Evidence map›Paper›PMID 39715362›Full record

ArticleGenome biology and evolution2025

CAT-Posterior Mean Site Frequencies Improves Phylogenetic Modeling Under Maximum Likelihood and Resolves Tardigrada as the Sister of Arthropoda Plus Onychophora.

Mattia Giacomelli, Matteo Vecchi, Roberto Guidetti, Lorena Rebecchi, Philip C J Donoghue, Jesus Lozano-Fernandez, Davide Pisani

Abstract read
In one paragraph

Article in Genome biology and evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Toward a genomic understanding of the tree of life.Molecular biology and evolution · 2026
    Article
  8. Article
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Mattia GiacomelliBristol Palaeobiology Group, School of Biological Sciences, Life Sciences Building, University of Bristol, Bristol, UK.ORCID 0000-0002-0554-3704
Matteo VecchiInstitute of Systematics and Evolution of Animals, Polish Academy of Sciences, Krakow, Poland.ORCID 0000-0002-7995-6827
Roberto GuidettiDepartment of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.ORCID 0000-0001-6079-2538
Lorena RebecchiDepartment of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.ORCID 0000-0001-5610-806X
Philip C J DonoghueBristol Palaeobiology Group, School of Earth Sciences, Life Sciences Building, University of Bristol, Bristol, UK.ORCID 0000-0003-3116-7463
Jesus Lozano-FernandezDepartment of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain.ORCID 0000-0003-3597-1221
Davide PisaniBristol Palaeobiology Group, School of Biological Sciences, Life Sciences Building, University of Bristol, Bristol, UK.ORCID 0000-0003-0949-6682

Funding

Biosphere Evolution, Transitions and ResilienceBiotechnology and Biological Sciences Research Council BB/Y003624/1European Union-NextGenerationEU CN_00000033European Union NextGenerationEU/PRTREuropean Union's Horizon 2020Italian Ministry of University and Research CUP E93C22001090001Leverhulme Grant RPG-2024-030Leverhulme Trust Research Fellowship RF-2022-167Marie Skłodowska-Curie 764840National Biodiversity Future CenterNBFC CNS2022-135805National Recovery and Resilience PlanNatural Environment Research Council NE/P013678/1Natural Science Foundation of ChinaUniversity of Bristol University Research Fellowship
6 · The paper itself

Abstract

Tardigrada, the water bears, are microscopic animals with walking appendages that are members of Ecdysozoa, the clade of molting animals that also includes Nematoda (round worms), Nematomorpha (horsehair worms), Priapulida (penis worms), Kinorhyncha (mud dragons), Loricifera (loricated animals), Arthropoda (insects, spiders, centipedes, crustaceans, and their allies), and Onychophora (velvet worms). The phylogenetic relationships within Ecdysozoa are still unclear, with analyses of molecular and morphological data yielding incongruent results. Accounting for across-site compositional heterogeneity using mixture models that partition sites in frequency categories, CATegories (CAT)-based models, has been shown to improve fit in Bayesian analyses. However, CAT-based models such as CAT-Poisson or CAT-GTR (where CAT is combined with a General Time Reversible matrix to account for replacement rate heterogeneity) have proven difficult to implement in maximum likelihood. Here, we use CAT-posterior mean site frequencies (CAT-PMSF), a new method to export dataset-specific mixture models (CAT-Poisson and CAT-GTR) parameterized using Bayesian methods to maximum likelihood software. We developed new maximum likelihood-based model adequacy tests using parametric bootstrap and show that CAT-PMSF describes across-site compositional heterogeneity better than other across-site compositionally heterogeneous models currently implemented in maximum likelihood software. CAT-PMSF suggests that tardigrades are members of Panarthropoda, a lineage also including Arthropoda and Onychophora. Within Panarthropoda, our results favor Tardigrada as sister to Onychophora plus Arthropoda (the Lobopodia hypothesis). Our results illustrate the power of CAT-PMSF to model across-site compositionally heterogeneous datasets in the maximum likelihood framework and clarify the relationships between the Tardigrada and the Ecdysozoa.

Indexed as

ArthropodsModels, GeneticPhylogenyTardigradaAnimalsBayes TheoremLikelihood FunctionsEcdysozoamodel adequacy testsparametric bootstrapphylogenomicsTardigrada

Identifiers

PMID39715362
PMCPMC11756273

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.