Reference-free data quality control using LongQC

Diverse applications using Oxford Nanopore sequencers enabled new usages in the field of genomics. One challenging task for data analysis is data quality assessment by various aspects all at once: coverage, contamination, read length, barcode ligations, etc. It is ideal to know and spot problems quickly, if exists, before the full analysis. Oxford Nanopore applications have a wide variety of usage; however, some key and shared components exist across such diverse applications. Here, we introduce a new tool, LongQC, to cope with the situation and demonstrate its capability on real datasets. It is possible to assess the data without any a priori knowledge or references by extracting those components. LongQC was tested on a wide range of datasets, finding that the difference between LongQC-estimated and actual mapping results was negligible.