Resources

notame

notame is an R package co-developed at the University of Turku, University of Eastern Finland, and Afekta Technologies for nontargeted metabolomics data processing. It features a streamlined workflow consisting of reading in exported data from signal detection software (currently MS-DIAL), correcting signal intensity drift and batch effects, identifying and flagging low-quality molecular features, imputing missing values, correcting batch effects, and clustering similar molecular features. notameStats is a separate package in the bundle that includes a variety of useful statistical tests for metabolomics data, and notameViz provides an array of data visualization features. We currently use these tools in all our metabolomics projects involving medium to large sample sets.

Want to know more? Visit the GitHub or Bioconductor links below to install and get started with the packages, or check out a book chapter on the practical use of the packages, available as a preprint!

Install the latest Bioconductor versions of notame, notameStats, and notameViz in R:

if (!require(“BiocManager”, quietly = TRUE))
     install.packages(“BiocManager”)
BiocManager::install(“notame”)
BiocManager::install(“notameStats”)
BiocManager::install(“notameViz”)

Citation:

Klåvus, A., Kokla, M., Noerman, S., Koistinen, V. M., Tuomainen, M., Zarei, I., ... & Hanhineva, K. (2020). “Notame”: workflow for non-targeted LC–MS metabolic profiling. Metabolites, 10(4), 135.

Plasma Benchmark Metabolite Visualisation Application

Plasma benchmark is a collaboration project between the Chalmers University of Technology, International Agency for Research on Cancer (IARC) in Lyon, France, the University of Eastern Finland, and the University of Turku. This project aims to robustly determine and catalogue the key human plasma metabolites and lipids detectable with typical LC-MS metabolomics approaches and to create a benchmark for a more comprehensive and reproducible way to assess the plasma metabolome in clinical human studies. We have designed an interactive platform to view the project results.

We are currently expanding the database and many typical plasma metabolites may still be missing. This is because we require that the signal of the metabolite must be reproducibly detected in the participating laboratories, which use UPLC-QTOF-MS instruments and a method modified from our untargeted metabolomics approach (Klåvus et al. 2020).