HAMRLNC: A Comprehensive Pipeline for High-throughput Analysis of Modified Ribonucleotides and Long Non-Coding Ribonucleic Acids

As sequencing technologies advance and costs decline, there has been a surge in the application of RNA sequencing (RNA-seq) in understanding biological processes. In addition to the typical uses of RNA-seq for transcriptomics, gene annotation, novel gene discovery, and network analysis, these data can enable a deeper understanding of cellular processes through the identification of RNA modifications (epitranscriptome) and long non-coding RNAs (lncRNAs). To expedite discovery, we developed a portable, centralized computational pipeline for H igh-throughput A nnotation of M odified R ibonucleotides and L ong N on- C oding ribonucleic acids (HAMRLNC). HAMRLNC differs from existing methods by incorporating three workflows for quantifying transcript abundance, inferring RNA modifications, and lncRNA annotation using the same RNA- seq pre-processing and mapping steps. This facilitates reproducibility across multiple analyses and allows researchers to perform post-hoc analyses of archived sequencing data. In addition, we include novel analysis features to enable downstream visualization of annotated modified RNAs. HAMRLNC generates over a dozen well-defined and labeled figures as output, including gene ontology heatmaps, modification enrichment landscape, and modification clustering statistics.