HAMRLNC: a comprehensive and scalable pipeline for integrated epitranscriptomic analysis Chosen E. Obih [1,†] , Jiatong Li [2,3,†] , Giovanni Melandri [1,4] , Duke Pauli [1,4] , Eric Lyons [1] , Andrew D. L. Nelson [5] , Brian D. Gregory* [2]

https://www.academia.edu/3064-9765/2/4/10.20935/AcadMolBioGen8059 As sequencing technologies advance and costs decline, there has been a surge in the application of RNA sequencing (RNA-seq) to understand the effects of gene expression regulation on specific 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 the high-throughput annotation of modified ribonucleotides and long non-coding ribonucleic acids (HAMRLNC). HAMRLNC differs from existing methods by integrating three workflows for transcript abundance quantification, RNA modification inference, 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 landscapes, and modification clustering statistics.