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Study applies a customizable RNA-seq pipeline to identify molecular signatures in Alzheimer's disease, suggesting improved analysis methodologies.
RNA-seq data analysis is a multi-step process that typically includes demultiplexing, alignment to the reference genome, read counting and genome annotation, ultimately leading to differential expression analysis (DEA). Several pipelines are currently available to support these steps and produce outputs adapted to specific experimental needs. Such pipelines must also ensure reproducibility, offer user-friendly implementation, support the analysis of complex genomes, allow batch correction, and remain sufficiently flexible to enable customization. Here , we present version 2.2 of a fully customizable command-line RNA-seq pipeline, developed within the Compi framework through the conversion and extension of the previously established DEWE graphical user interface tool. This updated version includes tasks for FASTQ quality control, two alternative read-counting strategies, and recently developed tools for DEA and pathway analysis, such as DElite and RCPA. Furthermore, we apply the pipeline to an exploratory RNA-seq study comparing transcriptomic profiles of Alzheimer’s disease (AD) patients with healthy controls (HC) to identify putative disease molecular signatures and assess the pathways affected by the disease.
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Gerlando et al. (2026) studied this question.
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