The article reviews the classification of long non-coding RNAs (lncRNAs) and discusses the challenges in their annotation and functional interpretation. It highlights the growing complexity of the non-coding transcriptome, which now accounts for a significant portion of the genome. Despite advances in transcriptome sequencing, lncRNA classification remains challenging due to overlapping terminology and a lack of a unified framework. The paper outlines various classification criteria, including transcript length, association with protein-coding genes, sequence conservation, functional roles, and subcellular localization. It also discusses the integration of large-scale datasets to improve classification and functional annotation. The authors propose a conceptual framework for lncRNA classification that incorporates multiple dimensions, such as transcript length, expression levels, and RNA modifications. This framework aims to enhance the understanding of lncRNA functions and facilitate the integration of genomic data. The review emphasizes the need for a systematic approach to lncRNA classification to address the challenges in annotating and interpreting non-coding transcriptome data.The article reviews the classification of long non-coding RNAs (lncRNAs) and discusses the challenges in their annotation and functional interpretation. It highlights the growing complexity of the non-coding transcriptome, which now accounts for a significant portion of the genome. Despite advances in transcriptome sequencing, lncRNA classification remains challenging due to overlapping terminology and a lack of a unified framework. The paper outlines various classification criteria, including transcript length, association with protein-coding genes, sequence conservation, functional roles, and subcellular localization. It also discusses the integration of large-scale datasets to improve classification and functional annotation. The authors propose a conceptual framework for lncRNA classification that incorporates multiple dimensions, such as transcript length, expression levels, and RNA modifications. This framework aims to enhance the understanding of lncRNA functions and facilitate the integration of genomic data. The review emphasizes the need for a systematic approach to lncRNA classification to address the challenges in annotating and interpreting non-coding transcriptome data.