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

Bragg, Lauren M., Stone, Glenn, Butler, Margaret K., Hugenholtz, Philip, Tyson, Gene W.·2013·PLoS Computational Biology
DOI 10.1371/journal.pcbi.1003031PMC PMC3623719
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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.

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Figures

Figure 1 Relationship between G+C% and the observed mean coverage for 100 bp bins in the reference genome. Panel (a) is a boxplot of the distribution of the square-root normalized mean read depth across the 100 bp windows for each reference genome, broken down further by sequencing kit and G+C% bin. The coverage for each run was normalised by the mean coverage –the boxplots show the square-root fold-change from the mean genomic coverage for each combination of species, kit and G+C% bin. Thus a value of 2 means the coverage was four times that of the mean for that sequencing run. The boxes display the central 50% of the values in each treatment, with the median represented by the solid black horizontal bar. The whiskers each extend for 1.5× the inter-quartile range, and the black dots represent extreme individual observations which fall outside this range. The variability observed in the high G+C bins are likely due to the small sample size for these G+C regions, shown in panel (b). The outliers are potentially due to repetitive content in the genome that failed to be masked by our perfect match repeat approach.
Figure 1 Relationship between G+C% and the observed mean coverage for 100 bp bins in the reference genome. Panel (a) is a boxplot of the distribution of the square-root normalized mean read depth across the 100 bp windows for each reference genome, broken down further by sequencing kit and G+C% bin. The coverage for each run was normalised by the mean coverage –the boxplots show the square-root fold-change from the mean genomic coverage for each combination of species, kit and G+C% bin. Thus a value of 2 means the coverage was four times that of the mean for that sequencing run. The boxes display the central 50% of the values in each treatment, with the median represented by the solid black horizontal bar. The whiskers each extend for 1.5× the inter-quartile range, and the black dots represent extreme individual observations which fall outside this range. The variability observed in the high G+C bins are likely due to the small sample size for these G+C regions, shown in panel (b). The outliers are potentially due to repetitive content in the genome that failed to be masked by our perfect match repeat approach.CC BY · PubMed Central
Figure 2 Mean rates of insertion, deletion and substitution errors across the three sequencing kits. Each box-plot shows the distribution of error rates for the specified type across the runs for the specified kit (species are aggregated).
Figure 2 Mean rates of insertion, deletion and substitution errors across the three sequencing kits. Each box-plot shows the distribution of error rates for the specified type across the runs for the specified kit (species are aggregated).CC BY · PubMed Central
Figure 3 Relationship between base position and error rate for homopolymer (over-call/under-call) versus substitution errors. Panel (a) shows the homopolymer error rate (insertion+deletion) by read base position, and panel (b) shows the substitution error rate by base position. Each line is the raw mean error rate for a single data-set with the kit and species as specified by the colour key.
Figure 3 Relationship between base position and error rate for homopolymer (over-call/under-call) versus substitution errors. Panel (a) shows the homopolymer error rate (insertion+deletion) by read base position, and panel (b) shows the substitution error rate by base position. Each line is the raw mean error rate for a single data-set with the kit and species as specified by the colour key.CC BY · PubMed Central
Figure 4 Examples of over-call/under-call errors in homopolymers of length less than 2. By aligning the read (derived from the rounded flow-values), and its corresponding reference sequence (considered the ‘true’ sequence) at the flow level, we can identify examples of over-calling a zero-length homopolymer (Flow Cycle #2), and under-calling a one-length homopolymer (Flow Cycle # 6). Flow Cycle #5 demonstrates a zero-length homopolymer being correctly called as zero.
Figure 4 Examples of over-call/under-call errors in homopolymers of length less than 2. By aligning the read (derived from the rounded flow-values), and its corresponding reference sequence (considered the ‘true’ sequence) at the flow level, we can identify examples of over-calling a zero-length homopolymer (Flow Cycle #2), and under-calling a one-length homopolymer (Flow Cycle # 6). Flow Cycle #5 demonstrates a zero-length homopolymer being correctly called as zero.CC BY · PubMed Central
Figure 5 Calling accuracy decreases with homopolymer length. Lines show mean accuracy for each kit by reference homopolymer length, across bases 10–100 and bases 10–200, the latter range only relevant for the two 200 bp kits.
Figure 5 Calling accuracy decreases with homopolymer length. Lines show mean accuracy for each kit by reference homopolymer length, across bases 10–100 and bases 10–200, the latter range only relevant for the two 200 bp kits.CC BY · PubMed Central
Figure 6 The influence of position in cycle (PIC, labelled 0–31) on flow-value distributions and consequently error rate and type. Panel (a) shows the coefficient (main-effect) of each flow cycle position as predictors of mean of the flow-value distribution. Panel (b) shows the error rate broken down by insertions and deletions for each PIC. These do not include flow-values for homopolymers where the reference homopolymer length is zero.
Figure 6 The influence of position in cycle (PIC, labelled 0–31) on flow-value distributions and consequently error rate and type. Panel (a) shows the coefficient (main-effect) of each flow cycle position as predictors of mean of the flow-value distribution. Panel (b) shows the error rate broken down by insertions and deletions for each PIC. These do not include flow-values for homopolymers where the reference homopolymer length is zero.CC BY · PubMed Central
Figure 7 Comparison of predicted versus empirical distributions of flow-values for homopolymers of length 1–5. Predicted (solid line) and empirical distributions (dotted line) of flow-values for homopolymers of length 1–5 (colours - black, red, green, blue and teal), for flow cycles 2,5,9 (rows) and PIC 1,10 and 12 (columns) for species B. amyloliquefaciens. The low number of observations of homopolymers of length 5 is the likely cause for abnormal distributions for this homopolymer length. The ‘shoulders’ observed in the data are often due to unexpectedly high popularity of boundary flow-values (eg. 0.51, 1.49, 1.51…).
Figure 7 Comparison of predicted versus empirical distributions of flow-values for homopolymers of length 1–5. Predicted (solid line) and empirical distributions (dotted line) of flow-values for homopolymers of length 1–5 (colours - black, red, green, blue and teal), for flow cycles 2,5,9 (rows) and PIC 1,10 and 12 (columns) for species B. amyloliquefaciens. The low number of observations of homopolymers of length 5 is the likely cause for abnormal distributions for this homopolymer length. The ‘shoulders’ observed in the data are often due to unexpectedly high popularity of boundary flow-values (eg. 0.51, 1.49, 1.51…).CC BY · PubMed Central
Figure 8 Breakdown of substitution type as a proportion of all substitutions for each sequencing kit.
Figure 8 Breakdown of substitution type as a proportion of all substitutions for each sequencing kit.CC BY · PubMed Central
Figure 9 Ion Torrent quality scores versus empirically estimated quality score for base. The grey cloud surrounding the LOESS smoother function indicates the 95% confidence interval for the conditional mean. Individual observations for each quality are plotted as black points.
Figure 9 Ion Torrent quality scores versus empirically estimated quality score for base. The grey cloud surrounding the LOESS smoother function indicates the 95% confidence interval for the conditional mean. Individual observations for each quality are plotted as black points.CC BY · PubMed Central

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Metadata

Authors
Bragg, L. M., Stone, G., Butler, M. K., Hugenholtz, P., & Tyson, G. W.
Venue
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2013-04-11
Volume
9
Issue
4
Pages
e1003031
ISSN
1553-7358
DOI
10.1371/journal.pcbi.1003031
PMC
PMC3623719
Cited by
261
References
23
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)

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Bragg, L. M., Stone, G., Butler, M. K., Hugenholtz, P., & Tyson, G. W. (2013). Shining a Light on Dark Sequencing: Characterising Errors in Ion Torrent PGM Data. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1003031

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