code: def _check_freqs(parts, indexes):
      
      thresh_ratio = 2.7
      try:  # FreeBayes
          ao_index = parts[8].split(":").index("AO")
          ro_index = parts[8].split(":").index("RO")
      except ValueError:
          ao_index, ro_index = None, None
      try:  # VarDict
          af_index = parts[8].split(":").index("AF")
      except ValueError:
          af_index = None
      if af_index is None and ao_index is None:
          # okay to skip if a gVCF record
          if parts[4].find("<*>") == -1:
              raise NotImplementedError("Unexpected format annotations: %s" % parts[8])
      def _calc_freq(item):
          try:
              if ao_index is not None and ro_index is not None:
                  ao = sum([int(x) for x in item.split(":")[ao_index].split(",")])
                  ro = int(item.split(":")[ro_index])
                  freq = ao / float(ao + ro)
              elif af_index is not None:
                  freq = float(item.split(":")[af_index])
              else:
                  freq = 0.0
          except (IndexError, ValueError, ZeroDivisionError):
              freq = 0.0
          return freq
      tumor_freq, normal_freq = _calc_freq(parts[indexes["tumor"]]), _calc_freq(parts[indexes["normal"]])
      return normal_freq <= 0.001 or normal_freq <= tumor_freq / thresh_ratio
docs: Ensure frequency of tumor to normal passes a reasonable threshold.
  
      Avoids calling low frequency tumors also present at low frequency in normals,
      which indicates a contamination or persistent error.