PC or HPC: The Real Truth About NGS Data Analysis

Is a personal computer enough for NGS data analysis, or do we really need a server?

Milad Eidi:
In my view, if you don’t run analyses every day and your workloads are not highly demanding, it is much more practical to use a personal computer-ideally a brand-new one.

The answer depends on the scale and complexity of the datasets you are working with. Next-generation sequencing (NGS) produces vast amounts of data: a single human whole genome at 30× coverage generates about 100–150 GB of raw FASTQ files, while large projects such as single-cell RNA sequencing can easily exceed several terabytes. The computational demands for processing such data—read alignment, variant calling, expression quantification, and downstream statistical analyses—can quickly outgrow the capacity of ordinary machines.

Personal computers (desktops):
A high-performance desktop with 12–32 CPU cores, 16–64 GB of RAM, and several terabytes of storage (Not necessarily SSD) can handle modest workloads, such as small RNA-seq studies or a number of exomes. For individual researchers or smaller labs, this remains the most cost-effective option. However, as projects scale up, bottlenecks appear in memory and disk I/O, and tasks may take days instead of hours (Schatz et al., 2010; Koboldt et al., 2013).

Servers and HPC clusters:
For large-scale genomics, servers or HPC infrastructures are the standard. They provide hundreds of cores, terabytes of RAM, GPUs for AI-driven analyses, and robust job schedulers (Slurm, PBS). These resources make it possible to run dozens or hundreds of samples in parallel, ensuring faster turnaround and reproducibility. This is critical for projects involving population genomics or clinical genomics pipelines where turnaround time matters (Lewin et al., 2019).

Laptops:
While laptops can, in theory, run bioinformatics tools, they are practical only for script testing, small QC tasks (e.g., FastQC, read trimming), or training purposes. Their limited memory (usually <16 GB) and storage, combined with thermal throttling under sustained load, make them unsuitable for real-world NGS workflows. Attempting whole-genome alignment or large-scale RNA-seq analysis on a laptop often results in system crashes or unacceptably long runtimes.

Conclusion:

• Laptop: educational or small toy datasets.
• Desktop PC: suitable for small projects and entry-level analyses.
• Server/HPC: essential for population-scale, multi-omics, or clinical-grade NGS data analysis.

As Schatz et al. (2010) noted more than a decade ago, “genomics is an information science,” and the hardware you choose directly impacts the scope and speed of discovery.

References

• Schatz, M. C., Langmead, B., & Salzberg, S. L. (2010). Cloud computing and the DNA data race. Nature ¹Biotechnology, 28(7), 691–693.
• Koboldt, D. C., Steinberg, K. M., Larson, D. E., Wilson, R. K., & Mardis, E. R. (2013). The next-generation sequencing revolution and its impact on genomics. Cell, 155(1), 27–38.
• Lewin, H. A., Robinson, G. E., Kress, W. J., et al. (2019). Earth BioGenome Project: Sequencing life for the future of life. PNAS, 115(17), 4325–4333.