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TADA: taxonomy-aware dataset aggregator.

Emil Hägglund1, Siv G E Andersson1, Lionel Guy2

  • 1Molecular Evolution, Department of Cell and Molecular Biology, Science for Life Laboratory, Biomedical Centre, Uppsala University, SE-751 24 Uppsala, Sweden.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

Selecting representative genomes for bacterial and archaeal phylogenetic analysis is crucial. TADA (Taxonomic-Aware Dataset selection) is a new workflow that automates this process, ensuring quality and diversity in genomic datasets.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Evolutionary Biology

Background:

  • The increasing number of sequenced bacterial and archaeal genomes enables advanced phylogenetic and comparative genomic studies.
  • However, utilizing all available genomic data for phylogenetic reconstruction is computationally challenging and can introduce biases due to uneven distribution across diversity.

Purpose of the Study:

  • To develop a user-friendly software solution for efficient and reliable subsampling of prokaryotic genomes for phylogenetic analysis.
  • To address the need for automated taxonomic-aware dataset selection in large-scale genomic studies.

Main Methods:

  • Implementation of TADA as a Snakemake workflow.
  • Development of a taxonomic-aware dataset selection process with adjustable granularity.
  • Inclusion of genome quality control and branch-balancing parameters.

Main Results:

  • TADA facilitates the selection of representative genomic subsets from diverse prokaryotic lineages.
  • The workflow allows for user-defined sampling strategies across prokaryotic diversity.
  • Constraints on genome quality and phylogenetic balance are integrated into the selection process.

Conclusions:

  • TADA provides a practical solution for constructing high-quality, diverse genomic datasets for phylogenetic inference.
  • This tool enhances the feasibility of large-scale phylogenetic analyses in prokaryotes.
  • Automated, taxonomic-aware subsampling improves the efficiency and accuracy of comparative genomics.