Lost in translation: the pitfalls of Ensembl gene annotations between human genome assemblies and their impact on diagnostics
Bibliographic record
Abstract
BACKGROUND: Gene models based on GRCh37 human genome assembly are preferred by many international projects over other updated assemblies (GRCh38 and T2T). Discrepant genes (DGs), those recognized as protein coding in the new but not the old assembly, are ignored by several genomic resources and discarded by variant prioritization tools relying on information based on GRCh37. METHODS: We curated a set of Ensembl genes with discrepant annotations between GRCh37 and GRCh38, additionally matching their RefSeq transcripts. Furthermore, we examined their clinical and phenotypic relevance. RESULTS: = 73). We found many clinically relevant genes in this group of neglected genes, and we anticipate that many more will be found relevant in the future. Important additional annotations such as evolutionary constraint metrics are also not calculated for these genes, further relegating them into oblivion. CONCLUSION: For discrepant genes, the inaccurate label of 'non-protein-coding' has relevant ramifications on clinical genetics. Accurate collation of these genes allows for manual curation in clinically relevant scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".