Cenny Taslim, Yuan Zhang, Galen Rask, Genevieve C Kendall, Emily R Theisen
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引用次数: 0
Abstract
Background: RNA sequencing (RNA-seq) analysis has become a routine task in numerous genomic research labs, driven by the reduced cost of bulk RNA sequencing experiments. These studies generate billions of reads that require easy-to-run, comprehensive, and reproducible analysis. However, many labs rely on in-house scripts, which can be challenging for bench scientists to use and hinder standardization and reproducibility. While existing RNA-seq pipelines attempt to address these challenges, they often lack a complete end-to-end user interface.
Findings: To bridge this gap, we developed RNA-SeqEZPZ, an automated pipeline with a user-friendly point-and-click interface, enabling rigorous and reproducible RNA-seq analysis without requiring programming or bioinformatics expertise. For advanced users, the pipeline can also be executed from the command line, allowing customization of steps to suit specific applications. The innovation of this pipeline lies in the combination of 3 key features: (i) all software is packaged within a Singularity container, eliminating installation issues; (ii) it offers a graphical, point-and-click interface from raw FASTQ files through differential expression and pathway analysis; and (iii) it includes a Nextflow implementation, enabling scalability and portability for seamless execution across various platforms, including job submission in the cloud and cluster computing. Additionally, RNA-SeqEZPZ generates a comprehensive statistical report and offers an option for batch adjustment to minimize effects of noise due to technical variation across replicates. Reports can also be reviewed by a bioinformatician to ensure the overall quality of the analysis.
Conclusions: RNA-SeqEZPZ is a robust, accessible, and scalable solution for comprehensive RNA-seq analysis, enabling researchers to focus on biological insights rather than computational challenges.
期刊介绍:
GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.