Shining a Light on Dark Sequencing: Characterising Errors in Ion Torrent PGM Data

Abstract

The Ion Torrent Personal Genome Machine (PGM) is a new sequencing platform that substantially differs from other sequencing technologies by measuring pH rather than light to detect polymerisation events. Using re-sequencing datasets, we comprehensively characterise the biases and errors introduced by the PGM at both the base and flow level, across a combination of factors, including chip density, sequencing kit, template species and machine. We found two distinct insertion/deletion (indel) error types that accounted for the majority of errors introduced by the PGM. The main error source was inaccurate flow-calls, which introduced indels at a raw rate of 2.84% (1.38% after quality clipping) using the OneTouch 200 bp kit. Inaccurate flow-calls typically resulted in over-called short-homopolymers and under-called long-homopolymers. Flow-call accuracy decreased with consecutive flow cycles, but we also found significant periodic fluctuations in the flow error-rate, corresponding to specific positions within the flow-cycle pattern. Another less common PGM error, high frequency indel (HFI) errors, are indels that occur at very high frequency in the reads relative to a given base position in the reference genome, but in the majority of instances were not replicated consistently across separate runs. HFI errors occur approximately once every thousand bases in the reference, and correspond to 0.06% of bases in reads. Currently, the PGM does not achieve the accuracy of competing light-based technologies. However, flow-call inaccuracy is systematic and the statistical models of flow-values developed here will enable PGM-specific bioinformatics approaches to be developed, which will account for these errors. HFI errors may prove more challenging to address, especially for polymorphism and amplicon applications, but may be overcome by sequencing the same DNA template across multiple chips.

Author Summary

DNA sequencing is used routinely within biology to reveal the genetic information of living organisms. In recent years, technological advances have led to the availability of high-throughput, low-cost DNA sequencing machines (‘sequencers’). In 2011, Life Sciences released a new sequencer, the Ion Torrent Personal Genome Machine (PGM). This is the first sequencer to measure changes in pH rather that emitted light to register sequencing reactions. Consequently, this unique technology is both cost-effective and advertised to have high accuracy, making it attractive for many laboratories. However, every sequencing technology introduces unique errors and biases into the resulting DNA sequences, and understanding PGM-specific characteristics is crucial to determining suitable applications for this new technology. We comprehensively examine the types of errors and biases in PGM-sequenced data across several experimental variables, including chip density, template kit, template DNA and across two machines. Using statistical approaches, we quantify the influence of experimental variables, as well as DNA sequence-specific effects, and find that the PGM has two types of technology-specific errors. We also find that the accuracy of the PGM is poorer than that of light-based technologies, and we make recommendations for this technology as well as provide statistical models for overcoming PGM sequencing errors.