A bioinformatics platform for metabolomics. Upload your LC-MS or NMR compound list and Quira automatically enriches each molecule with physicochemical data, literature, and web sources — delivering actionable insights for product development and circular-economy applications. All in one place.
Four integrated modules cover the full metabolomics workflow — compound enrichment, automated literature review, analytics, and formulation pathway exploration — all the way to a structured, paper-ready report. Without ever leaving the platform.
Upload your compound list from LC-MS or NMR and Quira automatically enriches each molecule with physicochemical properties, database records, literature findings, and web sources — eliminating weeks of manual data collection.
Run structured literature reviews across your full compound set — automatically surfacing relevant papers, clinical evidence, and pathway data without any manual searching.
Explore your enriched compound data through point-and-click visualisations. No programming required — designed so any researcher can interrogate their results immediately.
Drop into R, Python, or Julia for full scripting power. Your enriched compound dataset is ready to query, model, and visualise however your workflow demands.
Map how your identified compounds can be applied across formulation contexts — from nutraceuticals to cosmetics to therapeutics — supporting product development and circular-economy applications.
Quira is designed around the actual research workflow — not just the data. Every step outputs something usable.
Bring in the molecules identified from your LC-MS or NMR run. Quira accepts standard formats and immediately begins enrichment across databases, literature, and web sources.
Run literature reviews, explore formulation pathways, and perform analytics — no-code or with your preferred language. All on your enriched dataset.
Drag your findings into a structured, paper-ready report via Quarto and LaTeX. Submit-ready output, generated from within the platform.
"Researchers shouldn't spend weeks collecting data.
They should spend their time making discoveries."
— The Quira Team · Currently in Beta
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