🖥️ Building a Workstation for Single-Particle Analysis (SPA)
— Two Practical Paths: Research-Grade and Training-Grade Systems
1️⃣ Two Objectives
This series aims to achieve two goals:
(1) Build a research-grade workstation capable of processing full Cryo-EM SPA datasets, and
(2) Provide a training-grade workstation for students or researchers to practice the workflow with limited resources.
| Type | Research Workstation | Training Workstation |
|---|---|---|
| Purpose | Full Cryo-EM SPA processing | Learning Negative-stain and small Cryo-EM datasets |
| Budget | $8,000–15,000 | $1,500–3,000 |
| Users | Cryo-EM scientists, facility managers | Students, entry-level researchers |
| Dataset size | Hundreds of GB–TB | Hundreds of MB–tens of GB |
2️⃣ CPU — The Foundation of Parallel Processing
SPA relies heavily on CPU performance for motion correction, CTF estimation, and particle extraction.
Both CryoSPARC and RELION are optimized for Intel’s Math Kernel Library (MKL), making Intel CPUs the most stable and compatible choice.
| System | Recommended Specs | Example CPUs | Notes |
|---|---|---|---|
| Research-grade | 24–32 cores, ≥3.5 GHz | Intel Xeon | Excellent for MPI-based RELION and heavy parallelization |
| Training-grade | 8–16 cores, ≥3.0 GHz | Intel i9 or i7 | Strong single-core speed and wide software support |
💡 Xeon CPUs support ECC memory and multi-threaded workloads, ideal for large Cryo-EM projects.
i9 CPUs are well-balanced, affordable, and perfect for local learning environments.
3️⃣ GPU — The Core Accelerator
SPA’s 2D/3D classification and refinement steps depend primarily on GPU performance, particularly VRAM capacity.
| System | Minimum | Recommended | Example GPUs | Use Case |
|---|---|---|---|---|
| Research-grade | 16 GB VRAM | 24–48 GB VRAM | NVIDIA RTX A5000 / A6000 | Large datasets, atomic refinements |
| Training-grade | 8 GB VRAM | 12–16 GB VRAM | NVIDIA RTX 4070 / 4070 Ti / RTX 5000 | Negative-stain and small Cryo-EM datasets |
⚠️ If VRAM is insufficient, high-resolution refinements will fail.
A 16 GB GPU is sufficient for most learning and practice datasets.
4️⃣ RAM — The Working Memory
RAM serves as the buffer during massive particle classification or map refinement steps.
Insufficient memory will cause the system to swap to disk and drastically reduce performance.
| System | Minimum | Recommended | Example |
|---|---|---|---|
| Research-grade | 256 GB | 512 GB (ECC) | Xeon-based workstation memory |
| Training-grade | 32 GB | 64 GB | Standard DDR5 memory modules |
💡 64 GB is sufficient for running both CryoSPARC and RELION with small datasets.
For multi-million particle refinements, 128 GB or higher is strongly recommended.
5️⃣ Storage — Balancing Speed and Capacity
SPA generates massive intermediate files during motion correction, particle extraction, and refinement.
Using a fast NVMe SSD for temporary work and a large-capacity HDD or external SSD for long-term storage provides the best balance.
| Purpose | Research Setup | Training Setup |
|---|---|---|
| OS + Software | NVMe SSD 1 TB | NVMe SSD 500 GB |
| Scratch (Temp) | NVMe SSD 2 TB | NVMe SSD 1 TB |
| Archive | HDD/NAS 10–20 TB | External SSD 4 TB |
💡 CryoSPARC is highly sensitive to scratch disk speed. Always assign a dedicated NVMe drive for temporary files.
6️⃣ Example Configurations
| Type | CPU | GPU | RAM | Storage | Typical Use |
|---|---|---|---|---|---|
| Research-grade | Xeon(64 cores) | RTX A4000 (24 GB) x 4 | 512 GB | NVMe 1 TB + Scratch 2 TB + HDD 20 TB | Full Cryo-EM SPA pipeline |
| Training-grade | i9 (24 threads) | RTX 4070 Ti (12 GB) | 64 GB | NVMe 1 TB + External SSD 4 TB | Negative-stain & small Cryo-EM practice |
7️⃣ Budget Strategies
Not every lab or student needs a supercomputer.
The goal is to understand the workflow, not necessarily to achieve atomic resolution.
- Full setup → CryoSPARC + RELION + MotionCor2 + CTFFIND4 on one workstation
- Limited budget → CryoSPARC standalone + public Negative-stain dataset
💡 The key lesson is experience — learning how data flows through each stage of SPA analysis.
8️⃣ Conclusion
Building your own SPA workstation deepens your understanding of computational and hardware bottlenecks —
how CPU, GPU, RAM, and storage interact during each processing step.
This knowledge helps you design more efficient workflows, even on shared clusters.
The next article will cover Ubuntu setup, CUDA installation, and integrating CryoSPARC and RELION on a single workstation.
Summary
- Research-grade : Xeon + RTX A4000 x 4 + 512 GB RAM + NVMe 2 TB + HDD 20 TB
- Training-grade : i9 + RTX 4070 Ti + 64 GB RAM + NVMe 1 TB + External SSD 4 TB
- Goal : Experience the complete Cryo-EM SPA pipeline in a realistic, reproducible environment