A bioinformatics workflow is a defined sequence of computational steps used to process and analyze biological data, such as sequencing reads, gene expression profiles, or proteomics results. These workflows often integrate multiple tools and data formats to ensure reproducibility, scalability, and consistency across experiments

Optimized workflows reduce computational bottlenecks, minimize errors, and improve reproducibility—key factors for robust and scalable research. In high-throughput settings, inefficient pipelines can lead to delays, increased costs, and inconsistent results, especially when working across multiple datasets or cohorts.

Optimization becomes essential when datasets grow in volume or complexity, when analyses become a bottleneck, or when scaling across multiple projects, users, or environments. Organizations should also consider optimization when moving workloads to the cloud, adopting new tools, or preparing pipelines for clinical or regulatory use.

Yes. Ardigen contributes to and extends nf-core standards to build pipelines tailored to specific scientific use cases. These custom pipelines retain nf-core compliance (e.g., testing, modularity, documentation), ensuring they are both reusable and maintainable while meeting domain-specific requirements.

Yes. Ardigen offers full lifecycle support for Nextflow-based pipelines, including development, optimization, cloud deployment, and ongoing maintenance. We also help integrate pipelines with platforms such as Tower, nf-core, and internal data systems to streamline usage and governance.