As RNA sequencing becomes more widely used, researchers are generating massive amounts of data. While this creates new opportunities to understand biology and disease, it also introduces a challenge, how to process all of this data in a consistent and reproducible way across different experiments.

Researchers at the University of Nebraska-Lincoln have developed a solution called AutoRNAseq. This tool is designed to simplify and standardize how bulk RNA sequencing data is analyzed, reducing the need for manual setup and coordination between multiple software tools.

Traditionally, analyzing RNA sequencing data requires several separate steps. Scientists must download raw data, prepare reference genomes, perform quality control checks, align sequencing reads, and calculate gene expression levels. Each step may involve different tools, which can introduce variability and increase the chance of errors.

AutoRNAseq brings all of these steps together into a single automated workflow. Built using a framework called Snakemake, it handles everything from data retrieval to final gene quantification with minimal user input. This makes it easier for researchers to process datasets consistently, even when working across multiple projects or large collaborations.

Information flow and statistical comparisons

Panel 1A): AutoRNAseq’s information flow. At least one red input is required (local FASTQ files or NCBI GEO SRR accession numbers), and any blue processing step can be skipped by modifying the related setting. Panel 1B): Pearson, Spearman, and Kolmogorov-Smirnov statistics between AutoRNAseq and nf-core/rnaseq.

Another key advantage is reproducibility. Because the workflow is standardized, results can be more easily compared between studies. This is especially important in fields like drug development and computational biology, where consistent data processing is critical for making reliable conclusions.

AutoRNAseq is also flexible and can be applied across a wide range of research areas. Whether scientists are studying disease mechanisms, analyzing drug responses, or exploring gene expression patterns, this tool helps streamline the analysis process and reduce technical barriers.

As RNA sequencing continues to scale, tools like AutoRNAseq will play an important role in ensuring that data remains usable, consistent, and reliable across the scientific community.

Availability – AutoRNAseq is implemented in Snakemake and available at https://gitlab.com/unebraska/lagbh-public/autornaseq.