**4. Conclusion**

As NGS-based HLA typing is getting more momentum, there is more and more accumulated knowledge and experience concerning ambiguities. At the present state of art, apparently the bioinformatics workflow and data management is the main hurdle that a HLA biologist has to face. Therefore, it is important to know the main sources of sequencing and data errors leading to ambiguities: when switching to NGS HLA typing, besides cost, consider its benefits and drawbacks to make sure you are ready to change the laboratory and informatics workflow. NGS-HLA is not a remedy for all the problems we have in Sanger SBT or in traditional nonsequence-based HLA typing methods: to have a whole-gene fully resolved phased consensus you have to use a kit that is designed to provide this sequence and a bioinformatics pipeline that is delivering this result. Sequence annotation is mostly unresolved; we get a flood of novel sequences, but assigning exon/intron/UTR boundaries is still a manual process. Sequencing and assembling consensuses with UTRs are problematic and missing UTRs can lead to ambiguities.

Introducing QC metrics can help find out the nature of ambiguities and failures; studying these metrics, it is possible to decide whether it is the whole experiment, the sequencing part, or the final bioinformatics workflow that needs to be repeated with altered input. Do not accept genotyping results blindly, reconsider the QC metrics, look at the actual alignments, and interpret the obtained ambiguities.
