Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Performance comparison of benchtop high-throughput sequencing platforms

A Corrigendum to this article was published on 07 June 2012

This article has been updated


Three benchtop high-throughput sequencing instruments are now available. The 454 GS Junior (Roche), MiSeq (Illumina) and Ion Torrent PGM (Life Technologies) are laser-printer sized and offer modest set-up and running costs. Each instrument can generate data required for a draft bacterial genome sequence in days, making them attractive for identifying and characterizing pathogens in the clinical setting. We compared the performance of these instruments by sequencing an isolate of Escherichia coli O104:H4, which caused an outbreak of food poisoning in Germany in 2011. The MiSeq had the highest throughput per run (1.6 Gb/run, 60 Mb/h) and lowest error rates. The 454 GS Junior generated the longest reads (up to 600 bases) and most contiguous assemblies but had the lowest throughput (70 Mb/run, 9 Mb/h). Run in 100-bp mode, the Ion Torrent PGM had the highest throughput (80–100 Mb/h). Unlike the MiSeq, the Ion Torrent PGM and 454 GS Junior both produced homopolymer-associated indel errors (1.5 and 0.38 errors per 100 bases, respectively).

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Evaluation of read length and quality from benchtop sequencers.
Figure 2: N50 contig sizes from assemblies generated from sequence data for each sequencing platform.
Figure 3: An analysis of gaps when aligning draft de novo assemblies to the reference genome.

Accession codes


Sequence Read Archive

Change history

  • 23 April 2012

    On page 2, “error rates” has been corrected to “quality scores” in the sentence “Alignment quality scores measured in this way generally had good agreement with predicted scores, with the Ion Torrent PGM generally underestimating error rates and the other instruments slightly overestimating them (Supplementary Fig. 2).” The corrected sentence reads “Alignment quality scores measured in this way generally had good agreement with predicted scores, with the Ion Torrent PGM generally underestimating quality scores and the other instruments slightly overestimating them (Supplementary Fig. 2).”

  • 07 June 2012

    In the version of this article initially published online, in the Online Methods “Ion Torrent Sequencing” section, the sentence beginning with “Ten milligrams of this DNA was fragmented with a Bioruptor instrument….” should have read “Ten micrograms….” and in the “454 GS Junior sequencing” section, “(500 total)” should have read “(500 ng total).” The errors have been corrected in the PDF and HTML versions of this article.


  1. Pallen, M.J., Nelson, K. & Preston, G.M. Bacterial Pathogenomics (ASM Press, 2007).

  2. Metzker, M. Sequencing technologies—the next generation. Nat. Rev. Genet. 11, 31–46 (2010).

    CAS  Article  Google Scholar 

  3. Glenn, T. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759–769 (2011).

    CAS  Article  Google Scholar 

  4. Pallen, M., Loman, N. & Penn, C. High-throughput sequencing and clinical microbiology: progress, opportunities and challenges. Curr. Opin. Microbiol. 13, 625–631 (2010).

    CAS  Article  Google Scholar 

  5. Rothberg, J. et al. An integrated semiconductor device enabling non-optical genome sequencing. Nature 475, 348–352 (2011).

    CAS  Article  Google Scholar 

  6. Bentley, D. et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008).

    CAS  Article  Google Scholar 

  7. Frank, C. et al. Epidemic profile of Shiga-toxin-producing Escherichia coli O104:H4 outbreak in Germany. N. Engl. J. Med. 365, 1771–1780 (2011).

    CAS  Article  Google Scholar 

  8. Brzuszkiewicz, E. et al. Genome sequence analyses of two isolates from the recent Escherichia coli outbreak in Germany reveal the emergence of a new pathotype: entero-aggregative-haemorrhagic Escherichia coli (EAHEC). Arch. Microbiol. 193, 883–891 (2011).

    CAS  Article  Google Scholar 

  9. Mellmann, A. et al. Prospective genomic characterization of the German enterohemorrhagic Escherichia coli O104:H4 outbreak by rapid next generation sequencing technology. PLoS ONE 6, e22751 (2011).

    CAS  Article  Google Scholar 

  10. Rohde, H. et al. Open-source genomic analysis of Shiga-toxin-producing E. coli O104:H4. N. Engl. J. Med. 365, 718–724 (2011).

    CAS  Article  Google Scholar 

  11. Rasko, D. et al. Origins of the E. coli strain causing an outbreak of hemolytic-uremic syndrome in Germany. N. Engl. J. Med. 365, 709–717 (2011).

    CAS  Article  Google Scholar 

  12. Grad, Y. et al. Genomic epidemiology of the Escherichia coli O104:H4 outbreaks in Europe, 2011. Proc. Natl. Acad. Sci. USA 109, 3065–3070 (2012).

    CAS  Article  Google Scholar 

  13. Ewing, B. & Green, P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8, 186–194 (1998).

    CAS  Article  Google Scholar 

  14. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  15. Kingsford, C., Schatz, M. & Pop, M. Assembly complexity of prokaryotic genomes using short reads. BMC Bioinformatics 11, 21 (2010).

    Article  Google Scholar 

  16. Buffalo, V. qrqc: Quick Read Quality Control R package version 1.9.1 <> (2012).

  17. Chattaway, M., Dallman, T., Okeke, I. & Wain, J. Enteroaggregative E. coli O104 from an outbreak of HUS in Germany 2011, could it happen again? J. Infect. Dev. Ctries. 5, 425–436 (2011).

    Article  Google Scholar 

  18. Chaudhuri, R. et al. xBASE2: a comprehensive resource for comparative bacterial genomics. Nucleic Acids Res. 36, D543–546 (2008).

    CAS  Article  Google Scholar 

  19. Delcher, A., Bratke, K., Powers, E. & Salzberg, S. Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics 23, 673–679 (2007).

    CAS  Article  Google Scholar 

  20. Lowe, T. & Eddy, S. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997).

    CAS  Article  Google Scholar 

  21. Lagesen, K. et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 35, 3100–3108 (2007).

    CAS  Article  Google Scholar 

  22. Darling, A., Tritt, A., Eisen, J. & Facciotti, M. Mauve assembly metrics. Bioinformatics 27, 2756–2757 (2011).

    CAS  Article  Google Scholar 

  23. Milne, I. et al. Tablet–next generation sequence assembly visualization. Bioinformatics 26, 401–402 (2010).

    CAS  Article  Google Scholar 

  24. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  Google Scholar 

  25. Touchon, M. et al. Organised genome dynamics in the Escherichia coli species results in highly diverse adaptive paths. PLoS Genet. 5, e1000344 (2009).

    Article  Google Scholar 

Download references


We gratefully acknowledge the blogging community for helpful discussion in the comments section of our blog (, and in particular to B. Chevreux, J. Johnson, K. Robison and L. Nederbragt. We are grateful to C. Hercus at Novocraft for help with the Novoalign software and to A. Darling for help with Mauve Assembly Metrics. We thank Roche Diagnostics, UK, for 454 GS FLX+ and 454 FLX paired-end sequencing, technical support and helpful discussion. We thank Life Technologies for early access to 316 chips and instrument fluidics upgrade. We thank G. Smith and Illumina UK for early access to the MiSeq platform and public release of E. coli outbreak-strain data. We thank the three anonymous reviewers for their many helpful suggestions for improving the manuscript. The xBASE facility and N.J.L. are funded by BBSRC grant BBE0111791.

Author information

Authors and Affiliations



N.J.L., J.W., S.E.G. and M.J.P. conceived the experiments; J.W. and S.G. supplied the strains; N.J.L., R.V.M. and T.J.D. carried out the bioinformatics analysis; C.C. performed the Ion Torrent sequencing; and S.E.G. and R.V.M. performed the 454 GS Junior sequencing. N.J.L. and M.J.P. wrote the manuscript. All authors commented on the manuscript.

Corresponding authors

Correspondence to John Wain or Mark J Pallen.

Ethics declarations

Competing interests

Mark J. Pallen was a winner of an Ion Torrent Personal Genome Machine in the European PGM grant program. Nicholas J. Loman has had expenses paid to speak at an Ion Torrent meeting organized by Life Technologies, and has received honoraria and expenses to speak at Illumina meetings. The other authors declare no financial interest.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1-3 and Supplementary Figs. 1-5 (PDF 1037 kb)

Supplementary Table 4

Statistics from Mauve Assembly Metrics comparing each of the assemblies produced from bench-top instrument data (XLSX 13 kb)

Supplementary Table 5

Result of BLAST searches for important pathogen biology genes (XLSX 39 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Loman, N., Misra, R., Dallman, T. et al. Performance comparison of benchtop high-throughput sequencing platforms. Nat Biotechnol 30, 434–439 (2012).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing