After four years working on numerous projects using untargeted metabolomics, called by some colleagues "global metabolite profiling", my vision of this scientific discipline has been evolving largely as a result of the difficulties I've been encountering. Starting with the sample preparation (i.e., extraction of metabolites), through data processing, and ending with the identification of metabolites, each of these steps has caused me its own headaches. Still, I must admit, I have greatly simplified the whole process and now I can conduct much more pragmatic metabolomics studies. From this blog, I would like to begin a series of dialogues aimed at discussing the various methodological aspects of metabolomics. And I want to start with, perhaps, the subject that has suffered the largest transformation in my methodological workflow: data processing. Those who work with TOF instruments coupled to liquid chromatography will be familiar with the massive amount of data obtained with programs like XCMS. Well, in my opinion it all comes down to understanding the term “feature”. A very simple definition of feature is “a molecular entity with a unique m/z and retention time”. However, one feature does not necessarily correspond to a metabolite. The number of features is always much higher than the number of metabolites. How much? How many features do you typically detect in a regular untargeted metabolomics study? How do you filter features to end up identifying metabolites?
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| feature detection with XCMS |
These are some of the points I would like to discuss here. I bet you'll read a lot of different opinions on this matter, and I hope you can convey my experience and I can learn a bit more of all your points of view.
Welcome to metaBlogOmics!
Oscar
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