Computational identification and characterization of noncoding RNA-encoded peptides: tools, databases, and in silico strategies

Once dismissed as transcriptional artifacts, noncoding RNAs (ncRNAs) have gained recognition in recent years for their ability to participate in gene regulation, as well as their ability to encode functional molecules referred to as ncRNA-encoded peptides (ncPEPs). The discovery of ncPEPs has opened new avenues in proteomics and genomics research, revealing biological mechanisms that were previously unexplored. This review presents an extensive overview of the computational tools, databases, and in silico strategies used to identify ncRNA-encoded peptides across all major ncRNA classes, including long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and primary microRNAs (pri-miRNAs). Furthermore, we outline publicly available databases that compile experimentally validated and computationally predicted ncPEPs across multiple species, enabling systematic annotation and cross-referencing of candidate peptides. By highlighting the current challenges and emerging methodologies, we emphasize how computational methods continue to advance our ability to uncover hidden functional peptides within the noncoding transcriptome. These developments provide a framework for validating ncPEPs and elucidating their biological significance across diverse systems.