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Exploratory analysis and error modeling of a sequencing technology

Michael Inouye, Kerrin S. Small, Yik Y. Teo, Heng Li, Nava Whiteford, Tom Skelly, Irina Abnizova, Daniel J. Turner, Panos Deloukas, Dominic P. Kwiatkowski, Clive G. Brown, Taane G. Clark
doi: https://doi.org/10.1101/043042
Michael Inouye
1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom
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  • For correspondence: mi1@sanger.ac.uk
Kerrin S. Small
2Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
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Yik Y. Teo
2Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
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Heng Li
1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom
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Nava Whiteford
1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom
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Tom Skelly
1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom
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Irina Abnizova
1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom
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Daniel J. Turner
1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom
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Panos Deloukas
1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom
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Dominic P. Kwiatkowski
1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom
2Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
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Clive G. Brown
1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom
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Taane G. Clark
1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom
2Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
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Abstract

Next generation DNA sequencing methods have created an unprecedented leap in sequence data generation, thus novel computational tools and statistical models are required to optimize and assess the resulting data. In this report, we explore underlying causes of error for the Illumina Genome Analyzer (IGA) sequencing technology and attempt to quantify their effects using a human bacterial artificial chromosome sequenced to 60,000 fold coverage. Seven potential error predictors are considered: Phred score, read entropy, tile coordinates, local tile density, base position within read, nucleotide call, and lane. With these parameters, logistic regression and log-linear models are constructed and used to show that each of the potential predictors contributes to error (P<1×10−4). With this additional information, we apply the logistic model and achieve a 3% improvement in both the sensitivity and specificity to detect IGA errors. Further, we demonstrate that these modeling approaches can be used as a feedback loop to inform laboratory methods and identify specific machine or run bias.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 13, 2016.
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Exploratory analysis and error modeling of a sequencing technology
Michael Inouye, Kerrin S. Small, Yik Y. Teo, Heng Li, Nava Whiteford, Tom Skelly, Irina Abnizova, Daniel J. Turner, Panos Deloukas, Dominic P. Kwiatkowski, Clive G. Brown, Taane G. Clark
bioRxiv 043042; doi: https://doi.org/10.1101/043042
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Exploratory analysis and error modeling of a sequencing technology
Michael Inouye, Kerrin S. Small, Yik Y. Teo, Heng Li, Nava Whiteford, Tom Skelly, Irina Abnizova, Daniel J. Turner, Panos Deloukas, Dominic P. Kwiatkowski, Clive G. Brown, Taane G. Clark
bioRxiv 043042; doi: https://doi.org/10.1101/043042

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