Accurate genome annotation remains a major challenge in genomics, particularly for non-model organisms. Traditional annotation pipelines, such as MAKER or BRAKER, often rely on additional transcriptomic or protein evidence and may involve computationally intensive workflows requiring bioinformatics expertise.
However, recent advances in artificial intelligence are opening new possibilities for genomic research, including the development of novel approaches to genome annotation, such as GeneCAD.
GeneCAD is a recently developed deep-learning-based tool capable of predicting protein-coding gene structures directly from a genome assembly, without requiring additional RNA-seq or protein evidence. Initially developed for plant genomes, this programme represents a promising step towards more accessible, efficient, automated, and potentially cost-effective genome annotation.
The webinar will begin with a brief introductory presentation on genome annotation and its challenges, making the topic accessible to researchers with different levels of bioinformatics experience. We will then introduce GeneCAD from a practical perspective, exploring its capabilities, performance, recent developments, and potential applications in genomic research. We will also discuss its advantages and current limitations compared with conventional annotation approaches, and consider whether AI-driven tools could reshape genome annotation workflows in the future.
Registration
Registration is required.
Registration deadline: December 6, 2026.
A link to join the webinar will be emailed to all registered participants a few days before the event.