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SeqControl: process control for DNA sequencing

Nature Methods volume 11, pages 1071–1075 (2014)Cite this article

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Abstract

As high-throughput sequencing continues to increase in speed and throughput, routine clinical and industrial application draws closer. These 'production' settings will require enhanced quality monitoring and quality control to optimize output and reduce costs. We developed SeqControl, a framework for predicting sequencing quality and coverage using a set of 15 metrics describing overall coverage, coverage distribution, basewise coverage and basewise quality. Using whole-genome sequences of 27 prostate cancers and 26 normal references, we derived multivariate models that predict sequencing quality and depth. SeqControl robustly predicted how much sequencing was required to reach a given coverage depth (area under the curve (AUC) = 0.993), accurately classified clinically relevant formalin-fixed, paraffin-embedded samples, and made predictions from as little as one-eighth of a sequencing lane (AUC = 0.967). These techniques can be immediately incorporated into existing sequencing pipelines to monitor data quality in real time. SeqControl is available at http://labs.oicr.on.ca/Boutros-lab/software/SeqControl/.

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Figure 1: Correlations between single-lane and all-lane values.
Figure 2: Summary of tumor linear models for all metrics.
Figure 3: A Random Forest classifier accurately predicts collapsed coverage.
Figure 4: Classification from partial-lane sequencing yields high predictive accuracy.

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Acknowledgements

This study was conducted with the support of Movember funds through Prostate Cancer Canada and with the additional support of the Ontario Institute for Cancer Research, funded by the Government of Ontario; with the support of Genome Canada through a Large-Scale Applied Project contract to P.C.B., S. Shah and R. Morin; and with the support of Prostate Cancer Canada, funded by the Movember Foundation, grant #RS2014-01. P.C.B. was supported by a Terry Fox Research Institute New Investigator Award and a Canadian Institutes of Health Research New Investigator Award. The authors thank all members of the Boutros lab for their support and insightful discussions, J. Livingstone and L. Heisler for assistance in data handling, and L. Stein and J. Simpson for critical comments on the manuscript.

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Authors and Affiliations

  1. Informatics & Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada

    Lauren C Chong, Marco A Albuquerque, Nicholas J Harding, Cristian Caloian, Michelle Chan-Seng-Yue, Richard de Borja, Robert E Denroche, Timothy A Beck & Paul C Boutros

  2. Department of Pathology, University Health Network, Toronto, Ontario, Canada

    Michael Fraser & Theodorus van der Kwast

  3. Ontario Cancer Institute, University Health Network, Toronto, Ontario, Canada

    Robert G Bristow

  4. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Robert G Bristow, John D McPherson & Paul C Boutros

  5. Genomics Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada

    John D McPherson

  6. Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada

    Paul C Boutros

Authors
  1. Lauren C Chong
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Contributions

P.C.B. and L.C.C. initiated the project. M.F., T.v.d.K., R.G.B., J.D.M. and P.C.B. generated sequencing data. L.C.C., M.A.A., C.C., M.C.-S.-Y., R.d.B., R.E.D. and T.A.B. performed analysis on the sequencing data. L.C.C., M.A.A., C.C., N.J.H. and P.C.B. were responsible for statistical modeling. Research was supervised by R.G.B., J.D.M. and P.C.B. L.C.C. wrote the manuscript, which was edited and approved by all authors.

Corresponding author

Correspondence to Paul C Boutros.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–21, Supplementary Tables 2, 3, 11 and 15–18, Supplementary Note and Supplementary Results (PDF 9909 kb)

Supplementary Table 1

Information about the CPC-GENE sample data. (XLS 29 kb)

Supplementary Table 4

Metric values calculated for all lane groupings of the oldest ten samples (10 tumour, 9 matched normal). (XLSX 190 kb)

Supplementary Table 5

Metric values calculated for all lane groupings of the newest seventeen samples (tumour and matched normal). (XLSX 54 kb)

Supplementary Table 6

Metric data, true outcome and random forest-predicted outcome for all lane-level BAMs (tumour only). (XLSX 48 kb)

Supplementary Table 7

Metric data, true outcome and random forest-predicted outcome for all lane-level BAMs (normal only). (XLSX 34 kb)

Supplementary Table 8

Metric data, true outcome and random forest-predicted outcome for all half-lane BAMs (tumour only). (XLSX 87 kb)

Supplementary Table 9

Metric data, true outcome and random forest-predicted outcome for all quarter-lane BAMs (tumour only). (XLSX 160 kb)

Supplementary Table 10

Metric data, true outcome and random forest-predicted outcome for all eighth-lane BAMs (tumour only). (XLSX 308 kb)

Supplementary Table 12

Summary of preseq v0.0.1 prediction accuracy. (XLSX 25 kb)

Supplementary Table 13

Summary of preseq v0.1.0 prediction accuracy. (XLSX 25 kb)

Supplementary Table 14

Summary of preseq v1.0.0 prediction accuracy. (XLSX 25 kb)

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Chong, L., Albuquerque, M., Harding, N. et al. SeqControl: process control for DNA sequencing. Nat Methods 11, 1071–1075 (2014). https://doi.org/10.1038/nmeth.3094

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