[WIP] ProteoRE ID_converter tool

tools/proteore_id_converter/.shed.yml

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+categories: [Proteomics]
+description: "ProteoRE ID_Converter: A tool for converting public database identifiers"
+name: proteore_id_converter
+owner: galaxyp

tools/proteore_id_converter/README.rst

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+Wrapper for ID Converter tool
+=============================
+
+**Authors**
+
+David Christiany, T.P. Lien Nguyen, Florence Combes, Yves Vandenbrouck CEA, INSERM, CNRS, Grenoble-Alpes University, BIG Institute, FR
+
+Sandra Dérozier, Olivier Rué, Christophe Caron, Valentin Loux INRA, Paris-Saclay University, MAIAGE Unit, Migale Bioinformatics platform
+
+This work has been partially funded through the French National Agency for Research (ANR) IFB project.
+
+Contact [email protected] for any questions or concerns about the Galaxy implementation of this tool.
+
+=============================
+
+This tool converts a list of IDs to another identifier type, select the source and target type from the dropdown menus above (see below supported source and target types).
+
+After choosing the type of input IDs, you can choose one or more types of IDs you would like to map to. 
+
+If your input is a list of IDs or a single-column file, the tool will return a file containing the mapped IDs.
+
+If your input is a multiple-column file, the mapped IDs column(s) will be added at the end of the input file.
+
+**Available databases**
+
+* neXtProt ID (e.g. NX_P31946)
+
+* UniProt accession number (e.g. P31946 - reviewed entries only)
+
+* UniProt accession number (e.g. P31946 - reviewed and unreviewed entries)
+
+* Uniprot ID (e.g 1433B_HUMAN)
+
+* Entrez gene ID (e.g. 7529)
+
+* RefSeq (NCBI) protein (e.g.  NP_003395.1; NP_647539.1; XP_016883528.1)
+
+* GI (NCBI GI number) ID assigned to each sequence record processed by NCBI (e.g. 21328448; 377656701; 67464627; 78101741)
+
+* Protein DataBank ID (e.g. 2BR9:A; 3UAL:A;   3UBW:A)
+
+* GOterms (Gene Ontology) ID (e.g. GO:0070062; GO:0005925; GO:0042470; GO:0016020; GO:0005739; GO:0005634)
+
+* Protein Information Resource ID (e.g. S34755)
+
+* OMIM (Online Mendelian Inheritance in Man database) ID (e.g: 601289)
+
+* Unigene ID (e.g. Hs.643544)
+
+* Ensembl gene ID (e.g. ENSG00000166913)
+
+* Ensembl transcript ID (e.g. ENST00000353703; ENST00000372839)
+
+* Ensembl protein ID (e.g. ENSP00000300161; ENSP00000361930)
+
+* BioGrid (e.g. 113361)
+
+* STRING (e.g. 9606.ENSP00000300161)
+
+* KEGG gene id (e.g. hsa:7529)
+
+.. class:: warningmark 
+
+Nextprot and OMIM are only available for Human.
+
+.. class:: warningmark
+
+For Uniprot-AC, only Uniprot-AC reviewed are considered here, except for releases before 27-05-2019 where all uniprot-AC (at the time) are considered.
+
+This tool converts human IDs using file built from:
+
+* Current release of Uniprot, for idmapping_selected.tab and idmapping.dat for Human, Mouse and Rat: ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/idmapping/by_organism
+* All previous release of uniprot can be found here: ftp://ftp.uniprot.org/pub/databases/uniprot/previous_releases/
+* **nextprot_ac_list_all.txt (Nextprot released on 13/02/2019 - current)**: ftp://ftp.nextprot.org/pub/current_release/ac_lists/
+* All previous release of **nextprot_ac_list_all.txt** can be foud here: ftp://ftp.nextprot.org/pub/previous_releases/
+* `Human uniprot-AC entries reviewed (05/06/2019) <https://www.uniprot.org/uniprot/?query=reviewed:yes+AND+organism:9606+AND+created:[20120720%20TO%2020190605]&format=list>`_. 
+* `Mouse uniprot-AC entries reviewed (05/06/2019) <https://www.uniprot.org/uniprot/?query=reviewed:yes+AND+organism:10090+AND+created:[20120720%20TO%2020190605]&format=list>`_. 
+* `Rat uniprot-AC entries reviewed (05/06/2019) <https://www.uniprot.org/uniprot/?query=reviewed:yes+AND+organism:10116+AND+created:[20120720%20TO%2020190605]&format=list>`_.
+* `Human uniprot-AC entries reviewed (08/05/2019) <https://www.uniprot.org/uniprot/?query=reviewed:yes+AND+organism:9606+AND+created:[20120720%20TO%2020190508]&format=list>`_. 
+* `Mouse uniprot-AC entries reviewed (08/05/2019) <https://www.uniprot.org/uniprot/?query=reviewed:yes+AND+organism:10090+AND+created:[20120720%20TO%2020190508]&format=list>`_. 
+* `Rat uniprot-AC entries reviewed (08/05/2019) <https://www.uniprot.org/uniprot/?query=reviewed:yes+AND+organism:10116+AND+created:[20120720%20TO%2020190508]&format=list>`_.
+* `Human uniprot-AC entries reviewed (10/10/2018) <https://www.uniprot.org/uniprot/?query=reviewed:yes+AND+organism:9606+AND+created:[20120720%20TO%2020181010]&format=list>`_. 
+* `Mouse uniprot-AC entries reviewed (10/10/2018) <https://www.uniprot.org/uniprot/?query=reviewed:yes+AND+organism:10090+AND+created:[20120720%20TO%2020181010]&format=list>`_. 
+* `Rat uniprot-AC entries reviewed (10/10/2018) <https://www.uniprot.org/uniprot/?query=reviewed:yes+AND+organism:10116+AND+created:[20120720%20TO%2020181010]&format=list>`_.

tools/proteore_id_converter/id_converter.py

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+import argparse
+import csv
+import itertools
+import sys
+import os  # noqa 401
+import re
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-d", "--ref_file", help="path to reference file: <species>_id_mapping.tsv", required=True)  # noqa 501
+    parser.add_argument("--input_type",
+                        help="type of input (list of id or filename)",
+                        required=True)
+    parser.add_argument("-t", "--id_type", help="type of input IDs",
+                        required=True)
+    parser.add_argument("-i", "--input", help="list of IDs (text or filename)",
+                        required=True)
+    parser.add_argument("-c", "--column_number",
+                        help="list of IDs (text or filename)")
+    parser.add_argument("--header",
+                        help="true/false if your file contains a header")
+    parser.add_argument("--target_ids",
+                        help="target IDs to map to", required=True)
+    parser.add_argument("-o", "--output",
+                        help="output filename", required=True)
+    args = parser.parse_args()
+    return args
+
+# return list of (unique) ids from string
+
+
+def get_input_ids_from_string(input):
+    ids_list = list(set(re.split(r'\s+', input.replace("\r", "").replace("\n", " ").replace("\t", " "))))  # noqa 501
+    if "" in ids_list:
+        ids_list.remove("")
+    # if "NA" in ids_list : ids_list.remove("NA")
+    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("")
+    # if "NA" in ids_list : ids_list.remove("NA")
+
+    return input_file, ids_list
+
+# 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 = list(set(ids_list))
+
+    return new_file, ids_list
+
+# not used
+
+
+def output_one_id_one_line(line, convert_ids, target_ids):
+
+    # ids_not_processed = ["GI","PDB","GO","PIR","MIM","UniGene","BioGrid","STRING"]  # noqa 501
+    # ids with multiple ids per line in output file
+    ids_not_processed = ["UniProt-AC",
+                         "UniProt-AC_reviewed",
+                         "UniProt-ID",
+                         "GeneID",
+                         "RefSeq",
+                         "GI",
+                         "PDB",
+                         "GO",
+                         "PIR",
+                         "MIM",
+                         "UniGene",
+                         "Ensembl_Gene",
+                         "Ensembl_Transcript",
+                         "Ensembl_Protein",
+                         "BioGrid",
+                         "STRING",
+                         "KEGG"]  # All Ids
+    ids_not_processed = [id for id in ids_not_processed if id in target_ids]  # noqa 501
+    # ids present in target_ids with multiple ids per line in output file
+
+    for id_not_processed in ids_not_processed:
+        index = target_ids.index(id_not_processed)
+        convert_ids[index] = [";".join(convert_ids[index])]
+
+# getting all possibilities between lists of ids
+    res = itertools.product(*convert_ids)
+    res = [list(e) for e in res]   # convert to lists
+    res = [line + list(ids) for ids in res]   # adding the rest of the line
+
+    return(res)
+
+# 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):
+        sys.exit("Please specify the column where you would like to apply the filter with valid format")  # noqa 501
+
+# 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]  # noqa 501
+        tmp.append(line)
+
+    return tmp
+
+
+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 result dictionary
+
+
+def map_to_dictionary(ids, ids_dictionary, id_in, id_out):
+
+    result_dict = {}
+    for id in ids:
+        for target_id in id_out:
+            if id in ids_dictionary:
+                res = ids_dictionary[id][target_id]
+            else:
+                res = ""
+
+            if id in result_dict:
+                result_dict[id].append(res)
+            else:
+                result_dict[id] = [res]
+
+    return result_dict
+
+# create empty dictionary with index for tab
+
+
+def create_ids_dictionary(ids_list):
+    ids_dictionary = {}
+    ids_dictionary_index = {}
+    for i, id in enumerate(ids_list):
+        ids_dictionary_index[i] = id
+
+    return(ids_dictionary, ids_dictionary_index)
+
+
+def create_header(input_file, ncol, id_type, target_ids):
+    col_names = list(range(1, len(input_file[0]) + 1))
+    col_names = ["col" + str(e) for e in col_names]
+    col_names[ncol] = id_type
+    col_names = col_names + target_ids
+    return(col_names)
+
+
+def main():
+
+    # Get args from command line
+    args = get_args()
+    target_ids = args.target_ids.split(",")
+    header = False
+    if args.id_type in target_ids:
+        target_ids.remove(args.id_type)
+    if args.input_type == "file":
+        args.column_number = nb_col_to_int(args.column_number)
+        header = str2bool(args.header)
+
+    # Get ref file to build dictionary
+    csv.field_size_limit(sys.maxsize)  # to handle big files
+    with open(args.ref_file, "r") as csv_file:
+        tab = csv.reader(csv_file, delimiter='\t')
+        tab = [line for line in tab]
+
+    ids_list = tab[0]
+
+    # create empty dictionary and dictionary index
+    ids_dictionary, ids_dictionary_index = create_ids_dictionary(ids_list)
+
+    # fill dictionary and sub dictionaries with ids
+    id_index = ids_list.index(args.id_type)
+    for line in tab[1:]:
+        ref_ids = line[id_index]
+        other_id_type_index = [accession_id for accession_id in ids_dictionary_index.keys() if accession_id!=id_index]  # noqa 501
+        # if there's more than one id, one key per id (example : GO)
+        for id in ref_ids.replace(" ", "").split(";"):
+            if id not in ids_dictionary:    # if the key is not created yet
+                ids_dictionary[id] = {}
+            for other_id_type in other_id_type_index:
+                if ids_dictionary_index[other_id_type] not in ids_dictionary[id]:  # noqa 501
+                    ids_dictionary[id][ids_dictionary_index[other_id_type]] = set(line[other_id_type].replace("NA","").replace(" ","").split(";"))  # noqa 501
+                else:
+                    ids_dictionary[id][ids_dictionary_index[other_id_type]] |= set(line[other_id_type].replace("NA","").replace(" ","").split(";"))  # noqa 501
+                if len(ids_dictionary[id][ids_dictionary_index[other_id_type]]) > 1 and '' in ids_dictionary[id][ids_dictionary_index[other_id_type]]:  # noqa 501 
+                    ids_dictionary[id][ids_dictionary_index[other_id_type]].remove('')  # noqa 501
+
+    # 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,
+                                                  args.column_number,
+                                                  header)
+
+    # Mapping ids
+    result_dict = map_to_dictionary(ids, ids_dictionary,
+                                    args.id_type, target_ids)
+
+    # creating output file
+    with open(args.output, "w") as output:
+        writer = csv.writer(output, delimiter="\t")
+        # writer.writerows(output_file)
+
+        # write header
+        if header:
+            writer.writerow(input_file[0] + target_ids)
+            input_file = input_file[1:]
+        elif args.input_type == "file":
+            col_names = create_header(input_file, args.column_number,
+                                      args.id_type, target_ids)
+            writer.writerow(col_names)
+        else:
+            writer.writerow([args.id_type] + target_ids)
+
+        # write lines
+        previous_line = ""
+        if args.input_type == "file":
+            for line in input_file:
+                res = [";".join(list(res_ids)) for res_ids in result_dict[line[args.column_number]]]  # noqa 501
+                line = ["NA" if cell=="" or cell==" " or cell=="NaN" else cell for cell in line+res]  # noqa 501
+                if previous_line != line:
+                    writer.writerow(line)
+                    previous_line = line
+        elif args.input_type == "list":
+            for id in ids:
+                res = [";".join(list(res_ids)) for res_ids in result_dict[id]]
+                line = ["NA" if cell=="" or cell==" " or cell=="NaN" else cell for cell in [id]+res]  # noqa 501
+                if previous_line != line:
+                    writer.writerow(line)
+                    previous_line = line
+
+        # print ("output file created")
+
+
+if __name__ == "__main__":
+    main()