The Quality Sequencing Minimum (QSM): providing comprehensive, consistent, transparent next generation sequencing  data quality assurance

Abstract

Next generation sequencing (NGS) is routinely used in clinical genetic testing. Quality management of NGS testing is essential to ensure performance is consistently and rigorously evaluated. Three primary metrics are used in NGS quality evaluation: depth of coverage, base quality and mapping quality. To provide consistency and transparency in the utilisation of these metrics we present the Quality Sequencing Minimum (QSM). The QSM defines the minimum quality requirement a laboratory has selected for depth of coverage (C), base quality (B) and mapping quality (M) and can be applied per base, exon, gene or other genomic region, as appropriate. The QSM format is CX_BY(P Y)_MZ(P Z). X is the parameter threshold for C, Y the parameter threshold for B, P Y the percentage of reads that must reach Y, Z the parameter threshold for M, P Z the percentage of reads that must reach Z. The data underlying the QSM is in the BAM file, so a QSM can be easily and automatically calculated in any NGS pipeline. We used the QSM to optimise cancer predisposition gene testing using the TruSight Cancer Panel (TSCP). We set the QSM as C50_B10(85)_M20(95). Test regions falling below the QSM were automatically flagged for review, with 100/1471 test regions QSM-flagged in multiple individuals. Supplementing these regions with 132 additional probes improved performance in 85/100. We also used the QSM to optimise testing of genes with pseudogenes such as PTEN and PMS2. In TSCP data from 960 individuals the median number of regions that passed QSM per sample was 1429 (97%). Importantly, the QSM can be used at an individual report level to provide succinct, comprehensive quality assurance information about individual test performance. We believe many laboratories would find the QSM useful. Furthermore, widespread adoption of the QSM would facilitate consistent, transparent reporting of genetic test performance by different laboratories.

CoverView: a sequence quality evaluation tool for next generation sequencing data

“…Users can define the minimal requirements to ‘pass’ the quality test and if this is not met the region of interest is ‘flagged’. Defining these minimum requirements for depth of coverage, base and mapping quality are the basis of the QSM that is described in detail in the accompanying paper 8 . In CoverView a minimum or maximum value can be specified for any of the metrics in Table 2 .…”

“…This explains the striking difference between the COV and QCOV profiles since low quality reads are not counted as part of quality depth of coverage. PMS2 has a nearby pseudogene with strong homology to exons 9, 11–15 that causes ambiguous mapping and it is not possible to robustly analyse exon 12 by TSCP data alone 8 . However, the CoverView outputs show that every base in 1462/1471 (99%) TSCP regions in the GIAB sample pass the MINQCOV ≥50 quality threshold 19 .…”

“…False negative errors are often caused by insufficient depth of coverage 4 , and it is vital that regions with low coverage are flagged and reviewed, not least because they may require additional interrogation 5 . Coverage evaluation is also useful for comparing different NGS library generation strategies, to identify regions with suboptimal performance 6 , 7 and for probe design optimisation 8 .…”

“…CoverView was developed to provide the quality assurance and quality control information required by clinical NGS testing laboratories, though we believe it is equally useful for research use. We recently proposed the Quality Sequencing Minimum (QSM) to deliver comprehensive, consistent, transparent NGS quality assurance information about depth of coverage, base and mapping quality, and we use CoverView to evaluate fulfilment of a QSM in our laboratory 8 . We also use CoverView as the quality control tool for all our research and clinical NGS analyses and it is integrated into our exome analysis tool, OpEx (Optimised Exome) 18 .…”

The ICR639 CPG NGS validation series: A resource to assess analytical sensitivity of cancer predisposition gene testing

“…There are 502 pathogenic variants in BRCA1 or BRCA2 , making this an important comprehensive validation dataset for providers of BRCA1 and BRCA2 NGS testing. The vast majority of variants occur in extremely high-quality sequencing data, fulfilling a Quality Sequencing Minimum (QSM) of C50_B10(85)_M20(95) 12 . As such, it is anticipated that any accredited test provider will be able to detect these variants.…”