The past fifteen years have witnessed a new era in DNA sequencing technologies, starting from the release of the Roche 454 sequencer, which unveiled the curtain of next-generation sequencing (NGS). Compared to Sanger sequencing technology, NGS has remarkably higher throughput and reduced costs . As technology upgrades and iterates, NGS technologies have dramatically decreased the cost of human whole genome sequencing (WGS) and whole-exome sequencing (WES). As a result, the rapid development of technology leads to brilliant achievements in WGS projects such as the 1000 genome project , the HapMap project, and extensive cohort studies worldwide. WGS and WES have been and are being widely performed to discover genetic disease-associated genes and identify driver mutations in hereditary tumors [6–8]. It lays the foundations for the prior understanding of how mutated genes affect disease phenotype and the further interpretation of pathogenic mechanisms [6–8].
Since the completion of the Human Genome Project in 2003, various sequencing platforms have been developed: Roche 454, Illumina series (GA, HiSeq, Miseq, NextSeq, NovaSeq, etc.) , MGI (BGISEQ-500, MGISEQ2000, DNBSEQ-T7) , Ion Torrent , and GenapSys . Benefiting from continued technology development and product commercialization, Illumina’s sequencing by synthesis (SBS) based sequencers have dominated the sequencing market for a long time. In 2016, NextSeq 550 was released as mid-throughput desktop sequencing instrument, which can be applied in many fields, including transcriptome sequencing, targeted sequencing, WES, metagenomics sequencing, and genotyping. In June 2017, NovaSeq 6000 was launched, which incorporates Illumina’s SBS chemistry and two-color optics. Combined with patterned flow cell technology and reversible terminator-based method , it can produce 6 TB of sequencing data in a single run at a cost of approximately 10 USD/GB . As NGS applications expand in various research areas and clinical settings, there is an unmet demand to develop a novel NGS platform that is accurate, flexible, and cost-efficient for applications.
In October 2020, GeneMind Biosciences Company Limited (GeneMind) launched a new sequencing instrument (GenoLab M™) based on their previous work on single molecule sequencer GenoCareTM. The GenoLab M sequencer employs SBS techniques and reversible termination approaches . In 2021, the first study using GenoLab M was published , revealing that the GenoLab M is a p promising sequencing platform for transcriptomics and LncRNA studies in animal, plant, and human with comparable performance but a lower cost compared to NovaSeq 6000. However, the performance of the GenoLab M platform in other application areas has not yet been released, especially in WGS and WES.
In 2014, Genome in a Bottle (GIAB) published A golden standard genotype dataset (including reference sample NA12878), providing a resource for comparison of variants calling pipelines . Recently, several studies used the GIAB variant dataset for comparisons among different variants callers or sequencing platforms [17–20]. Generally, data depth of WGS and WES were above 30 fold and 100 fold [13, 18, 21–23]. Early in the history of WGS, the field converged around the concept that 30-fold represents a “high quality” genome with the ideal trade-off of accuracy and cost. Together with Genome Analysis Tool kit (GATK)  as the best practice analysis pipeline , this depth concept has become deeply ingrained in the community mindset, even when the sequencing and analysis fields have evolved rapidly. It is well recognized that GATK works well with dominated Illumia data, but is not yet proven on other sequencing platforms. Also, 30-fold data in WGS is potentially redundant, not only on the cost of sequencing but also the analysis computation and storage costs. There are quite a few previously published lower depth WGS studies, such as a large group WGS project of Icelanders in 2015 with a median sequencing depth was 20X . In 2018, Anna Supernat et al., have compared three variant callers (DeepVariant , GATK, and SpeedSeq ) for WGS reference sample sequenced at different depths (10X, 15X, and 30X). It was observed that the F-Scores obtained by DeepVariant at 15X were comparable to SpeedSeq and GATK at 30X. Yifan Jiang et al, found that the optimal sequencing depth for whole genome re-sequencing in pigs was 10X, an ideal practical depth for achieving plateau coverage and discovering accurate variants with greater than 99% genome coverage . With all these preliminary supporting studies and the emerging sequencing and analysis technologies with improved accuracy, a lower sequencing depth than 30X may be considered as the current best practice.
This study obtained both WES and WGS datasets of the NA12878 standard sample generated from multiple sequencing platforms, including NextSeq 550, NovaSeq 6000, and GenoLab M. On the analysis part, two pipelines were chosen: Sentieon DNAscope pipeline, a machine learning based variant calling workflow (https://github.com/Sentieon/sentieon-dnascope-ml), and DNAseq workflow, which is an accelerated GATK re-implementation . We compared WGS performance in GenoLab M with 22X data and NovaSeq 6000 with 33X data.