An all-inclusive, turnkey software for analysis of untargeted LC-MS data

Untargeted LC-MS is widely used in metabolomics, lipidomics, environmental analysis, etc to monitor virtually "all" metabolites, lipids, or compounds present in a sample. Analytically, this is possible with the use of high-resolution mass spectrometers, which enable parallel quantification and identification for thousands of features - known and unknown.

The challenge of untargeted studies is data processing and analysis. How to detect thousands of features over several orders of magnitude and across hundreds to thousands of files? How to address non-ideal behavior of LC-MS, i.e. drifts in time and m/z? How to groups MS peaks to pertain to the same chemical, and convolute data?   

We are happy to present external pageSLAWcall_made, a software that Alexis developed to address most of these problem in an efficient and scalable way. For us, it made a huge difference in performance and also ease of use. We introduce some of the functionalities in a external pageblog postcall_made. We will disclose more when the companion paper will be published. If you want to use it, we recommend pulling it from Docker or SingularityHub (as described on external pageGitHubcall_made).