Mercurial > repos > computational-metabolomics > metfrag_vis
changeset 0:3dbe79671820 draft default tip
"planemo upload for repository https://github.com/computational-metabolomics/metfrag-galaxy commit b337c6296968848e3214f4b51df3d86776f84b6a"
| author | computational-metabolomics |
|---|---|
| date | Tue, 14 Jul 2020 07:42:34 -0400 |
| parents | |
| children | |
| files | metfrag-vis.py metfrag-vis.xml metfrag_logo.png test-data/metfrag_vis_input.tabular test-data/metfrag_vis_output.html |
| diffstat | 5 files changed, 2170 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/metfrag-vis.py Tue Jul 14 07:42:34 2020 -0400 @@ -0,0 +1,695 @@ +#!/usr/bin/env python + +# Load modules +import argparse +import base64 +import csv +import os +import re +import time +import urllib.parse + +import matplotlib.pyplot as plt + +import pubchempy + +import requests + +# Parse arguments +parser = argparse.ArgumentParser( + description='Visualise MetFrag results in html.') +parser.add_argument('-v', '--version', action='version', + version='MetFrag-vis Version 0.9', + help='show version') +parser.add_argument('-i', '--input', metavar='metfrag_results.tsv', + dest="input_tsv", required=True, + help='MetFrag results as input') +parser.add_argument('-o', '--output', metavar='metfrag_results.html', + dest="output_html", required=True, + help='Write MetFrag results into this output file') +parser.add_argument('-m', '--max-candidates', metavar='10', + dest="max_candidates", default=10, type=int, + required=False, + help='Maximum number of candidates per compound [1-1000]') +parser.add_argument('-s', '--synonyms', dest='synonyms', action='store_true', + required=False, + help='Fetch synonyms from PubChem [disabled by default]') +parser.add_argument('-c', '--classyfire', dest='classyfire', + action='store_true', required=False, + help='Fetch compound classes from ClassyFire' + ' [disabled by default]') + +args = parser.parse_args() + +# Input CSV with MetFrag results +input_tsv = args.input_tsv + +# Output html of MetFrag results +output_html = args.output_html + +# Max number of candidates per compound +max_candidates = args.max_candidates + +# PubChem synonyms +pubchem_synonyms_enabled = args.synonyms + +# ClassyFire classes +classyfire_classes_enabled = args.classyfire + + +# ---------- cdk_inchi_to_svg ---------- +def cdk_inchi_to_svg(inchi): + if "cdk-inchi-to-svg" in os.environ: + JAVA_CMD = 'cdk-inchi-to-svg' + ' ' + str( + '\'' + inchi + '\'') + ' ' + 'cdk-inchi-to-svg-output.svg' + else: + JAVA_BINARY = '/usr/local/bin/java' + CDK_INCHI_TO_SVG_JAR = '/usr/local/bin/' \ + 'cdk-inchi-to-svg-0.0.1-' \ + 'SNAPSHOT-jar-with-dependencies.jar' + JAVA_CMD = str( + JAVA_BINARY + ' ' + '-jar' + ' ' + CDK_INCHI_TO_SVG_JAR + ' ' + + str('\'' + inchi + '\'') + ' ' + 'cdk-inchi-to-svg-output.svg') + + # Exec cdk-inchi-to-svg JAVA binary + exitcode = os.system(JAVA_CMD) + + # Check whether binary has successfully been run + if (exitcode == 0): + with open("cdk-inchi-to-svg-output.svg", "r") as svg_file: + svg_string = [] + for line in svg_file: + if not ('<?xml' in line) and not ('<!DOCTYPE' in line): + if (' fill=\'#FFFFFF\'' in line): + line = re.sub(' fill=\'#FFFFFF\'', + ' fill=\'#FFFFFF\' fill-opacity=\'0.0\'', + line) + svg_string.append(line) + svg_file.close() + os.remove("cdk-inchi-to-svg-output.svg") + return (str(''.join(svg_string))) + else: + return (' ') + + +# ---------- pubchem_link ---------- +def pubchem_link(compound_name): + return (str('https://pubchem.ncbi.nlm.nih.gov/#query=' + compound_name)) + + +# ---------- kegg_link ---------- +def kegg_link(compound_name): + return (str( + 'https://www.genome.jp/dbget-bin/' + 'www_bfind_sub?mode=bfind&max_hit=1000&dbkey=kegg&keywords=' + + compound_name)) + + +# ---------- biocyc_link ---------- +def biocyc_link(compound_name): + biocyc_url = urllib.parse.urlparse( + str( + 'https://www.biocyc.org/' + 'substring-search?type=NIL&object=' + + compound_name + '&quickSearch=Quick+Search')) + return (biocyc_url.geturl()) + + +# ---------- hmdb_link ---------- +def hmdb_link(compound_name): + hmdb_url = urllib.parse.urlparse( + str( + 'https://hmdb.ca/unearth/q?utf8=\xe2&query=' + + compound_name + '&searcher=metabolites&button=')) + return (hmdb_url.geturl()) + + +# ---------- hmdb_link ---------- +def chebi_link(inchi): + return (str( + 'https://www.ebi.ac.uk/chebi/advancedSearchFT.do?searchString=' + + inchi)) + + +# ---------- PubChem Synonyms ---------- +def fetch_pubchem_synonyms(inchi): + if not ('InChI=' in inchi): + return (' ') + + # Fetch CID from InChI + print('Retrieving PubChem CID from InChI...') + compound = pubchempy.get_compounds(identifier=inchi, namespace='inchi') + compound_cid = re.sub(r'\).*', '', re.sub(r'.*\(', '', str(compound))) + if len(compound_cid) <= 1: + print(str('Warning. No match for InChI \"' + str(inchi) + '\".')) + return (' ') + + # Retrieve compound + print('Retrieving PubChem compound information...') + compound = pubchempy.Compound.from_cid(compound_cid) + if ('synonyms' in dir(compound)): + return ('; '.join(compound.synonyms)) + else: + print(str('Warning. No synonyms found for CID \"' + str( + compound_cid) + '\".')) + return (' ') + + +# ---------- ClassyFire ---------- +def fetch_classyfire_classes(inchi): + if not ('InChI=' in inchi): + return (' ') + + # Send POST request to ClassyFire + print('Sending request to ClassyFire...') + classyfire_url = 'http://classyfire.wishartlab.com/queries.json' + classyfire_post = str( + '{\"label\":\"metfrag\",\"query_input\":\"' + inchi + + '\",\"query_type\":\"STRUCTURE\"}') + classyfire_headers = {'Content-Type': 'application/json'} + classyfire_request = requests.post(classyfire_url, data=classyfire_post, + headers=classyfire_headers) + + # Only continue when request has been successfully sent + if (classyfire_request.status_code != 201): + print('Error! Could not send request to ClassyFire. \"', + str(classyfire_request.status_code) + ': ' + str( + classyfire_request.reason), '\". Skipping entry.') + return (' ') + + # Get ClassyFire Query ID + classyfire_request.json() + classyfire_query_id = classyfire_request.json()['id'] + + # Query ClassyFire in max. 20 attempts + classyfire_request_loop = 0 + while (classyfire_request_loop < 20): + print(str( + 'Sending query ' + str(classyfire_query_id) + ' to ClassyFire...')) + time.sleep(10) + classyfire_query = requests.get( + str('http://classyfire.wishartlab.com/queries/' + str( + classyfire_query_id) + '.json')) + + if (classyfire_query.status_code == 200) and ( + classyfire_query.json()['classification_status'] == 'Done'): + classyfire_request_loop = 999 + break + else: + classyfire_request_loop += 1 + + if classyfire_request_loop == 999: + # Direct parent + direct_parent_name = classyfire_query.json()[ + 'entities'][0]['direct_parent']['name'] + direct_parent_url = classyfire_query.json()[ + 'entities'][0]['direct_parent']['url'] + direct_parent = str( + '<a target="_blank" href="' + direct_parent_url + '">' + + direct_parent_name + '</a>') + + # Alternative parents + alt_parents = [] + for i in range(0, len(classyfire_query.json()['entities'][0][ + 'alternative_parents'])): + alt_parent_name = classyfire_query.json()[ + 'entities'][0]['alternative_parents'][i]['name'] + alt_parent_url = classyfire_query.json()[ + 'entities'][0]['alternative_parents'][i]['url'] + alt_parent = str( + '<a target="_blank" href="' + alt_parent_url + '">' + + alt_parent_name + '</a>') + alt_parents.append(alt_parent) + + # Concat classes + classes = str('<b>' + direct_parent + '</b>, <br>' + str( + ', <br>'.join(alt_parents))) + else: + print('Warning. Timout sending query to ClassyFire. Skipping entry.') + classes = ' ' + + return (classes) + + +# ---------- Plot Spectrum ---------- +def plot_spectrum(spectrum, spectrum_explained, spectrum_explained_formulas): + # Plot + plt.figure(figsize=[5.5, 4.4]) + plt.xlabel('m/z') + plt.ylabel('intensity') + + # Plot spectrum + x = [] + y = [] + for i in spectrum.split(';'): + t = i.split('_') + x.append(t[0]) + y.append(t[1]) + + for i in range(0, len(x)): + plt.plot([float(x[i]), float(x[i])], [0, float(y[i])], linewidth=1, + color='black') + plt.plot(float(x[i]), float(y[i]), 'o', color='black', markersize=4) + + if not (spectrum_explained == 'NA') and not ( + spectrum_explained_formulas == 'NA'): + # Plot explained peaks + ex = [] + ey = [] + for i in spectrum_explained.split(';'): + t = i.split('_') + ex.append(t[0]) + ey.append(y[x.index(t[0])]) + + for i in range(0, len(ex)): + plt.plot([float(ex[i]), float(ex[i])], [0, float(ey[i])], + linewidth=3, color='#2b8126') + plt.plot(float(ex[i]), float(ey[i]), 'o', color='#2b8126', + markersize=8) + + # Plot formulas on explained peaks + ex = [] + ey = [] + ez = [] + for i in spectrum_explained_formulas.split(';'): + t = i.split(':') + ex.append(t[0]) + ey.append(y[x.index(t[0])]) + ez.append(t[1]) + + for i in range(0, len(ex)): + plt.text(float(ex[i]), float(ey[i]) + 1000, ez[i], color='#2b8126', + fontsize=8, + horizontalalignment='center', verticalalignment='bottom') + + # Save SVG + plt.savefig("metfrag-vis-spectrum.svg", format="svg", transparent=True) + plt.close() + + # Import SVG + with open("metfrag-vis-spectrum.svg", "r") as svg_file: + svg_string = [] + for line in svg_file: + if not ('<?xml' in line) and not ('<!DOCTYPE' in line) and not ( + ' "http://www.w3.org/Graphics' in line): + svg_string.append(line) + svg_file.close() + os.remove("metfrag-vis-spectrum.svg") + return (str(''.join(svg_string))) + + +# #################### MAIN #################### +if pubchem_synonyms_enabled: + print('Fetching of PubChem Synonyms enabled.') +if classyfire_classes_enabled: + print('Fetching of ClassyFire Classes enabled.') + +# Open output html file +try: + metfrag_html = open(output_html, "w") +except Exception as e: + print("Error writing output file. {}".format(e)) + exit(1) + +# Write html header +metfrag_html.write('<!DOCTYPE html>\n') +metfrag_html.write('<html>\n') +metfrag_html.write('<head>\n') +metfrag_html.write('<title>' + 'msPurity MetFrag results' + '</title>\n') +metfrag_html.write('<style type="text/css">\n') +metfrag_html.write('svg { width: 200px; height: 100%; }\n') +metfrag_html.write( + 'body { font-family: Lucida, Verdana, Arial, Helvetica, sans-serif; ' + 'font-size: 13px; text-align: left; ' + 'color: #000000; margin: 8px 8px 8px 8px; }\n') +metfrag_html.write( + 'A { color: #2b8126; text-decoration: none; background: transparent; }\n') +metfrag_html.write( + 'A:visited { ' + 'color: #19681a; text-decoration: none; background: transparent; ' + '}\n') +metfrag_html.write( + 'A:hover { ' + 'color: #8fc180; text-decoration: underline; background: transparent; ' + '}\n') +metfrag_html.write( + 'h1 { font-size: 32px; font-weight: bold; text-align: center; ' + 'padding: 0px 0px 4px 0px; margin: 26px 0px 0px 0px; }\n') +metfrag_html.write( + 'h2 { font-size: 24px; font-weight: bold; text-align: left; ' + 'padding: 0px 0px 4px 0px; margin: 26px 0px 0px 0px; }\n') +metfrag_html.write( + 'table { font-family: Lucida, Verdana, Arial, Helvetica, sans-serif; ' + 'font-size: 10px; text-align: left; ' + 'line-height: 10px; border: 1px solid #e3efdf; ' + 'background-color: #ecf5ea; margin-bottom: 8px; ' + 'min-width: 1600px; max-width: 2400px; }\n') +metfrag_html.write( + '#tablediv { width: 100%; min-width: 20px; max-width: 200px; }\n') +metfrag_html.write('.tdmax { min-width: 200px; max-width: 200px; }\n') +metfrag_html.write('.tdvar { min-width: 200px; max-width: 600px; }\n') +metfrag_html.write('tr:nth-child(even) { background-color: #f6faf5; }\n') +metfrag_html.write('</style>\n') +metfrag_html.write('</head>\n') +metfrag_html.write('<body>\n') + +# Read input csv file +with open(input_tsv, "r") as metfrag_file: + metfrag_results = csv.DictReader(metfrag_file, delimiter='\t') + # Parse each line + line_count = 0 + compound = "" + candidates = 0 + for row in metfrag_results: + + # Start new document + if (line_count == 0): + if os.path.join(os.path.dirname(os.path.abspath(__file__)), + 'metfrag_logo.png'): + logo_pth = os.path.join( + os.path.dirname(os.path.abspath(__file__)), + 'metfrag_logo.png') + else: + logo_pth = '/usr/local/share/metfrag/metfrag_logo.png' + + with open(logo_pth, "rb") as png_file: + png_encoded = base64.b64encode(png_file.read()) + metfrag_html.write(str( + '\n<h1><img style="vertical-align:bottom" ' + 'src="data:image/png;base64,' + + png_encoded.decode('utf-8') + + '" alt="metfrag-logo" width="150"></img><text ' + 'style="line-height:2.0"> results</text></h1>\n' + )) + else: + # Parameter list at beginning of document + if (line_count == 1): + metfrag_html.write('\n<h2>Parameter list</h2>\n') + metfrag_html.write(str('MetFragDatabaseType=' + + re.sub(' .*', '', + re.sub('.*MetFragDatabaseType=', + '', + row[ + "MetFragCLIString"])) + + '<br>\n') + ) + metfrag_html.write(str('PrecursorIonMode=' + + re.sub(' .*', '', + re.sub('.*PrecursorIonMode=', '', + row[ + "MetFragCLIString"])) + + '<br>\n') + ) + metfrag_html.write(str('DatabaseSearchRelativeMassDeviation=' + + re.sub(' .*', '', + re.sub( + '.*DatabaseSearchRelative' + 'MassDeviation=', + '', + row[ + "MetFragCLIString"])) + + '<br>\n') + ) + metfrag_html.write( + str('FragmentPeakMatchAbsoluteMassDeviation=' + + re.sub(' .*', '', + re.sub( + '.*FragmentPeakMatchAbsoluteMassDeviation=', + '', + row["MetFragCLIString"])) + '<br>\n') + ) + metfrag_html.write( + str('FragmentPeakMatchRelativeMassDeviation=' + + re.sub(' .*', '', + re.sub( + '.*FragmentPeakMatchRelativeMassDeviation=', + '', + row["MetFragCLIString"])) + '<br>\n') + ) + metfrag_html.write(str('FilterExcludedElements=' + + re.sub(' .*', '', + re.sub( + '.*FilterExcludedElements=', + '', + row[ + "MetFragCLIString"])) + + '<br>\n') + ) + metfrag_html.write(str('FilterIncludedElements=' + + re.sub(' .*', '', + re.sub( + '.*FilterIncludedElements=', + '', + row[ + "MetFragCLIString"])) + + '<br>\n') + ) + metfrag_html.write(str('MetFragScoreTypes=' + + re.sub(' .*', '', + re.sub('.*MetFragScoreTypes=', + '', + row[ + "MetFragCLIString"])) + + '<br>\n') + ) + # New compound in list + if (row["name"] != compound): + compound = row["name"] + candidates = 0 + identifier = row["name"] + monoisotopic_mass = row["MonoisotopicMass"] + precursor_mz = row["precursor_mz"] + + if "retention_time" in row: + precursor_rt = row["retention_time"] + try: + precursor_rt = round(float(precursor_rt), 4) + except ValueError: + continue + else: + precursor_rt = '' + + if "precursor_type" in row: + precursor_type = row["precursor_type"] + elif "adduct" in row: + precursor_type = row["adduct"] + else: + precursor_type = '' + + if line_count > 1: + metfrag_html.write(str('</table>\n')) + + metfrag_html.write(str('\n' + '<h2>' + identifier + '</h2>\n')) + metfrag_html.write(str('<p><b>Precursor Type:</b> ' + str( + precursor_type) + '<br>')) + metfrag_html.write(str('<b>Precursor Mass:</b> ' + str( + round(float(precursor_mz), 4)) + '<br>')) + metfrag_html.write( + str('<b>Precursor Retention Time:</b> ' + str( + precursor_mz) + '<br></p>')) + metfrag_html.write(str('\n' + '<table>\n')) + metfrag_html.write(str( + '<tr style="vertical-align:bottom; ' + 'background-color:#e3efdf;">' + + '<td class="tdmax">' + '<b>Spectrum</b>' + '</td>' + + '<td class="tdmax">' + '<b>Structure</b>' + '</td>' + + '<td>' + '<b>Monoisotopic Mass</b>' + '</td>' + + '<td>' + '<b>Molecular Formula</b>' + '</td>' + + '<td>' + '<b>Compound Name</b>' + '</td>' + + '<td class="tdvar">' + '<b>PubChem Synonyms</b>'+'</td>' + + '<td>' + '<b>Compound Classes</b>' + '</td>' + + '<td>' + '<b>MetFrag Score</b>' + '</td>' + + '<td>' + '<b>MetFusion Score</b>' + '</td>' + + '<td>' + '<b>Fragmenter Score</b>' + '</td>' + + '<td>' + '<b>Suspectlist Score</b>' + '</td>' + + '<td>' + '<b>Explained Peaks</b>' + '</td>' + + '<td>' + '<b>MetFrag Web</b>' + '</td>' + + '<td>' + '<b>External Links</b>' + '</td>' + + '<td class="tdmax">' + '<b>InChI</b>' + '</td>' + + '</tr>\n')) + + # Compound candidate + if (candidates < max_candidates): + # Column variables + inchi = row["InChI"] + smiles = row["SMILES"] + mol_formula = row["MolecularFormula"] + compound_name = row["IUPACName"] + frag_score = row["FragmenterScore"] + metfusion_score = row["OfflineMetFusionScore"] + score = row["Score"] + if "SuspectListScore" in row: + suspectlist_score = row["SuspectListScore"] + else: + suspectlist_score = 0 + peaks_explained = row["NoExplPeaks"] + peaks_used = row["NumberPeaksUsed"] + spectrum_explained = row["ExplPeaks"] + spectrum_explained_formulas = row["FormulasOfExplPeaks"] + identifier = row["Identifier"] + + # PubChem Synonyms + if pubchem_synonyms_enabled: + pubchem_synonyms = fetch_pubchem_synonyms(inchi) + else: + pubchem_synonyms = ' ' + + # Compound Classes + if classyfire_classes_enabled: + compound_classes = fetch_classyfire_classes(inchi) + else: + compound_classes = ' ' + + # Draw Spectrum + spectrum = re.sub(' .*', '', re.sub('.*PeakListString=', '', + row["MetFragCLIString"])) + spectrum_string = plot_spectrum(spectrum, spectrum_explained, + spectrum_explained_formulas) + + # Draw SVG + svg_string = cdk_inchi_to_svg(str(inchi)) + + # External links + external_links = str( + '<a target="_blank" href="' + pubchem_link( + compound_name) + '">PubChem</a>' + ', ' + + '<a target="_blank" href="' + kegg_link( + compound_name) + '">KEGG</a>' + ', ' + + '<a target="_blank" href="' + hmdb_link( + compound_name) + '">HMDB</a>' + ', ' + + '<a target="_blank" href="' + biocyc_link( + compound_name) + '">BioCyc</a>' + ', ' + + '<a target="_blank" href="' + chebi_link( + inchi) + '">ChEBI</a>') + + # MetFragWeb + FragmentPeakMatchAbsoluteMassDeviation = str( + '' + + re.sub(' .*', '', + re.sub( + '.*FragmentPeakMatchAbsoluteMassDeviation=', + 'FragmentPeakMatchAbsoluteMassDeviation=', + row["MetFragCLIString"])) + ) + FragmentPeakMatchRelativeMassDeviation = str( + '' + + re.sub(' .*', '', + re.sub( + '.*FragmentPeakMatchRelativeMassDeviation=', + 'FragmentPeakMatchRelativeMassDeviation=', + row["MetFragCLIString"])) + ) + DatabaseSearchRelativeMassDeviation = str( + '' + + re.sub(' .*', '', + re.sub( + '.*DatabaseSearchRelativeMassDeviation=', + 'DatabaseSearchRelativeMassDeviation=', + row["MetFragCLIString"])) + ) + IonizedPrecursorMass = str( + 'IonizedPrecursorMass=' + str(row["precursor_mz"])) + NeutralPrecursorMass = str( + '' + re.sub(' .*', '', + re.sub( + '.*NeutralPrecursorMass=', + 'NeutralPrecursorMass=', + row[ + "MetFragCLIString"])) + ) + NeutralPrecursorMolecularFormula = str( + 'NeutralPrecursorMolecularFormula=' + str( + row["MolecularFormula"])) + PrecursorIonMode = str( + '' + re.sub(' .*', '', re.sub('.*PrecursorIonMode=', + 'PrecursorIonMode=', + row["MetFragCLIString"]))) + PeakList = str( + '' + re.sub(' .*', '', + re.sub('.*PeakListString=', 'PeakList=', + row["MetFragCLIString"]))) + MetFragDatabaseType = str( + '' + re.sub(' .*', '', + re.sub( + '.*MetFragDatabaseType=', + 'MetFragDatabaseType=', + row["MetFragCLIString"]))) + + metfrag_web = str( + 'https://msbi.ipb-halle.de/MetFrag/landing.xhtml?' + + FragmentPeakMatchAbsoluteMassDeviation + '&' + + FragmentPeakMatchRelativeMassDeviation + '&' + + DatabaseSearchRelativeMassDeviation + '&' + + IonizedPrecursorMass + '&' + + NeutralPrecursorMass + '&' + + # NeutralPrecursorMolecularFormula + '&' + + PrecursorIonMode + '&' + + PeakList + '&' + + MetFragDatabaseType) + + # Write html code + metfrag_html.write(str('<tr style="vertical-align:center">' + + '<td class="tdmax">' + + spectrum_string + + '</td>' + + '<td class="tdmax">' + + svg_string + + '</td>' + + '<td>' + + monoisotopic_mass + + '</td>' + + '<td>' + + mol_formula + + '</td>' + + '<td>' + + compound_name + + '</td>' + + '<td class="tdvar">' + + pubchem_synonyms + + '</td>' + + '<td>' + + compound_classes + '</td>' + + '<td>' + + str(round(float(score), 3)) + + '</td>' + + '<td>' + + str(round(float(metfusion_score), 3)) + + '</td>' + + '<td>' + + str(round(float(frag_score), 3)) + + '</td>' + + '<td>' + + str( + round(float(suspectlist_score), 3) + ) + + '</td>' + + '<td>' + + peaks_explained + + ' / ' + + peaks_used + + '</td>' + + '<td>' + + '<a target="_blank" href="' + + metfrag_web + + '">MetFragWeb</a>' + + '</td>' + + '<td>' + + external_links + + '</td>' + + '<td class="tdmax">' + + inchi + + '</td>' + + '</tr>\n')) + + line_count += 1 + candidates += 1 + + # Finish candidate list + metfrag_html.write(str('</table>\n')) + +# Write html footer +metfrag_html.write('\n</body>\n') +metfrag_html.write('</html>\n') + +# Close output html file +metfrag_html.close()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/metfrag-vis.xml Tue Jul 14 07:42:34 2020 -0400 @@ -0,0 +1,154 @@ +<tool id="metfrag_vis" name="MetFrag Vis" version="2.4.5+galaxy0" profile="18.01"> + <description> + Visualisation for MetFrag results + </description> + <requirements> + <requirement type="package" version="2.4.5">metfrag</requirement> + <requirement type="package" version="0.9">cdk-inchi-to-svg</requirement> + <requirement type="package" version="3.7.3">python</requirement> + <requirement type="package" version="1.0.4">pubchempy</requirement> + <requirement type="package">matplotlib</requirement> + <requirement type="package">requests</requirement> + <requirement type="package">numpy</requirement> + </requirements> + <stdio> + <regex match="Cannot allocate memory" + source="stderr" + level="fatal_oom" + description="Out of memory error occurred" /> + </stdio> + <command detect_errors="exit_code"> + <![CDATA[ + python '$__tool_directory__/metfrag-vis.py' + -i $metfrag_vis_input + -o $metfrag_vis_output + -m $metfrag_vis_hits_limit + $metfrag_synonyms + $metfrag_classyfire + ]]></command> + <inputs> + <param argument="-i" name="metfrag_vis_input" type="data" format="tsv,tabular" multiple="False" optional="False" label="MetFrag result file (Output tabular file from MetFrag tool)" /> + <param argument="-m" name="metfrag_vis_hits_limit" type="text" value="10" label="MetFrag Hits Limit" optional="False" help="Limit of candidate hits returned per compound" /> + <param argument="-s" name="metfrag_synonyms" type="boolean" truevalue="-s" falsevalue="" checked="true" label="Synonyms" help="Fetch synonyms for each candidate from PubChem"/> + <param argument="-c" name="metfrag_classyfire" type="boolean" truevalue="-c" falsevalue="" label="ClassyFire" help="Fetch compound classes for each candidate using ClassyFire"/> + </inputs> + <outputs> + <data name="metfrag_vis_output" format="html" label="Summary HTML file" /> + </outputs> + + <tests> + <test> + <param name="metfrag_vis_input" value="metfrag_vis_input.tabular"/> + <output name="metfrag_vis_output" file="metfrag_vis_output.html" compare="sim_size"/> + </test> + </tests> + + <help> +--------------------- +MetFrag Visualisation +--------------------- + +Description +----------- + +MetFrag is a freely available software for the annotation of high precision tandem mass spectra of metabolites which is +a first and critical step for the identification of a molecule's structure. Candidate molecules of different databases +are fragmented "in silico" and matched against mass to charge values. A score calculated using the fragment peak +matches gives hints to the quality of the candidate spectrum assignment. + +This module summarises the results generated by MetFrag. It takes the (sometimes very large) output tabular file, parses the file and enriches it with images of the spectra showing the extracted and matched peaks and putative structures of the compound candidates. The module supports limiting the results per compound (default 10 candidates). Results can be enriched with further information from PubChem and ClassyFire to make it easier to select candidates. The information is summarised in a HTML5 file which can be viewed directly in Galaxy. + +Website: http://ipb-halle.github.io/MetFrag/ + +Parameters +---------- + +**\1. Tabular file** + +Tabular file created using the *MetFrag* tool. + +**\2. MetFrag Hits Limit** + +Defines the limit of candidates returned per compound. + +**\3. Synonyms** + +If set to True, synonyms for each candidate are fetched from PubChem. + +**\4. ClassyFire** + +If set to True, compound classes are fetched for each candidate using ClassyFire. + +Output +------- + +The output is similar to the MetFrag results tabular, but enriched with additional images of spectra, compound candidates and (if enabled) compound classes, alternative names and links to online services. + ++-------------+--------------------------------------------+---+ +| adduct | name |...| ++-------------+--------------------------------------------+---+ +| [M-H]- | D-Glucose; LC-ESI-QTOF; MS2; CE: 10; R=; |...| ++-------------+--------------------------------------------+---+ +| [M-H]- | D-Glucose; LC-ESI-QTOF; MS2; CE: 10; R=; |...| ++-------------+--------------------------------------------+---+ +| ... | ... |...| ++-------------+--------------------------------------------+---+ + +Table continued (these columns are derived from the MetFrag result): + ++---+------------------+----------------------------------------------------------+-------------------------------------------------------------------------------------+-----+ +|...| sample_name | ExplPeaks | FormulasOfExplPeaks | ... | ++---+------------------+----------------------------------------------------------+-------------------------------------------------------------------------------------+-----+ +|...| 1_metfrag_result | 59.0138_715.8;71.014_679.7;89.0251_999.0;101.0234_103.0 | 59.0138:[C2H4O2]-H-;71.014:[C3H5O2-H]-H-;89.0251:[C3H6O3]-H-;101.0234:[C4H7O3-H]-H- | ... | ++---+------------------+----------------------------------------------------------+-------------------------------------------------------------------------------------+-----+ +|...| 1_metfrag_result | 59.0138_715.8;71.014_679.7;89.0251_999.0;101.0234_103.0 | 59.0138:[C2H4O2]-H-;71.014:[C3H5O2-H]-H-;89.0251:[C3H6O3]-H-;101.0234:[C4H7O3-H]-H- | ... | ++---+------------------+----------------------------------------------------------+-------------------------------------------------------------------------------------+-----+ +|...| ... | ... | ... | ... | ++---+------------------+----------------------------------------------------------+-------------------------------------------------------------------------------------+-----+ + + +Table continued (columns are derived from the MetFrag result): + ++---+------------------+----------------------------+------------------------------------------------------+------------+---------------------------------------------------------------------------------+---+ +|...| FragmenterScore | FragmenterScore_Values | FormulasOfExplPeaks | Identifier | InChI |...| ++---+------------------+----------------------------+------------------------------------------------------+------------+---------------------------------------------------------------------------------+---+ +|...| 105.844569063138 | 696.0;1156.0;696.0;1156.0 | 6-(hydroxymethyl)oxane-2,3,4,5-tetrol | 206 | InChI=1S/C6H12O6/c7-1-2-3(8)4(9)5(10)6(11)12-2/h2-11H,1H |...| ++---+------------------+----------------------------+------------------------------------------------------+------------+---------------------------------------------------------------------------------+---+ +|...| 105.844569063138 | 696.0;1156.0;696.0;1156.0 | (3R,4S,5S,6R)-6-(hydroxymethyl)oxane-2,3,4,5-tetrol | 5793 | InChI=1S/C6H12O6/c7-1-2-3(8)4(9)5(10)6(11)12-2/h2-11H,1H2/t2-,3-,4+,5-,6?/m1/s1 |...| ++---+------------------+----------------------------+------------------------------------------------------+------------+---------------------------------------------------------------------------------+---+ +|...| ... | ... | ... | ... | ... |...| ++---+------------------+----------------------------+------------------------------------------------------+------------+---------------------------------------------------------------------------------+---+ + +Table continued (columns are derived from the MetFrag result): + + ++---+-------------+-----------------+-----------------------+----------------------------------------------+------------------+------------------+--------+ +|...| NoExplPeaks | NumberPeaksUsed | OfflineMetFusionScore | SMILES | Score | SuspectListScore | XlogP3 | ++---+-------------+-----------------+-----------------------+----------------------------------------------+------------------+------------------+--------+ +|...| 4 | 5 | 2.84566828424078 | C(C1C(C(C(C(O1)O)O)O)O)O | 1.82678219603441 | 1 | -2.6 | ++---+-------------+-----------------+-----------------------+----------------------------------------------+------------------+------------------+--------+ +|...| 4 | 5 | 2.84566828424078 | C([C@@H]1[C@H]([C@@H]([C@H](C(O1)O)O)O)O)O | 1.82678219603441 | 1 | -2.6 | ++---+-------------+-----------------+-----------------------+----------------------------------------------+------------------+------------------+--------+ +|...| ... | ... | ... | ... | ... | ... | ... | ++---+-------------+-----------------+-----------------------+----------------------------------------------+------------------+------------------+--------+ + +Additional notes +-------------------- + +This module queries the online services PubChem and ClassyFire. + +Feunang YD, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, Fahy E, Steinbeck C, Subramanian S, Bolton E, +Greiner R, Wishart DS (2016): ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform 8:61. doi: 10.1186/s13321-016-0174-y + +Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE (2019): PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 8;47(D1):D1102-D1109. doi: 10.1093/nar/gky1033 + +Developers and contributors +--------------------------- +- **Kristian Peters (kpeters@ipb-halle.de) - IPB Halle (Germany)** +- **Tom Lawson (t.n.lawson@bham.ac.uk) - University of Birmingham (UK)** +- **Christoph Ruttkies (christoph.ruttkies@ipb-halle.de) - IPB Halle (Germany)** + </help> + <citations> + <citation type="doi">10.1186/s13321-016-0115-9</citation> + </citations> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/metfrag_vis_input.tabular Tue Jul 14 07:42:34 2020 -0400 @@ -0,0 +1,4 @@ +MetFragCLIString name AlignmentID retention_time num_peaks sample_name precursor_type precursor_mz ExplPeaks FormulasOfExplPeaks FragmenterScore FragmenterScore_Values IUPACName Identifier InChI InChIKey InChIKey1 InChIKey2 MaximumTreeDepth MolecularFormula MonoisotopicMass NoExplPeaks NumberPeaksUsed OfflineMetFusionScore SMILES Score XlogP3 +metfrag PrecursorIonMode=1 MetFragScoreWeights=1.0,1.0 SampleName=1_metfrag_result FragmentPeakMatchAbsoluteMassDeviation=0.001 NumberThreads=1 DatabaseSearchRelativeMassDeviation=10.0 PeakListPath=./temp/1_tmpspec.txt MetFragDatabaseType=PubChem FragmentPeakMatchRelativeMassDeviation=5.0 MetFragCandidateWriter=CSV MetFragScoreTypes=FragmenterScore,OfflineMetFusionScore IsPositiveIonMode=True NeutralPrecursorMass=148.12786092 ResultsPath=./temp/ PeakListString=121.099329887276_182;149.133267159026_510;150.14016979533_114 pos_27_winter_Marpol_27_2.E.3_01_17272:1 1 720.852 3 1_metfrag_result [M+H]+ 149.13513692 NA NA 0.0 NA dimethyl(dipropyl)-lambda4-sulfane 71774044 InChI=1S/C8H20S/c1-5-7-9(3,4)8-6-2/h5-8H2,1-4H3 OPMSGHPOQIQQRS-UHFFFAOYSA-N OPMSGHPOQIQQRS UHFFFAOYSA 2 C8H20S 148.128572 0 1 0.37031226614982965 CCCS(C)(C)CCC 1.0 2.7 +metfrag PrecursorIonMode=1 MetFragScoreWeights=1.0,1.0 SampleName=1_metfrag_result FragmentPeakMatchAbsoluteMassDeviation=0.001 NumberThreads=1 DatabaseSearchRelativeMassDeviation=10.0 PeakListPath=./temp/1_tmpspec.txt MetFragDatabaseType=PubChem FragmentPeakMatchRelativeMassDeviation=5.0 MetFragCandidateWriter=CSV MetFragScoreTypes=FragmenterScore,OfflineMetFusionScore IsPositiveIonMode=True NeutralPrecursorMass=148.12786092 ResultsPath=./temp/ PeakListString=121.099329887276_182;149.133267159026_510;150.14016979533_114 pos_27_winter_Marpol_27_2.E.3_01_17272:1 1 720.852 3 1_metfrag_result [M+H]+ 149.13513692 NA NA 0.0 NA butyl-ethyl-dimethyl-lambda4-sulfane 90984195 InChI=1S/C8H20S/c1-5-7-8-9(3,4)6-2/h5-8H2,1-4H3 HCGXHQGSWBNABQ-UHFFFAOYSA-N HCGXHQGSWBNABQ UHFFFAOYSA 2 C8H20S 148.128572 0 1 0.37031226614982965 CCCCS(C)(C)CC 1.0 2.5 +metfrag PrecursorIonMode=1 MetFragScoreWeights=1.0,1.0 SampleName=1_metfrag_result FragmentPeakMatchAbsoluteMassDeviation=0.001 NumberThreads=1 DatabaseSearchRelativeMassDeviation=10.0 PeakListPath=./temp/1_tmpspec.txt MetFragDatabaseType=PubChem FragmentPeakMatchRelativeMassDeviation=5.0 MetFragCandidateWriter=CSV MetFragScoreTypes=FragmenterScore,OfflineMetFusionScore IsPositiveIonMode=True NeutralPrecursorMass=148.12786092 ResultsPath=./temp/ PeakListString=121.099329887276_182;149.133267159026_510;150.14016979533_114 pos_27_winter_Marpol_27_2.E.3_01_17272:1 1 720.852 3 1_metfrag_result [M+H]+ 149.13513692 NA NA 0.0 NA trimethyl(3-methylbutyl)-lambda4-sulfane 118334050 InChI=1S/C8H20S/c1-8(2)6-7-9(3,4)5/h8H,6-7H2,1-5H3 DFXVYXYADDYUJD-UHFFFAOYSA-N DFXVYXYADDYUJD UHFFFAOYSA 2 C8H20S 148.128572 0 1 0.3692650195260169 CC(C)CCS(C)(C)C 0.9971719904536216 2.6 \ No newline at end of file
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alt="metfrag-logo" width="150"></img><text style="line-height:2.0"> results</text></h1> + +<h2>Parameter list</h2> +MetFragDatabaseType=PubChem<br> +PrecursorIonMode=1<br> +DatabaseSearchRelativeMassDeviation=10.0<br> +FragmentPeakMatchAbsoluteMassDeviation=0.001<br> +FragmentPeakMatchRelativeMassDeviation=5.0<br> +FilterExcludedElements=metfrag<br> +FilterIncludedElements=metfrag<br> +MetFragScoreTypes=FragmenterScore,OfflineMetFusionScore<br> + +<h2>pos_27_winter_Marpol_27_2.E.3_01_17272:1</h2> +<p><b>Precursor Type:</b> [M+H]+<br><b>Precursor Mass:</b> 149.1351<br><b>Precursor Retention Time:</b> 720.852<br></p> +<table> +<tr style="vertical-align:bottom; background-color:#e3efdf;"><td class="tdmax"><b>Spectrum</b></td><td class="tdmax"><b>Structure</b></td><td><b>Monoisotopic Mass</b></td><td><b>Molecular Formula</b></td><td><b>Compound Name</b></td><td class="tdvar"><b>PubChem Synonyms</b></td><td><b>Compound Classes</b></td><td><b>MetFrag Score</b></td><td><b>MetFusion Score</b></td><td><b>Fragmenter Score</b></td><td><b>Suspectlist Score</b></td><td><b>Explained Peaks</b></td><td><b>MetFrag Web</b></td><td><b>External Links</b></td><td class="tdmax"><b>InChI</b></td></tr> +<tr style="vertical-align:center"><td class="tdmax"><!-- Created with matplotlib (https://matplotlib.org/) --> +<svg height="316.8pt" version="1.1" viewBox="0 0 396 316.8" width="396pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> + <defs> + <style type="text/css"> +*{stroke-linecap:butt;stroke-linejoin:round;} + </style> + </defs> + <g id="figure_1"> + <g id="patch_1"> + <path d="M 0 316.8 +L 396 316.8 +L 396 0 +L 0 0 +z +" style="fill:none;"/> + </g> + <g id="axes_1"> + <g id="patch_2"> + <path d="M 49.5 281.952 +L 356.4 281.952 +L 356.4 38.016 +L 49.5 38.016 +z +" style="fill:none;"/> + </g> + <g id="matplotlib.axis_1"> + <g id="xtick_1"> + <g id="line2d_1"> + <defs> + <path d="M 0 0 +L 0 3.5 +" id="mcdfcbc69ce" 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href="https://pubchem.ncbi.nlm.nih.gov/#query=butyl-ethyl-dimethyl-lambda4-sulfane">PubChem</a>, <a target="_blank" href="https://www.genome.jp/dbget-bin/www_bfind_sub?mode=bfind&max_hit=1000&dbkey=kegg&keywords=butyl-ethyl-dimethyl-lambda4-sulfane">KEGG</a>, <a target="_blank" href="https://hmdb.ca/unearth/q?utf8=✓&query=butyl-ethyl-dimethyl-lambda4-sulfane&searcher=metabolites&button=">HMDB</a>, <a target="_blank" href="https://www.biocyc.org/substring-search?type=NIL&object=butyl-ethyl-dimethyl-lambda4-sulfane&quickSearch=Quick+Search">BioCyc</a>, <a target="_blank" href="https://www.ebi.ac.uk/chebi/advancedSearchFT.do?searchString=InChI=1S/C8H20S/c1-5-7-8-9(3,4)6-2/h5-8H2,1-4H3">ChEBI</a></td><td class="tdmax">InChI=1S/C8H20S/c1-5-7-8-9(3,4)6-2/h5-8H2,1-4H3</td></tr> +<tr style="vertical-align:center"><td class="tdmax"><!-- Created with matplotlib (https://matplotlib.org/) --> +<svg height="316.8pt" version="1.1" viewBox="0 0 396 316.8" width="396pt" 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class="tdvar">SCHEMBL17037255</td><td> </td><td>0.997</td><td>0.369</td><td>0.0</td><td>0.0</td><td>0 / 1</td><td><a target="_blank" href="https://msbi.ipb-halle.de/MetFrag/landing.xhtml?FragmentPeakMatchAbsoluteMassDeviation=0.001&FragmentPeakMatchRelativeMassDeviation=5.0&DatabaseSearchRelativeMassDeviation=10.0&IonizedPrecursorMass=149.13513692&NeutralPrecursorMass=148.12786092&PrecursorIonMode=1&PeakList=121.099329887276_182;149.133267159026_510;150.14016979533_114&MetFragDatabaseType=PubChem">MetFragWeb</a></td><td><a target="_blank" href="https://pubchem.ncbi.nlm.nih.gov/#query=trimethyl(3-methylbutyl)-lambda4-sulfane">PubChem</a>, <a target="_blank" href="https://www.genome.jp/dbget-bin/www_bfind_sub?mode=bfind&max_hit=1000&dbkey=kegg&keywords=trimethyl(3-methylbutyl)-lambda4-sulfane">KEGG</a>, <a target="_blank" href="https://hmdb.ca/unearth/q?utf8=✓&query=trimethyl(3-methylbutyl)-lambda4-sulfane&searcher=metabolites&button=">HMDB</a>, <a target="_blank" href="https://www.biocyc.org/substring-search?type=NIL&object=trimethyl(3-methylbutyl)-lambda4-sulfane&quickSearch=Quick+Search">BioCyc</a>, <a target="_blank" href="https://www.ebi.ac.uk/chebi/advancedSearchFT.do?searchString=InChI=1S/C8H20S/c1-8(2)6-7-9(3,4)5/h8H,6-7H2,1-5H3">ChEBI</a></td><td class="tdmax">InChI=1S/C8H20S/c1-8(2)6-7-9(3,4)5/h8H,6-7H2,1-5H3</td></tr> +</table> + +</body> +</html>