Automated Navigation of the lncRNA Transcriptome: A comprehensive SnakeMake based computational Pipeline for robust Identification of lncRNAs and their putative targets

Abstract

ABSTRACTBackgroundLong non-coding RNAs (lncRNAs) have emerged as potent regulatory elements in cellular processes. The substantial increase in transcriptomic data resulting from high-throughput RNA sequencing necessitates effective approaches for the identification and functional annotation of lncRNAs.MethodTo address this need, we have developed a SnakeMake-based pipeline. Our pipeline automates and integrates several key steps: 1) RNA-seq analysis using Hisat2 and stringTie, (2) lncRNA identification using inhouse python scripts and tools CPC2 and BLASTX, (3) prediction ofcis-andtrans-gene targets of lncRNAs, and (4) KEGG pathway enrichment to obtain biological insights. Importantly, the pipeline allows users to customize parameters for each step through a user-friendly configuration file (config.yaml), enhancing flexibility and ease of use. One of the distinctive features of our approach is its single command execution, facilitating multiple runs without the need for extensive user intervention. This not only enhances user convenience but also ensures reproducibility of analyses across different studies.ResultWe applied our pipeline on rice, sorghum, and human RNA-seq data, to identify (1) List of all differentially expressed transcripts., (2) List of differentially expressed lncRNAs, (3) lncRNA target genes, (4) Enriched pathways to which target genes belong and (5) Obtain a visualization output in the form of a bubble plot that depicts the enriched pathways. Our approach can help researchers obtain valuable biological insights into how lncRNAs contribute to various biological functions.ConclusionThe distinctive features of our SnakeMake-based automation pipeline position it as a versatile asset for researchers seeking a user-friendly, adaptable, robust, and reproducible solution for pan species lncRNA analysis. By efficiently uncovering the regulatory roles of lncRNAs in cellular processes, this pipeline has the potential to shed light on various biological phenomena, such as developmental biology, disease progression, and cellular response to external stimuli.GRAPHICAL ABSTRACTThis study presents a SnakeMake-based pipeline for identifying and annotating long non-coding RNAs (IncRNAs) from RNA sequencing data. It integrates RNA-seq analysis, IncRNA identification, gene target prediction, and pathway enrichment, with customizable parameters through a user-friendly configuration file. The pipeline’s single command execution enhances convenience and reproducibility. (The bubble chart in the figure is a representative chart and provided as an example.)