An overview of computational methods for gene prediction in eukaryotes: strengths, limitations, and future directions.

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 c

SUBMITTER: Djossou A 

PROVIDER: S-EPMC12701808 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature