Evidence map›Paper›PMID 33837789›Full record

ArticleSystematic biology2021

Six-State Amino Acid Recoding is not an Effective Strategy to Offset Compositional Heterogeneity and Saturation in Phylogenetic Analyses.

Alexandra M Hernandez, Joseph F Ryan

Abstract read
In one paragraph

Article in Systematic biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Article
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  3. Toward a genomic understanding of the tree of life.Molecular biology and evolution · 2026
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  10. Incongruence in the phylogenomics era.Nature reviews. Genetics · 2023
    Review
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  12. Review
  13. Review
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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

2 authors.

Alexandra M HernandezWhitney Laboratory for Marine Bioscience, 9505 Ocean Shore Boulevard, St. Augustine, FL 32080, USA.
Joseph F RyanWhitney Laboratory for Marine Bioscience, 9505 Ocean Shore Boulevard, St. Augustine, FL 32080, USA.

Funding

National Science Foundation under Grant Number 1542597
6 · The paper itself

Abstract

Six-state amino acid recoding strategies are commonly applied to combat the effects of compositional heterogeneity and substitution saturation in phylogenetic analyses. While these methods have been endorsed from a theoretical perspective, their performance has never been extensively tested. Here, we test the effectiveness of six-state recoding approaches by comparing the performance of analyses on recoded and non-recoded data sets that have been simulated under gradients of compositional heterogeneity or saturation. In our simulation analyses, non-recoding approaches consistently outperform six-state recoding approaches. Our results suggest that six-state recoding strategies are not effective in the face of high saturation. Furthermore, while recoding strategies do buffer the effects of compositional heterogeneity, the loss of information that accompanies six-state recoding outweighs its benefits. In addition, we evaluate recoding schemes with 9, 12, 15, and 18 states and show that these consistently outperform six-state recoding. Our analyses of other recoding schemes suggest that under conditions of very high compositional heterogeneity, it may be advantageous to apply recoding using more than six states, but we caution that applying any recoding should include sufficient justification. Our results have important implications for the more than 90 published papers that have incorporated six-state recoding, many of which have significant bearing on relationships across the tree of life. [Compositional heterogeneity; Dayhoff 6-state recoding; S&R 6-state recoding; six-state amino acid recoding; substitution saturation.].

Indexed as

Amino AcidsPhylogenyAmino Acids

Identifiers

PMID33837789
PMCPMC8513762

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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.