Mercurial > repos > dchristiany > data_manager_proteore

annotate data_manager/resource_building.py @ 15:83f57ba70416 draft

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author dchristiany
date Tue, 15 Jan 2019 04:29:28 -0500
parents a1530507fee4
children 454c2e2984ea
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1 """
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2 The purpose of this script is to create source files from different databases to be used in other proteore tools
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3 """
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4
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5 import os, sys, argparse, requests, time, csv, re, json, zipfile, shutil
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6 from io import BytesIO
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7 from zipfile import ZipFile
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8 from galaxy.util.json import from_json_string, to_json_string
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9
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10 #######################################################################################################
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11 # General functions
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12 #######################################################################################################
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13 def unzip(url, output_file):
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14 """
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15 Get a zip file content from a link and unzip
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16 """
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17 content = requests.get(url)
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18 zipfile = ZipFile(BytesIO(content.content))
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19 output_content = ""
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20 output_content += zipfile.open(zipfile.namelist()[0]).read()
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21 output = open(output_file, "w")
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22 output.write(output_content)
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23 output.close()
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24
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25 def _add_data_table_entry(data_manager_dict, data_table_entry,data_table):
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26 data_manager_dict['data_tables'] = data_manager_dict.get('data_tables', {})
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27 data_manager_dict['data_tables'][data_table] = data_manager_dict['data_tables'].get(data_table, [])
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28 data_manager_dict['data_tables'][data_table].append(data_table_entry)
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29 return data_manager_dict
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30
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31 #######################################################################################################
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32 # 1. Human Protein Atlas
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33 # - Normal tissue
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34 # - Pathology
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35 # - Full Atlas
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36 #######################################################################################################
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37 def HPA_sources(data_manager_dict, tissue, target_directory):
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38 if tissue == "HPA_normal_tissue":
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39 tissue_name = "HPA normal tissue"
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40 url = "https://www.proteinatlas.org/download/normal_tissue.tsv.zip"
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41 elif tissue == "HPA_pathology":
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42 tissue_name = "HPA pathology"
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43 url = "https://www.proteinatlas.org/download/pathology.tsv.zip"
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44 elif tissue == "HPA_full_atlas":
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45 tissue_name = "HPA full atlas"
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46 url = "https://www.proteinatlas.org/download/proteinatlas.tsv.zip"
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47
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48 output_file = tissue +"_"+ time.strftime("%d-%m-%Y") + ".tsv"
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49 path = os.path.join(target_directory, output_file)
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50 unzip(url, path) #download and save file
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51 tissue_name = tissue_name + " " + time.strftime("%d/%m/%Y")
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52 tissue_id = tissue_name.replace(" ","_").replace("/","-")
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53
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54 data_table_entry = dict(id=tissue_id, name = tissue_name, value = tissue, path = path)
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55 _add_data_table_entry(data_manager_dict, data_table_entry, "proteore_protein_atlas")
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56
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57
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58 #######################################################################################################
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59 # 2. Peptide Atlas
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60 #######################################################################################################
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61 def peptide_atlas_sources(data_manager_dict, tissue, target_directory):
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62 # Define PA Human build released number (here early 2018)
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63 atlas_build_id = "472"
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64 # Define organism_id (here Human) - to be upraded when other organism added to the project
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65 organism_id = "2"
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66 # Extract sample_category_id and output filename
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67 sample_category_id = tissue.split("-")[0]
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68 output_file = tissue.split("-")[1] +"_"+ time.strftime("%d-%m-%Y") + ".tsv"
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69 query = "https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/GetPeptides?atlas_build_id=" + \
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70 atlas_build_id + "&display_options=ShowMappings&organism_id= " + \
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71 organism_id + "&sample_category_id=" + sample_category_id + \
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72 "&QUERY_NAME=AT_GetPeptides&output_mode=tsv&apply_action=QUERY"
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73 download = requests.get(query)
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74 decoded_content = download.content.decode('utf-8')
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75 cr = csv.reader(decoded_content.splitlines(), delimiter='\t')
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76
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77 #build dictionary by only keeping uniprot accession (not isoform) as key and sum of observations as value
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78 uni_dict = build_dictionary(cr)
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79
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80 #columns of data table peptide_atlas
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81 date = time.strftime("%d-%m-%Y")
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82 tissue = tissue.split("-")[1]
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83 tissue_id = tissue+"_"+date
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84 tissue_name = tissue_id.replace("-","/").replace("_"," ")
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85 path = os.path.join(target_directory,output_file)
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86
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87 with open(path,"wb") as out :
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88 w = csv.writer(out,delimiter='\t')
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89 w.writerow(["Uniprot_AC","nb_obs"])
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90 w.writerows(uni_dict.items())
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91
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92 data_table_entry = dict(id=tissue_id, name=tissue_name, value = path, tissue = tissue)
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93 _add_data_table_entry(data_manager_dict, data_table_entry, "proteore_peptide_atlas")
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94
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95 #function to count the number of observations by uniprot id
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96 def build_dictionary (csv) :
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97 uni_dict = {}
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98 for line in csv :
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99 if "-" not in line[2] and check_uniprot_access(line[2]) :
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100 if line[2] in uni_dict :
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101 uni_dict[line[2]] += int(line[4])
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102 else :
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103 uni_dict[line[2]] = int(line[4])
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104
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105 return uni_dict
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106
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107 #function to check if an id is an uniprot accession number : return True or False-
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108 def check_uniprot_access (id) :
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109 uniprot_pattern = re.compile("[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}")
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110 if uniprot_pattern.match(id) :
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111 return True
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112 else :
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113 return False
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114
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115
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116
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117 #######################################################################################################
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118 # 3. ID mapping file
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119 #######################################################################################################
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120 import ftplib, gzip
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121 csv.field_size_limit(sys.maxsize) # to handle big files
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122
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123 def id_mapping_sources (data_manager_dict, species, target_directory) :
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124
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125 human = species == "human"
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126 species_dict = { "human" : "HUMAN_9606", "mouse" : "MOUSE_10090", "rat" : "RAT_10116" }
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127 files=["idmapping_selected.tab.gz","idmapping.dat.gz"]
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128
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129 #header
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130 if human : tab = [["UniProt-AC","UniProt-ID","GeneID","RefSeq","GI","PDB","GO","PIR","MIM","UniGene","Ensembl_Gene","Ensembl_Transcript","Ensembl_Protein","neXtProt","BioGrid","STRING","KEGG"]]
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131 else : tab = [["UniProt-AC","UniProt-ID","GeneID","RefSeq","GI","PDB","GO","PIR","MIM","UniGene","Ensembl_Gene","Ensembl_Transcript","Ensembl_Protein","BioGrid","STRING","KEGG"]]
0
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132
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133 #print("header ok")
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134
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135 #get selected.tab and keep only ids of interest
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136 selected_tab_file=species_dict[species]+"_"+files[0]
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137 tab_path = download_from_uniprot_ftp(selected_tab_file,target_directory)
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138 with gzip.open(tab_path,"rt") as select :
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139 tab_reader = csv.reader(select,delimiter="\t")
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140 for line in tab_reader :
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141 tab.append([line[i] for i in [0,1,2,3,4,5,6,11,13,14,18,19,20]])
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142 os.remove(tab_path)
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143
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144 #print("selected_tab ok")
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145
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146 """
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147 Supplementary ID to get from HUMAN_9606_idmapping.dat :
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148 -NextProt,BioGrid,STRING,KEGG
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149 """
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150
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151 #there's more id type for human
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152 if human : ids = ['neXtProt','BioGrid','STRING','KEGG' ] #ids to get from dat_file
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153 else : ids = ['BioGrid','STRING','KEGG' ]
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154 unidict = {}
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155
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156 #keep only ids of interest in dictionaries
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157 dat_file=species_dict[species]+"_"+files[1]
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158 dat_path = download_from_uniprot_ftp(dat_file,target_directory)
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159 with gzip.open(dat_path,"rt") as dat :
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160 dat_reader = csv.reader(dat,delimiter="\t")
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161 for line in dat_reader :
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162 uniprotID=line[0] #UniProtID as key
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163 id_type=line[1] #ID type of corresponding id, key of sub-dictionnary
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164 cor_id=line[2] #corresponding id
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165 if "-" not in id_type : #we don't keep isoform
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166 if id_type in ids and uniprotID in unidict :
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167 if id_type in unidict[uniprotID] :
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168 unidict[uniprotID][id_type]= ";".join([unidict[uniprotID][id_type],cor_id]) #if there is already a value in the dictionnary
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169 else :
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170 unidict[uniprotID].update({ id_type : cor_id })
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171 elif id_type in ids :
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172 unidict[uniprotID]={id_type : cor_id}
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173 os.remove(dat_path)
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174
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175 #print("dat_file ok")
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176
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177 #add ids from idmapping.dat to the final tab
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178 for line in tab[1:] :
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179 uniprotID=line[0]
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180 if human :
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181 if uniprotID in unidict :
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182 nextprot = access_dictionary(unidict,uniprotID,'neXtProt')
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183 if nextprot != '' : nextprot = clean_nextprot_id(nextprot,line[0])
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184 line.extend([nextprot,access_dictionary(unidict,uniprotID,'BioGrid'),access_dictionary(unidict,uniprotID,'STRING'),
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185 access_dictionary(unidict,uniprotID,'KEGG')])
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186 else :
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187 line.extend(["","","",""])
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188 else :
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189 if uniprotID in unidict :
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190 line.extend([access_dictionary(unidict,uniprotID,'BioGrid'),access_dictionary(unidict,uniprotID,'STRING'),
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191 access_dictionary(unidict,uniprotID,'KEGG')])
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192 else :
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193 line.extend(["","",""])
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194
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195 #print ("tab ok")
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196
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197 #add missing nextprot ID for human
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198 if human :
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199 #build next_dict
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200 nextprot_ids = id_list_from_nextprot_ftp("nextprot_ac_list_all.txt",target_directory)
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201 next_dict = {}
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202 for nextid in nextprot_ids :
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203 next_dict[nextid.replace("NX_","")] = nextid
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204 os.remove(os.path.join(target_directory,"nextprot_ac_list_all.txt"))
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205
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206 #add missing nextprot ID
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207 for line in tab[1:] :
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208 uniprotID=line[0]
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209 nextprotID=line[13]
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210 if nextprotID == '' and uniprotID in next_dict :
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211 line[13]=next_dict[uniprotID]
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212
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213 output_file = species+"_id_mapping_"+ time.strftime("%d-%m-%Y") + ".tsv"
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214 path = os.path.join(target_directory,output_file)
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215
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216 with open(path,"w") as out :
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217 w = csv.writer(out,delimiter='\t')
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218 w.writerows(tab)
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219
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220 name_dict={"human" : "Homo sapiens", "mouse" : "Mus musculus", "rat" : "Rattus norvegicus"}
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221 name = name_dict[species]+" "+time.strftime("%d/%m/%Y")
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222 id = species+"_id_mapping_"+ time.strftime("%d-%m-%Y")
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223
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224 data_table_entry = dict(id=id, name = name, value = species, path = path)
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225 _add_data_table_entry(data_manager_dict, data_table_entry, "proteore_id_mapping")
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226
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227 def download_from_uniprot_ftp(file,target_directory) :
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228 ftp_dir = "pub/databases/uniprot/current_release/knowledgebase/idmapping/by_organism/"
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229 path = os.path.join(target_directory, file)
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230 ftp = ftplib.FTP("ftp.uniprot.org")
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231 ftp.login("anonymous", "anonymous")
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232 ftp.cwd(ftp_dir)
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233 ftp.retrbinary("RETR " + file, open(path, 'wb').write)
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234 ftp.quit()
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235 return (path)
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236
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237 def id_list_from_nextprot_ftp(file,target_directory) :
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238 ftp_dir = "pub/current_release/ac_lists/"
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239 path = os.path.join(target_directory, file)
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240 ftp = ftplib.FTP("ftp.nextprot.org")
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241 ftp.login("anonymous", "anonymous")
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242 ftp.cwd(ftp_dir)
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243 ftp.retrbinary("RETR " + file, open(path, 'wb').write)
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244 ftp.quit()
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245 with open(path,'r') as nextprot_ids :
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246 nextprot_ids = nextprot_ids.read().splitlines()
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247 return (nextprot_ids)
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248
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249 #return '' if there's no value in a dictionary, avoid error
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250 def access_dictionary (dico,key1,key2) :
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251 if key1 in dico :
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252 if key2 in dico[key1] :
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253 return (dico[key1][key2])
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254 else :
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255 return ("")
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256 #print (key2,"not in ",dico,"[",key1,"]")
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257 else :
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258 return ('')
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259
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260 #if there are several nextprot ID for one uniprotID, return the uniprot like ID
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261 def clean_nextprot_id (next_id,uniprotAc) :
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262 if len(next_id.split(";")) > 1 :
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263 tmp = next_id.split(";")
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264 if "NX_"+uniprotAc in tmp :
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265 return ("NX_"+uniprotAc)
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266 else :
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267 return (tmp[1])
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268 else :
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269 return (next_id)
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270
15
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271
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272 #######################################################################################################
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273 # 4. Build protein interaction maps files
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274 #######################################################################################################
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275
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276 def PPI_ref_files(data_manager_dict, species, interactome, target_directory):
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277
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278 species_dict={'human':'Homo sapiens',"mouse":"Mus musculus","rat":"Rattus norvegicus"}
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279
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280 ##BioGRID
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281 if interactome=="biogrid":
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282
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283 tab2_link="https://downloads.thebiogrid.org/Download/BioGRID/Release-Archive/BIOGRID-3.5.167/BIOGRID-ORGANISM-3.5.167.tab2.zip"
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284
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285 #dowload zip file
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286 r = requests.get(tab2_link)
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287 with open("BioGRID.zip", "wb") as code:
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288 code.write(r.content)
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289
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290 #unzip files
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291 with zipfile.ZipFile("BioGRID.zip", 'r') as zip_ref:
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292 if not os.path.exists("tmp_BioGRID"): os.makedirs("tmp_BioGRID")
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293 zip_ref.extractall("tmp_BioGRID")
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294
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295 #import file of interest and build dictionary
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296 file_path="tmp_BioGRID/BIOGRID-ORGANISM-"+species_dict[species].replace(" ","_")+"-3.5.167.tab2.txt"
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297 with open(file_path,"r") as handle :
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298 tab_file = csv.reader(handle,delimiter="\t")
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299 dico_network = {}
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300 GeneID_index=1
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301 network_cols=[1,2,7,8,11,12,18,20]
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302 for line in tab_file :
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303 dico_network[line[GeneID_index]]=[line[i] for i in network_cols]
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304
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305 #delete tmp_BioGRID directory
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306 os.remove("BioGRID.zip")
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307 shutil.rmtree("tmp_BioGRID", ignore_errors=True)
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308
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309 #download NCBI2Reactome.txt file and build dictionary
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310 download = requests.get('https://www.reactome.org/download/current/NCBI2Reactome.txt')
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311 decoded_content = download.content.decode('utf-8')
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312 tab_file = csv.reader(decoded_content.splitlines(), delimiter='\t')
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313 dico_nodes = {}
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314 GeneID_index=0
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315 pathway_description_index=3
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316 species_index=5
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317 for line in tab_file :
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318 if line[species_index]==species_dict[species]:
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319 if line[GeneID_index] in dico_nodes :
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320 dico_nodes[line[GeneID_index]].append(line[pathway_description_index])
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321 else :
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322 dico_nodes[line[GeneID_index]] = [line[pathway_description_index]]
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323
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324 dico={}
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325 dico['network']=dico_network
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326 dico['nodes']=dico_nodes
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327
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328 ##Bioplex
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329 elif interactome=="bioplex":
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330
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331 download = requests.get("http://bioplex.hms.harvard.edu/data/BioPlex_interactionList_v4a.tsv")
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332 decoded_content = download.content.decode('utf-8')
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333 bioplex = csv.reader(decoded_content.splitlines(), delimiter='\t')
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334 dico_network = {}
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335 dico_network["GeneID"]={}
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336 network_geneid_cols=[0,1,4,5,8]
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337 dico_network["UniProt-AC"]={}
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338 network_uniprot_cols=[2,3,4,5,8]
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339 dico_GeneID_to_UniProt = {}
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340 dico_nodes = {}
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341 for line in bioplex :
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342 dico_network["GeneID"][line[0]]=[line[i] for i in network_geneid_cols]
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343 dico_network["UniProt-AC"][line[2]]=[line[i] for i in network_uniprot_cols]
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344 dico_GeneID_to_UniProt[line[0]]=line[2]
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345
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346 download = requests.get("https://reactome.org/download/current/UniProt2Reactome.txt")
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347 decoded_content = download.content.decode('utf-8')
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348 tab_file = csv.reader(decoded_content.splitlines(), delimiter='\t')
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349 dico_nodes = {}
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350 uniProt_index=0
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351 pathway_description_index=3
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352 species_index=5
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353 for line in tab_file :
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354 if line[species_index]==species_dict[species]:
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355 if line[uniProt_index] in dico_nodes :
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356 dico_nodes[line[uniProt_index]].append(line[pathway_description_index])
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357 else :
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358 dico_nodes[line[uniProt_index]] = [line[pathway_description_index]]
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359
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360 dico={}
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361 dico['network']=dico_network
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362 dico['nodes']=dico_nodes
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363 dico['convert']=dico_GeneID_to_UniProt
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364
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365 #writing output
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366 output_file = species+'_'+interactome+'_dict_'+ time.strftime("%d-%m-%Y") + ".json"
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367 path = os.path.join(target_directory,output_file)
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368 name = species+" ("+species_dict[species]+") "+time.strftime("%d/%m/%Y")
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369 id = interactome+"_"+species+ time.strftime("%d-%m-%Y")
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370
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371 with open(path, 'w') as handle:
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372 json.dump(dico, handle, sort_keys=True)
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373
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374 data_table_entry = dict(id=id, name = name, value = species, path = path)
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375 _add_data_table_entry(data_manager_dict, data_table_entry, "proteore_"+interactome+"_dictionaries")
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376
0
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377
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378 #######################################################################################################
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379 # Main function
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380 #######################################################################################################
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381 def main():
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382 parser = argparse.ArgumentParser()
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383 parser.add_argument("--hpa", metavar = ("HPA_OPTION"))
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384 parser.add_argument("--peptideatlas", metavar=("SAMPLE_CATEGORY_ID"))
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385 parser.add_argument("--id_mapping", metavar = ("ID_MAPPING_SPECIES"))
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386 parser.add_argument("--interactome", metavar = ("PPI"))
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387 parser.add_argument("--species")
0
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388 parser.add_argument("-o", "--output")
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389 args = parser.parse_args()
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390
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391 data_manager_dict = {}
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392 # Extract json file params
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393 filename = args.output
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394 params = from_json_string(open(filename).read())
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395 target_directory = params[ 'output_data' ][0]['extra_files_path']
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396 os.mkdir(target_directory)
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397
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398 ## Download source files from HPA
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399 try:
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400 hpa = args.hpa
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401 except NameError:
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402 hpa = None
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403 if hpa is not None:
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404 #target_directory = "/projet/galaxydev/galaxy/tools/proteore/ProteoRE/tools/resources_building/test-data/"
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405 hpa = hpa.split(",")
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406 for hpa_tissue in hpa:
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407 HPA_sources(data_manager_dict, hpa_tissue, target_directory)
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408
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409 ## Download source file from Peptide Atlas query
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410 try:
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411 peptide_atlas = args.peptideatlas
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412 except NameError:
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413 peptide_atlas = None
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414 if peptide_atlas is not None:
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415 #target_directory = "/projet/galaxydev/galaxy/tools/proteore/ProteoRE/tools/resources_building/test-data/"
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416 peptide_atlas = peptide_atlas.split(",")
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417 for pa_tissue in peptide_atlas:
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418 peptide_atlas_sources(data_manager_dict, pa_tissue, target_directory)
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419
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420 ## Download ID_mapping source file from Uniprot
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421 try:
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422 id_mapping=args.id_mapping
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423 except NameError:
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424 id_mapping = None
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425 if id_mapping is not None:
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426 id_mapping = id_mapping .split(",")
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427 for species in id_mapping :
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428 id_mapping_sources(data_manager_dict, species, target_directory)
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429
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430 ## Download PPI ref files from biogrid/bioplex/humap
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431 try:
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432 interactome=args.interactome
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433 species=args.species
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434 except NameError:
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435 interactome=None
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436 species=None
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437 if interactome is not None and species is not None:
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438 PPI_ref_files(data_manager_dict, species, interactome, target_directory)
0
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439
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440 #save info to json file
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441 filename = args.output
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442 open(filename, 'wb').write(to_json_string(data_manager_dict))
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443
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444 if __name__ == "__main__":
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445 main()