Integrative multi-omics analysis widens annotation and functional insights into long non-coding RNAs of Arabidopsis thaliana

Long non-coding RNAs (lncRNAs) play key roles in regulating plant growth, development, and stress responses. Despite their increasing identification in plant transcriptomes, a systematic characterization of lncRNAs is still lacking, leaving a significant knowledge gap. To address this, we systematically identified and characterized Arabidopsis lncRNAs through integrative analysis of strand-specific RNA sequencing data and multi-omics datasets, revealing their genomic features, regulatory interactions, and evolutionary characteristics. Using a custom pipeline applied to hundreds of stranded RNA-seq datasets, we assembled a comprehensive catalog of 4,772 intergenic and antisense Arabidopsis lncRNAs. In comparing multiple key features of lncRNAs with those of protein-coding genes, we found that intergenic lncRNAs contain high transposable element-derived fragments and display broader TE diversity. Distinct DNA methylation and histone modification signatures further distinguished lncRNAs from protein-coding genes. We additionally uncovered R-loop connections and associations with sRNAs involved in post-transcriptional regulation and RNA-directed DNA methylation, with a minor subset classified as Pol V–transcribed. Of note, our results revealed lncRNAs mediating stress-responsive cis interactions and others linked to trait-associated loci. Probing further, an experimental evidence resource confirmed small peptide production from multiple lncRNA loci. Extending our investigation, comparative analyses across Brassicaceae species revealed syntenic lncRNAs enriched for shared sequence motifs despite substantial sequence divergence. This study provides a valuable and extensively annotated catalog of Arabidopsis lncRNAs, revealing their diverse genomic features, regulatory interactions, and evolutionary characteristics. Altogether, our work advocates for multi-omics integrative analysis as a potent strategy to efficiently enhance lncRNA annotation, providing insights into functionality and addressing annotation limitations. Our comprehensive bioinformatic analyses of Arabidopsis lncRNAs pave the way for future functional characterization of these transcripts.