Evidence map›Paper›PMID 26893301›Full record

ArticleMolecular biology and evolution2016

Clustering Genes of Common Evolutionary History.

Kevin Gori, Tomasz Suchan, Nadir Alvarez, Nick Goldman, Christophe Dessimoz

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
  2. Article
  3. Genomic analysis ofbioRxiv : the preprint server for biology · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. UNSUPERVISED CLUSTERING OF AIRWAY TREE STRUCTURES ON HIGH-RESOLUTION CT: THE MESA LUNG STUDY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2021
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

5 authors.

Kevin GoriEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Campus, Hinxton, United Kingdom.
Tomasz SuchanDepartment of Ecology and Evolution, Biophore Building, UNIL-Sorge, University of Lausanne, Lausanne, Switzerland.
Nadir AlvarezDepartment of Ecology and Evolution, Biophore Building, UNIL-Sorge, University of Lausanne, Lausanne, Switzerland.
Nick GoldmanEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Campus, Hinxton, United Kingdom goldman@ebi.ac.uk c.dessimoz@ucl.ac.uk.
Christophe DessimozEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Campus, Hinxton, United Kingdom Department of Ecology and Evolution, Biophore Building, UNIL-Sorge, University of Lausanne, Lausanne, Switzerland Department of Genetics, Evolution & Environment, University College London, London, United Kingdom Department of Computer Science, University College London, London, United Kingdom Centre for Integrative Genomics, University of Lausanne, Lausanne, Switzerland Swiss Institute of Bioinformatics, Biophore, Lausanne, Switzerland goldman@ebi.ac.uk c.dessimoz@ucl.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phylogenetic inference can potentially result in a more accurate tree using data from multiple loci. However, if the loci are incongruent-due to events such as incomplete lineage sorting or horizontal gene transfer-it can be misleading to infer a single tree. To address this, many previous contributions have taken a mechanistic approach, by modeling specific processes. Alternatively, one can cluster loci without assuming how these incongruencies might arise. Such "process-agnostic" approaches typically infer a tree for each locus and cluster these. There are, however, many possible combinations of tree distance and clustering methods; their comparative performance in the context of tree incongruence is largely unknown. Furthermore, because standard model selection criteria such as AIC cannot be applied to problems with a variable number of topologies, the issue of inferring the optimal number of clusters is poorly understood. Here, we perform a large-scale simulation study of phylogenetic distances and clustering methods to infer loci of common evolutionary history. We observe that the best-performing combinations are distances accounting for branch lengths followed by spectral clustering or Ward's method. We also introduce two statistical tests to infer the optimal number of clusters and show that they strongly outperform the silhouette criterion, a general-purpose heuristic. We illustrate the usefulness of the approach by 1) identifying errors in a previous phylogenetic analysis of yeast species and 2) identifying topological incongruence among newly sequenced loci of the globeflower fly genus Chiastocheta We release treeCl, a new program to cluster genes of common evolutionary history (http://git.io/treeCl).

Indexed as

Biological EvolutionModels, GeneticMultigene FamilyBase SequenceCluster AnalysisComputer SimulationPhylogenySequence Analysis, DNASoftwareYeastsclusteringincomplete lineage sorting.incongruencenonorthologyphylogenyprocess-agnostic

Identifiers

PMID26893301
PMCPMC4868114

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.