SeqControl: Process control for DNA sequencing

Lauren C. Chong, Marco A. Albuquerque, Nicholas J. Harding, Cristian Caloian, Michelle Chan-Seng-Yue, Richard De Borja, Michael Fraser, Robert E. Denroche, Timothy A. Beck, Theodorus Van Der Kwast, Robert G. Bristow, John D. McPherson, Paul C. Boutros

Research output: Contribution to journalArticlepeer-review


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

Original languageEnglish
Pages (from-to)1071-1075
Number of pages5
JournalNature Methods
Issue number10
Publication statusPublished - 1 Jan 2014

Research Beacons, Institutes and Platforms

  • Manchester Cancer Research Centre


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