Short Gene

Short genes, typically less than 180 nucleotides, pose a significant challenge for accurate gene prediction due to their limited sequence information. Current research focuses on improving gene prediction algorithms, particularly employing deep learning models like protein language models and neural networks, to enhance the identification of these short sequences in prokaryotic and other genomes. These advancements are crucial for a more complete understanding of genomic function, impacting fields like biotechnology and medicine through improved genome annotation and the identification of novel genes with potentially important biological roles. The development and validation of attribution methods, such as Layerwise Relevance Propagation (LRP), are also key to interpreting the predictions of these complex models and gaining biological insights.