Novel Integrated Galaxy Workflow Suites for comprehensive small non coding RNA Analysis
Year: 2026
Type: conference-paper
Abstract: Small non-coding RNAs (sncRNAs) are a class of regulatory RNAs that play a central role in gene expression and genome stability. Although this field has recently gained significant attention, many aspects of sncRNA biology remain poorly understood, especially regarding their emergence and functional interactions.As a representative use case, PIWI-interacting RNAs (piRNAs) illustrate the challenges of sncRNA analysis. Although their role in transposable element silencing is well characterized, the processes governing the emergence and activation of piRNA-producing genomic regions, known as piRNA clusters, remain less understood. Their activation can be assessed by quantifying their abundance using small RNA-seq across different conditions, complemented by the analysis of epigenetic markers, such as histone modifications, through ChIP-seq experiments. While several individual analysis tools such as FastQC for quality control, Bowtie for sequence alignment, and MACS3 for peak-calling are already available within the Galaxy platform, important components are still missing, including visualization tools such as Enhanced heatmap. More importantly, dedicated workflows tailored to sncRNA have not been developed yet. This limitation applies to other sncRNA classes such as tRNA-derived fragments (tRFs), whose elevated levels have been shown to disrupt other sncRNA-mediated gene repression pathways, including piRNAs. However, no specialized tool for tRF quantification, like MINTmap, is available within Galaxy, further limiting accessibility for non-computational users.In this context, we present two bioinformatics tool suites and associated workflows designed to investigate different aspects of sncRNA biology. One workflow is dedicated to the analysis of ChIP-seq data for the profiling of histone modifications and the second one focuses on the characterization of sncRNA population through small RNA-seq data. They will integrate existing tools, introduce missing functionalities, and improve accessibility through the Galaxy framework.
Source: HAL
Institution: Institut de Biologie Paris-Seine
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7205637320