ArticleArray (New York, N.Y.)2026
FairFlow: A transparency-first framework for verifiable and reproducible bioinformatics.
Article in Array (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.