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Estimating phylogenies from genomes: A beginners review of commonly used genomic data in vertebrate phylogenomics.

Javan K CarterRebecca T KimballErik R FunkNolan C KaneDrew R SchieldGarth M SpellmanRebecca J Safran
Published in: The Journal of heredity (2023)
Despite the increasing feasibility of sequencing whole genomes from diverse taxa, a persistent problem in phylogenomics is the selection of appropriate genetic markers or loci for a given taxonomic group or research question. In this review, we aim to streamline the decision-making process when selecting specific markers to use in phylogenomic studies by introducing commonly used types of genomic markers, their evolutionary characteristics, and their associated uses in phylogenomics. Specifically, we review the utilities of ultraconserved elements (including flanking regions), anchored hybrid enrichment loci, conserved nonexonic elements, untranslated regions, introns, exons, mitochondrial DNA, single nucleotide polymorphisms, and anonymous regions (nonspecific regions that are evenly or randomly distributed across the genome). These various genomic elements and regions differ in their substitution rates, likelihood of neutrality or of being strongly linked to loci under selection, and mode of inheritance, each of which are important considerations in phylogenomic reconstruction. These features may give each type of marker important advantages and disadvantages depending on the biological question, number of taxa sampled, evolutionary timescale, cost effectiveness, and analytical methods used. We provide a concise outline as a resource to efficiently consider key aspects of each type of genetic marker. There are many factors to consider when designing phylogenomic studies, and this review may serve as a primer when weighing options between multiple potential phylogenomic markers.
Keyphrases
  • genome wide
  • copy number
  • mitochondrial dna
  • dna methylation
  • decision making
  • genome wide association study
  • single cell
  • transcription factor
  • machine learning
  • deep learning