def merge_mhc_peptide_calls(job, antigen_predictions, transgened_files):
    """
    This module will merge all the calls from nodes 18 and 19, and will filter for the top X%% of
    binders of each allele.  The module will then call the rank boosting script to finish off the
    pipeline.

    This module corresponds to node 19 on the tree
    """
    job.fileStore.logToMaster('Merging MHC calls')
    work_dir = job.fileStore.getLocalTempDir()
    pept_files = {
        '10_mer.faa': transgened_files['transgened_tumor_10_mer_snpeffed.faa'],
        '10_mer.faa.map': transgened_files['transgened_tumor_10_mer_snpeffed.faa.map'],
        '15_mer.faa': transgened_files['transgened_tumor_15_mer_snpeffed.faa'],
        '15_mer.faa.map': transgened_files['transgened_tumor_15_mer_snpeffed.faa.map']}
    mhci_preds, mhcii_preds = antigen_predictions
    mhci_files = get_files_from_filestore(job, mhci_preds, work_dir)
    # First split mhcii_preds into prediction files and predictors and maintain keys so we can later
    # reference them in pairs
    mhcii_predictors = {x: y[1] for x, y in mhcii_preds.items()}
    mhcii_files = {x: y[0] for x, y in mhcii_preds.items()}
    mhcii_files = get_files_from_filestore(job, mhcii_files, work_dir)
    # Get peptide files
    pept_files = get_files_from_filestore(job, pept_files, work_dir)

    # Merge MHCI calls
    # Read 10-mer pepts into memory
    peptides = read_peptide_file(pept_files['10_mer.faa'])
    with open(pept_files['10_mer.faa.map'], 'r') as mapfile:
        pepmap = json.load(mapfile)
    # Incorporate peptide names into the merged calls
    with open('/'.join([work_dir, 'mhci_merged_files.list']), 'w') as mhci_resfile:
        for mhcifile in mhci_files.values():
            with open(mhcifile, 'r') as mf:
                for line in mf:
                    # Skip header lines
                    if not line.startswith('HLA'):
                        continue
                    line = line.strip().split('\t')
                    allele = line[0]
                    pept = line[5]
                    pred = line[7]
                    if float(pred) > 5.00:
                        continue
                    print_mhc_peptide((allele, pept, pred, pept), peptides, pepmap, mhci_resfile)
    # Merge MHCII calls
    # read 15-mer pepts into memory
    peptides = read_peptide_file(pept_files['15_mer.faa'])
    with open(pept_files['15_mer.faa.map'], 'r') as mapfile:
        pepmap = json.load(mapfile)
    # Incorporate peptide names into the merged calls
    with open('/'.join([work_dir, 'mhcii_merged_files.list']), 'w') as \
            mhcii_resfile:
        for mhciifile in mhcii_files.keys():
            core_col = None  # Variable to hold the column number with the core
            if mhcii_predictors[mhciifile] == 'Consensus':
                with open(mhcii_files[mhciifile], 'r') as mf:
                    for line in mf:
                        # Skip header lines
                        if not line.startswith('HLA'):
                            continue
                        line = line.strip().split('\t')
                        allele = line[0]
                        pept = line[4]
                        pred = line[6]
                        if core_col:
                            core = line[core_col] if core_col else 'NOCORE'
                        else:
                            methods = line[5].lstrip('Consensus(').rstrip(')')
                            methods = methods.split(',')
                            if 'NN' in methods:
                                core_col = 13
                            elif 'netMHCIIpan' in methods:
                                core_col = 17
                            elif 'Sturniolo' in methods:
                                core_col = 19
                            elif 'SMM' in methods:
                                core_col = 10
                            core = line[core_col] if core_col else 'NOCORE'
                        if float(pred) > 5.00:
                            continue
                        print_mhc_peptide((allele, pept, pred, core), peptides, pepmap,
                                          mhcii_resfile)
            elif mhcii_predictors[mhciifile] == 'Sturniolo':
                with open(mhcii_files[mhciifile], 'r') as mf:
                    for line in mf:
                        # Skip header lines
                        if not line.startswith('HLA'):
                            continue
                        line = line.strip().split('\t')
                        allele = line[0]
                        pept = line[5]
                        pred = line[6]
                        core = line[19]  #
                        if float(pred) > 5.00:
                            continue
                        print_mhc_peptide((allele, pept, pred, core), peptides, pepmap,
                                          mhcii_resfile)
            elif mhcii_predictors[mhciifile] == 'netMHCIIpan':
                with open(mhcii_files[mhciifile], 'r') as mf:
                    # Get the allele from the first line and skip the second line
                    allele = re.sub('-DQB', '/DQB', mf.readline().strip())
                    _ = mf.readline()
                    for line in mf:
                        line = line.strip().split('\t')
                        pept = line[1]
                        pred = line[5]
                        core = 'NOCORE'
                        peptide_name = line[2]
                        if float(pred) > 5.00:
                            continue
                        print(allele, pept, peptide_name, core, '0', pred, pepmap[peptide_name],
                              sep='\t', file=mhcii_resfile)
            else:
                raise RuntimeError('Shouldn\'t ever see this!!!')
    output_files = defaultdict()
    for mhc_file in [mhci_resfile.name, mhcii_resfile.name]:
        output_files[os.path.split(mhc_file)[1]] = job.fileStore.writeGlobalFile(mhc_file)
    return output_files
This module will merge all the calls from nodes 18 and 19, and will filter for the top X%% of
    binders of each allele.  The module will then call the rank boosting script to finish off the
    pipeline.

    This module corresponds to node 19 on the tree