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Background DNBSEQ-T7 is a new whole-genome sequencer developed by Complete Genomics and MGI using DNA nanoball and combinatorial probe anchor synthesis technologies to generate short reads at a very large scale—up to 60 human genomes per day. However, it has not been objectively and systematically compared against Illumina short-read sequencers. Findings By using the same KOREF sample, the Korean Reference Genome, we have compared 7 sequencing platforms including BGISEQ-500, DNBSEQ-T7, HiSeq2000, HiSeq2500, HiSeq4000, HiSeqX10, and NovaSeq6000. We measured sequencing quality by comparing sequencing statistics (base quality, duplication rate, and random error rate), mapping statistics (mapping rate, depth distribution, and percent GC coverage), and variant statistics (transition/transversion ratio, dbSNP annotation rate, and concordance rate with single-nucleotide polymorphism [SNP] genotyping chip) across the 7 sequencing platforms. We found that MGI platforms showed a higher concordance rate for SNP genotyping than HiSeq2000 and HiSeq4000. The similarity matrix of variant calls confirmed that the 2 MGI platforms have the most similar characteristics to the HiSeq2500 platform. Conclusions Overall, MGI and Illumina sequencing platforms showed comparable levels of sequencing quality, uniformity of coverage, percent GC coverage, and variant accuracy; thus we conclude that the MGI platforms can be used for a wide range of genomics research fields at a lower cost than the Illumina platforms.

peer-reviewed Background The use of shotgun metagenomics to analyse low-complexity microbial communities in foods has the potential to be of considerable fundamental and applied value. However, there is currently no consensus with respect to choice of species classification tool, platform, or sequencing depth. Here, we benchmarked the performances of three high-throughput short-read sequencing platforms, the Illumina MiSeq, NextSeq 500, and Ion Proton, for shotgun metagenomics of food microbiota. Briefly, we sequenced six kefir DNA samples and a mock community DNA sample, the latter constructed by evenly mixing genomic DNA from 13 food-related bacterial species. A variety of bioinformatic tools were used to analyse the data generated, and the effects of sequencing depth on these analyses were tested by randomly subsampling reads. Results Compositional analysis results were consistent between the platforms at divergent sequencing depths. However, we observed pronounced differences in the predictions from species classification tools. Indeed, PERMANOVA indicated that there was no significant differences between the compositional results generated by the different sequencers (p = 0.693, R2 = 0.011), but there was a significant difference between the results predicted by the species classifiers (p = 0.01, R2 = 0.127). The relative abundances predicted by the classifiers, apart from MetaPhlAn2, were apparently biased by reference genome sizes. Additionally, we observed varying false-positive rates among the classifiers. MetaPhlAn2 had the lowest false-positive rate, whereas SLIMM had the greatest false-positive rate. Strain-level analysis results were also similar across platforms. Each platform correctly identified the strains present in the mock community, but accuracy was improved slightly with greater sequencing depth. Notably, PanPhlAn detected the dominant strains in each kefir sample above 500,000 reads per sample. Again, the outputs from functional profiling analysis using SUPER-FOCUS were generally accordant between the platforms at different sequencing depths. Finally, and expectedly, metagenome assembly completeness was significantly lower on the MiSeq than either on the NextSeq (p = 0.03) or the Proton (p = 0.011), and it improved with increased sequencing depth. Conclusions Our results demonstrate a remarkable similarity in the results generated by the three sequencing platforms at different sequencing depths, and, in fact, the choice of bioinformatics methodology had a more evident impact on results than the choice of sequencer did. This research was funded by Science Foundation Ireland in the form of a centre grant (APC Microbiome Institute grant number SFI/12/RC/2273). Research in the Cotter laboratory is also funded by Science Foundation Ireland through the PI award “Obesibiotics” (11/PI/1137). Orla O’Sullivan is funded by Science Foundation Ireland through a Starting Investigator Research Grant award (13/SIRG/2160).