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Base-Calling Using a Random Effects Mixture Model on Next-Generation Sequencing Data

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Abstract

The emergence of next-generation sequencing technology has greatly influenced research in biology and clinical applications. This new technology allows millions of DNA fragments to be sequenced in parallel, reducing costs and increasing throughput. One of the most widely used DNA sequencing machines is the Illumina platform which contains a novel sequencing-by-synthesis method involving a series of chemical reactions and image processing. However, it suffers from biases inherent with the complex nature of the chemical processes involved. The process of converting the fluorescence intensity output of the sequencing-by-synthesis technology to the nucleotide bases is what is known as base-calling. The resulting DNA sequences are used in further downstream analyses such as in genome assemblies or variant detection in which the accuracy and quality of bases impact the results. In this paper, we introduce a random effects mixture model that captures the sequencing process and compare its performance to a model with fixed effects.

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Acknowledgements

The authors thank the Institute for Integrative Genome Biology Bioinformatics Facility at University of California, Riverside, for providing the bioinformatics cluster. This material was based upon work partially supported by the National Science Foundation (DMS ATD-1222718) and the University of California, Riverside (AES- CE RSAP A01869) for X.C. and A.C.

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  1. University of California Riverside, Riverside, USA

    Ashley Cacho, Weixin Yao & Xinping Cui

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  1. Ashley Cacho
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  2. Weixin Yao
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  3. Xinping Cui
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Correspondence to Xinping Cui.

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Cacho, A., Yao, W. & Cui, X. Base-Calling Using a Random Effects Mixture Model on Next-Generation Sequencing Data. Stat Biosci 10, 3–19 (2018). https://doi.org/10.1007/s12561-017-9190-3

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  • DOI: https://doi.org/10.1007/s12561-017-9190-3

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