Evidence map›Paper›PMID 37467477›Full record

ArticleMolecular biology and evolution2023

Computational Reproducibility of Molecular Phylogenies.

Sudhir Kumar, Qiqing Tao, Alessandra P Lamarca, Koichiro Tamura

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. A NovelBiomolecules · 2025
    Article
  5. 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

4 authors.

Sudhir KumarInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.ORCID 0000-0002-9918-8212
Qiqing TaoInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.
Alessandra P LamarcaInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.
Koichiro TamuraResearch Center for Genomics and Bioinformatics, Tokyo Metropolitan University, Hachioji, Tokyo, Japan.

Funding

Methods For Evolutionary Genomics AnalysisR35GM139540 · NIGMS · TEMPLE UNIV OF THE COMMONWEALTH · PI Sudhir Kumar · 2021 to 2026
$2.9M
Inferring molecular evolutionary rates and divergence datesR01GM126567 · NIGMS · TEMPLE UNIV OF THE COMMONWEALTH · PI KUMAR, SUDHIR · 2018 to 2020
$951k
6 · The paper itself

Abstract

Repeated runs of the same program can generate different molecular phylogenies from identical data sets under the same analytical conditions. This lack of reproducibility of inferred phylogenies casts a long shadow on downstream research employing these phylogenies in areas such as comparative genomics, systematics, and functional biology. We have assessed the relative accuracies and log-likelihoods of alternative phylogenies generated for computer-simulated and empirical data sets. Our findings indicate that these alternative phylogenies reconstruct evolutionary relationships with comparable accuracy. They also have similar log-likelihoods that are not inferior to the log-likelihoods of the true tree. We determined that the direct relationship between irreproducibility and inaccuracy is due to their common dependence on the amount of phylogenetic information in the data. While computational reproducibility can be enhanced through more extensive heuristic searches for the maximum likelihood tree, this does not lead to higher accuracy. We conclude that computational irreproducibility plays a minor role in molecular phylogenetics.

Indexed as

Biological EvolutionGenomicsComputer SimulationPhylogenyReproducibility of Resultsmaximum likelihoodmolecular phylogeniesoptimalityreproducibility

Identifiers

PMID37467477
PMCPMC10370456

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Registered trials

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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.