Bio Prefect + Dask + Nextflow

Choose a workflow engine for a local, distributed, or HPC bioinformatics pipeline and scaffold it.

Supplementary docs last verified: 2026-10-01 against Prefect 3.8.7, prefect-dask 0.3.7 (maintained in the Prefect repository; install with prefect[dask]), Dask/distributed 2026.8.0, and Nextflow v26.04.6.

Instructions

1. Collect requirements: scheduler, container policy, data location, scale, and whether steps are CLI tools or Python functions.
2. Choose the engine with decision-matrix.md: Nextflow for file-based CLI tools on a scheduler, Prefect+Dask for Python-heavy or dynamic work, or a hybrid with Prefect as the control plane and Nextflow as the compute plane.
3. Generate a runnable scaffold with a clear data layout, per-step resources, and explicit thread counts (task.cpus in Nextflow, threads_per_worker in Dask).
4. Pin the software: pixi for tool stacks or Apptainer images on HPC, uv for Python (uv add "prefect[dask]"). Nextflow v26.04+ parses scripts and configs with the strict syntax by default; write code that passes nextflow lint.
5. Run heavy work through the scheduler, never on a login node. Submit the Nextflow launcher with scripts/submit_nextflow.sh; it runs from the current directory, so call it from the project root.
6. Validate with a small test dataset, then check resume (-resume, or Prefect retries plus idempotent outputs) before scaling.

Quick Reference

Task	Action
Engine choice	decision-matrix.md
Prefect+Dask scaffold	prefect-dask.md
Prefect on Slurm	prefect-hpc-slurm.md
Nextflow on HPC	nextflow-hpc.md
Submit Nextflow through Slurm	SLURM_ACCOUNT=... scripts/submit_nextflow.sh main.nf 'data/*.fastq.gz' results
Multi-cluster sites	Export SBATCH_CLUSTERS, SBATCH_PARTITION, and SBATCH_QOS before the wrapper; --parsable then prints jobid;cluster, so strip the suffix and pass the cluster to squeue -M and sacct -M
Examples	examples.md

Input Requirements

• Workflow requirements and steps
• Target environment (local, cluster, cloud)
• Scheduler, account, partition, and container constraints
• Data locations and expected volumes

Output

• Engine recommendation with rationale
• Runnable scaffold (files and commands)
• Resource plan per step (CPUs, memory, walltime)
• Validation plan and checkpoints

Quality Gates

• A tiny test run completes end-to-end through the same launcher used for production.
• Resume and retry behavior is verified; Prefect and Nextflow retries are not stacked without a stated policy.
• The resource plan matches the cluster's limits, and every tool receives an explicit thread count.
• Temporary Dask clusters are created by the task runner at flow runtime and closed with the flow.
• Compound FASTQ suffixes (.fastq.gz) do not leak into sample output names.
• The Nextflow launch runs through sbatch and verifies the trace file and non-empty result artifacts.

Examples

Example 1: Engine recommendation

Choice: Nextflow
Why: CLI-heavy pipeline, HPC scheduler required, reproducible cache/resume needed.

Troubleshooting

Issue: Workflow fails on HPC because of an environment mismatch Solution: Pin the tool stack (pixi lockfile or Apptainer image) and validate with a minimal test dataset on a compute node.

Issue: Nextflow v26.04+ rejects a script or config that ran on an older version Solution: The strict parser is now the default. Fix the reported pattern (see the Nextflow strict-syntax migration guide), or set NXF_SYNTAX_PARSER=v1 as a temporary workaround and record it.