docs: add comprehensive MkDocs documentation website

Add a complete documentation site using MkDocs with Material theme:

- Getting Started: installation, quickstart, first analysis guides
- User Guide: configuration, sample formats, running pipeline, outputs
- Workflow: overview with mermaid diagrams, rules reference (all 21 rules),
  scripts reference (8 Python scripts), WarpDemuX demultiplexing guide
- Cluster Setup: LSF, SLURM, and GPU configuration guides
- Troubleshooting: common errors, FAQ, debugging techniques
- Development: architecture, adding rules, contributing guidelines

Infrastructure:
- mkdocs.yml with Material theme, mermaid support, code highlighting
- GitHub Actions workflow for auto-deployment to GitHub Pages
- Updated README with documentation badge and links

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Author: jayhesselberth

.github/workflows/docs.yml

@@ -0,0 +1,59 @@
+name: Deploy Documentation
+
+on:
+  push:
+    branches:
+      - main
+      - docs
+    paths:
+      - 'docs/**'
+      - 'mkdocs.yml'
+      - '.github/workflows/docs.yml'
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Install dependencies
+        run: |
+          pip install mkdocs mkdocs-material pymdown-extensions
+
+      - name: Build documentation
+        run: mkdocs build --strict
+
+      - name: Setup Pages
+        uses: actions/configure-pages@v4
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: 'site'
+
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    needs: build
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4

README.md

@@ -2,10 +2,11 @@
 
 [![CI](https://github.com/rnabioco/aa-tRNA-seq-pipeline/actions/workflows/ci.yml/badge.svg)](https://github.com/rnabioco/aa-tRNA-seq-pipeline/actions/workflows/ci.yml)
 [![Lint](https://github.com/rnabioco/aa-tRNA-seq-pipeline/actions/workflows/lint.yml/badge.svg)](https://github.com/rnabioco/aa-tRNA-seq-pipeline/actions/workflows/lint.yml)
+[![Documentation](https://img.shields.io/badge/docs-GitHub%20Pages-blue)](https://rnabioco.github.io/aa-tRNA-seq-pipeline)
 
 A Snakemake pipeline to process ONT aa-tRNA-seq data.
 
-Downstream analysis to generate figures for the initial preprint can be found at: [https://github.com/rnabioco/aa-tRNA-seq](https://github.com/rnabioco/aa-tRNA-seq)
+**[Documentation](https://rnabioco.github.io/aa-tRNA-seq-pipeline)** | **[Downstream Analysis](https://github.com/rnabioco/aa-tRNA-seq)**
 
 ## Usage
 

docs/cluster/gpu-configuration.md

@@ -0,0 +1,318 @@
+# GPU Configuration
+
+Configure GPU resources for the aa-tRNA-seq pipeline.
+
+## GPU Requirements
+
+Two rules require GPU access:
+
+| Rule | Purpose | GPU Usage |
+|------|---------|-----------|
+| `rebasecall` | Dorado basecalling | CUDA neural network inference |
+| `classify_charging` | Remora classification | PyTorch model inference |
+
+Both rules benefit significantly from GPU acceleration. CPU-only execution is possible but substantially slower.
+
+## GPU Resource Flow
+
+```mermaid
+flowchart LR
+    subgraph GPU Rules
+        A[rebasecall<br/>Dorado] --> B[classify_charging<br/>Remora]
+    end
+
+    subgraph Resources
+        C[POD5 Signal Data]
+        D[CUDA GPU]
+    end
+
+    C --> A
+    D --> A
+    D --> B
+```
+
+## Cluster Configuration
+
+### LSF GPU Settings
+
+In `cluster/lsf/config.yaml`:
+
+```yaml
+# Limit total concurrent GPU jobs
+resources:
+  - ngpu=12
+
+# GPU rule configuration
+set-resources:
+  - rebasecall:lsf_queue="gpu"
+  - rebasecall:lsf_extra="-gpu num=1:j_exclusive=yes"
+  - rebasecall:ngpu=1
+  - rebasecall:mem_mb=24
+
+  - classify_charging:lsf_queue="gpu"
+  - classify_charging:lsf_extra="-gpu num=1:j_exclusive=yes"
+  - classify_charging:ngpu=1
+  - classify_charging:mem_mb=24
+```
+
+### SLURM GPU Settings
+
+```yaml
+resources:
+  - ngpu=8
+
+set-resources:
+  - rebasecall:partition="gpu"
+  - rebasecall:gpu_opts="--gres=gpu:1"
+  - rebasecall:ngpu=1
+  - rebasecall:mem_mb=24000
+
+  - classify_charging:partition="gpu"
+  - classify_charging:gpu_opts="--gres=gpu:1"
+  - classify_charging:ngpu=1
+  - classify_charging:mem_mb=24000
+```
+
+## Configuration Options
+
+### GPU Concurrency Limit
+
+Control how many GPU jobs run simultaneously:
+
+```yaml
+resources:
+  - ngpu=8  # Max 8 concurrent GPU jobs
+```
+
+Set this to match your available GPUs or queue limits.
+
+### Exclusive GPU Access
+
+Request exclusive GPU access to avoid memory conflicts:
+
+=== "LSF"
+
+    ```yaml
+    set-resources:
+      - rebasecall:lsf_extra="-gpu num=1:j_exclusive=yes"
+    ```
+
+=== "SLURM"
+
+    ```yaml
+    set-resources:
+      - rebasecall:gpu_opts="--gres=gpu:1 --exclusive"
+    ```
+
+### GPU Type Selection
+
+If your cluster has multiple GPU types:
+
+=== "LSF"
+
+    ```yaml
+    set-resources:
+      - rebasecall:lsf_extra="-gpu num=1:j_exclusive=yes:gtile='!gv100'"
+    ```
+
+=== "SLURM"
+
+    ```yaml
+    set-resources:
+      - rebasecall:gpu_opts="--gres=gpu:v100:1"
+    ```
+
+## Local GPU Execution
+
+### CUDA_VISIBLE_DEVICES
+
+The pipeline respects `CUDA_VISIBLE_DEVICES`:
+
+```bash
+# Use specific GPU
+export CUDA_VISIBLE_DEVICES=0
+pixi run snakemake --cores 4 --configfile=config/config.yml
+
+# Use multiple GPUs (one per job)
+export CUDA_VISIBLE_DEVICES=0,1
+pixi run snakemake --cores 4 --resources gpu=2 --configfile=config/config.yml
+```
+
+### Limit GPU Jobs Locally
+
+```bash
+pixi run snakemake --cores 8 --resources gpu=1 \
+    --configfile=config/config.yml
+```
+
+## Memory Requirements
+
+GPU rules also require significant system memory:
+
+| Rule | GPU Memory | System Memory |
+|------|------------|---------------|
+| `rebasecall` | ~8-16 GB | 24 GB |
+| `classify_charging` | ~4-8 GB | 24 GB |
+
+## Performance Considerations
+
+### Dorado (rebasecall)
+
+- Processes POD5 signal data through neural network
+- Throughput: ~100-500 reads/second depending on GPU
+- Benefits from newer GPU architectures (Ampere, Ada Lovelace)
+
+### Remora (classify_charging)
+
+- Analyzes signal at CCA 3' end
+- Lower throughput than Dorado
+- Memory usage depends on batch size
+
+## Troubleshooting
+
+### CUDA Out of Memory
+
+**Symptom:**
+```
+RuntimeError: CUDA out of memory
+```
+
+**Solutions:**
+
+1. Ensure exclusive GPU access:
+   ```yaml
+   set-resources:
+     - rebasecall:lsf_extra="-gpu num=1:j_exclusive=yes"
+   ```
+
+2. Reduce concurrent GPU jobs:
+   ```yaml
+   resources:
+     - ngpu=4  # Reduce from default
+   ```
+
+3. Check for other GPU processes:
+   ```bash
+   nvidia-smi
+   ```
+
+### GPU Not Detected
+
+**Symptom:**
+```
+No CUDA GPUs are available
+```
+
+**Solutions:**
+
+1. Verify CUDA installation:
+   ```bash
+   nvidia-smi
+   ```
+
+2. Check CUDA_VISIBLE_DEVICES:
+   ```bash
+   echo $CUDA_VISIBLE_DEVICES
+   ```
+
+3. Verify job is on GPU node:
+   ```bash
+   # LSF
+   bjobs -l <job_id> | grep -i gpu
+
+   # SLURM
+   scontrol show job <job_id> | grep -i gres
+   ```
+
+### Wrong GPU Type
+
+**Symptom:**
+Job runs on incompatible GPU.
+
+**Solutions:**
+
+Specify GPU type explicitly in cluster profile:
+
+=== "LSF"
+
+    ```yaml
+    set-resources:
+      - rebasecall:lsf_extra="-gpu num=1:j_exclusive=yes:gmodel=NVIDIAA100"
+    ```
+
+=== "SLURM"
+
+    ```yaml
+    set-resources:
+      - rebasecall:gpu_opts="--gres=gpu:a100:1"
+    ```
+
+### Jobs Waiting for GPU
+
+**Symptom:**
+GPU jobs pending indefinitely.
+
+**Solutions:**
+
+1. Check GPU queue status:
+   ```bash
+   # LSF
+   bqueues -l gpu
+
+   # SLURM
+   sinfo -p gpu
+   ```
+
+2. Reduce concurrent GPU jobs:
+   ```yaml
+   resources:
+     - ngpu=2
+   ```
+
+3. Check fair share limits with your admin.
+
+## GPU Monitoring
+
+### NVIDIA SMI
+
+Monitor GPU usage during execution:
+
+```bash
+# Watch GPU utilization
+watch -n 1 nvidia-smi
+
+# Log GPU stats
+nvidia-smi --query-gpu=timestamp,name,utilization.gpu,utilization.memory,memory.used --format=csv -l 1 > gpu_log.csv
+```
+
+### Check Running GPU Jobs
+
+=== "LSF"
+
+    ```bash
+    bjobs -u $USER -q gpu
+    ```
+
+=== "SLURM"
+
+    ```bash
+    squeue -u $USER -p gpu
+    ```
+
+## CPU Fallback
+
+If GPUs are unavailable, Dorado can run on CPU (much slower):
+
+```bash
+# Force CPU-only execution
+export CUDA_VISIBLE_DEVICES=""
+pixi run snakemake --cores 12 --configfile=config/config.yml
+```
+
+!!! warning "Performance Impact"
+    CPU-only basecalling is 10-100x slower than GPU. Not recommended for production use.
+
+## Next Steps
+
+- [LSF Setup](lsf-setup.md) - LSF cluster configuration
+- [SLURM Setup](slurm-setup.md) - SLURM cluster configuration