In the past decade, RNA sequencing (RNA-seq) has revolutionized how researchers study gene expression, helping us understand how genes are turned on or off in different tissues, conditions, or species. But for many plants, animals, and microorganisms that haven’t been extensively studied—called non-model organisms—RNA-seq analysis presents a unique challenge.

That challenge stems from the fact that most non-model species don’t have a well-annotated reference genome, which is typically used to align RNA-seq data and identify differentially expressed genes (DEGs). Without a reference genome, scientists must first create a de novo transcriptome—an assembled version of all RNA transcripts from scratch—before any meaningful analysis can begin.

This de novo assembly process is computationally demanding, often requiring days of processing time and expertise in command-line tools, HPC environments, and bioinformatics workflows. It becomes even more complicated when scientists are working with multiple species in a single study, such as in biodiversity projects or environmental monitoring.

To make RNA-seq analysis more accessible and scalable, researchers at the University of Tuscia in Italy have introduced HPC-T-Assembly—a high-performance computing pipeline designed to streamline de novo transcriptome assembly for large, multi-species datasets.

What sets HPC-T-Assembly apart is its Web-based interface, which allows users to configure RNA-seq assembly jobs for multiple species in a straightforward way—without needing to manually code or install complex software. Once the user provides basic configuration data, the tool automatically generates the parallel computing scripts required to run the entire pipeline on a supercomputer or HPC cluster.

HPC-T-Assembly pipeline

The pipeline not only performs the transcriptome assembly but also includes essential post-processing steps such as:

• Assembly quality control to ensure data accuracy
• ORF prediction to identify protein-coding regions
• Transcript count matrix construction to support downstream analysis like DEG identification

By using parallel computing, HPC-T-Assembly dramatically reduces processing time and computational load, making large-scale transcriptomics and meta-transcriptomics projects more feasible.

This innovation is especially valuable for research fields such as conservation biology, marine biology, agriculture, and environmental genomics—where understanding gene expression in a wide range of lesser-studied organisms is increasingly important.

With HPC-T-Assembly, scientists now have a more efficient, scalable, and user-friendly way to analyze RNA-seq data from organisms without reference genomes—paving the way for broader and deeper biological insights.

Availability – HPC-T-Assembly is located at https://github.com/poset26/HPC-T-Assembly.