diff cravat_submit.py @ 0:7ebdd4ac13a2 draft

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/cravat_submit.py	Tue Apr 10 15:53:55 2018 -0400
@@ -0,0 +1,208 @@
+import requests
+import json
+import time
+import urllib
+import sys
+import csv
+import re
+
+try:
+    input_filename = sys.argv[1]
+    input_select_bar = sys.argv[2]
+    GRCh_build = sys.argv[3]
+    psm_filename = sys.argv[4]
+    output_filename = sys.argv[5]
+    file_3 = sys.argv[6]
+    file_4 = sys.argv[7]
+    file_5 = sys.argv[8]
+except:
+    input_filename = '1.) Galaxy2-[Human_Vcf_MCF7]-minimum.vcf'
+    input_filename = 'input/[tgriffin_cguerrer_20160726_MCF7_RNAseq_01_S13_R1_001.vcf].vcf'
+    input_select_bar = 'VEST'
+    GRCh_build = 'GRCh38'
+    output_filename = 'combined_variants.tsv'
+    psm_filename = 'input/[ERLIC_MCF7_110kb_R123-CustomProDB_RNA-Seq_cRAP_DB.psm-report].tabular'
+    file_3 = 'output/' + time.strftime("%H:%M") + '_Z_Gene_Level_Analysis.tsv'
+    file_4 = 'output/' + time.strftime("%H:%M") + '_Z_Variant_Non-coding.Result.tsv'
+    file_5 = 'output/' + time.strftime("%H:%M") + '_Z_Input_Errors.Result.tsv'
+
+
+#in_file = open('input_call.txt', "r")    
+#out_file = open('output_call.txt', "w")
+
+write_header = True
+
+GRCh37hg19 = 'off'
+if GRCh_build == 'GRCh37':
+    GRCh37hg19 = 'on'
+
+# http://staging.cravat.us/CRAVAT/rest/service/submit:
+
+#plugs in params to given URL
+submit = requests.post('http://staging.cravat.us/CRAVAT/rest/service/submit', files={'inputfile':open(input_filename)}, data={'email':'[e-mail]', 'analyses': input_select_bar, 'hg19': GRCh37hg19})
+
+#Makes the data a json dictionary, takes out only the job ID
+jobid = json.loads(submit.text)['jobid']
+#out_file.write(jobid)    
+submitted = json.loads(submit.text)['status']
+#out_file.write('\t' + submitted)
+
+input_file = open(input_filename)
+is_comment_line = re.compile(".*#+.*")
+
+
+#loops until we find a status equal to Success, then breaks
+while True:
+    check = requests.get('http://staging.cravat.us/CRAVAT/rest/service/status', params={'jobid': jobid})
+    status = json.loads(check.text)['status']
+    resultfileurl = json.loads(check.text)['resultfileurl']
+    #out_file.write(str(status) + ', ')
+    if status == 'Success':
+        #out_file.write('\t' + resultfileurl)
+        break
+    else:
+        time.sleep(2)
+
+#out_file.write('\n')
+
+#creates three files
+file_1 = time.strftime("%H:%M") + '_Z_Variant_Result.tsv'
+file_2 = time.strftime("%H:%M") + '_Z_Additional_Details.tsv'
+#file_3 = time.strftime("%H:%M") + 'Combined_Variant_Results.tsv'
+
+#Download the two results
+urllib.urlretrieve("http://staging.cravat.us/CRAVAT/results/" + jobid + "/" + "Variant.Result.tsv", file_1)
+urllib.urlretrieve("http://staging.cravat.us/CRAVAT/results/" + jobid + "/" + "Variant_Additional_Details.Result.tsv", file_2)
+urllib.urlretrieve("http://staging.cravat.us/CRAVAT/results/" + jobid + "/" + "Gene_Level_Analysis.Result.tsv", file_3)
+urllib.urlretrieve("http://staging.cravat.us/CRAVAT/results/" + jobid + "/" + "Variant_Non-coding.Result.tsv", file_4)
+urllib.urlretrieve("http://staging.cravat.us/CRAVAT/results/" + jobid + "/" + "Input_Errors.Result.tsv", file_5)
+
+headers = []
+duplicates = []
+
+#opens the Variant Result file and the Variant Additional Details file as csv readers, then opens the output file (galaxy) as a writer
+with open(file_1) as tsvin_1, open(file_2) as tsvin_2, open(output_filename, 'wb') as tsvout:
+    tsvreader_1 = csv.reader(tsvin_1, delimiter='\t')
+    tsvreader_2 = csv.reader(tsvin_2, delimiter='\t')
+    
+    tsvout = csv.writer(tsvout, delimiter='\t')
+
+    # Processes the PSM report
+    if (psm_filename != 'None'):
+        tsvin_3 = open(psm_filename)
+        psmreader = csv.reader(tsvin_3, delimiter='\t')
+        
+        psmreader.next()
+        peptide_map = {}
+        s = re.compile('[A-Z][0-9]+[A-Z]')
+        for row in psmreader:
+            pro_name = row[1]
+            pep_seq = row[2]
+            
+            prot_seq_changes = s.findall(pro_name)
+        
+            for change in prot_seq_changes:
+                if change in peptide_map:
+                    if pep_seq not in peptide_map[change].split(';'):
+                        peptide_map[change] = peptide_map[change] + ';' + pep_seq
+                else:
+                    peptide_map[change] = pep_seq
+    
+    #loops through each row in the Variant Additional Details file
+
+    print 'Checkpoint 3'
+    for row in tsvreader_2:
+        #sets row_2 equal to the same row in Variant Result file
+        row_2 = tsvreader_1.next()
+        #checks if row is empty or if the first term contains '#'
+        if row == [] or row[0][0] == '#':
+            tsvout.writerow(row)
+        else:
+        #checks if the row begins with input line
+            if row[0] == 'Input line':
+                #Goes through each value in the headers list in VAD
+                #print 'Original row'
+                #print row
+                #print row_2
+                for value in row:   
+                    #Adds each value into headers 
+                    headers.append(value)
+                #Loops through the Keys in VR
+                for value in row_2:
+                    #Checks if the value is already in headers
+                    if value in headers:
+                        continue
+                    #else adds the header to headers
+                    else:
+                        headers.append(value)
+                if (psm_filename != 'None'):
+                    headers.insert(1, 'Peptide')
+                #print headers
+                tsvout.writerow(headers)
+            else:
+                
+                cells = []
+                #Inserts a peptide column into the row
+                if (psm_filename != 'None'):
+                    if row[12] in peptide_map:
+                        row.insert(1, peptide_map[row[12]])
+                    else:
+                        row.insert(1, '')
+                    
+                #Goes through each value in the next list
+                for i,value in enumerate(row):
+                    #adds it to cells
+                    cells.append(value)
+                #Goes through each value from the VR file after position 11 (After it is done repeating from VAD file)
+                for i,value in enumerate(row_2[11:]):
+                    #adds in the rest of the values to cells
+
+                    # Skips the 2nd VEST p-value
+                    if (i != 49 - 11):
+                        cells.append(value)
+                    
+                print  cells
+                tsvout.writerow(cells)
+
+
+
+    
+    
+    
+
+            
+            
+            
+            
+
+            
+            
+            
+            
+  
+    
+            
+        
+         
+        
+        
+            
+            
+            
+       
+            
+    
+
+#a = 'col1\tcol2\tcol3'
+#header_list = a.split('\t')
+
+#loop through the two results, when you first hit header you print out the headers in tabular form
+#Print out each header only once
+#Combine both headers into one output file
+#loop through the rest of the data and assign each value to its assigned header
+#combine this all into one output file
+
+
+
+
+