In press at MCP right now, is this paper: "Automated Label-Free Quantification of Metabolites from LC-MS Data," by Erhan Kenar et al.,
Now, I know this is a proteomics blog, but I try to keep my ear to the ground in regard to this metabolomics thing that has been exploding. And this is a nice new one.
First of all, it is built into the OpenMS platform, which has a big support network and is available on all platforms (and crazy easy to install!). The cool part, however, is the use of a support vector machine (SVM) to rapidly and accurately identify metabolites. A SVM is a supervised learning algorithm (think, Percolator, or artificial neural networks in genomics) that makes classifications in a non-probabilistic manner. In this case, this sophisticated algorithm is used to determine whether ions in your run are metabolites of your ions of interest.
If you are doing metabolite ID and quan, you should take a minute to look through this paper and download OpenMS 1.11. You can find it here.
Very interesting results with deep implications for XCMS and other metabolomics soft, however actual workflow was not provided. So until TOPPAS workflow will be made publicly available for everybody to test it is quite difficult to evaluate validity of published claims.
ReplyDeleteThe workflow is available online now: http://open-ms.sourceforge.net/workflow-integration/toppasworkflows/
ReplyDelete