Mercurial > repos > dchristiany > data_manager_proteore

annotate data_manager/resource_building.py @ 3:c4ac72465a95 draft

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author dchristiany
date Fri, 19 Oct 2018 03:52:55 -0400
parents 2de84fea8367
children 9c75521e4a64
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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 tools
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3 """
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4
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5 import os, sys, argparse, requests, time, csv, re
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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 output_file = tissue +"_"+ time.strftime("%d-%m-%Y") + ".tsv"
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48 path = os.path.join(target_directory, output_file)
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49 unzip(url, path)
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50 print(str(os.path.isfile(path)))
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51 tmp=open(path,"r").readlines()
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52 tissue_name = tissue_name + " " + time.strftime("%d/%m/%Y")
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53 data_table_entry = dict(value = tissue, name = tissue_name, path = path)
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54 _add_data_table_entry(data_manager_dict, data_table_entry, "proteinatlas")
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55
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56
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57 #######################################################################################################
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58 # 2. Peptide Atlas
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59 #######################################################################################################
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60 def peptide_atlas_sources(data_manager_dict, tissue, target_directory):
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61 # Define PA Human build released number (here early 2018)
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62 atlas_build_id = "472"
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63 # Define organism_id (here Human) - to be upraded when other organism added to the project
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64 organism_id = "2"
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65 # Extract sample_category_id and output filename
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66 sample_category_id = tissue.split("-")[0]
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67 output_file = tissue.split("-")[1] +"_"+ time.strftime("%d-%m-%Y") + ".tsv"
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68 query = "https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/GetPeptides?atlas_build_id=" + \
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69 atlas_build_id + "&display_options=ShowMappings&organism_id= " + \
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70 organism_id + "&sample_category_id=" + sample_category_id + \
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71 "&QUERY_NAME=AT_GetPeptides&output_mode=tsv&apply_action=QUERY"
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72 download = requests.get(query)
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73 decoded_content = download.content.decode('utf-8')
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74 cr = csv.reader(decoded_content.splitlines(), delimiter='\t')
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75
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76 #build dictionary by only keeping uniprot accession (not isoform) as key and sum of observations as value
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77 uni_dict = build_dictionary(cr)
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78
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79 tissue_id = "_".join([atlas_build_id, organism_id, sample_category_id,time.strftime("%d-%m-%Y")])
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80 tissue_value = tissue.split("-")[1]
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81 tissue = tissue.split("-")[1] + "_" +time.strftime("%d-%m-%Y")
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82 tissue_name = " ".join(tissue_value.split("_")) + " " + time.strftime("%d/%m/%Y")
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83 path = os.path.join(target_directory,output_file)
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84
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85 with open(path,"wb") as out :
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86 w = csv.writer(out,delimiter='\t')
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87 w.writerow(["Uniprot_AC","nb_obs"])
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88 w.writerows(uni_dict.items())
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89
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90 data_table_entry = dict(value = path, name = tissue_name, tissue = tissue)
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91 _add_data_table_entry(data_manager_dict, data_table_entry, "peptide_atlas")
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92
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93 #function to count the number of observations by uniprot id
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94 def build_dictionary (csv) :
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95 uni_dict = {}
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96 for line in csv :
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97 if "-" not in line[2] and check_uniprot_access(line[2]) :
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98 if line[2] in uni_dict :
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99 uni_dict[line[2]] += int(line[4])
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100 else :
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101 uni_dict[line[2]] = int(line[4])
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102
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103 return uni_dict
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104
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105 #function to check if an id is an uniprot accession number : return True or False-
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106 def check_uniprot_access (id) :
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107 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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108 if uniprot_pattern.match(id) :
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109 return True
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110 else :
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111 return False
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112
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113
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114
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115 #######################################################################################################
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116 # 3. ID mapping file
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117 #######################################################################################################
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118 import ftplib, gzip
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119 csv.field_size_limit(sys.maxsize) # to handle big files
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120
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121 def id_mapping_sources (data_manager_dict, species, target_directory) :
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122
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123 human = species == "human"
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124 species_dict = { "human" : "HUMAN_9606", "mouse" : "MOUSE_10090", "rat" : "RAT_10116" }
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125 files=["idmapping_selected.tab.gz","idmapping.dat.gz"]
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126
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127 #header
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128 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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129 else : tab = [["UniProt-AC","UniProt-ID","GeneID","RefSeq","GI","PDB","GO","PIR","MIM","UniGene","Ensembl_Gene","Ensembl_Transcript","Ensembl_Protein","BioGrid","STRING","KEGG"]]
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130
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131 #print("header ok")
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132
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133 #selected.tab and keep only ids of interest
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134 selected_tab_file=species_dict[species]+"_"+files[0]
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135 tab_path = download_from_uniprot_ftp(selected_tab_file,target_directory)
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136 with gzip.open(tab_path,"rt") as select :
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137 tab_reader = csv.reader(select,delimiter="\t")
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138 for line in tab_reader :
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139 tab.append([line[i] for i in [0,1,2,3,4,5,6,11,13,14,18,19,20]])
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140 os.remove(tab_path)
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141
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142 #print("selected_tab ok")
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143
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144 """
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145 Supplementary ID to get from HUMAN_9606_idmapping.dat :
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146 -NextProt,BioGrid,STRING,KEGG
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147 """
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148
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149 if human : ids = ['neXtProt','BioGrid','STRING','KEGG' ] #ids to get from dat_file
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150 else : ids = ['BioGrid','STRING','KEGG' ]
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151 unidict = {}
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152
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153 #keep only ids of interest in dictionaries
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154 dat_file=species_dict[species]+"_"+files[1]
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155 dat_path = download_from_uniprot_ftp(dat_file,target_directory)
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156 with gzip.open(dat_path,"rt") as dat :
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157 dat_reader = csv.reader(dat,delimiter="\t")
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158 for line in dat_reader :
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159 uniprotID=line[0] #UniProtID as key
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160 id_type=line[1] #ID type of corresponding id, key of sub-dictionnary
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161 cor_id=line[2] #corresponding id
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162 if "-" not in id_type : #we don't keep isoform
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163 if id_type in ids and uniprotID in unidict :
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164 if id_type in unidict[uniprotID] :
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165 unidict[uniprotID][id_type]= ";".join([unidict[uniprotID][id_type],cor_id]) #if there is already a value in the dictionnary
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166 else :
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167 unidict[uniprotID].update({ id_type : cor_id })
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168 elif id_type in ids :
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169 unidict[uniprotID]={id_type : cor_id}
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170 os.remove(dat_path)
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171
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172 #print("dat_file ok")
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173
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174 #add ids from idmapping.dat to the final tab
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175 for line in tab[1:] :
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176 uniprotID=line[0]
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177 if human :
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178 if uniprotID in unidict :
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179 nextprot = access_dictionary(unidict,uniprotID,'neXtProt')
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180 if nextprot != '' : nextprot = clean_nextprot_id(nextprot,line[0])
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181 line.extend([nextprot,access_dictionary(unidict,uniprotID,'BioGrid'),access_dictionary(unidict,uniprotID,'STRING'),
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182 access_dictionary(unidict,uniprotID,'KEGG')])
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183 else :
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184 line.extend(["","","",""])
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185 else :
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186 if uniprotID in unidict :
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187 line.extend([access_dictionary(unidict,uniprotID,'BioGrid'),access_dictionary(unidict,uniprotID,'STRING'),
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188 access_dictionary(unidict,uniprotID,'KEGG')])
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189 else :
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190 line.extend(["","",""])
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191
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192 #print ("tab ok")
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193
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194 #add missing nextprot ID for human
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195 if human :
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196 #build next_dict
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197 nextprot_ids = id_list_from_nextprot_ftp("nextprot_ac_list_all.txt",target_directory)
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198 next_dict = {}
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199 for nextid in nextprot_ids :
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200 next_dict[nextid.replace("NX_","")] = nextid
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201 os.remove(os.path.join(target_directory,"nextprot_ac_list_all.txt"))
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202
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203 #add missing nextprot ID
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204 for line in tab[1:] :
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205 uniprotID=line[0]
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206 nextprotID=line[13]
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207 if nextprotID == '' and uniprotID in next_dict :
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208 line[13]=next_dict[uniprotID]
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209
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210 output_file = species+"_id_mapping_"+ time.strftime("%d-%m-%Y") + ".tsv"
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211 path = os.path.join(target_directory,output_file)
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212
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213 with open(path,"w") as out :
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214 w = csv.writer(out,delimiter='\t')
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215 w.writerows(tab)
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216
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217 name_dict={"human" : "Homo sapiens", "mouse" : "Mus musculus", "rat" : "Rattus norvegicus"}
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218 name = name_dict[species]+" ("+time.strftime("%d-%m-%Y")+")"
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219
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220 data_table_entry = dict(value = species+"_id_mapping_"+ time.strftime("%d-%m-%Y"), name = name, path = path)
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221 _add_data_table_entry(data_manager_dict, data_table_entry, "id_mapping_tab")
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222
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223 def download_from_uniprot_ftp(file,target_directory) :
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224 ftp_dir = "pub/databases/uniprot/current_release/knowledgebase/idmapping/by_organism/"
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225 path = os.path.join(target_directory, file)
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226 ftp = ftplib.FTP("ftp.uniprot.org")
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227 ftp.login("anonymous", "anonymous")
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228 ftp.cwd(ftp_dir)
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229 ftp.retrbinary("RETR " + file, open(path, 'wb').write)
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230 ftp.quit()
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231 return (path)
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232
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233 def id_list_from_nextprot_ftp(file,target_directory) :
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234 ftp_dir = "pub/current_release/ac_lists/"
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235 path = os.path.join(target_directory, file)
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236 ftp = ftplib.FTP("ftp.nextprot.org")
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237 ftp.login("anonymous", "anonymous")
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238 ftp.cwd(ftp_dir)
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239 ftp.retrbinary("RETR " + file, open(path, 'wb').write)
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240 ftp.quit()
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241 with open(path,'r') as nextprot_ids :
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242 nextprot_ids = nextprot_ids.read().splitlines()
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243 return (nextprot_ids)
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244
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245 #return '' if there's no value in a dictionary, avoid error
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246 def access_dictionary (dico,key1,key2) :
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247 if key1 in dico :
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248 if key2 in dico[key1] :
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249 return (dico[key1][key2])
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250 else :
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251 return ("")
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252 #print (key2,"not in ",dico,"[",key1,"]")
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253 else :
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254 return ('')
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255
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256 #if there are several nextprot ID for one uniprotID, return the uniprot like ID
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257 def clean_nextprot_id (next_id,uniprotAc) :
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258 if len(next_id.split(";")) > 1 :
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259 tmp = next_id.split(";")
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260 if "NX_"+uniprotAc in tmp :
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261 return ("NX_"+uniprotAc)
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262 else :
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263 return (tmp[1])
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264 else :
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265 return (next_id)
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266
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267
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268 #######################################################################################################
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269 # Main function
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270 #######################################################################################################
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271 def main():
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272 parser = argparse.ArgumentParser()
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273 parser.add_argument("--hpa", metavar = ("HPA_OPTION"))
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274 parser.add_argument("--peptideatlas", metavar=("SAMPLE_CATEGORY_ID"))
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275 parser.add_argument("--id_mapping", metavar = ("ID_MAPPING_SPECIES"))
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276 parser.add_argument("-o", "--output")
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277 args = parser.parse_args()
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278
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279 data_manager_dict = {}
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280 # Extract json file params
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281 filename = args.output
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282 params = from_json_string(open(filename).read())
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283 target_directory = params[ 'output_data' ][0]['extra_files_path']
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284 os.mkdir(target_directory)
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285
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286 ## Download source files from HPA
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287 try:
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288 hpa = args.hpa
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289 except NameError:
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290 hpa = None
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291 if hpa is not None:
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292 #target_directory = "/projet/galaxydev/galaxy/tools/proteore/ProteoRE/tools/resources_building/test-data/"
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293 hpa = hpa.split(",")
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294 for hpa_tissue in hpa:
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295 HPA_sources(data_manager_dict, hpa_tissue, target_directory)
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296
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297 ## Download source file from Peptide Atlas query
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298 try:
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299 peptide_atlas = args.peptideatlas
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300 except NameError:
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301 peptide_atlas = None
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302 if peptide_atlas is not None:
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303 #target_directory = "/projet/galaxydev/galaxy/tools/proteore/ProteoRE/tools/resources_building/test-data/"
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304 peptide_atlas = peptide_atlas.split(",")
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305 for pa_tissue in peptide_atlas:
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306 peptide_atlas_sources(data_manager_dict, pa_tissue, target_directory)
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307
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308 ## Download ID_mapping source file from Uniprot
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309 try:
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310 id_mapping=args.id_mapping
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311 except NameError:
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312 id_mapping = None
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313 if id_mapping is not None:
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314 id_mapping = id_mapping .split(",")
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315 for species in id_mapping :
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316 id_mapping_sources(data_manager_dict, species, target_directory)
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317
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318 #save info to json file
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319 filename = args.output
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320 open(filename, 'wb').write(to_json_string(data_manager_dict))
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321
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322 if __name__ == "__main__":
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323 main()