diff get_unique_srm.py @ 0:a2b06836de90 draft

planemo upload commit f9de6f4e3302c41e64c39d639bee780e5eafd84d-dirty

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/get_unique_srm.py	Fri Jul 12 07:49:45 2019 -0400
@@ -0,0 +1,192 @@
+import argparse, csv, re
+
+def get_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_type", help="type of input (list of id or filename)", required=True)
+    parser.add_argument("-i", "--input", help="list of IDs (text or filename)", required=True)
+    parser.add_argument("--header", help="true/false if your file contains a header")
+    parser.add_argument("-c", "--column_number", help="list of IDs (text or filename)")
+    parser.add_argument("-f", "--features", help="Protein features to return from SRM Atlas", required=True)
+    parser.add_argument("-d", "--ref_file", help="path to reference file", required=True)
+    parser.add_argument("-o", "--output", help="output filename", required=True)
+    args = parser.parse_args()
+    return args
+
+#return the column number in int format
+def nb_col_to_int(nb_col):
+    try :
+        nb_col = int(nb_col.replace("c", "")) - 1
+        return nb_col
+    except :
+        sys.exit("Please specify the column where you would like to apply the filter with valid format")
+
+#replace all blank cells to NA
+def blank_to_NA(csv_file) :
+    tmp=[]
+    for line in csv_file :
+        line = ["NA" if cell=="" or cell==" " or cell=="NaN" else cell for cell in line]
+        tmp.append(line)
+    
+    return tmp
+
+#convert string to boolean
+def str2bool(v):
+    if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        return True
+    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        return False
+    else:
+        raise argparse.ArgumentTypeError('Boolean value expected.')
+
+#return list of (unique) ids from string
+def get_input_ids_from_string(input) :
+
+    ids_list = list(set(re.split(r'\s+',input.replace("_SNP","").replace("d_","").replace("\r","").replace("\n"," ").replace("\t"," "))))
+    if "" in ids_list : ids_list.remove("")
+
+    return ids_list
+
+#return input_file and list of unique ids from input file path
+def get_input_ids_from_file(input,nb_col,header) :
+    with open(input, "r") as csv_file :
+        input_file= list(csv.reader(csv_file, delimiter='\t'))
+
+    input_file, ids_list = one_id_one_line(input_file,nb_col,header)
+    if "" in ids_list : ids_list.remove("")
+
+    return input_file, ids_list
+
+#function to check if an id is an uniprot accession number : return True or False-
+def check_uniprot (id):
+    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}")
+    if uniprot_pattern.match(id) :
+        return True
+    else :
+        return False
+
+#return input file by adding lines when there are more than one id per line
+def one_id_one_line(input_file,nb_col,header) :
+
+    if header : 
+        new_file = [input_file[0]]
+        input_file = input_file[1:]
+    else : 
+        new_file=[]
+    ids_list=[]
+
+    for line in input_file :
+        if line != [] and set(line) != {''}: 
+            line[nb_col] = re.sub(r"\s+","",line[nb_col])
+            if line[nb_col] == "" : line[nb_col]='NA'
+            if ";" in line[nb_col] :
+                ids = line[nb_col].split(";")
+                for id in ids :
+                    new_file.append(line[:nb_col]+[id]+line[nb_col+1:])
+                    ids_list.append(id)
+            else : 
+                new_file.append(line)
+                ids_list.append(line[nb_col])
+
+    ids_list=[e.replace("_SNP","").replace("d_","") for e in ids_list]
+    ids_list= list(set(ids_list))
+
+    return new_file, ids_list
+
+def create_srm_atlas_dictionary(features,srm_atlas_csv):
+
+    srm_atlas={}
+    features_index = {"PeptideSeq" : 0, "SSRT" : 1 , "Length" : 2 , "type": 3 , "PA_AccNum" : 4, "MW" : 5 }
+    features_to_get = [features_index[feature] for feature in features]
+    for line in srm_atlas_csv[1:]:
+        id = line[9].replace("_SNP","").replace("d_","")
+        if id not in srm_atlas:
+            srm_atlas[id]=[[line[i] for i in features_to_get]]
+        else: 
+            srm_atlas[id].append([line[i] for i in features_to_get])
+    return srm_atlas
+
+def retrieve_srm_features(srm_atlas,ids):
+
+    result_dict = {}
+    for id in ids:
+        if id in srm_atlas:
+            res = srm_atlas[id]
+        else :
+            res=""
+        result_dict[id]=res
+    return result_dict
+
+def create_header(input_file,ncol,features):
+    col_names = list(range(1,len(input_file[0])+1))
+    col_names = ["col"+str(e) for e in col_names]
+    col_names[ncol]="Uniprot-AC"
+    col_names = col_names+features
+    return(col_names)
+
+def main():
+    
+    #Get args from command line
+    args = get_args()
+    features=args.features.split(",")
+    header=False
+    if args.input_type=="file" :
+        column_number = nb_col_to_int(args.column_number)
+        header = str2bool(args.header)
+
+    #Get reference file (Human SRM Atlas)
+    with open(args.ref_file, "r") as csv_file :
+        srm_atlas_csv = csv.reader(csv_file, delimiter='\t')
+        srm_atlas_csv = [line for line in srm_atlas_csv]
+
+    #Create srm Atlas dictionary 
+    srm_atlas = create_srm_atlas_dictionary(features,srm_atlas_csv)
+        
+    #Get file and/or ids from input 
+    if args.input_type == "list" :
+        ids = get_input_ids_from_string(args.input)
+    elif args.input_type == "file" :
+        input_file, ids = get_input_ids_from_file(args.input,column_number,header)
+
+    #Check Uniprot-AC
+    if not any([check_uniprot(id) for id in ids]):
+        print ("No Uniprot-AC found, please check your input")
+        exit()
+
+    #retrieve features
+    result_dict = retrieve_srm_features(srm_atlas,ids)
+
+    #write output
+    with open(args.output,"w") as output :
+        writer = csv.writer(output,delimiter="\t")
+
+        #write header
+        if header : 
+            writer.writerow(input_file[0]+features)
+            input_file = input_file[1:]  
+        elif args.input_type=="file":
+            col_names = [create_header(input_file,column_number,features)]
+            writer.writerow(col_names)
+        else : 
+            writer.writerow(["Uniprot-AC"]+features)
+
+        #write lines 
+        previous_line=""
+        if args.input_type=="file" :
+            for line in input_file :
+                for res in result_dict[line[column_number]]:
+                    output_line = ["NA" if cell=="" or cell==" " or cell=="NaN" else cell for cell in line+res]
+                    if previous_line != output_line :
+                        writer.writerow(output_line)
+                        previous_line=output_line
+        elif args.input_type=="list" :
+            for id in ids :
+                for res in result_dict[id]:
+                    line = ["NA" if cell=="" or cell==" " or cell=="NaN" else cell for cell in [id]+res]
+                    if previous_line != line :
+                        writer.writerow(line)
+                        previous_line=line
+    
+
+if __name__ == "__main__":
+    main()
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