ArticleNature genetics2023
Maximum likelihood pandemic-scale phylogenetics.
Article in Nature genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
53 citing papers in PubMed.
- Computational approaches for multimodal lineage tracing.Nature reviews. Genetics · 2026Review
- Article
- A monoclonal antibody targeting OXA23 restores carbapenem susceptibility in global epidemic carbapenem-resistantAntimicrobial agents and chemotherapy · 2026Article
- Large-scale genomic surveillance reveals immunosuppression drives mutation dynamics in persistent SARS-CoV-2 infections.Nature communications · 2026Article
- Practical phylogenetic usage of theoretical advances in distance-based tree learning.BMC bioinformatics · 2026Article
- PRIME: An evaluation framework for protein representation inference and generalization in viral mutation space.BMC genomics · 2026Article
- IQ-TREE 3: phylogenomic inference software using complex evolutionary models.Molecular biology and evolution · 2026Article
- Rapid phylogenomic analysis for viral surveillance and metagenomic profiling with Omni2Tree.bioRxiv : the preprint server for biology · 2026Article
- Global approaches to infectious disease surveillance and modeling.Nature medicine · 2026Review
- Predicting the antigenic evolution of seasonal influenza viruses using phylogenetic convergence.bioRxiv : the preprint server for biology · 2026Article
- The emergence and molecular evolution of H5N1 influenza viruses in United States dairy cattle.bioRxiv : the preprint server for biology · 2026Article
- Rate variation and recurrent sequence errors in pandemic-scale phylogenetics.Nature methods · 2026Article
- PyEvoMotion: a Python tool for population-based time-course analysis of genome evolution.Bioinformatics (Oxford, England) · 2026Article
- ConvexML: Fast and Accurate Branch Length Estimation under Irreversible Mutation Models, Illustrated through Applications to CRISPR/Cas9-Based Lineage Tracing.Systematic biology · 2026Article
- Biological causes and impacts of rugged tree landscapes in phylodynamic inference.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Review
- A Pandemic-Scale Ancestral Recombination Graph for SARS-CoV-2.bioRxiv : the preprint server for biology · 2025Article
- Article
- On campus dormitories as viral transmission sinks: Phylodynamic insights into student housing networks during the COVID-19 pandemic.PLoS pathogens · 2025Article
- Tracing SARS-CoV-2 clusters across local scales using genomic data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
- Update of
Authors and funding
6 authors.
Funding
Abstract
Phylogenetics has a crucial role in genomic epidemiology. Enabled by unparalleled volumes of genome sequence data generated to study and help contain the COVID-19 pandemic, phylogenetic analyses of SARS-CoV-2 genomes have shed light on the virus's origins, spread, and the emergence and reproductive success of new variants. However, most phylogenetic approaches, including maximum likelihood and Bayesian methods, cannot scale to the size of the datasets from the current pandemic. We present 'MAximum Parsimonious Likelihood Estimation' (MAPLE), an approach for likelihood-based phylogenetic analysis of epidemiological genomic datasets at unprecedented scales. MAPLE infers SARS-CoV-2 phylogenies more accurately than existing maximum likelihood approaches while running up to thousands of times faster, and requiring at least 100 times less memory on large datasets. This extends the reach of genomic epidemiology, allowing the continued use of accurate phylogenetic, phylogeographic and phylodynamic analyses on datasets of millions of genomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.