--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/impc_tool.py	Thu Jun 09 10:51:23 2022 +0000
@@ -0,0 +1,587 @@
+import sys
+import requests
+import pandas as pd
+import urllib.request as url
+
+impc_api_url = "https://www.gentar.org/impc-dev-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':
+            full_gene_table()
+            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 = str(inp[0].values[0]).replace("'","")
+            else:
+                inp = inp.to_string(header=False, index=False).replace(" ",",")
+
+        if query == '8':
+            genes_in_pipeline(inp)
+            sys.exit(0)
+        elif query == '10':  # it's here but not totally implemented
+            par_pip_ma(inp)
+            sys.exit(0)
+        elif query == '11':  # it's here but not totally implemented
+            par_gen(inp)
+            sys.exit(0)
+        else:
+            tmp = inp.split(",")
+            final_list = []
+            sym_list = []
+            for i in tmp:
+                if 'MGI:' in i or 'MP:' in i:
+                    final_list.append(i)
+                else:
+                    sym_list.append(i)
+            del(i)
+            if len(sym_list) != 0:
+                sym_list = ",".join(sym_list)
+                biodbnet = f'https://biodbnet.abcc.ncifcrf.gov/webServices/rest.php/biodbnetRestApi.xml?method=db2db&format=row&input=genesymbol&inputValues={sym_list}&outputs=mgiid&taxonId=10090'
+                u = url.urlopen(biodbnet)
+                db = pd.read_xml(u, elems_only=True)
+                if len(db) == 0 and len(final_list) == 0:
+                    stop_err("It was not possible to map the input.")
+                else:
+                    for i in range(0,len(db)):
+                        final_list.append(db['MGIID'][i][4:])
+
+            inp= ",".join(final_list)
+
+
+        if query == '1':
+            get_pheno(inp)
+            sys.exit(0)
+        elif query == '2':
+            get_genes(inp)
+            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)
+        elif query == '9':
+            sign_mp(inp)
+            sys.exit(0)
+        else:
+            stop_err("Error, non-implemented query selected: " + query)
+    except Exception as ex:
+        stop_err('Error running get_pheno.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'] == 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):
+    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}/search/findAllBySignificantMpTermIdsContains?mpTermIds={target_mp_terms}&page=0&size=20"
+    genes_with_clinical_chemistry_phenotypes = requests.get(gene_by_phenotypes_query).json()
+    print(f"Genes with {target_mp_terms}: {genes_with_clinical_chemistry_phenotypes['page']['totalElements']}")
+    list_of_genes = pd.DataFrame(columns=['Gene accession id', 'Gene name', 'Gene bundle url'])
+    acc = []
+    name = []
+    url = []
+
+    for gene in genes_with_clinical_chemistry_phenotypes['_embedded']['genes']:
+        acc.append(gene['mgiAccessionId'])
+        name.append(gene['markerName'])
+        url.append(gene['_links']['geneBundle']['href'])
+
+    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/findAllByMgiAccessionIdIn?mgiAccessionIds={target_genes}"
+
+    genes_in_gene_list = requests.get(genes_in_gene_list_query).json()
+    list_of_mp_terms_vs_gene_index = {}
+
+    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 list_of_mp_terms_vs_gene_index:
+                list_of_mp_terms_vs_gene_index[mp_term_name['mpTermId']] = {"mp_term": mp_term_name['mpTermId'], "genes": []}
+            list_of_mp_terms_vs_gene_index[mp_term_name['mpTermId']]["genes"].append(gene_acc_id)
+    genes_by_mp_term = list(list_of_mp_terms_vs_gene_index.values())
+
+    df = pd.DataFrame()
+    terms = []
+    genes = []
+    for i in genes_by_mp_term:
+        terms.append(i['mp_term'])
+        genes.append(i['genes'])
+
+    df['mp_term'] = terms
+    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/findAllBySignificantMpTermIdsContains?mpTermIds={target_mp_terms}&page=0&size=20"
+    genes_with_morphology_mps = requests.get(gene_by_phenotypes_query).json()
+    list_of_gene_bundle_urls = [gene["_links"]["geneBundle"]['href'] for gene in
+                                genes_with_morphology_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}: {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 EASY
+
+
+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 = []
+        l = {}
+        for i in gen_imgs:
+            l = {"Parameter Name": i['parameterName']}
+            if l not in par_list:
+                par_list.append(l)
+        df = pd.DataFrame()
+        l = []
+
+        for i in par_list:
+            l.append(i['Parameter Name'])
+
+        df['Parameter'] = l
+        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) EASY
+def sign_par(inp):
+    head = sys.argv[4]
+    knockout = inp  # "MGI:104636"
+
+    gene_info = requests.get(f"{impc_api_url}statisticalResults/search/findAllByMarkerAccessionIdIsAndSignificantTrue?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):
+    head = sys.argv[4]
+    pip = inp
+
+    g_in_p_query = f"{impc_api_search_url}/search/findAllByTestedPipelineId?pipelineId={pip}&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']}")
+    d ={ }
+    list_d = []
+    list_of_genes = pd.DataFrame(columns=['Gene accession id', 'Gene name'])
+    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/findAllByTestedPipelineId?pipelineId={pip}&page={i}&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'])
+
+    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):
+    head = sys.argv[4]
+    mp_term = inp  # ['MP:0005391']
+
+    gene_by_mpterm_query = f"{impc_api_search_url}/search/findAllBySignificantMpTermIdsContains?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/findAllBySignificantMpTermIdsContains?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()
+    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():
+    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}&size=1000').json()['_embedded']['genes']
+        genes_info += gp
+
+    d = {}
+    list_d=[]
+
+    for i in genes_info:
+        l = []
+        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'])
+
+    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)
+
+# Old method, chech which is faster
+#    max_elem = gene_list['page']['totalElements']
+#    d = {}
+#    list_d = []
+#    for i in range(0, pages):
+#        gl = requests.get(impc_api_search_url + '?page=' + str(i) + '&size=' + str(max_elem)).json()
+#        for g in gl['_embedded']['genes']:
+#            if g['significantMpTerms'] is None:
+#                d = {"Gene": g['mgiAccessionId'], "Identified phenotypes": "None"}
+#            else:
+#                d = {"Gene": g['mgiAccessionId'], "Identified phenotypes": [ sub['mpTermId'] for sub in g['significantMpTerms'] ]}
+#            list_d.append(d)
+
+
+
+
+# 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/findAllByTestedParameterId?parameterId={id}&page=0&size=1000"
+    else:
+        ma_query = f"{impc_api_search_url}/search/findAllByTestedPipelineId?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']}")
+    d = {}
+    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/findAllByTestedParameterId?parameterId={id}&page={pn}&size=1000").json()['_embedded']['genes']
+            else:
+                pip = requests.get(f"{impc_api_search_url}/search/findAllByTestedPipelineId?pipelineId={id}&page={pn}&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):
+    head = sys.argv[4]
+    id = inp
+
+    pa_query = f"{impc_api_search_url}/search/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']}")
+    d = {}
+    list_d = []
+    list_of_genes = pd.DataFrame(columns=['Genes', 'Measured Values'])
+    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/findAllByTestedParameterId?parameterId={id}&page={pn}&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'])
+
+    list_of_genes['Genes'] = 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)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/impc_tool.xml	Thu Jun 09 10:51:23 2022 +0000
@@ -0,0 +1,244 @@
+<tool id="IMPC_query_tool" name="IMPC" version="0.9.0">
+  <description> query tool.</description>
+  <requirements>
+    <requirement type="package">requests</requirement>
+    <requirement type="package">pandas</requirement>
+	<requirement type="package">lxml</requirement>
+  </requirements>
+  <stdio>
+  </stdio>
+  <command>
+	  <![CDATA[python3 "$__tool_directory__/impc_tool.py" 
+	  #if $query_type.selector == '7'
+	  	'$query_type.input' '$output' '$query_type.selector' '$query_type.head'
+	  #else
+	  	#if $query_type.inp_q.inp_sel == 'str'
+	    	'$query_type.inp_q.input' '$output' '$query_type.selector' '$query_type.head' '$query_type.inp_q.inp_sel'
+		#else
+			'$query_type.inp_q.input' '$output' '$query_type.selector' '$query_type.head' '$query_type.inp_q.inp_sel' '$query_type.inp_q.sep'
+		#end if
+	  #end if]]>
+  </command>
+  <inputs>
+          <conditional name="query_type">
+                  <param name="selector" type="select" label="Select a query:">
+                          <option value="1">1 - Extract all measured phenotypes related to a gene</option>
+                          <option value="2">2 - Extract all genes having a particular phenotype or a set of phenotypes (e.g. relevant to a disease)</option>
+                          <option value="3">3 - Extract all phenotypes which are present in a particular gene set (e.g. genes together in a pathway)</option>
+                          <option value="4">4 - Extract images with a particular phenotype or a set of phenotypes</option>
+                          <option value="5">5 - Which parameters have been measured for a particular knockout</option>
+                          <option value="6">6 - Which parameters identified a significant finding for a particular knockout</option>
+                          <option value="7">7 - Full table of genes and all identified phenotypes, no input needed</option>
+						  <option value="8">8 - Extract all genes names and ID measured in a specific pipeline</option>
+						  <option value="9">9 - Extract all genes and corresponding phenotypes related to a particular phenotype major category (not working for now)</option>
+                  </param>
+                  <when value="1">
+					  <conditional name="inp_q">
+						  <param name="inp_sel" type="select" label="Select the type of input">
+							  <option value="str">1 - Direct input</option>
+							  <option value="txt">2 - Txt file</option>
+						  </param>
+						  <when value="str">
+							  <param name="input" type="text" label="Input gene:" help="Enter a single MGI gene id or gene symbol"/>
+						  </when>
+						  <when value="txt">
+							  <param name="input" type="data" format="data,tabular,txt" label="Input file:" help="Enter a txt file with the Gene MGI id or Gene Symbol."/>
+							  <param name="sep" type="select" label="Select the separator used in the file:">
+								  <option value="t">tab</option>
+								  <option value="s">single space</option>
+								  <option value=",">,</option>
+								  <option value=";">;</option>
+							  </param>
+						  </when>
+					  </conditional>
+						  <param name="head" type="boolean" checked="true" truevalue="True" falsevalue="False" label="Choose if include the header in the output. The default value is True."/>
+                  </when>
+                  <when value="2">
+					  <conditional name="inp_q">
+						  <param name="inp_sel" type="select" label="Select the type of input">
+							  <option value="str">1 - Direct input</option>
+							  <option value="txt">2 - Txt file</option>
+						  </param>
+						  <when value="str">
+                          		<param name="input" type="text" label="Input phenotype or set of phenotypes:" help="Enter a single MP term id or a list dividing each id with a comma (without spaces)"/>
+						  </when>
+						  <when value="txt">
+							  <param name="input" type="data" format="data,tabular,txt" label="Input file:" help="Enter a txt file with the MP terms."/>
+							  <param name="sep" type="select" label="Select the separator used in the file:">
+								  <option value="t">tab</option>
+								  <option value="s">single space</option>
+								  <option value=",">,</option>
+								  <option value=";">;</option>
+							  </param>
+						  </when>
+					  </conditional>
+						  <param name="head" type="boolean" checked="true" truevalue="True" falsevalue="False" label="Choose if include the header in the output. The default value is True."/>
+                  </when>
+                  <when value="3">
+					  <conditional name="inp_q">
+						  <param name="inp_sel" type="select" label="Select the type of input">
+							  <option value="str">1 - Direct input</option>
+							  <option value="txt">2 - Txt file</option>
+						  </param>
+						  <when value="str">
+                          	<param name="input" type="text" label="Input gene or set of genes:" help="Enter a single MGI gene id (or gene symbol) or a list dividing each id with a comma (without spaces)"/>
+						  </when>
+						  <when value="txt">
+							  <param name="input" type="data" format="data,tabular,txt" label="Input file:" help="Enter a txt file with the MP terms."/>
+							  <param name="sep" type="select" label="Select the separator used in the file:">
+								  <option value="t">tab</option>
+								  <option value="s">single space</option>
+								  <option value=",">,</option>
+								  <option value=";">;</option>
+							  </param>
+						  </when>
+					  </conditional>
+						  <param name="head" type="boolean" checked="true" truevalue="True" falsevalue="False" label="Choose if include the header in the output. The default value is True."/>
+                  </when>
+                  <when value="4">
+					  <conditional name="inp_q">
+						  <param name="inp_sel" type="select" label="Select the type of input">
+							  <option value="str">1 - Direct input</option>
+							  <option value="txt">2 - Txt file</option>
+						  </param>
+						  <when value="str">
+                          		<param name="input" type="text" label="Input phenotype or set of phenotypes:" help="Enter a single MP term id or a list dividing each id with a comma (without spaces)"/>
+						  </when>
+						  <when value="txt">
+							  <param name="input" type="data" format="data,tabular,txt" label="Input file:" help="Enter a txt file with the MP terms."/>
+							  <param name="sep" type="select" label="Select the separator used in the file:">
+								  <option value="t">tab</option>
+								  <option value="s">single space</option>
+								  <option value=",">,</option>
+								  <option value=";">;</option>
+							  </param>
+						  </when>
+					  </conditional>
+						  <param name="head" type="boolean" checked="true" truevalue="True" falsevalue="False" label="Choose if include the header in the output. The default value is True."/>
+                  </when>
+                  <when value="5">
+					  <conditional name="inp_q">
+						  <param name="inp_sel" type="select" label="Select the type of input">
+							  <option value="str">1 - Direct input</option>
+							  <option value="txt">2 - Txt file</option>
+						  </param>
+						  <when value="str">
+                          		<param name="input" type="text" label="Input gene:" help="Enter a single MGI gene id or gene symbol"/>
+						  </when>
+						  <when value="txt">
+							  <param name="input" type="data" format="data,tabular,txt" label="Input file:" help="Enter a txt file with a MGI id or a gene symbol."/>
+							  <param name="sep" type="select" label="Select the separator used in the file:">
+								  <option value="t">tab</option>
+								  <option value="s">single space</option>
+								  <option value=",">,</option>
+								  <option value=";">;</option>
+							  </param>
+						  </when>
+					  </conditional>
+						  <param name="head" type="boolean" checked="true" truevalue="True" falsevalue="False" label="Choose if include the header in the output. The default value is True."/>
+                  </when>
+                  <when value="6">
+					  <conditional name="inp_q">
+						  <param name="inp_sel" type="select" label="Select the type of input">
+							  <option value="str">1 - Direct input</option>
+							  <option value="txt">2 - Txt file</option>
+						  </param>
+						  <when value="str">
+                          		<param name="input" type="text" label="Input gene:" help="Enter a single MGI gene id or gene symbol"/>
+						  </when>
+						  <when value="txt">
+							  <param name="input" type="data" format="data,tabular,txt" label="Input file:" help="Enter a txt file with a MGI id or a gene symbol."/>
+							  <param name="sep" type="select" label="Select the separator used in the file:">
+								  <option value="t">tab</option>
+								  <option value="s">single space</option>
+								  <option value=",">,</option>
+								  <option value=";">;</option>
+							  </param>
+						  </when>
+					  </conditional>
+						  <param name="head" type="boolean" checked="true" truevalue="True" falsevalue="False" label="Choose if include the header in the output. The default value is True."/>
+                  </when>
+                  <when value="7">
+                          <param name="input" type="boolean" checked="true" truevalue="True" falsevalue="False" label="Include genes without identified phenotypes?" help="Choose if include in the output table also those genes that have no registred phenotypes. By default they are excluded."/>
+						  <param name="head" type="boolean" checked="true" truevalue="True" falsevalue="False" label="Choose if include the header in the output. The default value is True."/>
+                  </when>
+                  <when value="8">
+					  <conditional name="inp_q">
+						  <param name="inp_sel" type="select" label="Select the type of input">
+							  <option value="str">1 - Direct input</option>
+							  <option value="txt">2 - Txt file</option>
+						  </param>
+						  <when value="str">
+                          	<param name="input" type="text" label="Input pipeline:" help="Enter a IMPC pipeline id"/>
+						  </when>
+						  <when value="txt">
+							  <param name="input" type="data" format="data,tabular,txt" label="Input file:" help="Enter a txt file with a IMPC pipeline id."/>
+							  <param name="sep" type="select" label="Select the separator used in the file:">
+								  <option value="t">tab</option>
+								  <option value="s">single space</option>
+								  <option value=",">,</option>
+								  <option value=";">;</option>
+							  </param>
+						  </when>
+					  </conditional>
+						  <param name="head" type="boolean" checked="true" truevalue="True" falsevalue="False" label="Choose if include the header in the output. The default value is True."/>
+                  </when>
+                  <when value="9">
+					  <conditional name="inp_q">
+						  <param name="inp_sel" type="select" label="Select the type of input">
+							  <option value="str">1 - Direct input</option>
+							  <option value="txt">2 - Txt file</option>
+						  </param>
+						  <when value="str">
+                          		<param name="input" type="text" label="Input id:" help="Enter a phenotypic category mp id"/>
+						  </when>
+						  <when value="txt">
+							  <param name="input" type="data" format="data,tabular,txt" label="Input file:" help="Enter a txt file with a phenotypic category mp id."/>
+							  <param name="sep" type="select" label="Select the separator used in the file:">
+								  <option value="t">tab</option>
+								  <option value="s">single space</option>
+								  <option value=",">,</option>
+								  <option value=";">;</option>
+							  </param>
+						  </when>
+					  </conditional>
+						  <param name="head" type="boolean" checked="true" truevalue="True" falsevalue="False" label="Choose if include the header in the output. The default value is True."/>
+                  </when>
+          </conditional>
+  </inputs>
+  <outputs>
+	  <data format="tabular" name="output" label="${tool.name} query n° $query_type.selector"/>  
+  </outputs>
+  <help><![CDATA[
+	  **What it does**
+	  
+	  With this tool, it is possible to submit various types of queries to the IMPC database.
+	  Select the desired query from the drop down menu. As input both MGI ids or gene symbols are allowed (even mixed). If you want to input more than one id, separate them with a comma without spaces (eg: MGI:104636,MGI:104637). If a mixed input is retrieved, the order after the mapping will not be maintained.
+	  The mapping is performed using **bioDBnet** Python package (https://biodbnet.abcc.ncifcrf.gov/). Note that if the mapping between the two types of ids doesn't retrieves a result, that id will not be included in the query input, resulting in an error if all of the ids are not mapped. The output will be a table containing the data.
+	  For query 7 no inputs are required and you can choose if including genes without identified phenotypes or not.
+	  For each query is possible to choose if include or not an header row. Note that not all tools have an option to remove it automatically. In this case the user will have to remove it using the tool "Remove beginning of a file".
+	  The headers for each query are the following:
+	  
+	  **1-** MP term name, MP term ID
+	  
+	  **2-** Gene accession id, Gene name, Gene bundle url
+	  
+	  **3-** MP term, genes
+	  
+	  **4-** External sample id, Gene symbol, Biological sample group, Sex, Colony id, Zygosity, Parameter name, Download url, Thumbnail url
+	  
+	  **5-** Parameter name
+	  
+	  **6-** Parameter name, p-value
+	  
+	  **7-** Gene, Identified phenotypes
+	  
+	  **8-** Gene accession id, Gene name
+	  
+	  **9-** Gene accession id, Significant mp term id, Significant mp term name
+
+	  **Citation**
+	  
+	  If you use this tool in Galaxy, please cite Infrafrontier GmbH
+  ]]></help>
+</tool>
\ No newline at end of file