Illumina has dominated the next-generation sequencing market thanks to its high accuracy and high-throughput sequencing technology. However, MGI Tech, a Beijing Genomics Institute (BGI) Group subsidiary, is now shaking up the sector with its new MGI genetic sequencers. These MGI sequencers offer higher throughput and lower costs, but how do RNA sequencing (RNA-seq) results compare between Illumina and MGI sequencing platforms?
Here we look at the comparative studies that put Illumina and MGI sequencing platforms head-to-head for RNA-seq.
How similar are Illumina and MGI sequencing platforms?
Sequencing technologies from Illumina and MGI Tech are both short-read sequencing approaches.
They use different underlying chemistries to provide high-quality short sequencing reads on a large scale (Goodwin, McPherson and McCombie, 2016).
Illumina uses a cluster generation process and sequencing-by-synthesis with fluorescently-tagged chain terminators (Bentley et al., 2008).
MGI genetic sequencers use DNA nanoball (DNB) technology combined with combinatorial probe anchor synthesis (cPAS) or a novel technology called CoolMPS (Drmanac et al., 2010; Li et al., 2021).
Before opting for either Illumina or MGI sequencing technologies, researchers should know how they compare in RNA-seq.
Performance of older Illumina and MGI genetic sequencers in RNA-seq
In a study comparing RNA-seq data from earlier versions of sequencing machines, researchers used the Illumina HiSeq 2000 from 2010, and MGI BGISEQ-500 launched in 2016.
The two platforms produced nearly equivalent RNA-seq data, except in the most GC-rich regions (Patterson et al., 2019).
Similar results were also found in plants with the Illumina HiSeq 4000 from 2015 (Zhu et al., 2018).
Performance of Illumina and newer MGI genetic sequencers in RNA-seq
When comparing the Illumina HiSeq 4000 to a more recent MGI genetic sequencer model, the MGISEQ-2000 from 2017, RNA-seq gene expression levels were highly correlated across platforms for all samples (Jeon et al., 2019).
Recently, Wang et al. (2021) compared the performance of the Illumina NextSeq 500 from 2014 to the MGI DNBSEQ-G400RS sequencer (product names MGISEQ-2000 and DNBSEQ-G400 are used within and outside China, respectively).
They aimed to detect gene expression changes in 18 healthy endurance athletes at different time points when given a performance-enhancing substance called EPO (Wang et al., 2021).
Both platforms detected similar numbers of expressed protein-coding genes. For example, 16,581 expressed genes were detected for Illumina versus 16,738 for MGI, of which over 15,488 genes were common.
Data from the MGI DNBSEQ-G400RS genetic sequencer had an increased sensitivity to detect significant gene expression differences after EPO administration. For example, it detected nearly three times more differentially expressed genes than the Illumina platform (1552 versus 582 genes).
Despite this, the gene expression fold changes at different time points were significantly correlated between the two platforms for genes that were differentially expressed with both platforms.
The MGI sequencing platform performs as well as Illumina sequencing
Together, these cumulative studies indicate that the DNB technology in MGI sequencing platforms produces RNA-seq data of equivalent quality to that produced by Illumina platforms.
This comparable performance extends to whole-genome, single-cell transcriptome, and small RNA-seq applications, among others (Fehlmann et al., 2016; Senabouth et al., 2020; Jeon et al., 2021).
The access and cost factor
Alongside reliable and accurate sequencing data, current MGI genetic sequencers provide a lower cost per sample for researchers as they are more scalable than the most recent Illumina models.
Despite this, Illumina platforms remain more common than the MGI platform (Jeon et al., 2021).
For researchers with access to Illumina but not MGI sequencing platforms, low-cost 3’ mRNA-seq approaches such as MERCURIUS™ BRB-seq from Alithea Genomics perform as well as traditional Illumina-based RNA-seq methods but at a fraction of the cost (Alpern et al., 2019). This technology is also compatible with MGI genetic sequencers.
Please contact us here to learn more about Illumina sequencing, MGI sequencing, or MERCURIUS™ BRB-seq.
- Bentley, D.R. et al. (2008) ‘Accurate whole human genome sequencing using reversible terminator chemistry’, Nature, 456(7218), pp. 53–59. Available at: https://doi.org/10.1038/nature07517.
- Drmanac, R. et al. (2010) ‘Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays’, Science, 327(5961), pp. 78–81. Available at: https://doi.org/10.1126/science.1181498
- Fehlmann, T. et al. (2016) ‘cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs’, Clinical Epigenetics, 8(1), pp. 1-11. Available at: https://doi.org/10.1186/s13148-016-0287-1.
- Goodwin, S., McPherson, J.D. and McCombie, W.R. (2016) ‘Coming of age: Ten years of next-generation sequencing technologies’, Nature Reviews Genetics, 17(6), pp. 333–351. Available at: https://doi.org/10.1038/nrg.2016.49.
- Jeon, S.A. et al. (2019) ‘Comparison of the MGISEQ-2000 and Illumina hiseq 4000 sequencing platforms for RNA sequencing’, Genomics and Informatics, 17(3). Available at: https://doi.org/10.5808/GI.2019.17.3.e32.
- Jeon, S.A. et al. (2021) ‘Comparison between MGI and Illumina sequencing platforms for whole genome sequencing’, Genes and Genomics, 43(7), pp. 713–724. Available at: https://doi.org/10.1007/s13258-021-01096-x.
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- Li, Y. et al. (2021) ‘CoolMPS: Evaluation of antibody labeling based massively parallel non-coding RNA sequencing’, Nucleic Acids Research, 49(2), e10-e10. Available at: https://doi.org/10.1093/nar/gkaa1122.
- Patterson, J. et al. (2019) ‘Impact of sequencing depth and technology on de novo RNA-Seq assembly’, BMC Genomics, 20(1), pp. 1-14. Available at: https://doi.org/10.1186/s12864-019-5965-x.
- Senabouth, A. et al. (2020) ‘Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing’, NAR Genomics and Bioinformatics, 2(2), p.lqaa034. Available at: https://doi.org/10.1093/nargab/lqaa034.
- Wang, G. et al. (2021) ‘Cross-platform transcriptomic profiling of the response to recombinant human erythropoietin’, Scientific Reports, 11(1), p.21705. Available at: https://doi.org/10.1038/s41598-021-00608-9.
- Zhu, F.Y. et al. (2018) ‘Comparative performance of the BGISEQ-500 and Illumina HiSeq4000 sequencing platforms for transcriptome analysis in plants’, Plant Methods, 14(1). Available at: https://doi.org/10.1186/s13007-018-0337-0.