Data-Mining with Three Genome-Scale Approaches Supports that Lungfish is the Closest Living Relative of Land Vertebrate, but not Coelacanth
Bibliographic record
Abstract
The origin of tetrapod has been one of intense debating open questions for decades between coelacanth (Latimeria chalumnae) and lungfish (Protopterus annectens). For resolving this incongruence in phylogenies, a genome-wide data mining approach is used to retrieve 43 shared genes of seven taxa from GenBank and further 1001 orthologous genes of ten taxa from the Ensembl and NCBI. We used the maximum gene-support tree approach and the majority-rule branch approach to analyze 43 nuclear genes encoding amino acid residues, and compared these results to those inferred with the concatenation approach. Our results successfully provide strong evidence in favor of the lungfish-tetrapod hypothesis, but reject the coelacanth-tetrapod hypothesis based on significantly fewer gene support frequency and lower taxon jackknife probabilities for the coelacanth-tetrapod clade than the lungfish-tetrapod one with the maximum gene-support tree approach and the jackknife method for taxon subsampling. When more genomic data became available in recent years, sequencing data of 1001 shared genes was mined. We used the maximum gene-support approach with this larger dataset successfully to infer that lungfish is the closest relative of land vertebrates with a significant difference at p < 0.01 (Chi-Square test) in gene support values between the maximum gene-support tree and the second most gene-support tree with ML methods. The second most gene support frequency to the maximum gene support frequency (SM ratio), a combined index, is a better support measure than a single absolute value of support to reveal the insight of the phylogenetic support. Our results also show increasing the number of shared genes is much more effective than increasing the number of taxa.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".