Mercurial > repos > infr > impc_tools
changeset 0:0a9cf7f52b9c draft default tip
planemo upload commit 213f6eeb03f96bb13d0ace6e0c87e2562d37f728-dirty
| author | infr |
|---|---|
| date | Wed, 22 Jun 2022 13:36:44 +0000 |
| parents | |
| children | |
| files | impc_tool.py impc_tool.xml shed_example.yml |
| diffstat | 3 files changed, 967 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/impc_tool.py Wed Jun 22 13:36:44 2022 +0000 @@ -0,0 +1,636 @@ +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) + 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': + 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 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'] == 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'], "mp_name": mp_term_name['mpTermName'], "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 = [] + 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/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) + + +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) + 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) + empty = True + discarded = [] + for i in db.index: + if db['MGIID'][i] != '-': + empty = False + final_list.append(db['MGIID'][i][4:]) + break + else: + discarded.append(db['MGIID'][i][4:]) + + if (len(db) == 0 and len(final_list) == 0) or (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 gene symbols entry. Only MGI entries will be used.") + elif len(discarded) != 0: + print("Warning: it was not possible to map these elements: " + ",".join(discarded) + "\n") + return(final_list) + +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) + +if __name__ == "__main__": + main() \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/impc_tool.xml Wed Jun 22 13:36:44 2022 +0000 @@ -0,0 +1,325 @@ +<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 IMPReSS parameters have been measured for a particular knockout</option> + <option value="6">6 - Which IMPRess 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 IMPReSS pipeline</option> + <option value="9">9 - Extract all genes and corresponding phenotypes related to a particular top level phenotype category</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/HP 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/HP 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 genes MGI ids or symbols"/> + <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/HP 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/HP 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 an IMPReSS parameter id or a list of ids 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 an IMPReSS parameter id or a list of ids"/> + <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 an IMPReSS parameter id or a list of ids 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 an IMPReSS parameter id or a list of ids"/> + <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 IMPReSS pipeline id"/> + </when> + <when value="txt"> + <param name="input" type="data" format="data,tabular,txt" label="Input file:" help="Enter a txt file with an IMPReSS 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 top level phenotype category id"/> + </when> + <when value="txt"> + <param name="input" type="data" format="data,tabular,txt" label="Input file:" help="Enter a txt file with a top level phenotype category 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. + 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 the phenotypes, is possible to give as input both MP term ids or HP terms ids since they will be mapped to MP terms (also here the order of the input will not be maintained). + For both genes and phenotypes mapping, check the "View details" section of the job to check if some of them were not mapped (typo errors/id not present in the database). + For queries requiring an IMPReSS pipeline id, here_ is possible to find a complete list with details about each pipeline. + For query 7 no inputs are required and you can choose if including genes without identified phenotypes or not. + In query number 9, a top level phenotype category is required as input. On IMPC, phenotypes are divided into 20 categories to summarize wich systems are mainly influenced by the phenotype. In the database they are 24, since some of them are splitted into different groups: + + + +-----------------------------------------+---------------------------------------+ + | Top level phenotype category name | top level phenotype category id | + +=========================================+=======================================+ + | Immune system phenotype | MP:0005387 | + +-----------------------------------------+---------------------------------------+ + | Integument phenotype | MP:0010771 | + +-----------------------------------------+---------------------------------------+ + | Adipose tissue phenotype | MP:0005375 | + +-----------------------------------------+---------------------------------------+ + | Hearing/vestibular/ear phenotype | MP:0005377 | + +-----------------------------------------+---------------------------------------+ + | Hematopoietic system phenotype | MP:0005397 | + +-----------------------------------------+---------------------------------------+ + | Craniofacial phenotype | MP:0005382 | + +-----------------------------------------+---------------------------------------+ + | Cardiovascular system phenotype | MP:0005385 | + +-----------------------------------------+---------------------------------------+ + | Renal/urinary system phenotype | MP:0005367 | + +-----------------------------------------+---------------------------------------+ + | Homeostasis/metabolism phenotype | MP:0005376 | + +-----------------------------------------+---------------------------------------+ + | Pigmentation phenotype | MP:0001186 | + +-----------------------------------------+---------------------------------------+ + | Limbs/digits/tail phenotype | MP:0005371 | + +-----------------------------------------+---------------------------------------+ + | Nervous system phenotype | MP:0003631 | + +-----------------------------------------+---------------------------------------+ + | Vision/eye phenotype | MP:0005391 | + +-----------------------------------------+---------------------------------------+ + | Liver/biliary system phenotype | MP:0005370 | + +-----------------------------------------+---------------------------------------+ + | Respiratory system phenotype | MP:0005388 | + +-----------------------------------------+---------------------------------------+ + | Behavior/neurological phenotype | MP:0005386 | + +-----------------------------------------+---------------------------------------+ + | Skeleton phenotype | MP:0005390 | + +-----------------------------------------+---------------------------------------+ + | Mortality/aging | MP:0010768 | + +-----------------------------------------+---------------------------------------+ + | Reproductive system phenotype | MP:0005389 | + +-----------------------------------------+---------------------------------------+ + | Endocrine/exocrine gland phenotype | MP:0005379 | + +-----------------------------------------+---------------------------------------+ + | Growth/size/body region phenotype | MP:0005378 | + +-----------------------------------------+---------------------------------------+ + | Embryo phenotype | MP:0005380 | + +-----------------------------------------+---------------------------------------+ + | Muscle phenotype | MP:0005369 | + +-----------------------------------------+---------------------------------------+ + | Digestive/alimentary phenotype | MP:0005381 | + +-----------------------------------------+---------------------------------------+ + + | + | + + 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: + + +---------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------+ + | Query | Output header columns | + +===================================================================================================+==========================================================================+ + |Extract all measured phenotypes related to a gene |MP term name, MP term ID | + +---------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------+ + |Extract all genes having a particular phenotype or a set of phenotypes |Gene accession id, Gene name, Gene bundle url | + +---------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------+ + |Extract all phenotypes which are present in a particular gene set |MP term ID, MP term name, genes | + +---------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------+ + |Extract images with a particular phenotype or a set of phenotypes |External sample id, Gene symbol, Biological sample group, Sex, Colony id, | + | |Zygosity, Parameter name, Download url, Thumbnail url | + +---------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------+ + |Which IMPReSS parameters have been measured for a particular knockout |IMPReSS Parameter name | + +---------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------+ + |Which IMPRess parameters identified a significant finding for a particular knockout |IMPReSS Parameter name, p-value | + +---------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------+ + |Full table of genes and all identified phenotypes |Gene, Identified phenotypes | + +---------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------+ + |Extract all genes names and ID measured in a specific IMPReSS pipeline |Gene accession id, Gene name | + +---------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------+ + |Extract all genes and corresponding phenotypes related to a particular top level phenotype category|Gene accession id, Significant mp term id, Significant mp term name | + +---------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------+ + + | + | + + **Credits** + + * `Infrafrontier GmbH`_ + + * `International Mouse Phenotype Consortium`_ + +.. _here: https://www.mousephenotype.org/impress/pipelines +.. _`Infrafrontier GmbH`: https://infrafrontier.eu +.. _`International Mouse Phenotype Consortium`: https://mousephenotype.org +]]></help> +<citations> + <citation type="doi">https://doi.org/10.1093/nar/gku1193</citation> + <citation type="doi">https://doi.org/10.1038/nature19356</citation> +</citations> +</tool> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/shed_example.yml Wed Jun 22 13:36:44 2022 +0000 @@ -0,0 +1,6 @@ +categories: [Web Services] +description: "A collection of tools that allow to query the IMPC database" +name: "impc_tools" +owner: andrea.furlani +type: "unrestricted" +long_description: "This tool allow the user to retrieve different types of information from the IMPC database. The tool allows as input both files (txt,csv,tab) or direct input. If you are using any query inside a workflow, only the file input option is usable. Moreover, when using a file as input please take note on which separator are you using in that file. If you are not sure on which separator is used in a file, as default consider to use the 'tab' option." \ No newline at end of file