A comprehensive assessment of Next‐Generation Sequencing variants validation using a secondary technology

Abstract

Background Recently, increasing innovations improved the accuracy of next generation sequencing (NGS) data. However, the validation of all NGS variants increased the cost and turn‐around time of clinical diagnosis, and therefore limited the further development of clinical applications. We aimed to comprehensively assess the necessity of validating NGS variants. Methods Validation data of 7,601 NGS variants involving 1,045 genes were collected from 5,190 clinical samples and sequenced by one of five targeted capture panels and two NGS chemistries, respectively. These genes and variants were widely distributed in 24 human chromosomes and mitochondrial genome. Variants validation was firstly processed by Sanger sequencing. If validation results were unavailable or inconsistent with NGS calls, another validation test would be performed by mass spectrometry genotyping. Results A total of 6,939 high quality NGS variants with ≥35 × depth coverage and ≥35% heterozygous ratio were 100% confirmed by a secondary methodology. 5,775 heterozygous variants were separated from 760 homozygous variants and 404 hemizygous variants by 80% heterozygous ratio. A total of 1.5% (98/6,939) of NGS variants were validated by mass spectrometry genotyping. Conclusion Considering of the above comprehensive assessment, a new variant with high quality from a well‐validated capture‐based NGS workflow can be reported directly without validation.

Modeling of shotgun sequencing of DNA plasmids using experimental and theoretical approaches

“…Currently, several sequencing strategies are available to determine the correct DNA sequence [38,39]. Further applications of in silico sequencing algorithms might include the single-molecule sequencing method, able to analyze short-length segments in a large volume, which does not require the amplification of a DNA template [40] together with old-fashioned but very precise methods, such as the Sanger sequencing [41,42].…”

Sanger Validation of High-Throughput Sequencing in Genetic Diagnosis: Still the Best Practice?

“…Our data are in keeping with previous studies, suggesting that Sanger sequencing may not represent a necessary step to validate NGS variants when dealing with data meeting high-quality scores and an adequate depth of coverage. In fact, several previous studies evaluating data from different NGS platforms and approaches (targeted, exome, or whole-genome sequencing) identified almost 100% Sanger validation rate on a total of 14,495 variants ( McCourt et al, 2013 ; Sikkema-Raddatz et al, 2013 ; Strom et al, 2014 ; Baudhuin et al, 2015 ; Beck et al, 2016 ; Zheng et al, 2019 ). In these studies, variants not validated by Sanger sequencing did not match adequate quality scores ( Strom et al, 2014 ; Beck et al, 2016 ; Zheng et al, 2019 ).…”

“…In fact, several previous studies evaluating data from different NGS platforms and approaches (targeted, exome, or whole-genome sequencing) identified almost 100% Sanger validation rate on a total of 14,495 variants ( McCourt et al, 2013 ; Sikkema-Raddatz et al, 2013 ; Strom et al, 2014 ; Baudhuin et al, 2015 ; Beck et al, 2016 ; Zheng et al, 2019 ). In these studies, variants not validated by Sanger sequencing did not match adequate quality scores ( Strom et al, 2014 ; Beck et al, 2016 ; Zheng et al, 2019 ). Both ours and literature data move in the direction of a limited usefulness of Sanger validation for NGS-derived variants associated with robust quality scores, suggesting a re-consideration of its application in routine diagnostics that should be limited to validation of a specific clinical phenotype-associated variant, quality assurance, and risk-avoidance purposes.…”

“…Apart from those factors regarding the starting material (e.g., quality and storage conditions of the starting biological material as well as molecules interfering with DNA extraction from blood of patients treated with drug or medication) or the analytical confounders [GC- and AT-rich areas, repeat sequences, large (>20-bp) deletions ( Ilyas, 2017 )], a significant issue in NGS is essentially represented by the accuracy of the different bioinformatic pipelines in (a) filtering low-quality reads, (b) discriminating clinically relevant variation from background “noise” (due, for instance, to spontaneous PCR errors and deamination), and (c) accurately aligning the reads to a reference sequence. Also, a low read depth (below 10 reads per base on average for whole-exome sequencing on Illumina platforms, 20–30 reads for gene panel sequencing on Ion Torrent and Illumina) determines false-positive results due to sequencing errors especially in GC-rich regions ( McCourt et al, 2013 ; Strom et al, 2014 ; Beck et al, 2016 ; Zheng et al, 2019 ). Further, clinically relevant mutations can be missed (false-negative results) as some exons may be not completely represented or sufficiently covered ( Sikkema-Raddatz et al, 2013 ; Beck et al, 2016 ).…”

“…However, the necessity of validating genetic variants identified through high-performance instruments, which are increasingly accurate and affordable, with a more expensive technology, raised a question about the actual cost-effectiveness of such an approach, both in research and in diagnostic fields. Several recent reports suggested that NGS data, reaching established quality scores, are as accurate as Sanger sequencing ( McCourt et al, 2013 ; Sikkema-Raddatz et al, 2013 ; Strom et al, 2014 ; Baudhuin et al, 2015 ; Beck et al, 2016 ; Zheng et al, 2019 ) as the latter was able to confirm nearly 100% of NGS detected variants at an initial or a second sequencing run. A low number of NGS variants were not validated even after a second Sanger run.…”