Modern bioinformatics demands powerful, scalable computing infrastructure. Whether you’re running genomic variant calling, RNA-seq analysis, or single-cell pipelines, you need a compute cluster that scales with your workload, supports modern workflow engines, and doesn’t require a dedicated IT team to manage.
SciTechLink deploys Kubernetes-based HPC clusters optimized for bioinformatics workflows. Choose cloud deployment (AWS, Azure, GCP) or on-premises bare-metal infrastructure. We handle setup, configuration, and—if you need it—ongoing management.

What We Deliver

Production-Ready Kubernetes Clusters

Not just a vanilla Kubernetes installation. We deploy bioinformatics-optimized clusters with:

  • Pre-configured workflow engines (Nextflow, WDL, ARGO)
  • Autoscaling to handle variable workloads efficiently
  • Data storage integration (S3, Azure Blob, GCS, NFS)
  • Security hardening for sensitive genomic data
  • Monitoring and logging for performance tracking and troubleshooting

Typical Cluster Size: 10-50 nodes (scalable based on workload)

Supported Workflow Engines

Nextflow

Why Nextflow:
Industry-standard workflow language for bioinformatics, with extensive public pipelines (nf-core) and strong community support.

What We Set Up:

  • Nextflow runtime environment
  • Tower (workflow monitoring) integration (optional)
  • Pre-configured executors for Kubernetes
  • Integration with public workflow repositories

Common Workflows:

  • nf-core/rnaseq
  • nf-core/sarek (variant calling)
  • nf-core/scrnaseq (single-cell)
  • Custom Nextflow pipelines

WDL (Workflow Description Language)

Why WDL:
Developed by the Broad Institute, WDL is widely used in genomics research and clinical sequencing.

What We Set Up:

  • Cromwell workflow engine on Kubernetes
  • WDL runtime configuration
  • Integration with Terra/FireCloud (if applicable)
  • Support for public WDL workflows

Common Workflows:

  • GATK Best Practices pipelines
  • Broad Institute production workflows
  • Custom WDL pipelines

ARGO Workflows

Why ARGO:
Cloud-native workflow engine designed for Kubernetes, ideal for complex multi-step bioinformatics pipelines.

What We Set Up:

  • ARGO Workflows platform
  • Workflow templates for common bioinformatics tasks
  • Integration with artifact repositories
  • Web-based workflow monitoring UI

Common Use Cases:

  • Complex multi-stage pipelines
  • Parallel sample processing
  • Data preprocessing and QC workflows

Deployment Options

Cloud Deployment (Recommended for Most Labs)

AWS (Primary Platform)

  • EC2 spot instances for cost optimization
  • S3 for data storage
  • Elastic Block Store (EBS) for persistent volumes
  • CloudWatch for monitoring

Azure (Also Supported)

  • Azure Kubernetes Service (AKS)
  • Azure Blob Storage
  • Azure Monitor integration

Google Cloud Platform (Also Supported)

  • Google Kubernetes Engine (GKE)
  • Google Cloud Storage
  • Stackdriver monitoring

Benefits of Cloud:

  • No upfront hardware costs
  • Scale up/down based on workload
  • Pay only for what you use
  • High availability and disaster recovery built-in

On-Premises Bare-Metal Deployment

Why On-Premises:

  • Data sovereignty requirements
  • Existing hardware investment
  • Predictable long-term costs
  • Air-gapped or sensitive data environments

What We Provide:

  • Bare-metal Kubernetes cluster setup
  • Network and storage configuration
  • High-availability control plane
  • Monitoring and logging infrastructure

Benefits:

  • Full control over hardware
  • No cloud egress fees
  • Predictable performance
  • Compliance with on-prem data requirements

Common Bioinformatics Workflows

Primary Analysis

base2fastq (Illumina BCL → FASTQ conversion)
Convert raw sequencer data to analysis-ready FASTQ files. Essential first step for NGS data processing.

Secondary Analysis

Amplicon Sequencing
Targeted sequencing analysis for gene panels, 16S rRNA, or custom amplicon assays.

RNA-seq Analysis
Gene expression quantification, differential expression, and transcript discovery.

Single-Cell Analysis
Cell clustering, marker gene identification, trajectory analysis for single-cell RNA-seq data.

Metagenomics
Taxonomic classification and functional profiling of microbial communities.

Variant Calling (Germline & Somatic)
Identify SNVs, indels, and structural variants from whole-genome or exome sequencing.

Tertiary Analysis

Annotation and Interpretation
Functional annotation of variants, pathway analysis, and clinical interpretation.

How It Works

Step 1: Requirements Assessment

We discuss:

  • Workload type (RNA-seq, variant calling, metagenomics, etc.)
  • Sample volume (samples per week/month)
  • Data volume (GB/TB per run)
  • Workflow engine preference (Nextflow, WDL, ARGO)
  • Deployment preference (AWS, Azure, GCP, or on-premises)
  • Budget constraints

Step 2: Cluster Design

We design a cluster configuration optimized for your workload:

  • Node types and sizes (compute-optimized, memory-optimized, GPU if needed)
  • Storage architecture (object storage, NFS, persistent volumes)
  • Autoscaling policies
  • Cost optimization strategies (spot instances, reserved capacity)

Step 3: Deployment & Configuration

We deploy and configure:

  • Kubernetes cluster with bioinformatics-specific optimizations
  • Workflow engine(s) of choice (Nextflow, WDL, ARGO)
  • Data storage and backup solutions
  • Monitoring, logging, and alerting
  • Security hardening and access controls

Step 4: Workflow Integration

We help you:

  • Deploy your existing workflows or public pipelines
  • Optimize workflow configurations for Kubernetes execution
  • Set up input/output data paths
  • Configure resource requests and limits

Step 5: Training & Handoff (or Ongoing Management)

Option A: Handoff
We train your team to manage the cluster and execute workflows independently.

Option B: Managed Service
We provide ongoing cluster management, monitoring, troubleshooting, and optimization—ideal for labs without dedicated IT teams.

Typical Deployment Timeline: 2-4 weeks

Who Benefits from Kubernetes Bioinformatics Clusters?

Biotech Startups

Challenge: Need enterprise-grade compute infrastructure without the budget for dedicated HPC staff.

Solution: Cloud-based Kubernetes cluster with managed services. Scale as you grow, pay only for what you use.

Clinical Genomics Labs

Challenge: Process variable workloads—quiet periods and high-volume surges—without over-provisioning infrastructure.

Solution: Autoscaling Kubernetes cluster that scales up during high-volume periods and scales down to save costs during quiet times.

Research Facilities

Challenge: Run diverse bioinformatics workflows (RNA-seq, single-cell, metagenomics) on a single platform without managing multiple compute environments.

Solution: Kubernetes cluster supporting Nextflow, WDL, and ARGO—one platform for all workflows.

Labs Migrating from Legacy HPC

Challenge: Aging SGE or Slurm clusters are expensive to maintain and don’t support modern workflow engines.

Solution: Migrate to Kubernetes-based infrastructure for better scalability, easier management, and support for modern tools like Nextflow and ARGO.

Why Choose SciTechLink for Bioinformatics Compute?

1. Bioinformatics-Specific Expertise

We don’t just deploy generic Kubernetes clusters—we optimize for bioinformatics workloads. We understand workflow engines, data storage patterns, and the unique compute/memory/storage requirements of genomic analysis.

2. Cloud & On-Prem Flexibility

Choose the deployment model that fits your budget, compliance, and operational needs. We support AWS, Azure, GCP, and on-premises bare-metal.

3. Workflow Engine Expertise

We’re fluent in Nextflow, WDL, and ARGO. We don’t just set up infrastructure—we help you deploy, optimize, and troubleshoot your workflows.

4. Cost Optimization

Cloud compute can get expensive fast. We design clusters with cost optimization in mind—spot instances, autoscaling, right-sizing nodes, and smart storage policies.

5. Managed Services Available

Don’t have a DevOps team? We provide ongoing cluster management so you can focus on science, not infrastructure.

Get Started

Ready to deploy a production-ready bioinformatics compute cluster?

Schedule a consultation to discuss:

  • Your workflow requirements and sample volume
  • Cloud vs. on-premises deployment
  • Cluster sizing and cost estimates
  • Managed services vs. self-managed options

Frequently Asked Questions

Can you migrate our existing SGE/Slurm workflows to Kubernetes?

Yes. We help migrate workflows from legacy HPC schedulers to Kubernetes-native execution using Nextflow, WDL, or ARGO.

Do we need Kubernetes expertise to use the cluster?

Not if you choose managed services. We handle cluster management, and your team just submits workflows using familiar tools like Nextflow.

Can we run workflows from public repositories (nf-core, Broad, etc.)?

Absolutely. Nextflow and WDL support public workflow repositories out of the box. We help you configure and run these workflows on your cluster.

What about data security and compliance?

We implement security best practices including encryption at rest and in transit, role-based access control (RBAC), network policies, and audit logging. For compliance-sensitive data (HIPAA, etc.), we can deploy in private cloud environments or on-premises.

Can the cluster autoscale?

Yes. Cloud-based clusters support autoscaling—nodes are added during high workload periods and removed when idle, minimizing costs.

Do you provide ongoing support?

Yes. Managed service subscriptions include cluster monitoring, troubleshooting, updates, and workflow optimization support.