An overview of computational methods for gene prediction in eukaryotes: strengths, limitations, and future directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary: Advances in Next-Generation Sequencing (NGS) and machine-learning methods have improved eukaryotic gene prediction. Despite this progress, computational prediction remains crucial for complementing empirical data and annotating newly sequenced genomes, given the complexity of eukaryotic gene structures. Recent deep-learning approaches further enhance accuracy by learning gene-structure patterns directly from genomic sequences, enabling stronger cross-species generalization without predefined gene models. This review introduces a new classification of gene prediction methods-gene-model-based, gene-model-free, and hybrid-and examines representative tools with respect to their algorithmic strategies, input data, strengths, and limitations. It also updates previously reported challenges and outlines new issues arising from modern deep-learning techniques. To support these discussions, we extended the G3PO benchmark of gene-model-based predictors (Augustus, GenScan, GeneID, GlimmerHMM, and SNAP) to additionally include a gene-model-free method, sensor-NN, and a hybrid method, Helixer. Availability and implementation: Benchmark DNA and protein sequences are available in the G3PO repository (http://git.lbgi.fr/scalzitti/Benchmark_study). Scripts for Augustus and Helixer, along with all prediction outputs, are accessible at https://github.com/UdeS-CoBIUS/GenePredictionReviewBenchmark.
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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 | 0.000 |
| 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 it