptic_peptide = sorted(pepToParts[peptide], key=lambda x: len(x))[-1] |
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665 match = {'peptide': longest_tryptic_peptide} |
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666 match['tryptic_match'] = tryptic_match_string(peptide, tryptic_match) |
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667 match['tryptic_peptide'] = match['peptide'] |
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668 match['peptide'] = peptide |
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669 peptideMatches.append(match) |
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670 resp = peptideMatches |
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671 if options.debug: |
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672 print("\nmapped response: %s\n" % str(resp), file=sys.stderr) |
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673 # output results |
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674 if not (options.unmatched or options.json or options.tsv or options.csv): |
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675 print(str(resp)) |
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676 if options.unmatched: |
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677 with open(options.unmatched, 'w') as outputFile: |
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678 for peptide in peptides: |
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679 if peptide in unmatched_peptides: |
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680 outputFile.write("%s\n" % peptide) |
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681 if options.json: |
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682 if options.unipept in ['pept2lca', 'pept2taxa', 'peptinfo']: |
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683 root = get_taxon_json(resp) |
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684 with open(options.json, 'w') as outputFile: |
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685 outputFile.write(json.dumps(root)) |
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686 elif options.unipept in ['pept2prot', 'pept2ec', 'pept2go', 'pept2interpro', 'pept2funct']: |
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687 with open(options.json, 'w') as outputFile: |
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688 outputFile.write(str(resp)) |
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689 if options.ec_json: |
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690 if options.unipept in ['pept2ec', 'pept2funct', 'peptinfo']: |
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691 root = get_ec_json(resp) |
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692 with open(options.ec_json, 'w') as outputFile: |
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693 outputFile.write(json.dumps(root)) |
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694 if options.tsv or options.csv: |
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695 rows = [] |
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696 column_names = None |
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697 if options.unipept in ['pept2ec', 'pept2go', 'pept2interpro', 'pept2funct', 'peptinfo']: |
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698 taxa = None |
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699 ec_dict = None |
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700 go_dict = None |
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701 ipr_dict = None |
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702 if options.unipept in ['peptinfo']: |
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703 (taxa, taxon_cols) = get_taxon_dict(resp, column_order, extra=options.extra, names=options.names) |
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704 if options.unipept in ['pept2ec', 'pept2funct', 'peptinfo']: |
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705 (ec_dict, ec_cols) = get_ec_dict(resp, extra=options.extra) |
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706 if options.unipept in ['pept2go', 'pept2funct', 'peptinfo']: |
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707 (go_dict, go_cols) = get_go_dict(resp, extra=options.extra) |
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708 if options.unipept in ['pept2interpro', 'pept2funct', 'peptinfo']: |
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709 (ipr_dict, ipr_cols) = get_ipr_dict(resp, extra=options.extra) |
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710 for i, pdict in enumerate(resp): |
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711 peptide = pdict['peptide'] |
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712 total_protein_count = str(pdict['total_protein_count']) if 'total_protein_count' in pdict else '0' |
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713 column_names = ['peptide', 'total_protein_count'] |
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714 vals = [peptide, total_protein_count] |
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715 if options.peptide_match == 'report': |
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716 column_names = ['peptide', 'tryptic_match', 'total_protein_count'] |
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717 tryptic_match = pdict.get('tryptic_match', '') |
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718 vals = [peptide, tryptic_match, total_protein_count] |
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719 if ec_dict: |
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720 vals += ec_dict[peptide] |
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721 column_names += ec_cols |
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722 if go_dict: |
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723 vals += go_dict[peptide] |
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724 column_names += go_cols |
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725 if ipr_dict: |
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726 vals += ipr_dict[peptide] |
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727 column_names += ipr_cols |
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728 if taxa: |
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729 vals += taxa[peptide][1:] |
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730 column_names += taxon_cols[1:] |
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731 rows.append(vals) |
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732 elif options.unipept in ['pept2lca', 'pept2taxa', 'pept2prot']: |
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733 if options.peptide_match == 'report': |
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734 column_order.insert(1, 'tryptic_match') |
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735 (taxa, taxon_cols) = get_taxon_dict(resp, column_order, extra=options.extra, names=options.names) |
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736 column_names = taxon_cols |
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737 rows = list(taxa.values()) |
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738 if options.tsv: |
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739 with open(options.tsv, 'w') as outputFile: |
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740 if column_names: |
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741 outputFile.write("#%s\n" % '\t'.join(column_names)) |
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742 for vals in rows: |
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743 outputFile.write("%s\n" % '\t'.join(vals)) |
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744 if options.csv: |
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745 with open(options.csv, 'w') as outputFile: |
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746 if column_names: |
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747 outputFile.write("%s\n" % ','.join(column_names)) |
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748 for vals in rows: |
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749 outputFile.write("%s\n" % ','.join(['"%s"' % (v if v else '') for v in vals])) |
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750 if options.ec_tsv and options.unipept in ['pept2ec', 'pept2funct', 'peptinfo']: |
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751 column_order = pept2ec_extra_column_order if options.extra else pept2ec_column_order |
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752 write_ec_table(options.ec_tsv, resp, column_order) |
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753 if options.go_tsv and options.unipept in ['pept2go', 'pept2funct', 'peptinfo']: |
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754 column_order = pept2go_extra_column_order if options.extra else pept2go_column_order |
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755 write_go_table(options.go_tsv, resp, column_order) |
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756 if options.ipr_tsv and options.unipept in ['pept2interpro', 'pept2funct', 'peptinfo']: |
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757 column_order = pept2interpro_extra_column_order if options.extra else pept2interpro_column_order |
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758 write_ipr_table(options.ipr_tsv, resp, column_order) |
0 | 759 |
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760 |
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761 if __name__ == "__main__": |
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762 __main__() |