Phylogenomic analyses of ochrophytes (stramenopiles) with an emphasis on neglected lineages
Bibliographic record
Abstract
Ochrophyta is a photosynthetic lineage that crowns the phylogenetic tree of stramenopiles, one of the major eukaryotic supergroups. Due to their ecological impact as a major primary producer, ochrophytes are relatively well-studied compared to the rest of the stramenopiles, yet their evolutionary relationships remain poorly understood. This is in part due to a number of missing lineages in large-scale multigene analyses, and an apparently rapid radiation leading to many short internodes between ochrophyte subgroups in the tree. These short internodes are also found across deep-branching lineages of stramenopiles with limited phylogenetic signal, leaving many relationships controversial overall. We have addressed this issue with other deep-branching stramenopiles recently, and now examine whether contentious relationships within the ochrophytes may be resolved with the help of filling in missing lineages in an updated phylogenomic dataset of ochrophytes, along with exploring various gene filtering criteria to identify the most phylogenetically informative genes. We generated ten new transcriptomes from various culture collections and single-cell isolation from an environmental sample, added these to an existing phylogenomic dataset, and examined the effects of selecting genes with high phylogenetic signal or low phylogenetic noise. For some previously contentious relationships, we find a variety of analyses and gene filtering criteria consistently unite previously unstable grouping with strong statistical support. For example, we recovered a robust grouping of Eustigmatophyceae with Raphidophyceae-Phaeophyceae-Xanthophyceae while Olisthodiscophyceae formed a sister lineage to Pinguiophyceae. Selecting genes with high phylogenetic signal or data quality recovered more stable topologies. Overall, we find that adding under-represented groups across different lineages is still crucial in resolving phylogenomic relationships, and discrete gene properties affect lineages of stramenopiles differently which may be further explored to better understand the molecular evolution of stramenopiles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".