An Empirical Assessment of Long-Branch Attraction Artefacts in Deep Eukaryotic Phylogenomics
Bibliographic record
Abstract
In the context of exponential growing molecular databases, it becomes increasingly easy to assemble large multigene data sets for phylogenomic studies. The expected increase of resolution due to the reduction of the sampling (stochastic) error is becoming a reality. However, the impact of systematic biases will also become more apparent or even dominant. We have chosen to study the case of the long-branch attraction artefact (LBA) using real instead of simulated sequences. Two fast-evolving eukaryotic lineages, whose evolutionary positions are well established, microsporidia and the nucleomorph of cryptophytes, were chosen as model species. A large data set was assembled (44 species, 133 genes, and 24,294 amino acid positions) and the resulting rooted eukaryotic phylogeny (using a distant archaeal outgroup) is positively misled by an LBA artefact despite the use of a maximum likelihood-based tree reconstruction method with a complex model of sequence evolution. When the fastest evolving proteins from the fast lineages are progressively removed (up to 90%), the bootstrap support for the apparently artefactual basal placement decreases to virtually 0%, and conversely only the expected placement, among all the possible locations of the fast-evolving species, receives increasing support that eventually converges to 100%. The percentage of removal of the fastest evolving proteins constitutes a reliable estimate of the sensitivity of phylogenetic inference to LBA. This protocol confirms that both a rich species sampling (especially the presence of a species that is closely related to the fast-evolving lineage) and a probabilistic method with a complex model are important to overcome the LBA artefact. Finally, we observed that phylogenetic inference methods perform strikingly better with simulated as opposed to real data, and suggest that testing the reliability of phylogenetic inference methods with simulated data leads to overconfidence in their performance. Although phylogenomic studies can be affected by systematic biases, the possibility of discarding a large amount of data containing most of the nonphylogenetic signal allows recovering a phylogeny that is less affected by systematic biases, while maintaining a high statistical support.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".