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Statistical and Computational Methods for Microbial Strain Analysis

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Statistical Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2629))

Abstract

Microbial strains are interpreted as a lineage derived from a recent ancestor that have not experienced “too many” recombination events and can be successfully retrieved with culture-independent techniques using metagenomic sequencing. Such a strain variability has been increasingly shown to display additional phenotypic heterogeneities that affect host health, such as virulence, transmissibility, and antibiotics resistance. New statistical and computational methods have recently been developed to track the strains in samples based on shotgun metagenomics data either based on reference genome sequences or Metagenome-assembled genomes (MAGs). In this paper, we review some recent statistical methods for strain identifications based on frequency counts at a set of single nucleotide variants (SNVs) within a set of single-copy marker genes. These methods differ in terms of whether reference genome sequences are needed, how SNVs are called, what methods of deconvolution are used and whether the methods can be applied to multiple samples. We conclude our review with areas that require further research.

Short title: Resolving Microbial Strain Mixtures

This research is supported by NIH grants GM123056 and P30DK050306.

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Acknowledgements

The authors would like to thank Andrew Ghazi, Phillip Münch, Di Chen, Jordan Jensen, Yancong Zhang, Curtis Huttenhower, and Christopher Quince for their helpful input and discussions on this review.

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Authors and Affiliations

  1. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Siyuan Ma & Hongzhe Li

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  1. Siyuan Ma
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  2. Hongzhe Li
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Correspondence to Hongzhe Li .

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Editors and Affiliations

  1. Moffitt Cancer Center, Tampa, FL, USA

    Brooke Fridley

  2. Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

    Xuefeng Wang

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Ma, S., Li, H. (2023). Statistical and Computational Methods for Microbial Strain Analysis. In: Fridley, B., Wang, X. (eds) Statistical Genomics. Methods in Molecular Biology, vol 2629. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2986-4_11

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  • DOI: https://doi.org/10.1007/978-1-0716-2986-4_11

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2985-7

  • Online ISBN: 978-1-0716-2986-4

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