Never Deal with 'It Works on My Machine' Again: Docker and Sigularity for NGS Analysis

Описание: End dependency nightmares and ensure reproducible NGS analyses with Docker and Singularity containers. Learn to build portable analysis environments that run anywhere—from your laptop to HPC clusters.

📚 FULL TUTORIAL WITH CODE:
https://ngs101.com/build-once-run-any...

⏱️ TIMESTAMPS:
0:00 - Why Environment Migration Fails in NGS Analysis
0:58 - Limitations of Conda Environment Migration
1:47 - Docker Solution
2:45 - Create Docker Image - Step 1: Prepare Build Environment
3:11 - Create Docker Image - Step 2: Write the Dockerfile
5:14 - Create Docker Image - Step 3: Build Your Docker Image
5:29 - Create Docker Image - Step 4: Test the Docker Image
6:09 - Understanding Docker Container Directory Structure
6:26 - Interactive Docker Usage with Data Mounting
7:00 - Docker Usage on HPC with Singularity Conversion

🔬 WHAT YOU'LL LEARN:
✅ Eliminate dependency conflicts and version mismatches
✅ Install Docker and pull prebuilt NGS containers
✅ Mount host directories for data access
✅ Write Dockerfiles to bundle your analysis tools
✅ Share containers via Docker Hub for collaboration
✅ Convert Docker images to Singularity for HPC
✅ Run containers without root privileges on clusters
✅ Version control your entire computational environment
✅ Achieve bit-for-bit reproducible analyses

🧬 WHY CONTAINERIZATION MATTERS:

**The "Works on My Machine" Problem**:
→ Different Python/R versions across collaborators
→ Missing system libraries break tool installation
→ Conda environments conflict and grow unwieldy
→ HPC admins won't install your specific tool versions
→ Reviewers can't reproduce your computational results

**How Containers Solve This**:
→ Package ALL dependencies in isolated environment
→ Identical execution on laptop, server, or cloud
→ Share exact environment via single image file
→ Version entire analysis stack, not just code
→ Enable true computational reproducibility

🔧 DOCKER VS SINGULARITY:

**Docker**:
Best for: Development on personal computers
Requires: Root/admin privileges
Strengths: Easy to use, massive ecosystem (Docker Hub)
Limitations: Security concerns on shared HPC systems
Use when: Building containers, testing on local machine

**Singularity**:
Best for: HPC clusters and shared computing
Requires: NO root privileges (user-level execution)
Strengths: HPC-friendly, converts Docker images
Limitations: Smaller ecosystem than Docker
Use when: Running on institutional clusters

**Typical Workflow**:
1. Build container with Docker on your laptop
2. Push to Docker Hub
3. Pull and convert to Singularity on HPC
4. Run analysis on cluster with Singularity

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