code: def gatk_filter_rnaseq(vrn_file, data):
      
      out_file = "%s-filter%s" % utils.splitext_plus(vrn_file)
      if not file_exists(out_file):
          ref_file = dd.get_ref_file(data)
          with file_transaction(data, out_file) as tx_out_file:
              params = ["VariantFiltration",
                        "-R", ref_file,
                        "-V", vrn_file,
                        "--cluster-window-size", "35",
                        "--cluster-size", "3",
                        "--filter-expression", "'FS > 30.0'",
                        "--filter-name", "FS",
                        "--filter-expression", "'QD < 2.0'",
                        "--filter-name", "QD",
                        "--output", tx_out_file]
              # Use GATK4 for filtering, tools_off is for variant calling
              config = utils.deepish_copy(dd.get_config(data))
              if "gatk4" in dd.get_tools_off({"config": config}):
                  config["algorithm"]["tools_off"].remove("gatk4")
              jvm_opts = broad.get_gatk_opts(config, os.path.dirname(tx_out_file))
              do.run(broad.gatk_cmd("gatk", jvm_opts, params, config), "Filter RNA-seq variants.")
      return out_file
docs: this incorporates filters listed here, dropping clusters of variants
      within a 35 nucleotide window, high fischer strand values and low
      quality by depth
      https://software.broadinstitute.org/gatk/guide/article?id=3891
      java -jar GenomeAnalysisTK.jar -T VariantFiltration -R hg_19.fasta -V
      input.vcf -window 35 -cluster 3 -filterName FS -filter "FS > 30.0"
      -filterName QD -filter "QD < 2.0" -o output.vcf