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Methodologies for Microbial Ancestral Sequence Reconstruction

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Environmental Microbial Evolution

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2569))

Abstract

The reconstruction of genetic material of ancestral organisms constitutes a powerful application of evolutionary biology. A fundamental step in this inference is the ancestral sequence reconstruction (ASR), which can be performed with diverse methodologies implemented in computer frameworks. However, most of these methodologies ignore evolutionary properties frequently observed in microbes, such as genetic recombination and complex selection processes, that can bias the traditional ASR. From a practical perspective, here I review methodologies for the reconstruction of ancestral DNA and protein sequences, with particular focus on microbes, and including biases, recommendations, and software implementations. I conclude that microbial ASR is a complex analysis that should be carefully performed and that there is a need for methods to infer more realistic ancestral microbial sequences.

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Acknowledgments

This work was supported by the Spanish Ministry of Economy and Competitiveness [RYC-2015-18241] and by Xunta de Galicia [ED431F 2018/08].

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Authors and Affiliations

  1. Biomedical Research Center (CINBIO), University of Vigo, Vigo, Spain

    Miguel Arenas

  2. Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain

    Miguel Arenas

  3. Galicia Sur Health Research Institute (IIS Galicia Sur), Vigo, Spain

    Miguel Arenas

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  1. Miguel Arenas
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Correspondence to Miguel Arenas .

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Editors and Affiliations

  1. School of Life Sciences, Earth and Environmental Sciences Programme, and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China

    Haiwei Luo

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Arenas, M. (2022). Methodologies for Microbial Ancestral Sequence Reconstruction. In: Luo, H. (eds) Environmental Microbial Evolution. Methods in Molecular Biology, vol 2569. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2691-7_14

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  • DOI: https://doi.org/10.1007/978-1-0716-2691-7_14

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