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Advances using molecular data in insect systematics.

Karl Kjer1, Marek L Borowiec2, Paul B Frandsen3

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Molecular datasets are rapidly expanding, aiding insect phylogeny research. New methods improve data analysis, leading to a consensus on insect evolutionary history and better dating analyses.

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Area of Science:

  • * Molecular biology and evolutionary biology.
  • * Bioinformatics and computational phylogenetics.

Background:

  • * Exponential growth in molecular datasets (genomes, transcriptomes, hybrid capture) since the 1980s.
  • * Technological advancements driving increased data generation, including hundreds or thousands of genetic loci.
  • * Existing methods facing challenges with large datasets, necessitating new analytical approaches.

Purpose of the Study:

  • * To leverage massive molecular datasets for resolving higher-level insect phylogeny.
  • * To address challenges in interpreting phylogenetic branch support with large datasets.
  • * To advance methods for phylogenetic dating and analysis of large-scale molecular data.

Main Methods:

  • * Analysis of large-scale molecular datasets including genomes, transcriptomes, and hybrid capture data.
  • * Development and application of sophisticated partitioning schemes and phylogenetic models.
  • * Utilizing coalescent methods with thousands of available genes.
  • * Incorporating expanded barcode libraries into phylogenies with numerous individuals.

Main Results:

  • * Approaching a consensus on higher-level insect phylogeny due to extensive datasets.
  • * Identification of new opportunities for improved phylogenetic models and partitioning strategies.
  • * Advancements in molecular dating analyses through careful fossil calibration.
  • * Maturation of coalescent methods for large-scale phylogenetic inference.

Conclusions:

  • * Large molecular datasets are crucial for resolving deep evolutionary relationships in insects.
  • * Continued development of analytical methods is essential for interpreting complex phylogenomic data.
  • * Phylogenetic dating and individual-based phylogenies are benefiting from new computational approaches.