def run_fusion(job,
               fastqs,
               junction_file,
               univ_options,
               star_fusion_options,
               fusion_inspector_options):
    """
    Runs STAR-Fusion and filters fusion calls using FusionInspector

    :param tuple fastqs: RNA-Seq FASTQ Filestore IDs
    :param toil.fileStore.FileID junction_file: Chimeric junction file
    :param dict univ_options: universal arguments used by almost all tools
    :param dict star_fusion_options: STAR-Fusion specific parameters
    :return: Transgene BEDPE file
    :rtype: toil.fileStore.FileID
    """
    work_dir = job.fileStore.getLocalTempDir()

    input_files = {'rna_1.fq.gz': fastqs[0],
                   'rna_2.fq.gz': fastqs[1],
                   'tool_index.tar.gz': star_fusion_options['index']}

    parameters = []

    # If there isn't a junction file, then we can run STAR-Fusion from the fastq files
    if junction_file:
        input_files['STAR.junction'] = junction_file
        parameters.extend(['--chimeric_junction', '/data/STAR.junction'])

    else:
        parameters.extend(['--left_fq', '/data/rna_1.fq.gz', '--right_fq', '/data/rna_2.fq.gz'])

    input_files = get_files_from_filestore(job, input_files, work_dir, docker=False)
    input_files['tool_index'] = os.path.basename(untargz(input_files['tool_index.tar.gz'],
                                                         work_dir))

    cores = star_fusion_options['n']
    parameters.extend(['--output_dir', '/data/fusion-output',
                       '--genome_lib_dir', input_files['tool_index'],
                       '--CPU', str(cores)])

    docker_call(tool='star-fusion',
                tool_parameters=parameters,
                work_dir=work_dir,
                dockerhub=univ_options['dockerhub'],
                tool_version=star_fusion_options['version'])

    star_output = 'fusion-output/star-fusion.fusion_candidates.final.abridged'
    fusion_path = os.path.join(work_dir, star_output)

    # Export the STAR-Fusion predictions
    export_results(job,
                   job.fileStore.writeGlobalFile(fusion_path),
                   'star-fusion-predictions.tsv',
                   univ_options, subfolder='mutations/fusions')

    # Check for fusion prediction
    with open(fusion_path, 'r') as f:
        # Skip header
        f.next()
        try:
            f.next()
        except StopIteration:
            logging.warning('%s: Did not find any fusions!' % univ_options['patient'])
            return

    parameters = ['--fusions', '/data/%s' % star_output,
                  '--genome_lib', input_files['tool_index'],
                  '--left_fq', '/data/rna_1.fq.gz',
                  '--right_fq', '/data/rna_2.fq.gz',
                  '--out_dir', '/data/FusionInspector',
                  '--out_prefix', 'FusionInspector',
                  '--CPU', str(cores)]

    if fusion_inspector_options['run_trinity']:
        parameters.append('--include_Trinity')

    docker_call(tool='fusion-inspector',
                tool_parameters=parameters,
                work_dir=work_dir,
                dockerhub=univ_options['dockerhub'],
                tool_version=fusion_inspector_options['version'])

    found_fusion = False
    inspector_output = 'FusionInspector/FusionInspector.fusion_predictions.final.abridged.FFPM'
    fusion_path = os.path.join(work_dir, inspector_output)
    output_path = os.path.join(work_dir, 'fusion.final')

    # Export the FusionInpsector predictions
    export_results(job,
                   job.fileStore.writeGlobalFile(fusion_path),
                   'fusion-inspector-predictions.tsv',
                   univ_options, subfolder='mutations/fusions')

    # Remove fusions without a large anchor sequence and at least 0.1
    # fusion fragments per million reads
    if os.path.exists(fusion_path):
        with open(fusion_path, 'r') as f, open(output_path, 'w') as g:
            g.write(f.next())
            for line in f:
                fields = line.strip().split()

                # Check for a large anchor support
                ldas = fields[10]

                assert ldas in {'YES', 'NO'}, 'FusionInpsector file is malformed!'

                j_ffpm, s_ffpm = fields[-2:]

                # Fusions without a larger anchor support or low read support
                # are suspicious and should not be consider for further analysis
                if ldas == 'YES' and sum([float(j_ffpm), float(s_ffpm)]) > 0.1:
                    found_fusion = True
                    g.write(line)

    if found_fusion:
        fusion_bed_f = 'FusionInspector/FusionInspector.bed'
        fusion_bed_path = os.path.join(work_dir, fusion_bed_f)
        transcript_f = 'FusionInspector/FusionInspector.gmap_trinity_GG.fusions.fasta'
        transcript_path = os.path.join(work_dir, transcript_f)
        transcript_gff_f = 'FusionInspector/FusionInspector.gmap_trinity_GG.fusions.gff3'
        transcript_gff_path = os.path.join(work_dir, transcript_gff_f)

        transcripts = None
        transcript_annotation = None

        if os.path.exists(transcript_path):
            transcripts = job.fileStore.writeGlobalFile(transcript_path)
            export_results(job,
                           transcripts,
                           transcript_path,
                           univ_options,
                           subfolder='mutations/fusions')

        if os.path.exists(transcript_gff_path):
            transcript_annotation = job.fileStore.writeGlobalFile(transcript_gff_path)
            export_results(job,
                           transcript_annotation,
                           transcript_gff_path,
                           univ_options,
                           subfolder='mutations/fusions')

        fusion_annotation = job.fileStore.writeGlobalFile(fusion_bed_path)
        filtered_fusions = job.fileStore.writeGlobalFile(output_path)

        export_results(job,
                       filtered_fusions,
                       output_path,
                       univ_options,
                       subfolder='mutations/fusions')
        job.fileStore.logToMaster('Ran STAR-Fusion on %s successfully' % univ_options['patient'])
        return job.addChildJobFn(reformat_star_fusion_output,
                                 fusion_annotation,
                                 filtered_fusions,
                                 transcripts,
                                 transcript_annotation,
                                 univ_options).rv()

    else:
        job.fileStore.logToMaster('No fusions detected for %s' % univ_options['patient'])