PLncFire: A Scalable Pipeline for Transcriptome-wide Discovery of Plant lncRNAs

Abstract

BackgroundLong non-coding RNAs (lncRNAs) play important regulatory roles in plant growth, development, and stress responses. However, their genome-wide identification remains challenging due to low sequence conservation, incomplete reference annotations, and variability across species. Existing workflows often lack standardization and reproducibility, limiting large-scale comparative studies. To address these challenges, we developed PLncFire, a modular computational pipeline designed for automated and reproducible identification and annotation of plant lncRNAs using RNA-seq data. MethodsPLncFire processes standard RNA-seq datasets through a structured workflow comprising quality control, read alignment, transcript assembly, and transcript filtering. A consensus-based coding potential assessment strategy was implemented using CPC2, PlantLncPipe, and FEELnc to improve prediction reliability. Transcripts were filtered based on length, exon structure, and coding probability thresholds to generate high-confidence lncRNA candidates. Identified lncRNAs were further classified as known or novel by comparison with reference annotations. The pipeline also incorporates differential expression analysis to support functional prioritization. Workflow modularity ensures scalability across plant species and enables reproducible execution in diverse computational environments. ResultsApplication of PLncFire to plant RNA-seq datasets enabled systematic identification of high-confidence lncRNA candidates, including both previously annotated and novel transcripts. The consensus coding-potential framework reduced false-positive predictions compared to single-tool approaches. Integration of differential expression analysis facilitated prioritization of lncRNAs associated with specific developmental stages or stress conditions. The modular design demonstrated compatibility across datasets from different plant species, supporting cross-species adaptability and comparative analysis. ConclusionsPLncFire provides a standardized and reproducible framework for genome-wide lncRNA discovery in plants. By integrating multi-tool consensus coding assessment with transcript assembly and expression analysis, the pipeline enhances prediction confidence and scalability. This platform supports large-scale functional genomics studies and facilitates systematic exploration of plant lncRNA landscapes. The source code is available at https://github.com/ahsan-rizvi/PLncFire.git. Trial registrationNot applicable.