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

Author

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  • Ashley Cacho

    (University of California Riverside)

  • Weixin Yao

    (University of California Riverside)

  • Xinping Cui

    (University of California Riverside)

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.

Suggested Citation

  • Ashley Cacho & Weixin Yao & Xinping Cui, 2018. "Base-Calling Using a Random Effects Mixture Model on Next-Generation Sequencing Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 3-19, April.
    • Handle: RePEc:spr:stabio:v:10:y:2018:i:1:d:10.1007_s12561-017-9190-3
    DOI: 10.1007/s12561-017-9190-3
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    1. Biscarat, Jean Claude, 1994. "Almost sure convergence of a class of stochastic algorithms," Stochastic Processes and their Applications, Elsevier, vol. 50(1), pages 83-99, March.
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