Pitfalls in supermatrix phylogenomics
Open accessAbstract
In the mid-2000s, molecular phylogenetics turned into phylogenomics, a development that improved the resolution of phylogenetic trees through a dramatic reduction in stochastic error. While some then predicted “the end of incongruence”, it soon appeared that analysing large amounts of sequence data without an adequate model of sequence evolution amplifies systematic error and leads to phylogenetic artefacts. With the increasing flood of (sometimes low-quality) genomic data resulting from the rise of high-throughput sequencing, a new type of error has emerged. Termed here “data errors”, it lumps together several kinds of issues affecting the construction of phylogenomic supermatrices (e.g., sequencing and annotation errors, contaminant sequences). While easy to deal with at a single-gene scale, such errors become very difficult to avoid at the genomic scale, both because hand curating thousands of sequences is prohibitively time-consuming and because the suitable automated bioinformatics tools are still in their infancy. In this paper, we first review the pitfalls affecting the construction of supermatrices and the strategies to limit their adverse effects on phylogenomic inference. Then, after discussing the relative non-issue of missing data in supermatrices, we briefly present the approaches commonly used to reduce systematic error.
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Commonly applied phylogenetic models do not account for a wide array of factors that are known to cause positively misleading phylogenetic estimates when overlooked.
“While phylogenomics has revolutionized our understanding of the tree of life, commonly applied phylogenetic models do not account for a wide array of factors that are known to cause positively misleading phylogenetic estimates when overlooked18,19.”
From Evidence for sponges as sister to all other animals from partitioned phylogenomics with mixture models and recoding (2021) · cites this paper as [19]
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How to build phylogenetic species trees with OMA
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- Authors
- Philippe, H., Vienne, D. M. D., Ranwez, V., Roure, B., Baurain, D., & Delsuc, F.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Lyon 1 Université (FR)
- Institut Agro Montpellier (FR)
- University of Liège (BE)
- Venue
- European Journal of Taxonomy
- Publisher
- Museum National D'Histoire Naturelle
- Published
- 2017-02-21
- Volume
- 283
- Issue
- 283
- Pages
- 1-25
- ISSN
- 2118-9773 (ISSN-L)
- OpenAlex
- W2590646912
- Cited by
- 173
- References
- 119
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 12 · 2023: 16 · 2022: 21 · 2021: 23 · 2020: 41 · 2019: 22 · 2018: 18 · 2017: 11 · 2015: 1
- FWCI
- 10.28
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- European Journal of Taxonomy (publishedVersion, cc-by)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ORBi (University of Liège) (submittedVersion, other-oa)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- Agence Nationale de la Recherche (ANR-10-LABX-0041, ANR-10-LABX- 41, ANR-10-LABX-0025, ANR-10, 10-LABX-41)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protist diversity and phylogeny
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Plant Diversity and Evolution
- Keywords
- phylogenetic incongruence, data errors, sequencing errors, annotation errors, systematic errors, missing data, high-throughput sequencing data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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