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Statistical Analyses of Next Generation Sequencing Data: An Overview

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Statistical Analysis of Next Generation Sequencing Data

Part of the book series: Frontiers in Probability and the Statistical Sciences ((FROPROSTAS))

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Abstract

Next generation sequencing (NGS) is a significant technological advance in biomedical sciences. The sequencing platforms have advanced rapidly to the point that several genomes can now be sequenced simultaneously in a single instrument run in under two weeks. Its applications range from detecting transcription factor binding sites and quantifying gene expression to discovering methylation patterns and comparing genomes. We discuss and review some of the major NGS platforms that are currently in use. Some of these platforms like Illumina represent the fastest evolving genomic technologies in terms of cost, throughput and speed. However, despite overcoming the limitations of first generation platforms and microarray based studies, the generated data is not free of noise. The sources of noise are diverse and complex depending on the generating platform and sequencing chemistry. For example, errors can creep in from any intermediate sequencing steps like ligand adaption, fragmentation, Polymerase Chain Reaction (PCR) amplification and nucleotide removal. In methods like Chromatin Immunoprecipitation Sequencing (ChIP-Seq), non-specific binding is a major source of noise. All of this raises novel statistical and computational challenges, e.g., in basecalling and differential profiling. In this chapter, we point out the critical challenges that arise in NGS data analysis and provide an objective overview of the existing literature. As we shall see, NGS is not only transforming genomics but driving new methodological development in several branches of quantitative science.

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  1. Department of Bioinformatics and Biostatistics, School of Public Health and Information Science, University of Louisville, 485 E. Gray St., Louisville, KY, 40205, USA

    Riten Mitra, Susmita Datta & Somnath Datta

  2. Department of Mathematics, University of Louisville, Louisville, KY, 40292, USA

    Ryan Gill

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    Somnath Datta

  2. Department of Statistics, Iowa State University, Ames, Iowa, USA

    Dan Nettleton

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Mitra, R., Gill, R., Datta, S., Datta, S. (2014). Statistical Analyses of Next Generation Sequencing Data: An Overview. In: Datta, S., Nettleton, D. (eds) Statistical Analysis of Next Generation Sequencing Data. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-07212-8_1

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