Helixer – Deep Learning-Based Gene Prediction for Eukaryotic Genomes
Helixer is an advanced gene prediction tool that applies deep learning models to identify protein-coding genes in eukaryotic genomes. It combines neural-network–based sequence analysis with structured post-processing to generate high-quality gene models that outperform or complement traditional ab initio methods.
Key Benefits
- State-of-the-art accuracy powered by deep learning trained on high-quality reference genomes.
- Robust across diverse taxa, including plants, fungi, and other eukaryotes.
- Fast and scalable, suitable for large genomes and high-throughput annotation projects.
- Minimal manual tuning, making genome annotation accessible to non-specialists.
Features
- Neural-network–based exon, intron, and CDS boundary prediction directly from DNA sequence.
- Automated post-processing to generate complete gene models.
- Pretrained models for multiple organism groups.
- Command-line implementation optimized for HPC and pipeline integration.
Applications
- Structural genome annotation for new eukaryotic assemblies.
- Improving or refining existing gene annotations.
- Complementary prediction layer in multi-tool annotation workflows.
- Educational use in training gene annotation and machine-learning concepts.
Intended Use
Designed for genome researchers, plant scientists, and bioinformaticians who require accurate, modern gene prediction tools for newly sequenced or reannotated eukaryotic genomes.