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Computational Tools for Population Genomics

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Book cover Population Genomics

Part of the book series: Population Genomics ((POGE))

Abstract

With the rapidly dropping costs of sequencing, it is now possible to study the genomes and populations of any species to obtain precise evidence about their evolution and adaptation. Here, we will give an overview of software tools for processing raw sequencing reads into population-level data, and then go through the common population genomics analyses on these data and computational tools developed for them, as well as give insights into the computational solutions and their efficiency.

We first address the tools and pipelines for processing next-generation sequencing data from heterogeneous data sources into population-level data comprising single nucleotide polymorphisms or copy-number variants. After a brief discussion on all-purpose software tools for carrying out standard population genetic analyses, we provide a more detailed overview of different types of population genomics data analyses, loosely grouped under population genetics and demography, evolutionary population genomics, phylogenomics, and comparative genomics, as well as suggest current tools for the analyses. Under population genetics and demography analyses, we discuss methods for exploring population genomic diversity and genetic structure, population admixture, interspecific introgression events, and inferences about overall population history. The evolutionary genomics analyses include methods and tools for studying patterns of selection, such as hard and soft sweeps and population differentiation but also genome-wide association studies and pan-genomes between individuals and populations, as well as paleogenomics research. Under phylogenomics and comparative genomics, we provide an overview of the computational tools used for studies on polyploid species, phylogenomics, and comparative genomics of gene space evolution within and between species.

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Salojärvi, J. (2018). Computational Tools for Population Genomics. In: Rajora, O. (eds) Population Genomics. Population Genomics. Springer, Cham. https://doi.org/10.1007/13836_2018_57

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