LinguaPhylo: A probabilistic model specification language for reproducible phylogenetic analyses

Alexei J Drummond, Kylie Chen, Fábio K Mendes and Dong Xie

PLOS Computational Biology, 2023, vol. 19, issue 7, 1-16

Abstract: Phylogenetic models have become increasingly complex, and phylogenetic data sets have expanded in both size and richness. However, current inference tools lack a model specification language that can concisely describe a complete phylogenetic analysis while remaining independent of implementation details. We introduce a new lightweight and concise model specification language, ‘LPhy’, which is designed to be both human and machine-readable. A graphical user interface accompanies ‘LPhy’, allowing users to build models, simulate data, and create natural language narratives describing the models. These narratives can serve as the foundation for manuscript method sections. Additionally, we present a command-line interface for converting LPhy-specified models into analysis specification files (in XML format) compatible with the BEAST2 software platform. Collectively, these tools aim to enhance the clarity of descriptions and reporting of probabilistic models in phylogenetic studies, ultimately promoting reproducibility of results.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1011226

DOI: 10.1371/journal.pcbi.1011226

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