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

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.

[1]  Pedro M. Valero-Mora,et al.  ggplot2: Elegant Graphics for Data Analysis , 2010 .

[2]  Inge Jonassen,et al.  Systematic exploration of error sources in pyrosequencing flowgram data , 2011, Bioinform..

[3]  Emese Meglécz,et al.  Accuracy and quality assessment of 454 GS-FLX Titanium pyrosequencing , 2011, BMC Genomics.

[4]  A. Elliott,et al.  Rapid detection of the ACMG/ACOG-recommended 23 CFTR disease-causing mutations using ion torrent semiconductor sequencing. , 2012, Journal of biomolecular techniques : JBT.

[5]  Tracy K. Teal,et al.  Systematic artifacts in metagenomes from complex microbial communities , 2009, The ISME Journal.

[6]  T. Dallman,et al.  Performance comparison of benchtop high-throughput sequencing platforms , 2012, Nature Biotechnology.

[7]  Susan M. Huse,et al.  Accuracy and quality of massively parallel DNA pyrosequencing , 2007, Genome Biology.

[8]  Limin Fu,et al.  Artificial and natural duplicates in pyrosequencing reads of metagenomic data , 2010, BMC Bioinformatics.

[9]  Richard Durbin,et al.  A large genome center's improvements to the Illumina sequencing system , 2008, Nature Methods.

[10]  S. Salzberg,et al.  Versatile and open software for comparing large genomes , 2004, Genome Biology.

[11]  Russell J. Davenport,et al.  Removing Noise From Pyrosequenced Amplicons , 2011, BMC Bioinformatics.

[12]  C. Quince,et al.  Accurate determination of microbial diversity from 454 pyrosequencing data , 2009, Nature Methods.

[13]  J. Braman,et al.  PCR fidelity of pfu DNA polymerase and other thermostable DNA polymerases. , 1996, Nucleic acids research.

[14]  Bernard P. Puc,et al.  An integrated semiconductor device enabling non-optical genome sequencing , 2011, Nature.

[15]  Peter F. Stadler,et al.  Fast Mapping of Short Sequences with Mismatches, Insertions and Deletions Using Index Structures , 2009, PLoS Comput. Biol..

[16]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[17]  Howard Ochman,et al.  Neutral mutations and neutral substitutions in bacterial genomes. , 2003, Molecular biology and evolution.

[18]  H. Swerdlow,et al.  A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers , 2012, BMC Genomics.

[19]  Jeffrey E. Barrick,et al.  Genome evolution and adaptation in a long-term experiment with Escherichia coli , 2009, Nature.

[20]  T. Fennell,et al.  Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries , 2011, Genome Biology.

[21]  R. Knight,et al.  Rapid denoising of pyrosequencing amplicon data: exploiting the rank-abundance distribution , 2010, Nature Methods.

[22]  Christophe Klopp,et al.  Assessment of replicate bias in 454 pyrosequencing and a multi-purpose read-filtering tool , 2011, BMC Research Notes.

[23]  Inge Jonassen,et al.  Characteristics of 454 pyrosequencing data—enabling realistic simulation with flowsim , 2010, Bioinform..