Genomics and Bioinformatics HPC

Genomics and bioinformatics research has undergone a dramatic transformation over the last decade, both in data volume and analytical depth. A single whole genome sequencing (WGS) study generates hundreds of gigabytes of raw data, while large cohort studies reach petabyte scale. Converting this data into biological meaning — alignment, variant calling, functional annotation, and statistical analysis — requires a computing capacity far beyond standard server hardware.

Why Is HPC Essential in Genomics Research?

If a clinical research laboratory is processing dozens of WGS samples daily, the GATK Best Practices pipeline for each sample takes 48–72 hours on CPU-based infrastructure. Twenty samples running in parallel create a backlog as their processing times stack up; scientific results are delayed and experimental flow is disrupted.

HPC clusters solve this problem through two channels:

1. Horizontal scaling: Each sample is distributed to different nodes for parallel processing; as the cohort grows, cluster capacity can be expanded.
2. GPU acceleration: GPU-accelerated tools like NVIDIA Parabricks accelerate the GATK pipeline by 50×, reducing per-sample processing time to 1–2 hours.

Across a wide spectrum — from academia to clinical diagnostic labs, agricultural genetics companies to biotech startups — HPC is the core infrastructure for competitive research speed.

Core Workload Categories

1. Whole Genome and Exome Sequencing (WGS / WES)

The WGS pipeline consists of alignment, duplicate marking, variant calling, and filtering stages. Each stage has a different compute profile:

• BWA-MEM / BWA-MEM2: Reference genome alignment; achieves near-linear scaling with multi-core CPU. BWA-MEM2 is approximately 2× faster than BWA-MEM due to SIMD optimizations.
• SAMtools / Picard: Sorting, deduplication, and quality metrics; requires high I/O bandwidth.
• GATK HaplotypeCaller: Variant calling standard; consumes high RAM (64–128 GB/sample).
• NVIDIA Parabricks: Runs GATK germline and somatic pipelines on GPU; completes 30× WGS in ~45 minutes on H100.

High I/O requirement: During alignment and sorting stages, dozens of GB of temporary files are generated per sample. Even single nodes with NVMe hit I/O bottlenecks without a parallel file system (BeeGFS or Lustre).

2. RNA-seq and Transcriptomic Analyses

The standard workflow for RNA sequencing, gene expression measurement, and alternative splicing research includes these tools:

• STAR / HISAT2: Splice-aware alignment; requires high memory (32–64 GB) for large reference genome index.
• Salmon / Kallisto: Pseudo-alignment-based fast quantification; completes in minutes on a single CPU node.
• DESeq2 / edgeR: R-based differential expression analysis; adequate RAM is essential for large matrices.
• Seurat / Scanpy: Single-cell RNA sequencing (scRNA-seq) analysis; datasets containing hundreds of thousands of cells benefit from GPU acceleration.

scRNA-seq workloads are memory-intensive: a dataset of 500,000 cells in Seurat may require 256–512 GB RAM.

3. Protein Structure Prediction and Structural Bioinformatics

With the public release of AlphaFold 2 and AlphaFold 3, structural bioinformatics research has gained new momentum.

• AlphaFold 2 / 3: NVIDIA GPU required; 1–3 hours for a 100 amino acid protein on A100/H100, 6–24 hours for large complexes.
• ESMFold: Language model-based fast structure prediction; faster than AlphaFold but less accurate.
• RoseTTAFold: Strong alternative for multi-chain protein complex prediction.
• HADDOCK / ZDOCK: Protein-protein docking simulations; scales well on CPU clusters.
• GROMACS / NAMD: Protein conformational dynamics and drug binding simulations; GPU acceleration provides 10–30× performance improvement.

4. Variant Annotation and Large Cohort Analyses

• ANNOVAR / VEP (Ensembl): Variant functional annotation; parallel processing for large VCF files.
• PLINK 2 / REGENIE: Genome-wide association analysis (GWAS); high core counts required for tens of thousands of samples.
• SAIGE: Linear mixed model for large cohort unbalanced phenotypes; scales with SPARK-based distributed computing.

Reference HPC Configurations

Medium-Scale Genomics Laboratory (20–50 WGS Samples Daily)

Login Nodes (2×, load-balanced)
│
├── CPU Compute Nodes (12–20 units)
│   └── 2× AMD EPYC 9654 (192 cores/node), 512 GB DDR5
│       ├── BWA-MEM2, SAMtools, STAR alignment
│       ├── GATK HaplotypeCaller, PLINK, ANNOVAR
│       └── InfiniBand HDR 200 Gb/s (MPI connectivity)
│
├── GPU Compute Nodes (4–8 units)
│   └── 2× Intel Xeon Gold + 4× NVIDIA H100 SXM5 80 GB
│       ├── NVIDIA Parabricks (WGS pipeline acceleration)
│       ├── AlphaFold 2/3 and ESMFold
│       └── scRNA-seq GPU analysis (Rapids cuML)
│
├── High-Memory Nodes (2–4 units)
│   └── AMD EPYC 9654, 1.5 TB DDR5
│       └── Seurat / Scanpy large cell atlas, Gaussian
│
└── Storage Layer
    ├── BeeGFS NVMe (scratch): 200 TB, >20 GB/s total bandwidth
    ├── Capacity storage (SAS/SATA): 2–4 PB raw data archive
    └── S3-compatible object storage (completed projects)

Clinical Diagnostic Laboratory (Parabricks-Focused, Speed-Critical)

GPU Compute Nodes (4 units)
└── 2× AMD EPYC 9554 + 8× NVIDIA L40S 48 GB
    ├── Parabricks germline pipeline: 30× WGS → ~40 min
    ├── Parabricks somatic (tumor-normal pair): ~90 min
    └── Parabricks RNA-seq (STAR + GATK): ~20 min

Software Ecosystem: Requirements per Tool

Data Security and KVKK Compliance

Genomic data constitutes the most sensitive subset of personal data. An individual’s complete genome can identify not just themselves but their first-degree relatives as well. This places genomics research under the special category personal data processing regime within KVKK scope.

Critical KVKK requirements:

• Processing and storing genomic data on Turkey-located infrastructure provides legal assurance; overseas cloud servers create additional regulatory risk.
• Recording data access permissions (audit log) and role-based access control (RBAC) are mandatory.
• Anonymization or pseudonymization should be applied at the first stage of the pipeline.

Mevasis infrastructure is located in data centers in Turkey. Genomics research institutions can directly satisfy KVKK requirements without additional legal arrangements with cloud providers.

Mevasis Genomics HPC Services

Mevasis provides HPC infrastructure design, installation, and managed operations services for genomics laboratories, university research groups, and clinical diagnostic centers.

Our areas of expertise:

• NVIDIA Parabricks installation, licensing, and pipeline optimization
• BWA-MEM2, GATK, STAR, Salmon, DeepVariant integration and configuration
• AlphaFold 2/3 setup (including 2.2 TB database management)
• GROMACS and NAMD GPU optimization and benchmarking
• BeeGFS / Lustre parallel file system installation and tuning
• KVKK-compliant data flow design and access control

Related Mevasis services:

• HPC Infrastructure Setup — Turnkey cluster design and installation
• HPC Consulting — Pipeline analysis, hardware selection, and performance optimization
• GPU Cluster Rental — Project-based GPU access, Parabricks license included
• Managed HPC Service — Operations, monitoring, and update support

Contact us to evaluate your genomics research infrastructure or accelerate your existing pipeline. Our expert team will provide technical recommendations tailored to your needs.

Frequently Asked Questions

For WGS analysis, should I choose Parabricks or CPU-based GATK? Parabricks is the clearly recommended option for laboratories processing 5 or more samples daily. With H100 GPU, the 30× WGS pipeline completes in ~45 minutes, while the same job takes 48–72 hours on a CPU cluster. When initial hardware costs are factored in, the GPU investment amortizes quickly as annual throughput increases.

What is the minimum GPU requirement for AlphaFold installation? A100 80 GB is recommended for AlphaFold 2; the 40 GB memory limit can cause issues with large proteins. H100 80 GB or H100 NVL 94 GB is more suitable for large multimer complexes. AlphaFold 3 officially requires A100 80 GB.

How much RAM is sufficient for scRNA-seq analyses? Standard datasets with 10,000–50,000 cells require 128–256 GB RAM. Large atlas projects with 200,000+ cells may require 512 GB–1 TB RAM. GPU-based analysis with Rapids cuML both increases speed and reduces RAM requirements for large datasets.

What is the advantage of keeping genomic data on-premise versus cloud? There are three primary advantages: speed, cost predictability, and KVKK compliance. For large cohort studies, petabyte-scale data transfer is disadvantageous for cloud use in both time and cost. Under KVKK, processing special category health/genomic data outside Turkey’s borders carries legal risk.

What is the difference between BeeGFS and NFS, and which is necessary for genomics? NFS is adequate for serial workloads, but creates bottlenecks under high I/O loads like parallel alignment (BWA-MEM2) or simultaneous multiple Parabricks jobs. BeeGFS or Lustre provides 5–20× higher total bandwidth under this load and is strongly recommended for genomics HPC.