The article reviews the landscape of long non-coding RNA (lncRNA) classification, highlighting the challenges and advancements in understanding the complexity of the non-coding transcriptome. It discusses the evolution of lncRNA classification from a mRNA-centric paradigm to a more comprehensive view that includes various classes of non-coding RNAs. The authors emphasize the need for a clear conceptual framework to guide the annotation and interpretation of non-coding transcriptome data. They review existing classifications based on transcript length, association with annotated protein-coding genes, other DNA elements, mRNA resemblance, sequence conservation, biological states, subcellular localization, and function. The article also addresses the limitations of current classifications, such as overlapping annotations and the lack of systematic organization. To address these challenges, the authors propose a consolidated conceptual framework that integrates multiple layers of information, including mapping the longest unprocessed transcript, defining processed transcripts, incorporating expression levels, and considering RNA modifications. This framework aims to provide a more structured and comprehensive approach to understanding the functional roles of lncRNAs.The article reviews the landscape of long non-coding RNA (lncRNA) classification, highlighting the challenges and advancements in understanding the complexity of the non-coding transcriptome. It discusses the evolution of lncRNA classification from a mRNA-centric paradigm to a more comprehensive view that includes various classes of non-coding RNAs. The authors emphasize the need for a clear conceptual framework to guide the annotation and interpretation of non-coding transcriptome data. They review existing classifications based on transcript length, association with annotated protein-coding genes, other DNA elements, mRNA resemblance, sequence conservation, biological states, subcellular localization, and function. The article also addresses the limitations of current classifications, such as overlapping annotations and the lack of systematic organization. To address these challenges, the authors propose a consolidated conceptual framework that integrates multiple layers of information, including mapping the longest unprocessed transcript, defining processed transcripts, incorporating expression levels, and considering RNA modifications. This framework aims to provide a more structured and comprehensive approach to understanding the functional roles of lncRNAs.