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Choosing molecular markers for phylogenomics is crucial. This study compares ultraconserved elements (UCE), exon-capture, and RADseq methods, finding most perform equally well for clear evolutionary relationships, but modeling data heterogeneity is key for complex cases.

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Area of Science:

  • Genomics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • The selection of molecular markers is critical for successful phylogenetic studies, especially with the advent of genome-scale DNA sequencing.
  • Four common phylogenomic protocols—ultraconserved elements (UCE), exon-capture, RADseq, and ddRADseq—are frequently employed but require standardized comparison.

Purpose of the Study:

  • To standardize and compare the performance of UCE, exon-capture, RADseq, and ddRADseq methods in phylogenomic analyses.
  • To evaluate their effectiveness in phylogenetic resolution and accurate estimation of clade ages using molecular clock models.
  • To compare these marker classes against traditional Sanger-sequenced nuclear exons and mitochondrial genomes.

Main Methods:

  • In silico extraction of data from published primate genomes for four marker classes: UCE, exon-capture, RADseq, and ddRADseq.
  • Phylogenetic analysis to assess resolution and clade age estimation using fossil-calibrated molecular-clock models.
  • Comparison with existing data from nuclear exons and mitochondrial genomes under identical analytical conditions.

Main Results:

  • Most data classes recovered identical, well-supported topologies for uncontroversial phylogenetic nodes.
  • A difficult-to-resolve, rapidly diversifying clade showed conflicting topologies among marker classes, attributed to mismodeling gene tree heterogeneity.
  • Clade age estimates varied between data sets, with recent nodes showing younger ages for nuclear exons and deeper nodes for UCE, RADseq, and mitochondrial data, linked to temporal differences in phylogenetic informativeness.

Conclusions:

  • For straightforward phylogenetic questions, numerous loci are not essential, and practical factors like data generation ease become more important.
  • Accurate modeling of data heterogeneity remains critical for resolving complex evolutionary histories and recalcitrant phylogenetic problems.