TempNet: a method to display statistical parsimony networks for heterochronous DNA sequence data


  • Stefan Prost,

    Corresponding author
    1. Department of Anatomy and Structural Biology, Allan Wilson Centre for Molecular Ecology and Evolution, University of Otago, Dunedin 9054, New Zealand
    2. Department of Integrative Biology, University of California, Berkeley, CA 94720-3140, USA
      Corresponding author. E-mail: stefan.prost@anatomy.otago.ac.nz
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  • Christian N. K. Anderson

    1. Department of Organismic and Evolutionary Biology, Museum of Comparative Zoology, Harvard University, Cambridge, MA 02138, USA
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Corresponding author. E-mail: stefan.prost@anatomy.otago.ac.nz


1. Heterochronous data have been used to study demographic changes in epidemiology and ancient DNA studies, revolutionizing our understanding of complex evolutionary processes such as invasions, migrations and responses to drugs or climate change. While there are sophisticated applications based on Markov-Chain Monte Carlo or Approximate Bayesian Computation to study these processes through time, summarizing the raw genetic data in an intuitively meaningful graphic can be challenging, most notably if identical haplotypes are present at different points in time.

2. We present temporal networks, an attractive way to display and summarize relationships within the heterochronous data so commonly used in ancient DNA or epidemiological research. TempNet is a user-friendly R script that creates journal-quality figures from genetic data in standard formats (FASTA, CLUSTAL, etc.). These figures are customizable and interactive within the R graphics window. Using three examples, we demonstrate that TempNet can deal with standard-sized datasets, as well as datasets of hundreds of sequences from fast-evolving organisms.

3. Temporal networks are flexible ways to illustrate genetic relationships through time. Furthermore, this approach is not limited to time-stamped data, but can also be used for different data partitioning strategies, such as spatial or phenotypic groupings. The R script presented here will be useful in illustrating complex genetic relationships between groups.