ArticleMolecular biology and evolution2013
FUBAR: a fast, unconstrained bayesian approximation for inferring selection.
Article in Molecular biology and evolution, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 859 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
859 citing papers in PubMed.
- Article
- Mitogenomic Insights into the Tinfoil Barb Barbonymus schwanefeldii from Sumatra, Indonesia: Structural Features, Selective Pressure, Genetic Diversity, and Phylogenetic Placement.Journal of molecular evolution · 2026Article
- Into the wild: a long-term study of begomovirus community dynamics in a natural landscape.The Journal of general virology · 2026Article
- Plastomes of TenEcology and evolution · 2026Article
- Molecular Detection and VP2 Characterization of Feline and Canine Parvoviruses in Domestic Cats in Central Vietnam.International journal of molecular sciences · 2026Article
- Evolutionary Dynamics-AwareACS omega · 2026Article
- Balancing Genome Plasticity and Evolutionary Constraint During the Long-Term Evolution of Feline Coronaviruses.Viruses · 2026Article
- Intersecting experimental evolution and CRISPR screens to identify novel toxin resistance loci.eLife · 2026Article
- Adaptive Diversification of theEvolutionary applications · 2026Article
- Genomic characterization of LaoThe Journal of general virology · 2026Article
- Genetic Variability and Evolutionary Dynamics of A(H1N1)pdm09 in Italy Before, During, and After the COVID-19 Pandemic.Pathogens (Basel, Switzerland) · 2026Article
- Genome-informed structural analysis of polymerase and glycoprotein adaptation in H5N1 clade 2.3.4.4b.Journal of veterinary science · 2026Article
- Emergence of canine parvovirus-2c in domestic cats and adaptive evolution of the NS1 gene: Molecular epidemiology of feline parvoviruses in Northern Vietnam (2022-2025).Veterinary world · 2026Article
- Article
- Feline Kobuvirus in Domestic Cats in Northern Vietnam: Molecular Insights Reveal Adaptive Evolution in VP1 Protein.International journal of molecular sciences · 2026Article
- Comparative Chloroplast Genomics, Codon Usage Bias, and Phylogenetic Placement ofCurrent issues in molecular biology · 2026Article
- Article
- Novel insights into tomato leaf curl New Delhi virus introduction and evolution in Southeastern France using an advanced long-read sequencing workflow.PLoS pathogens · 2026Article
- Beyond Invariable Sites: Using Evolutionary Stasis to Map Multilayered Constraints on the Evolution of Viral and Mammalian Genomes.Genome biology and evolution · 2026Article
- Article
799 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Model-based analyses of natural selection often categorize sites into a relatively small number of site classes. Forcing each site to belong to one of these classes places unrealistic constraints on the distribution of selection parameters, which can result in misleading inference due to model misspecification. We present an approximate hierarchical Bayesian method using a Markov chain Monte Carlo (MCMC) routine that ensures robustness against model misspecification by averaging over a large number of predefined site classes. This leaves the distribution of selection parameters essentially unconstrained, and also allows sites experiencing positive and purifying selection to be identified orders of magnitude faster than by existing methods. We demonstrate that popular random effects likelihood methods can produce misleading results when sites assigned to the same site class experience different levels of positive or purifying selection--an unavoidable scenario when using a small number of site classes. Our Fast Unconstrained Bayesian AppRoximation (FUBAR) is unaffected by this problem, while achieving higher power than existing unconstrained (fixed effects likelihood) methods. The speed advantage of FUBAR allows us to analyze larger data sets than other methods: We illustrate this on a large influenza hemagglutinin data set (3,142 sequences). FUBAR is available as a batch file within the latest HyPhy distribution (http://www.hyphy.org), as well as on the Datamonkey web server (http://www.datamonkey.org/).
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.