Data from: Conserved non-exonic elements: a novel class of marker for phylogenomics
Bibliographic record
Abstract
Noncoding markers have a particular appeal as tools for phylogenomic analysis because, at least in vertebrates, they appear less subject to strong variation in GC content among lineages. Thus far, ultraconserved elements (UCEs) and introns have been the most widely used noncoding markers. Here we analyze and study the evolutionary properties of a new type of noncoding marker, conserved non-exonic elements (CNEEs), which consists of noncoding elements that are estimated to evolve slower than the neutral rate across a set of species. Although they often include UCEs, CNEEs are distinct from UCEs because they are not ultraconserved, and, most importantly, the core region alone is analyzed, rather than both the core and its flanking regions. Using a data set of 16 birds plus an alligator outgroup, and ∼3600 - ∼3800 loci per marker type, we found that although CNEEs were less variable than bioinformatically-derived UCEs or introns and in some cases exhibited a slower approach to branch resolution as determined by phylogenomic subsampling, the quality of CNEE alignments was superior to those of the other markers, with fewer gaps and missing species. Phylogenetic resolution using coalescent approaches was comparable among the three marker types, with most nodes being fully and congruently resolved. Comparison of phylogenetic results across the three marker types indicated that one branch, the sister group to the passerine+falcon clade, was resolved differently and with moderate (> 70%) bootstrap support between CNEEs and UCEs or introns. Overall, CNEEs appear to be promising as phylogenomic markers, yielding phylogenetic resolution as high as for UCEs and introns but with fewer gaps, less ambiguity in alignments and with patterns of nucleotide substitution more consistent with the assumptions of commonly used methods of phylogenetic analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".