ArticleBioinformatics and biology insights2026
SeqExpressionAnalyser: An R Package for Automated End-to-End RNA-Seq Analysis From Reads to Differential Expression.
Article in Bioinformatics and biology insights, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
8 authors.
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Abstract
Differential gene expression analysis of RNA Sequencing (RNA-Seq) data is crucial for understanding key patterns of gene regulation and enhancing our knowledge of biological processes and diseases. The workflow of this analysis comprises quality control, filtering of low-quality data, alignment, read counting, and final differential analysis. In this case, users often need to manually combine several tools and write multiple scripts to cover the entire pipeline. This fragmented approach is time-consuming and not user-friendly, especially for non-expert users. There is a need for an integrated, automated and accessible solution that unifies the entire analysis process within a single, easy-to-use platform. To address this need, we developed SeqExpressionAnalyser, an R package that provides a web application for interactive differential gene-expression analysis of RNA-seq data, making it accessible to R users for the first time. Built on the Shiny framework, SeqExpressionAnalyser enables users to read FASTQ files and perform analyses, including quality control, filtering, alignment, read counting, and differential expression analysis. The tool generates multiple outputs, including data tables, an HTML report and visualisations. The source code is available on GitHub (https://github.com/sanaeesskhayry/SeqExpressionAnalyser) and is licensed under the GPLv3 license. Also available as a Docker image at https://hub.docker.com/repository/docker/biomix/seq-expression-analyser/general.
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Registered trials
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