Nextflow

Nextflow is a domain-specific language (DSL) and runtime designed to simplify the construction, execution and sharing of complex, data-driven computational pipelines. Built on a dataflow programming model, Nextflow lets you express pipelines as a graph of processes connected by asynchronous channels. This model makes parallelism implicit: tasks run whenever their inputs are available, which simplifies writing distributed and concurrent pipelines while keeping code declarative and portable. Nextflow has been widely adopted in bioinformatics and other scientific domains for producing reproducible analyses and large-scale production workflows. At its core, Nextflow separates pipeline functional logic from the underlying execution layer. A pipeline is composed of processes (units that execute a tool or script) and channel declarations that route data between them. Processes can be written in any scripting language supported by the platform (Bash, Python, Perl, R, etc.), and each process declares its inputs and outputs so Nextflow can orchestrate execution and parallelization automatically. Key runtime features include an incremental task cache and a resumable work directory so runs can be restarted from the last successful step; fine-grained resource and environment control via process directives; configuration profiles for different compute environments; and selectors that let you apply CPU, memory or queue settings to groups of processes. Parameters may be supplied on the command line, via parameter files (JSON/YAML), or in config files, enabling reproducible and configurable executions. Nextflow integrates with the ecosystems scientists already use to achieve reproducibility and portability. It supports container runtimes such as Docker, Singularity and Podman, and package managers like Conda and Spack, letting you pin and isolate software dependencies per process. The runtime provides out-of-the-box executors for common HPC batch schedulers (GridEngine, SLURM, LSF, PBS, Moab, HTCondor) as well as cloud services and orchestration platforms (AWS Batch, Azure Batch, Google Cloud Batch, Kubernetes). Additional executors and layers—Bridge, Flux, HyperQueue and others—expand support for heterogeneous environments. Nextflow also integrates tightly with Git: you can run pipelines directly from remote repositories or cached local copies, specify branches/tags/commits, and clone pipelines for local modification. The nf-core community curates high-quality, peer-reviewed Nextflow pipelines for common bioinformatics tasks, and Seqera Labs provides the Nextflow extension for Visual Studio Code, training materials, community forums and an optional managed/analytic layer through Seqera Cloud and Seqera Platform. Typical use cases where Nextflow excels include scalable omics workflows (RNA‑seq quantification, genome variant calling, single-cell processing), distributed BLAST and database queries (split queries into chunks for parallel searches and merge results), and production machine-learning pipelines that fetch datasets, run parallel model training and select the best models. Because processes are isolated and inputs/outputs are tracked, Nextflow workflows are suitable for both exploratory analyses and production deployments: you can develop locally, then run the same pipeline on an HPC cluster or in the cloud by switching configuration profiles. Installing Nextflow is straightforward (curl -fsSL https://get.nextflow.io | bash), or via Bioconda, or as a standalone binary for offline environments. Java 17 or later is required. Beyond the runtime, Nextflow benefits from an active open-source ecosystem and community. It is released under the Apache 2.0 license, has extensive documentation, training material and community channels (forum, Slack, GitHub) for support, and a vibrant catalog of shared pipelines (nf-core). Seqera Labs augments the project with cloud services and analytics—Seqera Cloud Basic offers a free tier for small teams and academic researchers can apply for Pro access—while community resources provide templates, VS Code tooling, and example pipelines that accelerate adoption. For scientific publications, the canonical citation is Di Tommaso et al., "Nextflow enables reproducible computational workflows" (Nature Biotechnology), reflecting the tool's emphasis on reproducibility across platforms.