comparison metfrag.py @ 8:9a3019c609d9 draft

planemo upload for repository https://github.com/computational-metabolomics/metfrag-galaxy commit febaaca439248736775db9ad0a857e30463d10aa

7:0b3816a7a14b

    meta_regex['precursor_mz'].extend(regex_msp['precursor_mz'])
    meta_regex['precursor_type'].extend(regex_msp['precursor_type'])
    meta_regex['num_peaks'].extend(regex_msp['num_peaks'])
    meta_regex['msp'] = regex_msp['msp']

    print(meta_regex)

adduct_types = {
    '[M+H]+': 1.007276,
    '[M+NH4]+': 18.034374,
    '[M+Na]+': 22.989218,

    '[M+HCOO]-': 44.99819,
    '[M-H+HCOOH]-': 44.99819,  # same as above but different style of writing adduct
    '[M+CH3COO]-': 59.01385,
    '[M-H+CH3COOH]-': 59.01385  # same as above but different style of writing adduct
}

# function to extract the meta data using the regular expressions
def parse_meta(meta_regex, meta_info={}):
    for k, regexes in six.iteritems(meta_regex):
        for reg in regexes:

        paramd['additional_details'] = meta_info
    else:
        # Just have and index of the spectra in the MSP file
        paramd['additional_details'] = {'spectra_idx': spectrac}

    paramd["SampleName"] = "{}_metfrag_result".format(spectrac)

    # =============== Output peaks to txt file  ==============================
    paramd["PeakListPath"] = os.path.join(wd, "{}_tmpspec.txt".format(spectrac))

            outfile.write(p[0] + "\t" + p[1] + "\n")

    # =============== Update param based on MSP metadata ======================
    # Replace param details with details from MSP if required
    if 'precursor_type' in meta_info and meta_info['precursor_type'] in adduct_types:
        nm = float(meta_info['precursor_mz']) - adduct_types[meta_info['precursor_type']]
        paramd["PrecursorIonMode"] = int(round(adduct_types[meta_info['precursor_type']], 0))
    elif not args.skip_invalid_adducts:
        paramd["PrecursorIonMode"] = paramd['PrecursorIonModeDefault']
        nm = float(meta_info['precursor_mz']) - paramd['nm_mass_diff_default']
    else:
        print('Skipping {}'.format(paramd["SampleName"]))
        return '', ''

    paramd["NeutralPrecursorMass"] = nm

    # =============== Create CLI cmd for metfrag ===============================
    cmd = "metfrag"
    for k, v in six.iteritems(paramd):
        if not k in ['PrecursorIonModeDefault', 'nm_mass_diff_default', 'additional_details']:
            cmd += " {}={}".format(str(k), str(v))

    # =============== Run metfrag ==============================================
    print(cmd)
    # Filter before process with a minimum number of MS/MS peaks
    if plinesread >= float(args.minMSMSpeaks):

        if int(args.cores_top_level) == 1:
            os.system(cmd)

    return paramd, cmd


def work(cmds):

additional_detail_headers = ['sample_name']
for k, paramd in six.iteritems(paramds):
    additional_detail_headers = list(set(additional_detail_headers + list(paramd['additional_details'].keys())))

headers = additional_detail_headers + sorted(list(set(headers)))


# Sort files nicely
outfiles.sort(key = lambda s: int(re.match('^.*/(\d+)_metfrag_result.csv', s).group(1)))

                # Write the line if it pass all filters
                if bewrite:
                    bfn = os.path.basename(fn)
                    bfn = bfn.replace(".csv", "")
                    ad = paramds[bfn]['additional_details']
                    line.update(ad)
                    line['sample_name'] = paramds[bfn]['SampleName']

                    dwriter.writerow(line)

8:9a3019c609d9

    meta_regex['precursor_mz'].extend(regex_msp['precursor_mz'])
    meta_regex['precursor_type'].extend(regex_msp['precursor_type'])
    meta_regex['num_peaks'].extend(regex_msp['num_peaks'])
    meta_regex['msp'] = regex_msp['msp']



adduct_types = {
    '[M+H]+': 1.007276,
    '[M+NH4]+': 18.034374,
    '[M+Na]+': 22.989218,

    '[M+HCOO]-': 44.99819,
    '[M-H+HCOOH]-': 44.99819,  # same as above but different style of writing adduct
    '[M+CH3COO]-': 59.01385,
    '[M-H+CH3COOH]-': 59.01385  # same as above but different style of writing adduct
}
inv_adduct_types = {int(round(v, 0)): k for k, v in adduct_types.iteritems()}

# function to extract the meta data using the regular expressions
def parse_meta(meta_regex, meta_info={}):
    for k, regexes in six.iteritems(meta_regex):
        for reg in regexes:

        paramd['additional_details'] = meta_info
    else:
        # Just have and index of the spectra in the MSP file
        paramd['additional_details'] = {'spectra_idx': spectrac}


    paramd["SampleName"] = "{}_metfrag_result".format(spectrac)

    # =============== Output peaks to txt file  ==============================
    paramd["PeakListPath"] = os.path.join(wd, "{}_tmpspec.txt".format(spectrac))

            outfile.write(p[0] + "\t" + p[1] + "\n")

    # =============== Update param based on MSP metadata ======================
    # Replace param details with details from MSP if required
    if 'precursor_type' in meta_info and meta_info['precursor_type'] in adduct_types:
        adduct = meta_info['precursor_type']
        nm = float(meta_info['precursor_mz']) - adduct_types[meta_info['precursor_type']]
        paramd["PrecursorIonMode"] = int(round(adduct_types[meta_info['precursor_type']], 0))
    elif not args.skip_invalid_adducts:
        adduct = inv_adduct_types[int(paramd['PrecursorIonModeDefault'])]
        paramd["PrecursorIonMode"] = paramd['PrecursorIonModeDefault']
        nm = float(meta_info['precursor_mz']) - paramd['nm_mass_diff_default']
    else:
        print('Skipping {}'.format(paramd["SampleName"]))
        return '', ''

    paramd['additional_details']['adduct'] = adduct
    paramd["NeutralPrecursorMass"] = nm

    # =============== Create CLI cmd for metfrag ===============================
    cmd = "metfrag"
    for k, v in six.iteritems(paramd):
        if not k in ['PrecursorIonModeDefault', 'nm_mass_diff_default', 'additional_details']:
            cmd += " {}={}".format(str(k), str(v))

    # =============== Run metfrag ==============================================
    #print(cmd)
    # Filter before process with a minimum number of MS/MS peaks
    if plinesread >= float(args.minMSMSpeaks):

        if int(args.cores_top_level) == 1:
            os.system(cmd)



    return paramd, cmd


def work(cmds):

additional_detail_headers = ['sample_name']
for k, paramd in six.iteritems(paramds):
    additional_detail_headers = list(set(additional_detail_headers + list(paramd['additional_details'].keys())))

# add inchikey if not already present (missing in metchem output)
if 'InChIKey' not in headers:
    headers.append('InChIKey')

headers = additional_detail_headers + sorted(list(set(headers)))



# Sort files nicely
outfiles.sort(key = lambda s: int(re.match('^.*/(\d+)_metfrag_result.csv', s).group(1)))

                # Write the line if it pass all filters
                if bewrite:
                    bfn = os.path.basename(fn)
                    bfn = bfn.replace(".csv", "")
                    line['sample_name'] = paramds[bfn]['SampleName']
                    ad = paramds[bfn]['additional_details']

                    if  args.MetFragDatabaseType == "MetChem":
                        # for some reason the metchem database option does not report the full inchikey (at least
                        # in the Bham setup. This ensures we always get the fully inchikey
                        line['InChIKey'] = '{}-{}-{}'.format(line['InChIKey1'], line['InChIKey2'], line['InChIKey3'])

                    line.update(ad)
                    dwriter.writerow(line)