diff impc_tool.py @ 0:d319dc5f3ea8 draft default tip

planemo upload for repository https://github.com/INFRAFRONTIERDIB/tools-iuc/tree/query_impc/tools/query_impc commit 991881b5df5f5228ecf4445ee2cc1431b9602ea8

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/impc_tool.py	Wed Oct 11 14:51:02 2023 +0000
@@ -0,0 +1,759 @@
+import sys
+
+import mygene
+import pandas as pd
+import requests
+
+
+impc_api_url = "https://www.ebi.ac.uk/mi/impc/bulkdata-api"
+impc_api_search_url = f"{impc_api_url}/genes"
+impc_api_gene_bundle_url = f"{impc_api_url}/geneBundles"
+
+
+def stop_err(msg):
+    sys.exit(msg)
+
+
+def main():
+    inp = str(sys.argv[1])
+    query = str(sys.argv[3])
+
+    try:
+        if query == "7":
+            g_out = str(sys.argv[5])
+            full_gene_table(g_out)
+            sys.exit(0)
+
+        if str(sys.argv[5]) == "txt":
+            s = str(sys.argv[6])
+            if s == "t":
+                sep = "\t"
+            elif s == "s":
+                sep = " "
+            elif s in ",;.":
+                sep = s
+            else:
+                sys.exit("Separator not valid, please change it.")
+            inp = pd.read_csv(inp, header=None, delimiter=sep)
+            if len(inp.columns) == 1:
+                inp = inp.to_csv(header=None,
+                                 index=False).strip("\n").split("\n")
+                inp = ",".join(inp)
+            else:
+                inp = inp.to_csv(header=None,
+                                 index=False).strip(sep).split(sep)
+                inp = ",".join(inp)
+
+        if query == "8":
+            if str(sys.argv[5]) == "txt":
+                g_out = str(sys.argv[7])
+            else:
+                g_out = str(sys.argv[6])
+            genes_in_pipeline(inp, g_out)
+            sys.exit(0)
+        elif query == "9":
+            if str(sys.argv[5]) == "txt":
+                g_out = str(sys.argv[7])
+            else:
+                g_out = str(sys.argv[6])
+            sign_mp(inp, g_out)
+            sys.exit(0)
+        elif query == "10":
+            par_pip_ma(inp)
+            sys.exit(0)
+        elif query == "11":
+            par_gen(inp)
+            sys.exit(0)
+        elif query == "2" or query == "4":
+            final_list = pheno_mapping(inp)
+        else:
+            final_list = gene_mapping(inp)
+        inp = ",".join(final_list)
+
+        if query == "1":
+            get_pheno(inp)
+            sys.exit(0)
+        elif query == "2":
+            if str(sys.argv[5]) == "txt":
+                g_out = str(sys.argv[7])
+            else:
+                g_out = str(sys.argv[6])
+            get_genes(inp, g_out)
+            sys.exit(0)
+        elif query == "3":
+            gene_set(inp)
+            sys.exit(0)
+        elif query == "4":
+            extr_img(inp)
+            sys.exit(0)
+        elif query == "5":
+            parameters(inp)
+            sys.exit(0)
+        elif query == "6":
+            sign_par(inp)
+            sys.exit(0)
+        else:
+            stop_err("Error, non-implemented query selected: " + query)
+    except Exception as ex:
+        stop_err("Error running impc_tool.py:\n" + str(ex))
+
+
+# 1-Given a gene id, retrieve all the phenotypes related to it (id and name)
+def get_pheno(inp):
+    head = sys.argv[4]
+    mgi_accession_id = inp
+
+    gene_url = f"{impc_api_search_url}/{mgi_accession_id}"
+    gene_data = requests.get(gene_url).json()
+
+    p_list = []
+    id_list = []
+
+    if gene_data["significantMpTerms"] is None:
+        stop_err("No significant MP terms found for this gene")
+    else:
+        for x in gene_data["significantMpTerms"]:
+            p_list.append(x["mpTermId"])
+            id_list.append(x["mpTermName"])
+
+    df = pd.DataFrame()
+    df["MP term name"] = p_list
+    df["MP term id"] = id_list
+
+    if head == "True":
+        df.to_csv(sys.argv[2], header=True, index=False,
+                  sep="\t", index_label=False)
+    else:
+        df.to_csv(sys.argv[2], header=False, index=False,
+                  sep="\t", index_label=False)
+
+
+# 3-Extract all genes having a particular phenotype or a set of phenotypes
+# (e.g. relevant to a disease)
+def get_genes(inp, g_out):
+    head = sys.argv[4]
+    target_mp_terms = inp
+
+# All the data is paginated using the page and size parameters,
+# by default the endpoint returns the first 20 hits
+    gene_by_phenotypes_query = f"{impc_api_search_url}" \
+                               f"/search/findAllBySignificantMpTermIdsContains" \
+                               f"?mpTermIds={target_mp_terms}&page=0&size=20"
+    genes_with_clinical_chemistry_phen = \
+        requests.get(gene_by_phenotypes_query).json()
+    print(f"Genes with {target_mp_terms}: "
+          f"{genes_with_clinical_chemistry_phen['page']['totalElements']}")
+    acc = []
+    name = []
+    url = []
+
+    for gene in genes_with_clinical_chemistry_phen["_embedded"]["genes"]:
+        acc.append(gene["mgiAccessionId"])
+        name.append(gene["markerName"])
+        url.append(gene["_links"]["geneBundle"]["href"])
+
+    if g_out == "sym":
+        list_of_genes = pd.DataFrame(columns=["Gene symbol id", "Gene name",
+                                              "Gene bundle url"])
+        list_of_genes["Gene symbol id"] = mgi_sym_map(acc)
+    else:
+        list_of_genes = pd.DataFrame(columns=["Gene accession id",
+                                              "Gene name", "Gene bundle url"])
+        list_of_genes["Gene accession id"] = acc
+    list_of_genes["Gene name"] = name
+    list_of_genes["Gene bundle url"] = url
+
+    if head == "True":
+        list_of_genes.to_csv(sys.argv[2], header=True, index=False,
+                             sep="\t", index_label=False)
+    else:
+        list_of_genes.to_csv(sys.argv[2], header=False, index=False,
+                             sep="\t", index_label=False)
+
+
+# 4. Extract all phenotypes which are present in a particular gene set
+# (e.g. genes together in a pathway)
+def gene_set(inp):
+    head = sys.argv[4]
+    target_genes = inp
+
+    genes_in_gene_list_query = f"{impc_api_search_url}/search/" \
+                               f"findAllByMgiAccessionIdIn?" \
+                               f"mgiAccessionIds={target_genes}"
+
+    genes_in_gene_list = requests.get(genes_in_gene_list_query).json()
+    mp_terms_vs_gene_idx = {}
+
+    for gene in genes_in_gene_list["_embedded"]["genes"]:
+        mp_terms = gene["significantMpTerms"]
+        gene_acc_id = gene["mgiAccessionId"]
+        if mp_terms is None:
+            continue
+        for mp_term_name in mp_terms:
+            if mp_term_name["mpTermId"] not in mp_terms_vs_gene_idx:
+                mp_terms_vs_gene_idx[mp_term_name["mpTermId"]] = \
+                    {"mp_term": mp_term_name["mpTermId"],
+                     "mp_name": mp_term_name["mpTermName"], "genes": []}
+            mp_terms_vs_gene_idx[mp_term_name["mpTermId"]]["genes"].\
+                append(gene_acc_id)
+    genes_by_mp_term = list(mp_terms_vs_gene_idx.values())
+
+    df = pd.DataFrame()
+    terms = []
+    names = []
+    genes = []
+    for i in genes_by_mp_term:
+        terms.append(i["mp_term"])
+        names.append(i["mp_name"])
+        genes.append(",".join(i["genes"]))
+
+    df["mp_term"] = terms
+    df["mp_name"] = names
+    df["genes"] = genes
+
+    if head == "True":
+        df.to_csv(sys.argv[2], header=True, index=False,
+                  sep="\t", index_label=False)
+    else:
+        df.to_csv(sys.argv[2], header=False, index=False,
+                  sep="\t", index_label=False)
+
+
+# 7. Extract images with a particular phenotype or a set of phenotypes
+def extr_img(inp):
+    head = sys.argv[4]
+    target_mp_terms = inp  # ["MP:0002110", "MP:0000559"]
+
+# All the data is paginated using the page and size parameters,
+# by default the endpoint returns the first 20 hits
+    gene_by_phenotypes_query = f"{impc_api_search_url}/search/" \
+                               f"findAllBySignificantMpTermIdsContains?" \
+                               f"mpTermIds={target_mp_terms}&page=0&size=20"
+    genes_with_morph_mps = requests.get(gene_by_phenotypes_query).json()
+    list_of_gene_bundle_urls = [
+        gene["_links"]["geneBundle"]["href"] for gene in
+        genes_with_morph_mps["_embedded"]["genes"]
+    ]
+
+    gene_bundles = []
+    for gene_bundle_url in list_of_gene_bundle_urls:
+        gene_bundle = requests.get(gene_bundle_url).json()
+        gene_bundles.append(gene_bundle)
+
+    images_with_morphology_mps = []
+
+    # Doing just the first 20 and filtering out fields on the images
+    display_fields = ["geneSymbol", "parameterName", "biologicalSampleGroup",
+                      "colonyId", "zygosity", "sex", "downloadUrl",
+                      "externalSampleId", "thumbnailUrl"]
+
+    for gene_bundle in gene_bundles[:20]:
+        if len(gene_bundle) == 4:
+            continue
+        if gene_bundle["geneImages"] is not None:
+            images = gene_bundle["geneImages"]
+            for image in images:
+                display_image = {k: v for k, v in image.items()
+                                 if k in display_fields}
+                images_with_morphology_mps.append(display_image)
+
+    images_table = []
+    print(f"Images related to phenotype {target_mp_terms}: "
+          f"{len(images_with_morphology_mps)}")
+    # Displaying just the first 20 images
+    for i in images_with_morphology_mps[:20]:
+        row = [f"<img src='{i['thumbnailUrl']}' />"] + list(i.values())
+        images_table.append(row)
+
+    df = pd.DataFrame()
+    externalSampleId = []
+    geneSymbol = []
+    biologicalSampleGroup = []
+    sex = []
+    colonyId = []
+    zygosity = []
+    parameterName = []
+    downloadUrl = []
+    thumbnailUrl = []
+
+    for i in images_table:
+        externalSampleId.append(i[1])
+        geneSymbol.append(i[2])
+        biologicalSampleGroup.append(i[3])
+        sex.append(i[4])
+        colonyId.append(i[5])
+        zygosity.append(i[6])
+        parameterName.append(i[7])
+        downloadUrl.append(i[8])
+        thumbnailUrl.append(i[9])
+
+    df["externalSampleId"] = externalSampleId
+    df["geneSymbol"] = geneSymbol
+    df["biologicalSampleGroup"] = biologicalSampleGroup
+    df["sex"] = sex
+    df["colonyId"] = colonyId
+    df["zygosity"] = zygosity
+    df["parameterName"] = parameterName
+    df["downloadUrl"] = downloadUrl
+    df["thumbnailUrl"] = thumbnailUrl
+
+    if head == "True":
+        df.to_csv(sys.argv[2], header=True, index=False,
+                  sep="\t", index_label=False)
+    else:
+        df.to_csv(sys.argv[2], header=False, index=False,
+                  sep="\t", index_label=False)
+
+
+# 11- Which parameters have been measured for a particular knockout
+def parameters(inp):
+    head = sys.argv[4]
+    knockout = inp  # "MGI:104636"
+    gene_info = requests.get(impc_api_search_url + "/" + knockout).json()
+
+    if gene_info["phenotypingDataAvailable"]:
+        geneBundle = requests.get(gene_info["_links"]["geneBundle"]["href"])\
+            .json()
+        gen_imgs = geneBundle["geneImages"]
+        par_list = []
+        lis = {}
+        for i in gen_imgs:
+            lis = {"Parameter Name": i["parameterName"]}
+            if lis not in par_list:
+                par_list.append(lis)
+        df = pd.DataFrame()
+        li = []
+
+        for i in par_list:
+            li.append(i["Parameter Name"])
+
+        df["Parameter"] = li
+        if head == "True":
+            df.to_csv(sys.argv[2], header=True, index=False,
+                      sep="\t", index_label=False)
+        else:
+            df.to_csv(sys.argv[2], header=False, index=False,
+                      sep="\t", index_label=False)
+
+    else:
+        stop_err("No parameters available for this knockout gene")
+
+
+# 12- Which parameters identified a significant finding for a particular
+# knockout line (colony)
+def sign_par(inp):
+    head = sys.argv[4]
+    knockout = inp  # "MGI:104636"
+
+    gene_info = requests.get(f"{impc_api_url}statisticalResults/search/"
+                             f"findAllByMarkerAccessionIdIsAndSignificantTrue?"
+                             f"mgiAccessionId=" + knockout).json()
+    gene_stats = gene_info["_embedded"]["statisticalResults"]
+
+    if len(gene_stats) == 0:
+        stop_err("No statistically relevant parameters found "
+                 "for this knockout gene")
+    else:
+        df = pd.DataFrame()
+        n = []
+        p = []
+        for g in gene_stats:
+            n.append(g["parameterName"])
+            p.append(g["pvalue"])
+
+        df["Parameter name"] = n
+        df["p-value"] = p
+        if head == "True":
+            df.to_csv(sys.argv[2], header=True, index=False,
+                      sep="\t", index_label=False)
+        else:
+            df.to_csv(sys.argv[2], header=False, index=False,
+                      sep="\t", index_label=False)
+
+
+# 13- List of genes names and ID measured in a pipeline
+def genes_in_pipeline(inp, g_out):
+    head = sys.argv[4]
+    pip = inp
+
+    g_in_p_query = f"{impc_api_search_url}/search/" \
+                   f"findAllByTestedPipelineId?pipelineId={pip}&" \
+                   f"page=0&size=1000"
+    genes_in_pip = requests.get(g_in_p_query).json()
+    pages = genes_in_pip["page"]["totalPages"]
+    max_elem = genes_in_pip["page"]["totalElements"]
+
+    print(f"Genes with {pip}: {genes_in_pip['page']['totalElements']}")
+    list_d = []
+    acc = []
+    name = []
+
+    if max_elem > 1000:
+        g_in_p_query = genes_in_pip["_embedded"]["genes"]
+        for i in range(1, pages):
+            gl = requests.get(f"{impc_api_search_url}/search/"
+                              f"findAllByTestedPipelineId?pipelineId={pip}&"
+                              f"page={i}&"
+                              f"size=1000").json()["_embedded"]["genes"]
+            g_in_p_query += gl
+    else:
+        g_in_p_query = genes_in_pip["_embedded"]["genes"]
+
+    for g in g_in_p_query:
+        d = {"Gene Accession ID": g["mgiAccessionId"],
+             "Gene Name": g["markerName"]}
+        list_d.append(d)
+
+    for i in list_d:
+        acc.append(i["Gene Accession ID"])
+        name.append(i["Gene Name"])
+    if g_out == "sym":
+        list_of_genes = pd.DataFrame(columns=["Gene symbol", "Gene name"])
+        list_of_genes["Gene symbol"] = mgi_sym_map(acc)
+    else:
+        list_of_genes = pd.DataFrame(columns=["Gene accession id",
+                                              "Gene name"])
+        list_of_genes["Gene accession id"] = acc
+    list_of_genes["Gene name"] = name
+
+    if head == "True":
+        list_of_genes.to_csv(sys.argv[2], header=True, index=False,
+                             sep="\t", index_label=False)
+    else:
+        list_of_genes.to_csv(sys.argv[2], header=False, index=False,
+                             sep="\t", index_label=False)
+
+
+# 14- Extract all genes and corresponding phenotypes related to a
+# particular organ system (eg: significatMPTerm)
+def sign_mp(inp, g_out):
+    head = sys.argv[4]
+    mp_term = inp  # ["MP:0005391"]
+
+    gene_by_mpterm_query = f"{impc_api_search_url}/search/" \
+                           f"findAllBySignificantMpTermIdsContains?" \
+                           f"mpTermIds={mp_term}&size=1000"
+    genes_with_mpterm = requests.get(gene_by_mpterm_query).json()
+
+    pages = genes_with_mpterm["page"]["totalPages"]
+    genes_info = genes_with_mpterm["_embedded"]["genes"]
+
+    for pn in range(1, pages):
+        pq = f"{impc_api_search_url}/search/" \
+             f"findAllBySignificantMpTermIdsContains?" \
+             f"mpTermIds={mp_term}&page={pn}&size=1000"
+        g = requests.get(pq).json()["_embedded"]["genes"]
+        genes_info += g
+
+    list_d = []
+    d = {}
+    for g in genes_info:
+        names = []
+        ids = []
+        for s in g["significantMpTerms"]:
+            names.append(s["mpTermName"])
+            ids.append(s["mpTermId"])
+        d = {"Gene": g["mgiAccessionId"], "mpTermId": ids, "mpTermName": names}
+        list_d.append(d)
+
+    g = []
+    ids = []
+    names = []
+    for i in list_d:
+        g.append(i["Gene"])
+        ids.append(i["mpTermId"])
+        names.append(i["mpTermName"])
+
+    df = pd.DataFrame()
+    if g_out == "sym":
+        df["Gene symbol"] = mgi_sym_map(g)
+    else:
+        df["Gene Id"] = g
+    df["Significant MP terms Ids"] = ids
+    df["Significant MP terms Names"] = names
+
+    if head == "True":
+        df.to_csv(sys.argv[2], header=True, index=False,
+                  sep="\t", index_label=False)
+    else:
+        df.to_csv(sys.argv[2], header=False, index=False,
+                  sep="\t", index_label=False)
+
+
+# 16- Full table of genes and all identified phenotypes
+def full_gene_table(g_out):
+    head = sys.argv[4]
+    gene_list = requests.get(impc_api_search_url + "?page=0&size=1000").json()
+    pages = gene_list["page"]["totalPages"]
+    genes_info = gene_list["_embedded"]["genes"]
+
+    for pn in range(1, pages):
+        gp = requests.get(impc_api_search_url
+                          + f"?page={pn}&"
+                            f"size=1000").json()["_embedded"]["genes"]
+        genes_info += gp
+
+    d = {}
+    list_d = []
+
+    for i in genes_info:
+        if i["significantMpTerms"] is None:
+            d = {"Gene": i["mgiAccessionId"], "Identified phenotypes": "None"}
+        else:
+            d = {"Gene": i["mgiAccessionId"],
+                 "Identified phenotypes": [
+                     sub["mpTermId"] for sub in i["significantMpTerms"]
+            ]}
+        list_d.append(d)
+
+    df = pd.DataFrame()
+    g = []
+    p = []
+    for i in list_d:
+        g.append(i["Gene"])
+        p.append(i["Identified phenotypes"])
+
+    if g_out == "sym":
+        df["Gene symbol"] = mgi_sym_map(g)
+    else:
+        df["MGI id"] = g
+    df["MP term list"] = p
+
+    for i in range(0, len(df)):
+        if df["MP term list"][i] != "None":
+            df["MP term list"][i] = str(
+                df["MP term list"][i]
+            )[1:-1].replace("'", "")
+
+    if str(sys.argv[1]) == "True":
+        if head == "True":
+            df.to_csv(sys.argv[2], header=True, index=False,
+                      sep="\t", index_label=False)
+        else:
+            df.to_csv(sys.argv[2], header=False, index=False,
+                      sep="\t", index_label=False)
+    else:
+        df = df[df["MP term list"] != "None"]
+        df.reset_index(drop=True, inplace=True)
+        if head == "True":
+            df.to_csv(sys.argv[2], header=True, index=False,
+                      sep="\t", index_label=False)
+        else:
+            df.to_csv(sys.argv[2], header=False, index=False,
+                      sep="\t", index_label=False)
+
+
+# 18- Extract measurements and analysis for a parameter or pipeline
+def par_pip_ma(inp):
+    head = sys.argv[4]
+    id = inp
+
+    if id[0:4] == "IMPC":
+        par = True
+        ma_query = f"{impc_api_search_url}/search/" \
+                   f"findAllByTestedParameterId?" \
+                   f"parameterId={id}&page=0&size=1000"
+    else:
+        ma_query = f"{impc_api_search_url}/search/" \
+                   f"findAllByTestedPipelineId?" \
+                   f"pipelineId={id}&page=0&size=1000"
+        par = False
+
+    ma_in_pip = requests.get(ma_query).json()
+    pages = ma_in_pip["page"]["totalPages"]
+    max_elem = ma_in_pip["page"]["totalElements"]
+
+    print(f"Genes with {id}: {ma_in_pip['page']['totalElements']}")
+    list_d = []
+    list_of_genes = pd.DataFrame(columns=["Measurements", "Analysis"])
+    mes = []
+    an = []
+
+    if max_elem > 1000:
+
+        ma_in_pip = ma_in_pip["_embedded"]["genes"]
+        for pn in range(1, pages):
+            if par:
+                pip = requests.get(f"{impc_api_search_url}/search/"
+                                   f"findAllByTestedParameterId?"
+                                   f"parameterId={id}&page={pn}&"
+                                   f"size=1000").json()["_embedded"]["genes"]
+            else:
+                pip = requests.get(f"{impc_api_search_url}/search/"
+                                   f"findAllByTestedPipelineId?"
+                                   f"pipelineId={id}&page={pn}&"
+                                   f"size=1000").json()["_embedded"]["genes"]
+            ma_in_pip += pip
+
+    else:
+        ma_in_pip = ma_in_pip["_embedded"]["genes"]
+
+    for g in ma_in_pip:
+        d = {"Measurements": g[""], "Analysis": g[""]}
+        list_d.append(d)
+
+    for i in list_d:
+        mes.append(i[""])
+        an.append(i[""])
+
+    list_of_genes["Analysis"] = an
+    list_of_genes["Measurements"] = mes
+
+    if head == "True":
+        list_of_genes.to_csv(sys.argv[2], header=True, index=False,
+                             sep="\t", index_label=False)
+    else:
+        list_of_genes.to_csv(sys.argv[2], header=False, index=False,
+                             sep="\t", index_label=False)
+
+
+# 19- Get all genes and measured values for a particular parameter
+def par_gen(inp, g_out):
+    head = sys.argv[4]
+    id = inp
+
+    pa_query = f"{impc_api_search_url}/search/" \
+               f"findAllByTestedParameterId?parameterId={id}&page=0&size=1000"
+
+    gm_par = requests.get(pa_query).json()
+    pages = gm_par["page"]["totalPages"]
+    max_elem = gm_par["page"]["totalElements"]
+
+    print(f"Genes with {id}: {gm_par['page']['totalElements']}")
+    list_d = []
+    gen = []
+    mes = []
+
+    if max_elem > 1000:
+
+        gm_par = gm_par["_embedded"]["genes"]
+
+        for pn in range(1, pages):
+            pip = requests.get(f"{impc_api_search_url}/search/"
+                               f"findAllByTestedParameterId?"
+                               f"parameterId={id}&page={pn}&"
+                               f"size=1000").json()["_embedded"]["genes"]
+            gm_par += pip
+
+    else:
+        gm_par = gm_par["_embedded"]["genes"]
+
+    for g in gm_par:
+        d = {"Genes": g["mgiAccessionId"], "Measured Values": g[""]}
+        list_d.append(d)
+
+    for i in list_d:
+        gen.append(i["Genes"])
+        mes.append(i["Measured Values"])
+
+    if g_out == "sym":
+        list_of_genes = pd.DataFrame(columns=["Gene symbol",
+                                              "Measured Values"])
+        list_of_genes["Gene symbol"] = mgi_sym_map(gen)
+    else:
+        list_of_genes = pd.DataFrame(columns=["Gene accession id",
+                                              "Measured Values"])
+        list_of_genes["Gene accession id"] = gen
+    list_of_genes["Measured Values"] = mes
+
+    if head == "True":
+        list_of_genes.to_csv(sys.argv[2], header=True, index=False,
+                             sep="\t", index_label=False)
+    else:
+        list_of_genes.to_csv(sys.argv[2], header=False, index=False,
+                             sep="\t", index_label=False)
+
+
+# Function to map gene symbol to MGI ids
+def gene_mapping(inp):
+    tmp = inp.split(",")
+    final_list = []
+    sym_list = []
+    for i in tmp:
+        if "MGI:" in i:
+            final_list.append(i)
+        else:
+            sym_list.append(i)
+    del i
+
+    # symbol for symbols, mgi for MGI :
+    # https://docs.mygene.info/en/latest/doc/query_service.html#available-fields
+    if len(sym_list) != 0:
+        mg = mygene.MyGeneInfo()
+        ginfo = mg.querymany(sym_list, scopes="symbol", fields="symbol,MGI",
+                             species="mouse")
+        empty = True
+        discarded = []
+        for i in ginfo:
+            try:
+                final_list.append(i["MGI"])
+                empty = False
+            except KeyError:
+                discarded.append(i["query"])
+        if empty and len(final_list) == 0:
+            stop_err("Error: it was not possible to map the input.")
+        elif empty:
+            print("Warning: it was not possible to map any of the symbol ids. "
+                  "Only MGI ids will be used.")
+        elif len(discarded) != 0:
+            print("Warning: it was not possible to map these elements: "
+                  "" + ",".join(discarded) + "\n")
+
+    return final_list
+
+
+# Function to map phenotypes ids to names
+def pheno_mapping(inp):
+    tmp = inp.split(",")
+    final_list = []
+    sym_list = []
+    for i in tmp:
+        if "MP:" in i:
+            final_list.append(i)
+        else:
+            sym_list.append(i)
+    del i
+    if len(sym_list) != 0:
+        url = "https://raw.githubusercontent.com/AndreaFurlani/" \
+              "hp_mp_mapping_test/main/hp_mp_mapping.csv"
+        mapper = pd.read_csv(url, header=0, index_col=2)
+        empty = True
+        discarded = []
+        for i in sym_list:
+            try:
+                final_list.append(mapper.loc[i]["mpId"])
+                empty = False
+            except KeyError:
+                discarded.append(i)
+                continue
+        if empty and len(final_list) == 0:
+            stop_err("Error: it was not possible to map the input.")
+        elif empty:
+            print("Warning: it was not possible to map any of the "
+                  "HP term entries. Only MP entries will be used.")
+        elif len(discarded) != 0:
+            print("Warning: it was not possible to "
+                  "map these elements: " + ",".join(discarded) + "\n")
+    return final_list
+
+
+# Function to map MGI ids to Gene Symbols
+def mgi_sym_map(mgi_list):
+    sym_list = []
+    mg = mygene.MyGeneInfo()
+    ginfo = mg.querymany(mgi_list, scopes="MGI", fields="symbol,MGI",
+                         species="mouse")
+    discarded = []
+    for i in ginfo:
+        try:
+            sym_list.append(i["symbol"])
+        except KeyError:
+            sym_list.append(i["query"])
+            discarded.append(i["query"])
+    if len(discarded) != 0:
+        print("It was not possible to map these genes: " + ",".join(discarded))
+    return sym_list
+
+
+if __name__ == "__main__":
+    main()