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

By Lauren M. Bragg, Glenn Stone, Margaret K. Butler, Philip Hugenholtz, Gene W. Tyson. PLoS Comput. Biol., 2013

327 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a595cee62f80240d3d2389c9354a30e28cf780ed

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

Protist diversity along a salinity gradient in a coastal lagoon (2015, background): “Replicate sequencing of known bacterial genomes highlighted that the quality scores provided by the Ion Torrent run reports underestimated the real base accuracy (Bragg et al. 2013).”
Methods for the extraction, storage, amplification and sequencing of DNA from environmental samples (2018, background): “However, the semiconductor sequencing technology suffers from high error rates for homopolymers because there is not a perfect correlation between the number of identical bases incorporated and the observed voltage change (Bragg et al. 2013).”
Application of high-throughput sequencing (HTS) metabarcoding to diatom biomonitoring: Do DNA extraction methods matter? (2017, background): “…amplification efficiency that may differ among species; DNA sequencing errors and bioinformatics filtering that may affect DNA sequence reliability (Jeon et al. 2008, Amend et al. 2010, Bragg et al. 2013, Weber and Pawlowski 2013, Deiner et al. 2015, Elbrecht and Leese 2015, Schmidt et al. 2015).”
Creating a universal SNP and small indel variant caller with deep neural networks (2016, background): “; ://doi.org/10.1101/092890 with hand-crafted features capturing common error modes 6 .”