Detecting unannotated splicing events in short-read RNA-seq with SAMI, a UMI-aware Nextflow pipeline

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ID: 314602
2026
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Abstract
Abstract Summary Although RNA-sequencing has replaced microarrays for gene expression profiling over the past 15 years, its full potential for splicing analysis in clinical settings remains underexploited. Most available tools are tailored for large cohorts or known isoforms, limiting their applicability in routine diagnostics where non-recurring events must be identified in low-dimension datasets. We present SAMI (Splicing Analysis with Molecular Indexes), a fully-integrated UMI-aware pipeline designed to detect splicing events diverging from transcript annotations. Building upon the well-proven STAR aligner, SAMI introduces original post-processing of gaps and potential intron retentions to maximize accuracy, along with clear graphical representations and tunable filtering stringency. The ability of SAMI and concurrent software to detect intragenic splicing aberrations and gene fusions was assessed, both on real data from a commercial control sample and simulated data generated with ASimulatoR. Availability and implementation Nextflow pipeline and Singularity container recipe freely available under GPL 3 licence at https://github.com/HCL-HUBL/SAMI
Reference Key
openalex_W7161998668 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sylvain Mareschal, Valentin Wucher, Sarah Huet, Camille Léonce, Kaddour Chabane, Sandrine Hayette, Pierre‐Paul Bringuier, S. Pinson, Marc Barritault, Claire Bardel
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag252
URL
https://doi.org/10.1093/bioinformatics/btag252
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