bioRxivBioinformaticsPreprint
HAMRLNC: A Comprehensive Pipeline for High-throughput Analysis of Modified Ribonucleotides and Long Non-Coding Ribonucleic Acids
Chosen E. ObihiD, Jiatong LiiD, Giovanni MelandriiD, Duke PauliiD, Eric H. LyonsiD, Andrew D. L. NelsoniD, Brian D. GregoryiD
Abstract
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 High-throughput Annotation of Modified Ribonucleotides and Long Non-Coding 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. Availability and ImplementationHAMRLNC is an open-source software, and the source code is available at https://github.com/bdgregory/HAMRLNC. The pipeline can be installed and used through a docker container (https://hub.docker.com/r/chosenobih/HAMRLNC/tags). HAMRLNC is also available as an app in the CyVerse Discovery Environment https://de.cyverse.org/. Supplementary informationSupplementary data are available at BioRxiv online.
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: database / resource
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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