An overview of computational methods for gene prediction in eukaryotes: strengths, limitations, and future directions
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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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".