ArticleMolecular biology and evolution2024
CMAPLE: Efficient Phylogenetic Inference in the Pandemic Era.
Article in Molecular biology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- IQ-TREE 3: phylogenomic inference software using complex evolutionary models.Molecular biology and evolution · 2026Article
- Next-Generation Sequencing Strategies During the 2024-2025 Avian Influenza A(H5N1) Emergency Response in the U.S.Viruses · 2026Article
- Predicting the antigenic evolution of seasonal influenza viruses using phylogenetic convergence.bioRxiv : the preprint server for biology · 2026Article
- Rate variation and recurrent sequence errors in pandemic-scale phylogenetics.Nature methods · 2026Article
- Article
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Authors and funding
4 authors.
Funding
Abstract
We have recently introduced MAPLE (MAximum Parsimonious Likelihood Estimation), a new pandemic-scale phylogenetic inference method exclusively designed for genomic epidemiology. In response to the need for enhancing MAPLE's performance and scalability, here we present two key components: (i) CMAPLE software, a highly optimized C++ reimplementation of MAPLE with many new features and advancements, and (ii) CMAPLE library, a suite of application programming interfaces to facilitate the integration of the CMAPLE algorithm into existing phylogenetic inference packages. Notably, we have successfully integrated CMAPLE into the widely used IQ-TREE 2 software, enabling its rapid adoption in the scientific community. These advancements serve as a vital step toward better preparedness for future pandemics, offering researchers powerful tools for large-scale pathogen genomic analysis.
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Registered trials
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