**Author details**

underlying technology and workflow. Failure for these QC tests generally indicates issues with the wet lab part of the genotyping workflow (e.g., over-fragmentation, unnoticed low input DNA concentration). These QC failures can usually be eliminated by repeating the

**•** Data qualification (e.g., cross-mapping read ratio, crossover PCR artifact ratio): the thresh‐ olds for these metrics are also experiment dependent, but a QC test failure is not necessarily a consequence of an error during the sequencing process, therefore, a repeated experiment won't necessarily resolve the issue. In most cases, these QC failures can be eliminated by

**•** Result qualification (e.g., consensus continuity, consensus phasing, consensus coverage minimum depth, mismatch count): these metrics qualify the output, the result consensus

A special case of QC is the concordance calculation between two independent genotyping methods. In this case a complete alternative/secondary genotyping method is introduced to provide results comparable to the controlled primary genotyping method and the result is expressed as a concordance value that can be mapped to the standard QC result scheme (e.g.,

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

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

further optimization of the workflow (e.g., PCR cycle number optimization).

and genotype, regardless of the input quality.

382 Next Generation Sequencing - Advances, Applications and Challenges

experiment.

passed/failed).

**4. Conclusion**

ambiguities.

interpret the obtained ambiguities.

Szilveszter Juhos\* , Krisztina Rigó and György Horváth

\*Address all correspondence to: szilveszter.juhos@omixon.com

Omixon Ltd, H- Budapest, Hungary
