Evaluating the Performance of Probabilistic Algorithms for Phylogenetic Analysis of Big Morphological Datasets: A Simulation Study Article Swipe
Oksana Vernygora
,
Tiago R. Simões
,
Erin O. Campbell
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1093/sysbio/syaa020
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1093/sysbio/syaa020
Reconstructing the tree of life is an essential task in evolutionary biology. It demands accurate phylogenetic inference for both extant and extinct organisms, the latter being almost entirely dependent on morphological data. While parsimony methods have traditionally dominated the field of morphological phylogenetics, a rapidly growing number of studies are now employing probabilistic methods (maximum likelihood and Bayesian inference). The present-day toolkit of probabilistic methods offers varied software with distinct algorithms and assumptions for reaching global optimality. However, benchmark performance assessments of different software packages for the analyses of morphological data, particularly in the era of big data, are still lacking. Here, we test the performance of four major probabilistic software under variable taxonomic sampling and missing data conditions: the Bayesian inference-based programs MrBayes and RevBayes, and the maximum likelihood-based IQ-TREE and RAxML. We evaluated software performance by calculating the distance between inferred and true trees using a variety of metrics, including Robinson-Foulds (RF), Matching Splits (MS), and Kuhner-Felsenstein (KF) distances. Our results show that increased taxonomic sampling improves accuracy, precision, and resolution of reconstructed topologies across all tested probabilistic software applications and all levels of missing data. Under the RF metric, Bayesian inference applications were the most consistent, accurate, and robust to variation in taxonomic sampling in all tested conditions, especially at high levels of missing data, with little difference in performance between the two tested programs. The MS metric favored more resolved topologies that were generally produced by IQ-TREE. Adding more taxa dramatically reduced performance disparities between programs. Importantly, our results suggest that the RF metric penalizes incorrectly resolved nodes (false positives) more severely than the MS metric, which instead tends to penalize polytomies. If false positives are to be avoided in systematics, Bayesian inference should be preferred over maximum likelihood for the analysis of morphological data.
Related Topics
Concepts
Inference
Metric (unit)
Probabilistic logic
Sampling (signal processing)
Bayesian probability
Tree (set theory)
Computer science
Phylogenetic tree
Benchmark (surveying)
Bayesian network
Artificial intelligence
Statistics
Machine learning
Data mining
Biology
Mathematics
Filter (signal processing)
Biochemistry
Geodesy
Economics
Gene
Operations management
Computer vision
Mathematical analysis
Geography
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/sysbio/syaa020
- OA Status
- green
- Cited By
- 31
- References
- 73
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3010730509
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3010730509Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/sysbio/syaa020Digital Object Identifier
- Title
-
Evaluating the Performance of Probabilistic Algorithms for Phylogenetic Analysis of Big Morphological Datasets: A Simulation StudyWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-03-16Full publication date if available
- Authors
-
Oksana Vernygora, Tiago R. Simões, Erin O. CampbellList of authors in order
- Landing page
-
https://doi.org/10.1093/sysbio/syaa020Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://zenodo.org/record/5000809Direct OA link when available
- Concepts
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Inference, Metric (unit), Probabilistic logic, Sampling (signal processing), Bayesian probability, Tree (set theory), Computer science, Phylogenetic tree, Benchmark (surveying), Bayesian network, Artificial intelligence, Statistics, Machine learning, Data mining, Biology, Mathematics, Filter (signal processing), Biochemistry, Geodesy, Economics, Gene, Operations management, Computer vision, Mathematical analysis, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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31Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 5, 2023: 6, 2022: 6, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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73Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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