FairFlow: A transparency-first framework for verifiable and reproducible bioinformatics

Bioinformatics analyses depend on external software that itself relies on further programs, or dependencies, each with its own release cycle. Because tools are updated to fix bugs and add features, identical code can produce different results depending on which versions are installed, and incomplete documentation of dependencies and versions therefore undermines reproducibility. Containerization packages software with all its dependencies, but demands technical expertise and lacks a standard operating procedure. Existing workflow managers and container tools have improved reproducibility, yet each works differently and requires specialized knowledge, forcing developers to rebuild equivalent solutions from scratch. Here we present FairFlow, a framework that makes rigorous, reproducible analysis accessible without specialized technical knowledge. Developers describe a pipeline once in a declarative, INI-style specification file written in Baryon, and FairFlow automatically generates ready-to-run interfaces for R, Python, Bash, Galaxy, Nextflow and StreamFlow, each executing the analysis inside a version-locked container. The generated interfaces are self-contained: end users need only Docker and their preferred language. As a proof of concept we reanalyzed a published study in two ways: following the original protocol, which does not account for dependency installation, and with a FairFlow-based script. The first yielded only ∼21% concordance with the original findings, traced to an unspecified sequence-alignment tool version, whereas the FairFlow-based reanalysis achieved 98.8-100% concordance across operating systems and reduced setup from months of troubleshooting to a single Docker installation.