Close sequence comparisons are sufficient to identify human <i>cis</i> -regulatory elements
Bibliographic record
Abstract
Cross-species DNA sequence comparison is the primary method used to identify functional noncoding elements in human and other large genomes. However, little is known about the relative merits of evolutionarily close and distant sequence comparisons. To address this problem, we identified evolutionarily conserved noncoding regions in primate, mammalian, and more distant comparisons using a uniform approach (Gumby) that facilitates unbiased assessment of the impact of evolutionary distance on predictive power. We benchmarked computational predictions against previously identified cis -regulatory elements at diverse genomic loci and also tested numerous extremely conserved human–rodent sequences for transcriptional enhancer activity using an in vivo enhancer assay in transgenic mice. Human regulatory elements were identified with acceptable sensitivity (53%–80%) and true-positive rate (27%–67%) by comparison with one to five other eutherian mammals or six other simian primates. More distant comparisons (marsupial, avian, amphibian, and fish) failed to identify many of the empirically defined functional noncoding elements. Our results highlight the practical utility of close sequence comparisons, and the loss of sensitivity entailed by more distant comparisons. We derived an intuitive relationship between ancient and recent noncoding sequence conservation from whole-genome comparative analysis that explains most of the observations from empirical benchmarking. Lastly, we determined that, in addition to strength of conservation, genomic location and/or density of surrounding conserved elements must also be considered in selecting candidate enhancers for in vivo testing at embryonic time points.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".