Bioinformatics Standards for Data Reproducibility

Abstract

Computational reproducibility, the ability to obtain identical results from the same data using the same analytical code, remains elusive in bioinformatics despite widespread recognition that irreproducible analyses undermine scientific credibility and waste resources. We evaluated 218 bioinformatics reproducibility programmes across centres in Austria, France, and Germany between 2017 and 2021, spanning five standards categories: workflow management and containerisation, data format and metadata standardisation, software environment capture, provenance tracking and audit trails, and benchmark dataset curation. A Bioinformatics Reproducibility Standards Index (BRSI) was constructed from five sub-scores -- computational reproducibility rate, metadata completeness, workflow portability across platforms, long-term archival sustainability, and community adoption breadth -- with weights from regression against sustained institutional adoption. BRSI correlated with adoption at r = +0.84 and discriminated adopted from non-adopted standards with an AUC of 0.884. Workflow management and containerisation scored highest (mean BRSI 0.826), while provenance tracking trailed at 0.600. Only 35.3 percent exceeded the 0.75 threshold. Computational reproducibility rate carried the largest weight (beta = +0.280), followed by workflow portability (beta = +0.228)