Thinking on
reproducible science
Long-form writing about bioinformatics methodology, reproducibility in research, and the systems we are building at NuHelix AI.
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First articles in the pipeline
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Reproducible bioinformatics: why provenance metadata matters
Most published analyses cannot be reproduced — not because the authors were careless, but because the tooling never made it easy. We explore what provenance metadata actually means in practice and why recording it at the infrastructure level changes everything.
Interpreting differential expression results in a clinical context
A statistically significant log2 fold change is not the same as a biologically meaningful one. This piece walks through the conceptual gaps that trip up researchers moving from basic science RNA-seq to clinical applications, and how to communicate results responsibly.
How we built our workflow orchestration layer
A behind-the-scenes look at the architecture decisions behind NuHelix's analysis pipeline: why we chose Celery over more heavyweight orchestrators, how the workflow registry pattern keeps executors cleanly separated, and what we got wrong the first time.