Mercurial > repos > proteore > proteore_data_manager

annotate data_manager/resource_building.py @ 5:b05fa99ddda2 draft

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"planemo upload commit a23ff40d482d58e0425108bf60372e5fcafb847e"
author proteore
date Thu, 06 Feb 2020 04:02:50 -0500
parents e967a99d66b3
children 8f33a6e6e36c
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1 # -*- coding: utf-8 -*-
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2 """
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3 The purpose of this script is to create source files from different databases to be used in other proteore tools
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4 """
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5
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6 import os, shutil, sys, argparse, requests, time, csv, re, json, shutil, zipfile, subprocess
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7 from io import BytesIO
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8 from zipfile import ZipFile
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9 from galaxy.util.json import from_json_string, to_json_string
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10
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11 #######################################################################################################
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12 # General functions
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13 #######################################################################################################
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14 def unzip(url, output_file):
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15 """
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16 Get a zip file content from a link and unzip
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17 """
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18 content = requests.get(url)
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19 zipfile = ZipFile(BytesIO(content.content))
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20 output_content = ""
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21 output_content += zipfile.open(zipfile.namelist()[0]).read()
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22 output = open(output_file, "w")
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23 output.write(output_content)
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24 output.close()
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25
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26 def _add_data_table_entry(data_manager_dict, data_table_entry,data_table):
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27 data_manager_dict['data_tables'] = data_manager_dict.get('data_tables', {})
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28 data_manager_dict['data_tables'][data_table] = data_manager_dict['data_tables'].get(data_table, [])
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29 data_manager_dict['data_tables'][data_table].append(data_table_entry)
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30 return data_manager_dict
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31
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32 #######################################################################################################
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33 # 1. Human Protein Atlas
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34 # - Normal tissue
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35 # - Pathology
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36 # - Full Atlas
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37 #######################################################################################################
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38 def HPA_sources(data_manager_dict, tissue, target_directory):
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39 if tissue == "HPA_normal_tissue":
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40 tissue_name = "HPA normal tissue"
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41 url = "https://www.proteinatlas.org/download/normal_tissue.tsv.zip"
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42 table = "proteore_protein_atlas_normal_tissue"
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43 elif tissue == "HPA_pathology":
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44 tissue_name = "HPA pathology"
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45 url = "https://www.proteinatlas.org/download/pathology.tsv.zip"
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46 table = "proteore_protein_atlas_tumor_tissue"
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47 elif tissue == "HPA_full_atlas":
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48 tissue_name = "HPA full atlas"
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49 url = "https://www.proteinatlas.org/download/proteinatlas.tsv.zip"
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50 table = "proteore_protein_full_atlas"
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51 elif tissue == "HPA_RNA_tissue":
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52 tissue_name = "HPA RNA tissue"
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53 url = "https://www.proteinatlas.org/download/rna_tissue_consensus.tsv.zip"
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54 table = "proteore_protein_atlas_rna_tissue"
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55
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56 output_file = tissue +"_"+ time.strftime("%d-%m-%Y") + ".tsv"
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57 path = os.path.join(target_directory, output_file)
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58 unzip(url, path) #download and save file
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59 tissue_name = tissue_name + " " + time.strftime("%d/%m/%Y")
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60 release = tissue_name.replace(" ","_").replace("/","-")
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61 id = str(10000000000 - int(time.strftime("%Y%m%d")))
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62
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63
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64 data_table_entry = dict(id=id, release=release, name = tissue_name, tissue = tissue, value = path)
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65 _add_data_table_entry(data_manager_dict, data_table_entry, table)
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66
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67
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68 #######################################################################################################
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69 # 2. Peptide Atlas
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70 #######################################################################################################
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71 def peptide_atlas_sources(data_manager_dict, tissue, date, target_directory):
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72 # Define organism_id (here Human) - to be upraded when other organism added to the project
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73 organism_id = "2"
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74 # Extract sample_category_id and output filename
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75 tissue=tissue.split(".")
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76 sample_category_id = tissue[0]
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77 tissue_name = tissue[1]
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78 output_file = tissue_name+"_"+date + ".tsv"
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79
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80 query="https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/GetProteins?&atlas_build_id="+ \
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81 sample_category_id+"&display_options=ShowAbundances&organism_id="+organism_id+ \
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82 "&redundancy_constraint=4&presence_level_constraint=1%2C2&gene_annotation_level_constraint=leaf\
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83 &QUERY_NAME=AT_GetProteins&action=QUERY&output_mode=tsv&apply_action=QUERY"
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84
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85 with requests.Session() as s:
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86 download = s.get(query)
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87 decoded_content = download.content.decode('utf-8')
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88 cr = csv.reader(decoded_content.splitlines(), delimiter='\t')
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89
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90 uni_dict = build_dictionary(cr)
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91
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92 #columns of data table peptide_atlas
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93 tissue_id = tissue_name+"_"+date
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94 name = tissue_id.replace("-","/").replace("_"," ")
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95 path = os.path.join(target_directory,output_file)
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96
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97 with open(path,"w") as out :
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98 w = csv.writer(out,delimiter='\t')
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99 w.writerow(["Uniprot_AC","nb_obs"])
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100 w.writerows(uni_dict.items())
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101
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102 data_table_entry = dict(id=tissue_id, name=name, value = path, tissue = tissue_name)
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103 _add_data_table_entry(data_manager_dict, data_table_entry, "proteore_peptide_atlas")
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104
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105 #function to count the number of observations by uniprot id
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106 def build_dictionary (csv) :
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107 uni_dict = {}
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108 for line in csv :
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109 if "-" not in line[0] and check_uniprot_access(line[0]) :
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110 if line[0] in uni_dict :
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111 uni_dict[line[0]] += int(line[5])
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112 else :
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113 uni_dict[line[0]] = int(line[5])
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114
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115 return uni_dict
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116
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117 #function to check if an id is an uniprot accession number : return True or False-
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118 def check_uniprot_access (id) :
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119 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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120 if uniprot_pattern.match(id) :
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121 return True
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122 else :
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123 return False
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124
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125 def check_entrez_geneid (id) :
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126 entrez_pattern = re.compile("[0-9]+|[A-Z]{1,2}_[0-9]+|[A-Z]{1,2}_[A-Z]{1,4}[0-9]+")
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127 if entrez_pattern.match(id) :
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128 return True
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129 else :
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130 return False
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131
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132 #######################################################################################################
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133 # 3. ID mapping file
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134 #######################################################################################################
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135 import ftplib, gzip
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136 csv.field_size_limit(sys.maxsize) # to handle big files
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137
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138 def id_mapping_sources (data_manager_dict, species, target_directory, tool_data_path) :
0
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139
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140 human = species == "Human"
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141 species_dict = { "Human" : "HUMAN_9606", "Mouse" : "MOUSE_10090", "Rat" : "RAT_10116" }
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142 files=["idmapping_selected.tab.gz","idmapping.dat.gz"]
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143 archive = os.path.join(tool_data_path, "id_mapping/ID_mapping_archive_"+species+"_"+str(time.strftime("%Y%m%d")))
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144 if os.path.isdir(archive) is False : os.mkdir(archive)
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145
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146 #header
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147 if human : tab = [["UniProt-AC","UniProt-AC_reviewed","UniProt-ID","GeneID","RefSeq","GI","PDB","GO","PIR","MIM","UniGene","Ensembl_Gene","Ensembl_Transcript","Ensembl_Protein","neXtProt","BioGrid","STRING","KEGG",'Gene_Name']]
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148 else : tab = [["UniProt-AC","UniProt-AC_reviewed","UniProt-ID","GeneID","RefSeq","GI","PDB","GO","PIR","MIM","UniGene","Ensembl_Gene","Ensembl_Transcript","Ensembl_Protein","BioGrid","STRING","KEGG",'Gene_Name']]
0
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149
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150 #get selected.tab and keep only ids of interest
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151 selected_tab_file=species_dict[species]+"_"+files[0]
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152 tab_path = download_from_uniprot_ftp(selected_tab_file,target_directory)
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153 with gzip.open(tab_path,"rt") as select :
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154 tab_reader = csv.reader(select,delimiter="\t")
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155 for line in tab_reader :
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156 tab.append([line[0]]+[line[i] for i in [0,1,2,3,4,5,6,11,13,14,18,19,20]])
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157 if os.path.exists(os.path.join(archive,tab_path.split("/")[-1])) : os.remove(os.path.join(archive,tab_path.split("/")[-1]))
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158 shutil.move(tab_path, archive)
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159 #print("selected_tab ok")
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160
2
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161 #get uniprot-AC reviewed
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162 organism = species_dict[species].split("_")[1]
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163 query = "https://www.uniprot.org/uniprot/?query=reviewed:yes+AND+organism:"+organism+"&format=list"
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164
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165 with requests.Session() as s:
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166 download = s.get(query)
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167 decoded_content = download.content.decode('utf-8')
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168 uniprot_reviewed_list = decoded_content.splitlines()
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169
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170 #save reviewed list
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171 reviewed_list_path = os.path.join(archive,'uniprot_reviewed_list.txt')
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172 with open(reviewed_list_path,'w') as reviewed_list_file:
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173 for id in uniprot_reviewed_list:
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174 reviewed_list_file.write(id+"\n")
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175
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176 #remove unreviewed uniprot-AC
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177 for line in tab[1:]:
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178 UniProtAC = line[1]
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179 if UniProtAC not in uniprot_reviewed_list :
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180 line[1]=""
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181
0
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182 """
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183 Supplementary ID to get from HUMAN_9606_idmapping.dat :
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184 -NextProt,BioGrid,STRING,KEGG
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185 """
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186
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187 #there's more id type for human
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188 if human : ids = ['neXtProt','BioGrid','STRING','KEGG','Gene_Name' ] #ids to get from dat_file
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189 else : ids = ['BioGrid','STRING','KEGG','Gene_Name' ]
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190 unidict = {}
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191
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192 #keep only ids of interest in dictionaries
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193 dat_file = species_dict[species]+"_"+files[1]
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194 dat_path = download_from_uniprot_ftp(dat_file,target_directory)
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195 with gzip.open(dat_path,"rt") as dat :
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196 dat_reader = csv.reader(dat,delimiter="\t")
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197 for line in dat_reader :
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198 uniprotID=line[0] #UniProtID as key
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199 id_type=line[1] #ID type of corresponding id, key of sub-dictionnary
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200 cor_id=line[2] #corresponding id
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201 if "-" not in id_type : #we don't keep isoform
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202 if id_type in ids and uniprotID in unidict :
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203 if id_type in unidict[uniprotID] :
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204 unidict[uniprotID][id_type]= ";".join([unidict[uniprotID][id_type],cor_id]) #if there is already a value in the dictionnary
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205 else :
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206 unidict[uniprotID].update({ id_type : cor_id })
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207 elif id_type in ids :
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208 unidict[uniprotID]={id_type : cor_id}
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209 if os.path.exists(os.path.join(archive,dat_path.split("/")[-1])) : os.remove(os.path.join(archive,dat_path.split("/")[-1]))
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210 shutil.move(dat_path, archive)
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211
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212 #print("dat_file ok")
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213
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214 #add ids from idmapping.dat to the final tab
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215 for line in tab[1:] :
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216 uniprotID=line[0]
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217 if human :
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218 if uniprotID in unidict :
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219 nextprot = access_dictionary(unidict,uniprotID,'neXtProt')
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220 if nextprot != '' : nextprot = clean_nextprot_id(nextprot,line[0])
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221 line.extend([nextprot,access_dictionary(unidict,uniprotID,'BioGrid'),access_dictionary(unidict,uniprotID,'STRING'),
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222 access_dictionary(unidict,uniprotID,'KEGG'),access_dictionary(unidict,uniprotID,'Gene_Name')])
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223 else :
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224 line.extend(["","","","",""])
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225 else :
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226 if uniprotID in unidict :
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227 line.extend([access_dictionary(unidict,uniprotID,'BioGrid'),access_dictionary(unidict,uniprotID,'STRING'),
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228 access_dictionary(unidict,uniprotID,'KEGG'),access_dictionary(unidict,uniprotID,'Gene_Name')])
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229 else :
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230 line.extend(["","","",""])
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231
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232 #print ("tab ok")
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233
2
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234 #add missing nextprot ID for human or replace old ones
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235 if human :
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236 #build next_dict
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237 nextprot_path = id_list_from_nextprot_ftp("nextprot_ac_list_all.txt",target_directory)
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238 with open(nextprot_path,'r') as nextprot_ids :
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239 nextprot_ids = nextprot_ids.read().splitlines()
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240 if os.path.exists(os.path.join(archive,nextprot_path.split("/")[-1])) : os.remove(os.path.join(archive,nextprot_path.split("/")[-1]))
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241 shutil.move(nextprot_path,archive)
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242 next_dict = {}
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243 for nextid in nextprot_ids :
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244 next_dict[nextid.replace("NX_","")] = nextid
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245 #os.remove(os.path.join(target_directory,"nextprot_ac_list_all.txt"))
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246
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247 #add missing nextprot ID
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248 for line in tab[1:] :
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249 uniprotID=line[0]
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250 nextprotID=line[14]
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251 if uniprotID in next_dict and (nextprotID == '' or (nextprotID != "NX_"+uniprotID and next_dict[uniprotID] == "NX_"+uniprotID)) :
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252 line[14]=next_dict[uniprotID]
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253
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254 output_file = species+"_id_mapping_"+ time.strftime("%d-%m-%Y") + ".tsv"
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255 path = os.path.join(target_directory,output_file)
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256
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257 with open(path,"w") as out :
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258 w = csv.writer(out,delimiter='\t')
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259 w.writerows(tab)
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260
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261 subprocess.call(['tar', '-czvf', archive+".tar.gz", archive])
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262 shutil.rmtree(archive, ignore_errors=True)
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263
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264 name_dict={"Human" : "Homo sapiens", "Mouse" : "Mus musculus", "Rat" : "Rattus norvegicus"}
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265 name = species +" (" + name_dict[species]+" "+time.strftime("%d/%m/%Y")+")"
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266 release = species+"_id_mapping_"+ time.strftime("%d-%m-%Y")
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267 id = str(10000000000 - int(time.strftime("%Y%m%d"))) #new ids must be inferior to previous id -> sort by <filter> in xml only in descending order
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268
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269 data_table_entry = dict(id=id, release=release , name = name, species = species, value = path)
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270 _add_data_table_entry(data_manager_dict, data_table_entry, "proteore_id_mapping_"+species)
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271
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272 def download_from_uniprot_ftp(file,target_directory) :
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273 ftp_dir = "pub/databases/uniprot/current_release/knowledgebase/idmapping/by_organism/"
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274 path = os.path.join(target_directory, file)
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275 ftp = ftplib.FTP("ftp.uniprot.org")
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276 ftp.login("anonymous", "anonymous")
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277 ftp.cwd(ftp_dir)
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278 ftp.retrbinary("RETR " + file, open(path, 'wb').write)
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279 ftp.quit()
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280 return (path)
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281
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282 def id_list_from_nextprot_ftp(file,target_directory) :
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283 ftp_dir = "pub/current_release/ac_lists/"
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284 path = os.path.join(target_directory, file)
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285 ftp = ftplib.FTP("ftp.nextprot.org")
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286 ftp.login("anonymous", "anonymous")
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287 ftp.cwd(ftp_dir)
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288 ftp.retrbinary("RETR " + file, open(path, 'wb').write)
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289 ftp.quit()
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290
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291 return (path)
0
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292
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293 #return '' if there's no value in a dictionary, avoid error
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294 def access_dictionary (dico,key1,key2) :
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295 if key1 in dico :
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296 if key2 in dico[key1] :
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297 return (dico[key1][key2])
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298 else :
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299 return ("")
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300 #print (key2,"not in ",dico,"[",key1,"]")
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301 else :
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302 return ('')
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303
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304 #if there are several nextprot ID for one uniprotID, return the uniprot like ID
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305 def clean_nextprot_id (next_id,uniprotAc) :
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306 if len(next_id.split(";")) > 1 :
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307 tmp = next_id.split(";")
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308 if "NX_"+uniprotAc in tmp :
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309 return ("NX_"+uniprotAc)
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310 else :
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311 return (tmp[1])
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312 else :
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313 return (next_id)
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314
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315
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316 #######################################################################################################
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317 # 4. Build protein interaction maps files
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318 #######################################################################################################
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319
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320 def get_interactant_name(line,dico):
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321
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322 if line[0] in dico :
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323 interactant_A = dico[line[0]]
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324 else :
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325 interactant_A = "NA"
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326
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327 if line[1] in dico :
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328 interactant_B = dico[line[1]]
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329 else :
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330 interactant_B = "NA"
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331
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332 return interactant_A, interactant_B
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333
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334 def PPI_ref_files(data_manager_dict, species, interactome, target_directory):
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335
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336 species_dict={'Human':'Homo sapiens',"Mouse":"Mus musculus","Rat":"Rattus norvegicus"}
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337
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338 ##BioGRID
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339 if interactome=="biogrid":
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340
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341 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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342
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343 #download zip file
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344 r = requests.get(tab2_link)
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345 with open("BioGRID.zip", "wb") as code:
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346 code.write(r.content)
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347
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348 #unzip files
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349 with zipfile.ZipFile("BioGRID.zip", 'r') as zip_ref:
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350 if not os.path.exists("tmp_BioGRID"): os.makedirs("tmp_BioGRID")
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351 zip_ref.extractall("tmp_BioGRID")
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352
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353 #import file of interest and build dictionary
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354 file_path="tmp_BioGRID/BIOGRID-ORGANISM-"+species_dict[species].replace(" ","_")+"-3.5.167.tab2.txt"
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355 with open(file_path,"r") as handle :
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356 tab_file = csv.reader(handle,delimiter="\t")
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357 dico_network = {}
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358 GeneID_index=1
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359 network_cols=[1,2,7,8,11,12,14,18,20]
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360 for line in tab_file :
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361 if line[GeneID_index] not in dico_network:
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362 dico_network[line[GeneID_index]]=[[line[i] for i in network_cols]]
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363 else:
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364 dico_network[line[GeneID_index]].append([line[i] for i in network_cols])
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365
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366 #delete tmp_BioGRID directory
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367 os.remove("BioGRID.zip")
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368 shutil.rmtree("tmp_BioGRID", ignore_errors=True)
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proteore
parents:
diff changeset
369
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proteore
parents:
diff changeset
370 #download NCBI2Reactome.txt file and build dictionary
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proteore
parents:
diff changeset
371 with requests.Session() as s:
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proteore
parents:
diff changeset
372 r = s.get('https://www.reactome.org/download/current/NCBI2Reactome.txt')
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proteore
parents:
diff changeset
373 r.encoding ="utf-8"
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proteore
parents:
diff changeset
374 tab_file = csv.reader(r.content.splitlines(), delimiter='\t')
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proteore
parents:
diff changeset
375
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proteore
parents:
diff changeset
376 dico_nodes = {}
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proteore
parents:
diff changeset
377 geneid_index=0
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proteore
parents:
diff changeset
378 pathway_description_index=3
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proteore
parents:
diff changeset
379 species_index=5
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proteore
parents:
diff changeset
380 for line in tab_file :
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proteore
parents:
diff changeset
381 if line[species_index]==species_dict[species]:
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proteore
parents:
diff changeset
382 if line[geneid_index] in dico_nodes :
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proteore
parents:
diff changeset
383 dico_nodes[line[geneid_index]].append(line[pathway_description_index])
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proteore
parents:
diff changeset
384 else :
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proteore
parents:
diff changeset
385 dico_nodes[line[geneid_index]] = [line[pathway_description_index]]
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proteore
parents:
diff changeset
386
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proteore
parents:
diff changeset
387 dico={}
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proteore
parents:
diff changeset
388 dico['network']=dico_network
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proteore
parents:
diff changeset
389 dico['nodes']=dico_nodes
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proteore
parents:
diff changeset
390
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proteore
parents:
diff changeset
391 ##Bioplex
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proteore
parents:
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392 elif interactome=="bioplex":
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proteore
parents:
diff changeset
393
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proteore
parents:
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394 with requests.Session() as s:
4
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proteore
parents: 3
diff changeset
395 r = s.get('http://bioplex.hms.harvard.edu/data/BioPlex_interactionList_v4a.tsv')
0
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proteore
parents:
diff changeset
396 r = r.content.decode('utf-8')
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proteore
parents:
diff changeset
397 bioplex = csv.reader(r.splitlines(), delimiter='\t')
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proteore
parents:
diff changeset
398
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proteore
parents:
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399 dico_network = {}
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proteore
parents:
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400 dico_network["GeneID"]={}
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proteore
parents:
diff changeset
401 network_geneid_cols=[0,1,4,5,8]
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proteore
parents:
diff changeset
402 dico_network["UniProt-AC"]={}
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proteore
parents:
diff changeset
403 network_uniprot_cols=[2,3,4,5,8]
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proteore
parents:
diff changeset
404 dico_GeneID_to_UniProt = {}
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proteore
parents:
diff changeset
405 for line in bioplex :
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proteore
parents:
diff changeset
406 if line[0] not in dico_network["GeneID"]:
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proteore
parents:
diff changeset
407 dico_network["GeneID"][line[0]]=[[line[i] for i in network_geneid_cols]]
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proteore
parents:
diff changeset
408 else :
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proteore
parents:
diff changeset
409 dico_network["GeneID"][line[0]].append([line[i] for i in network_geneid_cols])
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proteore
parents:
diff changeset
410 if line[1] not in dico_network["UniProt-AC"]:
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proteore
parents:
diff changeset
411 dico_network["UniProt-AC"][line[2]]=[[line[i] for i in network_uniprot_cols]]
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proteore
parents:
diff changeset
412 else:
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proteore
parents:
diff changeset
413 dico_network["UniProt-AC"][line[2]].append([line[i] for i in network_uniprot_cols])
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proteore
parents:
diff changeset
414 dico_GeneID_to_UniProt[line[0]]=line[2]
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proteore
parents:
diff changeset
415
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proteore
parents:
diff changeset
416 with requests.Session() as s:
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proteore
parents:
diff changeset
417 r = s.get('https://reactome.org/download/current/UniProt2Reactome.txt')
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proteore
parents:
diff changeset
418 r.encoding ="utf-8"
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proteore
parents:
diff changeset
419 tab_file = csv.reader(r.content.splitlines(), delimiter='\t')
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proteore
parents:
diff changeset
420
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proteore
parents:
diff changeset
421 dico_nodes_uniprot = {}
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proteore
parents:
diff changeset
422 uniProt_index=0
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proteore
parents:
diff changeset
423 pathway_description_index=3
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proteore
parents:
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424 species_index=5
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proteore
parents:
diff changeset
425 for line in tab_file :
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proteore
parents:
diff changeset
426 if line[species_index]==species_dict[species]:
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proteore
parents:
diff changeset
427 if line[uniProt_index] in dico_nodes_uniprot :
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proteore
parents:
diff changeset
428 dico_nodes_uniprot[line[uniProt_index]].append(line[pathway_description_index])
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proteore
parents:
diff changeset
429 else :
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proteore
parents:
diff changeset
430 dico_nodes_uniprot[line[uniProt_index]] = [line[pathway_description_index]]
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proteore
parents:
diff changeset
431
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proteore
parents:
diff changeset
432 with requests.Session() as s:
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proteore
parents:
diff changeset
433 r = s.get('https://www.reactome.org/download/current/NCBI2Reactome.txt')
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proteore
parents:
diff changeset
434 r.encoding ="utf-8"
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proteore
parents:
diff changeset
435 tab_file = csv.reader(r.content.splitlines(), delimiter='\t')
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proteore
parents:
diff changeset
436
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proteore
parents:
diff changeset
437 dico_nodes_geneid = {}
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proteore
parents:
diff changeset
438 geneid_index=0
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proteore
parents:
diff changeset
439 pathway_description_index=3
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proteore
parents:
diff changeset
440 species_index=5
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proteore
parents:
diff changeset
441 for line in tab_file :
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proteore
parents:
diff changeset
442 if line[species_index]==species_dict[species]:
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proteore
parents:
diff changeset
443 if line[geneid_index] in dico_nodes_geneid :
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proteore
parents:
diff changeset
444 dico_nodes_geneid[line[geneid_index]].append(line[pathway_description_index])
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proteore
parents:
diff changeset
445 else :
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proteore
parents:
diff changeset
446 dico_nodes_geneid[line[geneid_index]] = [line[pathway_description_index]]
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proteore
parents:
diff changeset
447
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proteore
parents:
diff changeset
448 dico={}
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proteore
parents:
diff changeset
449 dico_nodes={}
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proteore
parents:
diff changeset
450 dico_nodes['GeneID']=dico_nodes_geneid
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proteore
parents:
diff changeset
451 dico_nodes['UniProt-AC']=dico_nodes_uniprot
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proteore
parents:
diff changeset
452 dico['network']=dico_network
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proteore
parents:
diff changeset
453 dico['nodes']=dico_nodes
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proteore
parents:
diff changeset
454 dico['convert']=dico_GeneID_to_UniProt
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proteore
parents:
diff changeset
455
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proteore
parents:
diff changeset
456 ##Humap
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proteore
parents:
diff changeset
457 elif interactome=="humap":
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proteore
parents:
diff changeset
458
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proteore
parents:
diff changeset
459 with requests.Session() as s:
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proteore
parents:
diff changeset
460 r = s.get('http://proteincomplexes.org/static/downloads/nodeTable.txt')
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proteore
parents:
diff changeset
461 r = r.content.decode('utf-8')
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proteore
parents:
diff changeset
462 humap_nodes = csv.reader(r.splitlines(), delimiter=',')
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proteore
parents:
diff changeset
463
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proteore
parents:
diff changeset
464 dico_geneid_to_gene_name={}
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proteore
parents:
diff changeset
465 dico_protein_name={}
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proteore
parents:
diff changeset
466 for line in humap_nodes :
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proteore
parents:
diff changeset
467 if check_entrez_geneid(line[4]):
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proteore
parents:
diff changeset
468 if line[4] not in dico_geneid_to_gene_name:
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proteore
parents:
diff changeset
469 dico_geneid_to_gene_name[line[4]]=line[3]
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proteore
parents:
diff changeset
470 if line[4] not in dico_protein_name:
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proteore
parents:
diff changeset
471 dico_protein_name[line[4]]=line[5]
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proteore
parents:
diff changeset
472
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proteore
parents:
diff changeset
473 with requests.Session() as s:
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proteore
parents:
diff changeset
474 r = s.get('http://proteincomplexes.org/static/downloads/pairsWprob.txt')
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proteore
parents:
diff changeset
475 r = r.content.decode('utf-8')
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proteore
parents:
diff changeset
476 humap = csv.reader(r.splitlines(), delimiter='\t')
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proteore
parents:
diff changeset
477
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proteore
parents:
diff changeset
478 dico_network = {}
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proteore
parents:
diff changeset
479 for line in humap :
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proteore
parents:
diff changeset
480 if check_entrez_geneid(line[0]) and check_entrez_geneid(line[1]):
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proteore
parents:
diff changeset
481
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proteore
parents:
diff changeset
482 interactant_A, interactant_B = get_interactant_name(line,dico_geneid_to_gene_name)
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proteore
parents:
diff changeset
483
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proteore
parents:
diff changeset
484 #first interactant (first column)
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proteore
parents:
diff changeset
485 if line[0] not in dico_network:
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proteore
parents:
diff changeset
486 dico_network[line[0]]=[line[:2]+[interactant_A,interactant_B,line[2]]]
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proteore
parents:
diff changeset
487 else :
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proteore
parents:
diff changeset
488 dico_network[line[0]].append(line[:2]+[interactant_A,interactant_B,line[2]])
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proteore
parents:
diff changeset
489
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proteore
parents:
diff changeset
490 #second interactant (second column)
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proteore
parents:
diff changeset
491 if line[1] not in dico_network:
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proteore
parents:
diff changeset
492 dico_network[line[1]]=[[line[1],line[0],interactant_B,interactant_A,line[2]]]
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proteore
parents:
diff changeset
493 else :
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proteore
parents:
diff changeset
494 dico_network[line[1]].append([line[1],line[0],interactant_B,interactant_A,line[2]])
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proteore
parents:
diff changeset
495
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proteore
parents:
diff changeset
496 with requests.Session() as s:
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proteore
parents:
diff changeset
497 r = s.get('https://www.reactome.org/download/current/NCBI2Reactome.txt')
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proteore
parents:
diff changeset
498 r.encoding ="utf-8"
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proteore
parents:
diff changeset
499 tab_file = csv.reader(r.content.splitlines(), delimiter='\t')
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proteore
parents:
diff changeset
500
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proteore
parents:
diff changeset
501 dico_nodes = {}
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proteore
parents:
diff changeset
502 geneid_index=0
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proteore
parents:
diff changeset
503 pathway_description_index=3
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proteore
parents:
diff changeset
504 species_index=5
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proteore
parents:
diff changeset
505 for line in tab_file :
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proteore
parents:
diff changeset
506 if line[species_index]==species_dict[species]:
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proteore
parents:
diff changeset
507 #Fill dictionary with pathways
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proteore
parents:
diff changeset
508 if line[geneid_index] in dico_nodes :
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proteore
parents:
diff changeset
509 dico_nodes[line[geneid_index]].append(line[pathway_description_index])
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proteore
parents:
diff changeset
510 else :
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proteore
parents:
diff changeset
511 dico_nodes[line[geneid_index]] = [line[pathway_description_index]]
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proteore
parents:
diff changeset
512
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proteore
parents:
diff changeset
513 dico={}
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proteore
parents:
diff changeset
514 dico['network']=dico_network
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proteore
parents:
diff changeset
515 dico['nodes']=dico_nodes
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proteore
parents:
diff changeset
516 dico['gene_name']=dico_geneid_to_gene_name
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proteore
parents:
diff changeset
517 dico['protein_name']=dico_protein_name
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proteore
parents:
diff changeset
518
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proteore
parents:
diff changeset
519 #writing output
2
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proteore
parents: 1
diff changeset
520 output_file = species+'_'+interactome+'_'+ time.strftime("%Y-%m-%d") + ".json"
0
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proteore
parents:
diff changeset
521 path = os.path.join(target_directory,output_file)
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proteore
parents:
diff changeset
522 name = species+" ("+species_dict[species]+") "+time.strftime("%d/%m/%Y")
2
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proteore
parents: 1
diff changeset
523 release = species+"_"+interactome+"_"+ time.strftime("%Y-%m-%d")
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proteore
parents: 1
diff changeset
524 id = str(10000000000 - int(time.strftime("%Y%m%d")))
0
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proteore
parents:
diff changeset
525
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proteore
parents:
diff changeset
526 with open(path, 'w') as handle:
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proteore
parents:
diff changeset
527 json.dump(dico, handle, sort_keys=True)
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proteore
parents:
diff changeset
528
2
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proteore
parents: 1
diff changeset
529 data_table_entry = dict(id=id, release=release, name = name, species = species, value = path)
0
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proteore
parents:
diff changeset
530 _add_data_table_entry(data_manager_dict, data_table_entry, "proteore_"+interactome+"_dictionaries")
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proteore
parents:
diff changeset
531
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proteore
parents:
diff changeset
532 #######################################################################################################
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proteore
parents:
diff changeset
533 # 5. nextprot (add protein features)
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proteore
parents:
diff changeset
534 #######################################################################################################
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proteore
parents:
diff changeset
535
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proteore
parents:
diff changeset
536 def Build_nextprot_ref_file(data_manager_dict,target_directory):
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proteore
parents:
diff changeset
537 nextprot_ids_file = "nextprot_ac_list_all.txt"
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proteore
parents:
diff changeset
538 ids = id_list_from_nextprot_ftp(nextprot_ids_file,target_directory)
2
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proteore
parents: 1
diff changeset
539
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proteore
parents: 1
diff changeset
540 output_file = 'nextprot_ref_'+ time.strftime("%d-%m-%Y") + ".tsv"
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proteore
parents: 1
diff changeset
541 path = os.path.join(target_directory,output_file)
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proteore
parents: 1
diff changeset
542 name = "neXtProt release "+time.strftime("%d-%m-%Y")
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proteore
parents: 1
diff changeset
543 release_id = "nextprot_ref_"+time.strftime("%d-%m-%Y")
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proteore
parents: 1
diff changeset
544
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proteore
parents: 1
diff changeset
545 output = open(path, 'w')
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proteore
parents: 1
diff changeset
546 writer = csv.writer(output,delimiter="\t")
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proteore
parents: 1
diff changeset
547
0
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proteore
parents:
diff changeset
548 nextprot_file=[["NextprotID","MW","SeqLength","IsoPoint","Chr","SubcellLocations","Diseases","TMDomains","ProteinExistence"]]
2
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proteore
parents: 1
diff changeset
549 writer.writerows(nextprot_file)
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proteore
parents: 1
diff changeset
550
0
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proteore
parents:
diff changeset
551 for id in ids :
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proteore
parents:
diff changeset
552 #print (id)
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proteore
parents:
diff changeset
553 query="https://api.nextprot.org/entry/"+id+".json"
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proteore
parents:
diff changeset
554 resp = requests.get(url=query)
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proteore
parents:
diff changeset
555 data = resp.json()
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proteore
parents:
diff changeset
556
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proteore
parents:
diff changeset
557 #get info from json dictionary
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proteore
parents:
diff changeset
558 mass_mol = data["entry"]["isoforms"][0]["massAsString"]
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proteore
parents:
diff changeset
559 seq_length = data['entry']["isoforms"][0]["sequenceLength"]
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proteore
parents:
diff changeset
560 iso_elec_point = data['entry']["isoforms"][0]["isoelectricPointAsString"]
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proteore
parents:
diff changeset
561 chr_loc = data['entry']["chromosomalLocations"][0]["chromosome"]
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proteore
parents:
diff changeset
562 protein_existence = "PE"+str(data['entry']["overview"]['proteinExistence']['level'])
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proteore
parents:
diff changeset
563
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proteore
parents:
diff changeset
564 #put all subcell loc in a set
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proteore
parents:
diff changeset
565 if "subcellular-location" in data['entry']["annotationsByCategory"].keys() :
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proteore
parents:
diff changeset
566 subcell_locs = data['entry']["annotationsByCategory"]["subcellular-location"]
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proteore
parents:
diff changeset
567 all_subcell_locs = set()
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proteore
parents:
diff changeset
568 for loc in subcell_locs :
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proteore
parents:
diff changeset
569 all_subcell_locs.add(loc['cvTermName'])
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proteore
parents:
diff changeset
570 all_subcell_locs.discard("")
9e31ea9fc7ea planemo upload commit 567ba7934c0ca55529dfeb5e7ca0935ace260ad7-dirty
proteore
parents:
diff changeset
571 all_subcell_locs = ";".join(all_subcell_locs)
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proteore
parents:
diff changeset
572 else :
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proteore
parents:
diff changeset
573 all_subcell_locs = "NA"
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proteore
parents:
diff changeset
574
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proteore
parents:
diff changeset
575 #put all subcell loc in a set
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proteore
parents:
diff changeset
576 if ('disease') in data['entry']['annotationsByCategory'].keys() :
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proteore
parents:
diff changeset
577 diseases = data['entry']['annotationsByCategory']['disease']
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proteore
parents:
diff changeset
578 all_diseases = set()
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proteore
parents:
diff changeset
579 for disease in diseases :
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proteore
parents:
diff changeset
580 if (disease['cvTermName'] is not None and disease['cvTermName'] != ""):
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proteore
parents:
diff changeset
581 all_diseases.add(disease['cvTermName'])
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proteore
parents:
diff changeset
582 if len(all_diseases) > 0 : all_diseases = ";".join(all_diseases)
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proteore
parents:
diff changeset
583 else : all_diseases="NA"
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proteore
parents:
diff changeset
584 else :
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proteore
parents:
diff changeset
585 all_diseases="NA"
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proteore
parents:
diff changeset
586
1
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proteore
parents: 0
diff changeset
587 #get all tm domain
0
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proteore
parents:
diff changeset
588 nb_domains = 0
1
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proteore
parents: 0
diff changeset
589 if "transmembrane-region" in data['entry']['annotationsByCategory'].keys():
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proteore
parents: 0
diff changeset
590 tm_domains = data['entry']['annotationsByCategory']["transmembrane-region"]
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proteore
parents: 0
diff changeset
591 all_tm_domains = set()
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proteore
parents: 0
diff changeset
592 for tm in tm_domains :
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proteore
parents: 0
diff changeset
593 all_tm_domains.add(tm['cvTermName'])
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proteore
parents: 0
diff changeset
594 nb_domains+=1
2
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proteore
parents: 1
diff changeset
595 #print "nb domains ++"
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proteore
parents: 1
diff changeset
596 #print (nb_domains)
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proteore
parents: 1
diff changeset
597 nextprot_file[:] = []
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proteore
parents: 1
diff changeset
598 nextprot_file.append([id,mass_mol,str(seq_length),iso_elec_point,chr_loc,all_subcell_locs,all_diseases,str(nb_domains),protein_existence])
0
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proteore
parents:
diff changeset
599 writer.writerows(nextprot_file)
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proteore
parents:
diff changeset
600
2
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proteore
parents: 1
diff changeset
601 id = str(10000000000 - int(time.strftime("%Y%m%d")))
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proteore
parents: 1
diff changeset
602
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proteore
parents: 1
diff changeset
603 data_table_entry = dict(id=id, release=release_id, name = name, value = path)
0
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proteore
parents:
diff changeset
604 _add_data_table_entry(data_manager_dict, data_table_entry, "proteore_nextprot_ref")
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proteore
parents:
diff changeset
605
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proteore
parents:
diff changeset
606 #######################################################################################################
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proteore
parents:
diff changeset
607 # Main function
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proteore
parents:
diff changeset
608 #######################################################################################################
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proteore
parents:
diff changeset
609 def main():
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proteore
parents:
diff changeset
610 parser = argparse.ArgumentParser()
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proteore
parents:
diff changeset
611 parser.add_argument("--hpa", metavar = ("HPA_OPTION"))
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proteore
parents:
diff changeset
612 parser.add_argument("--peptideatlas", metavar=("SAMPLE_CATEGORY_ID"))
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proteore
parents:
diff changeset
613 parser.add_argument("--id_mapping", metavar = ("ID_MAPPING_SPECIES"))
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proteore
parents:
diff changeset
614 parser.add_argument("--interactome", metavar = ("PPI"))
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proteore
parents:
diff changeset
615 parser.add_argument("--species")
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proteore
parents:
diff changeset
616 parser.add_argument("--date")
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proteore
parents:
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617 parser.add_argument("-o", "--output")
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618 parser.add_argument("--database")
4
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619 parser.add_argument("--tool_data_path")
0
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620 args = parser.parse_args()
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621
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622 data_manager_dict = {}
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623 # Extract json file params
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proteore
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624 filename = args.output
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625 params = from_json_string(open(filename).read())
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626 target_directory = params[ 'output_data' ][0]['extra_files_path']
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627 os.mkdir(target_directory)
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proteore
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628
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proteore
parents:
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629 ## Download source files from HPA
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proteore
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630 try:
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proteore
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631 hpa = args.hpa
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proteore
parents:
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632 except NameError:
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proteore
parents:
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633 hpa = None
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proteore
parents:
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634 if hpa is not None:
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proteore
parents:
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635 #target_directory = "/projet/galaxydev/galaxy/tools/proteore/ProteoRE/tools/resources_building/test-data/"
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proteore
parents:
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636 hpa = hpa.split(",")
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proteore
parents:
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637 for hpa_tissue in hpa:
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proteore
parents:
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638 HPA_sources(data_manager_dict, hpa_tissue, target_directory)
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proteore
parents:
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639
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proteore
parents:
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640 ## Download source file from Peptide Atlas query
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proteore
parents:
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641 try:
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proteore
parents:
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642 peptide_atlas = args.peptideatlas
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proteore
parents:
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643 date = args.date
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proteore
parents:
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644 except NameError:
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proteore
parents:
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645 peptide_atlas = None
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proteore
parents:
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646 if peptide_atlas is not None:
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proteore
parents:
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647 #target_directory = "/projet/galaxydev/galaxy/tools/proteore/ProteoRE/tools/resources_building/test-data/"
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proteore
parents:
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648 peptide_atlas = peptide_atlas.split(",")
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proteore
parents:
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649 for pa_tissue in peptide_atlas:
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proteore
parents:
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650 peptide_atlas_sources(data_manager_dict, pa_tissue, date, target_directory)
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proteore
parents:
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651
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proteore
parents:
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652 ## Download ID_mapping source file from Uniprot
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proteore
parents:
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653 try:
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654 id_mapping = args.id_mapping
0
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proteore
parents:
diff changeset
655 except NameError:
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proteore
parents:
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656 id_mapping = None
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proteore
parents:
diff changeset
657 if id_mapping is not None:
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proteore
parents:
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658 id_mapping = id_mapping .split(",")
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proteore
parents:
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659 for species in id_mapping :
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parents: 3
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660 id_mapping_sources(data_manager_dict, species, target_directory, args.tool_data_path)
0
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proteore
parents:
diff changeset
661
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proteore
parents:
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662 ## Download PPI ref files from biogrid/bioplex/humap
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proteore
parents:
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663 try:
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proteore
parents:
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664 interactome=args.interactome
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proteore
parents:
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665 if interactome == "biogrid" :
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proteore
parents:
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666 species=args.species
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proteore
parents:
diff changeset
667 else :
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proteore
parents:
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668 species="Human"
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proteore
parents:
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669 except NameError:
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proteore
parents:
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670 interactome=None
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proteore
parents:
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671 species=None
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proteore
parents:
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672 if interactome is not None and species is not None:
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proteore
parents:
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673 PPI_ref_files(data_manager_dict, species, interactome, target_directory)
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proteore
parents:
diff changeset
674
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proteore
parents:
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675 ## Build nextprot ref file for add protein features
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proteore
parents:
diff changeset
676 try:
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proteore
parents:
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677 database=args.database
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proteore
parents:
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678 except NameError:
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proteore
parents:
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679 database=None
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proteore
parents:
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680 if database is not None :
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proteore
parents:
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681 Build_nextprot_ref_file(data_manager_dict,target_directory)
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proteore
parents:
diff changeset
682
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proteore
parents:
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683 #save info to json file
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proteore
parents:
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684 filename = args.output
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proteore
parents:
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685 open(filename, 'wb').write(to_json_string(data_manager_dict))
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proteore
parents:
diff changeset
686
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proteore
parents:
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687 if __name__ == "__main__":
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proteore
parents:
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688 main()