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Statistical Considerations on NGS Data for Inferring Copy Number Variations

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Deep Sequencing Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2243))

Abstract

The next-generation sequencing (NGS) technology has revolutionized research in genetics and genomics, resulting in massive NGS data and opening more fronts to answer unresolved issues in genetics. NGS data are usually stored at three levels: image files, sequence tags, and alignment reads. The sizes of these types of data usually range from several hundreds of gigabytes to several terabytes. Biostatisticians and bioinformaticians are typically working with the aligned NGS read count data (hence the last level of NGS data) for data modeling and interpretation.

To horn in on the use of NGS technology, researchers utilize it to profile the whole genome to study DNA copy number variations (CNVs) for an individual subject (or patient) as well as groups of subjects (or patients). The resulting aligned NGS read count data are then modeled by proper mathematical and statistical approaches so that the loci of CNVs can be accurately detected. In this book chapter, a summary of most popularly used statistical methods for detecting CNVs using NGS data is given. The goal is to provide readers with a comprehensive resource of available statistical approaches for inferring DNA copy number variations using NGS data.

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Authors and Affiliations

  1. Division of Biostatistics and Data Science, Department of Population Health Sciences, Medical College of Georgia, Augusta University, Augusta, GA, USA

    Jie Chen

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  1. Jie Chen
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Correspondence to Jie Chen .

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  1. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Noam Shomron

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Chen, J. (2021). Statistical Considerations on NGS Data for Inferring Copy Number Variations. In: Shomron, N. (eds) Deep Sequencing Data Analysis. Methods in Molecular Biology, vol 2243. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1103-6_2

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  • DOI: https://doi.org/10.1007/978-1-0716-1103-6_2

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1102-9

  • Online ISBN: 978-1-0716-1103-6

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