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Annot: a Django-based sample, reagent, and experiment metadata tracking system.

Elmar BucherCheryl J ClaunchDerrick HeeRebecca L SmithKaylyn DevlinWallace ThompsonJames E KorkolaLaura M Heiser
Published in: BMC bioinformatics (2019)
Annot offers a robust solution to annotate samples, reagents, and experimental protocols for established assays where multiple laboratory scientists are involved. Further, it provides a framework to store and retrieve metadata for data analysis and integration, and therefore ensures that data generated in different experiments can be integrated and jointly analyzed. This type of solution to metadata tracking can enhance the utility of large-scale datasets, which we demonstrate here with a large-scale microenvironment microarray study.
Keyphrases
  • data analysis
  • stem cells
  • high throughput
  • solid state
  • rna seq
  • machine learning
  • single cell
  • bioinformatics analysis